AI and the Visualization of Paradise: Cultural Paradigms, Aesthetic Evolution, and Cognitive Exploration, From Varanasi to Sinaia - 3rd and 4th World Congresses on Logic and Religion, Proposal for organizing the 5th Congress on Logic and Religion in Vancouver, July 6-10, 2025
Bibliographic record
Abstract
AI and the Visualization of Paradise: Cultural Paradigms, Aesthetic Evolution, and Cognitive Exploration This study investigates the intricate process of visualizing the concept of paradise through the lens of Artificial Intelligence (AI), employing neural networks to craft intricate visual depictions of utopian realms. The project scrutinizes the prevalent themes associated with paradisiacal imagery, dissecting the intricate weave of religious doctrines, mythologies, and the intrinsic nature of paradise – be it a tangible realm or a metaphysical state. This inquiry critically assesses the role of AI-generated art, derived from complex algebraic formulations, in mirroring conventional iconography and exposing inherent biases. Such revelations underscore the cultural resonance of diverse traditions and the potential presence of subconscious prejudices. Employing AI algorithms capable of transforming textual prompts into vivid illustrations, this research unveils insights into the nexus of AI and cultural portrayals of utopia, thereby provoking profound philosophical deliberations. Central to these contemplations is the extent of human dominion over conceptualized ideals and the prospect of AI-crafted art inadvertently molding our paradisiacal perceptions, with implications ranging from reinforcement of stereotypes to shaping intrinsic cognitive schemas. From Varanasi to Sinaia - 3rd and 4th World Congresses on Logic and Religion This talk discusses the development of the Logic and Religion project and the various events organized within this framework, particularly the 3rd and 4th editions, with this volume including papers presented at both. Proposal for organizing the 5th Congress on Logic and Religion in Vancouver, July 6-10, 2025
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".