Simulation of Creative Manifestation by Functional Ensembles of Intellectual Agents Based on Live Information in Various Spheres of Life Activity
Bibliographic record
Abstract
Neural networks with deep learning and reinforcement are able to compose poetry and music, draw paintings, and write short stories, as well as come up with scripts for films. Functional ensembles of harmoniously interacting intellectual agents with living information can virtually model creativity for various spheres of life activity. Virtual modeling of creativity by harmoniously interacting intellectual agents is carried out based on living creative processes represented by acts of creation accumulated by humanity in a certain sphere of life. Live information of creative acts of creation for functional ensembles from harmoniously interacting intellectual agents is revealed from the effective creative practice of specialists in specific conditions and presented in the format of smart ethical communicative-associative cases. To model creativity, a virtual environment of a certain sphere of activity is formed, in which the ensemble gives birth to a creative fruit according to the plan of a specialist. Functional ensembles of harmoniously interacting intellectual agents with live creative practice can cooperate with a person, and can also independently virtually model the creative creation of new designs of a specialist, if the ensemble has enough acts of creation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".