Art creation as an emergent multimodal journey in Artificial Intelligence latent space
Bibliographic record
Abstract
This research explores emergent creative processes that develop between artist (interactor) and generative artificially intelligent (AI) technology when an AI system is positioned as a latent space n-dimensional journeying tool or more semantically as an ontological other that the artist works through. The authors investigate a new artistic process, building a latent space journeying AI diffusion system, and with it examining a more multimodal emotional approach to art making including how intentions of the artist are reshaped by journeying through the algorithmic transformation and re-presentation to question what is preserved, nurtured, lost, or irrevocably altered in the interplay of the autographic and the algorithmic. The study finds that neural media, as the authors term it, becomes a non-deterministic multimodal process of moving (an emotion journey) through AI latent space. This time and space n-dimensional artist journey is achieved through a series of choices and creative forks, including external perturbation from the AI system, the ontological “other” leading to the artist's innate expression of their emotional reactions of intermediate art artifacts on that journey to a final aesthetic artifact.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".