Agence, A Dynamic Film about (and with) Artificial Intelligence
Bibliographic record
Abstract
Agence is a short “dynamic film” that uses AI to power a real-time story. It was co-produced by Transitional Forms and the National Film Board of Canada (NFB). It is available on VR, PC and mobile, but for the purposes of this paper, we will be talking about the VR version, since it most closely matches the director’s vision. The film is directed by Pietro Gagliano whose work on interactive stories has spanned many years and technologies. A few years ago he started Transitional Forms to combine real-time storytelling with artificial intelligence. The intention behind that process is twofold: First, we believe that entertainment will soon be driven by AI. And secondly, artificial intelligence is poised to be humanity’s greatest tool, and stories might be the best way to make sense of it. To this end, we believe that Agence is an innovative production with bold strides in immersion, interactivity and technology. The approaches taken in this film are novel and unique in their propositions, and may open the door to many new projects that may build upon them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".