Brushstrokes of Tomorrow: Exploring the Art of AI
Bibliographic record
Abstract
In recent years, the advancement of Artificial Intelligence (AI) technology, particularly in deep learning algorithms like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), has led to significant developments in AI-based art generation across various sectors within the art industry. The year 2022 witnessed an explosion of AI-generated art, particularly in creative design, resulting in the production of numerous outstanding works that have enhanced the efficiency of art design processes. This study delves into the application and design characteristics of AI generation technology within two specific sub-fields: AI painting and AI animation production. A comparative analysis between traditional painting methods and AI-generated painting techniques is conducted to discern differences. Through this research, the paper synthesizes the advantages and challenges inherent in the AI creative design process. Despite technical limitations and issues such as copyright and income distribution, AI art designs demonstrate promise in facilitating artistic innovation and technological integration within the art domain. Their potential for advancing sub divisional artistic practices and their intersection with technology renders them highly valuable subjects for further research and exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".