Notice bibliographique
Résumé
Artificial Intelligence and Education in our TimeThere is presently a lot of excitement around the importance of educating ourselves at all levels about the uses and abuses of Artificial Intelligence (AI).Research applications to funding agencies abound that wish to show to students of different ages and abilities how to access some of the most recent general AI programs such as ChatGPT and their equivalents for purposes their academic work.Among the commonest is aid in writing scholarly essays.In this part of the world, essays in English are paramount.But any language will do these days.Certainly, it is important that many who need it can get help in producing a written piece of a topic of one's choice.But of course, the other side of this is that such written pieces can be produced as one's own work for credit in some educational arrangement or another at school, college, or university level.So, there is equal interest in teaching learners at all levels the moral wrongness of how one might be offering work as one's own that is really just the product of access to these generals AI programs like ChatGPT.It seems to me that teaching how to properly use such AI systems for particular purposes is a valuable thing.And teaching the ethics of their use is another valuable thing.But it also seems to me that the most valuable thing that we might each learn, in an era of omnipresent AI, is what thinking is necessary in order to create a new and useful piece of AI that can perform something valuable that some of us can do or perform that could be turned into a system that could be turned into AI for the benefit of others too.That is learning ourselves how to break down the steps we actually use to accomplish an important task that we can do and other find difficult.When one understood these important steps, the steps that we uniquely easily and automatically perform , could be used to create new and useful artificial intelligence.One can illustrate what I mean by a problem that occurred to historians in the 1970's who were interested in the history of populations and social structure.In order to look at whole populations, historians had discovered that by the mid-19th century large files of entire populations were being routinely generated.An obvious one was census of entire populations in many countries.The Bible tells us that the Romans took such censuses of everybody in their empire at the time of Jesus's birth.And most European countries and perhaps China took such censuses routinely inte-mid 19th century.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,016 |
| Communication savante | 0,011 | 0,009 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».