The Application and Practice of Artificial Intelligence in the Entertainment Field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) technology has witnessed unprecedented advancements and a gradual penetration into civilian applications. This paper aims to thoroughly investigate the application of AI in the entertainment industry, with a particular focus on the principles and cross-disciplinary implementations of 3D real-life scanning, AI for non-player characters (NPCs), and AI video generation. By synthesizing how these technologies streamline content creation processes, lower technical barriers, and inspire novel approaches to game design, we observe that AI is not only reshaping the ecosystem of the entertainment sector but also facilitating the entry of newcomers into game development. However, alongside the benefits, this study identifies several challenges and limitations associated with current AI technologies, such as accuracy, cost-effectiveness, and ethical concerns, which require attention and resolution in future research and practice. Through a detailed examination and synthesis of these phenomena, this research provides a reference for practitioners and suggests directions for subsequent studies.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it