The Development Trend of Digital Art in the Age of Artificial Intelligence
Bibliographic record
Abstract
With the continuous growth of artificial intelligence (AI) technology, AI has been widely used in various fields. In the field of digital art, the application of AI has gradually become a trend, which brings more possibilities and innovations to the creation, expression and dissemination of digital art. Digital artistic creation has gradually become more intelligent, autonomous and diversified. Nowadays, AI has become an important tool for digital art creation. It can not only simulate the creative process of artists, but also produce unique and unprecedented works of art. Firstly, this paper introduces the connotation and characteristics of AI and digital art, and expounds the relationship between digital art and AI. Secondly, the application status and existing problems of AI in digital art are analyzed from the aspects of digital art creation, appraisal and evaluation, market transaction and audience promotion. Finally, some ideas and suggestions for the growth of digital art combined with AI are put forward, so as to provide reference for the future growth of digital art.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".