Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Creativity and expression are no longer a forte of a human being. Advanced algorithms, also known as AI, are capable of creating content of their own such as painting and composing music. In the era of Industry 5.0, Google's AI company, DeepMind, has created software that can produce novel music sounds and unique images. If such things had been created by humans, they would have been subject to protection under copyright laws but since machine lacks the characteristics of humans, the question arises about who owns the copyrights for the content. US circuit court has recently held in the case of Naruto et al . vs David Slater that animals, other than humans, cannot sue for copyright protection. Furthermore, WIPO member countries enacted a law that states non-humans are not subjected to protection under IP laws. Furthermore, it depends a lot upon the interpretation of courts for originality requirements under authorship and if it requires creative inputs from humans. In India, to get protection under copyright, one must prove creativity in addition to variation from previous works. Another issue that this paper reflects on would be the determination of several possibilities for assigning authorship where an object has been designed by AI. This paper will analyse the claims of the stakeholders such as programmers, users, and AI software itself to determine who has the greatest claim towards ownership of products and further possibilities for assigning authorship where an object has been designed by AI, thus analysing the question of “who is the author” of the works created by AI. This piece contributes to the literature on conundrums arising in the Industry 5.0 era, which on hand is striving hard to develop technologies in assistance of AI but is also facing issues on who owns the assets and liabilities arising out from the labour of AI. With the press release of the Japanese government that plans to create a legal framework, especially for the works created by AI to protect copyrights on novels, music, and other works, it becomes a relevant question if laws, including international treaties, should redefine the term “authorship” and include non-legal entities as well under the umbrella or should develop a new legal framework for AI.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle