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Record W4313143655 · doi:10.1609/aiide.v10i5.12764

Generating Structure – Towards Large-Scale Formal Generation

2014· article· en· W4313143655 on OpenAlexaff
Arne Eigenfeldt

Bibliographic record

VenueProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2014
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsImprovisationGenerative grammarEvent (particle physics)Computer scienceScale (ratio)Performing artsOperator (biology)MusicalHuman–computer interactionArtificial intelligenceCognitive sciencePsychologyVisual artsArt

Abstract

fetched live from OpenAlex

Metacreative systems have been very successful at generating event-to-event musical details and generating short forms. However, large-scale formal decisions have tended to remain in the hands of a human, either through an operator guiding an improvisational system along in performance, or the assemblage of shorter generated sections into longer forms. This paper will describe the open problem of large-scale generative form, and the author’s attempts at delegating such decisions to a metacreative system by describing several of his generative systems and their approach to structure. The author will describe in greater detail his latest system — The Indifference Engine — and how it negotiates between agent intentions and performance information derived from a live performer.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.264
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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