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Record W4390486156 · doi:10.23977/jaip.2023.060809

The Transformation of Photography by Artificial Intelligence Generative AI Technology

2023· article· en· W4390486156 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2023
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
Fundersnot available
KeywordsGenerative grammarPhotographyTransformation (genetics)Computer scienceArtificial intelligenceGenerative DesignComputer technologyExpression (computer science)Visual artsMultimediaArtEngineering

Abstract

fetched live from OpenAlex

With the development of technology, the art form of artificial intelligence generative AI technology, created through code, has become a new form of artistic creation. The advancement of computer technology has expanded the creative dimensions of AI generative technology from two-dimensional to three-dimensional, and further enabled effective cross-fusion with various artistic media. This not only brings new modes of artistic expression for creators but also offers a fresh experience for viewers. This paper analyzes the application of AI generative AI technology in image-based artistic creation, elucidates the contemporary value of photographic art's essential language, discusses the impact of intervention by new imaging techniques on photographic art, and further explores the potential development of photographic art in the era of artificial intelligence.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.361
Teacher spread0.310 · 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