AI and ML in the Workplace: Introducing an AI Elective
Notice bibliographique
Résumé
The popular and rapidly evolving application of AI has been the subject of a great deal of attention over the last two years. According to a report by investment bank Goldman Sachs, artificial intelligence (AI) could replace the equivalent of 300 million full-time jobs [Goldman Sachs]. The report suggests that AI could replace a quarter of work tasks in the US and Europe, but it may also lead to new jobs and a productivity boom. Similarly, according to a recent survey by PwC, almost a third of respondents said they were worried about the prospect of their role being replaced by technology in three years [PWC, 2022]. However, a year later the next iteration of that survey found that a majority of the respondents anticipated that AI would have one or more positive impacts on their careers. [PWC, 2023] Consequently, most educators would agree that the current generation of college graduates should enter the workforce with some readiness to make use of AI concepts, AI applications, and (at the very least) some awareness of how AI promises (or threatens) to influence our near and foreseeable future. This is too large of a topic to ignore. However, for many educators, it is not immediately clear how AI topics should be integrated into individual courses, degree programs and fields of study. This TREO talk recounts how the Fox School of Business at Temple University introduced its first undergraduate elective in AI as a course offered this past spring. It provides specific details on how the course was presented and framed for the approval of college administrators. Also presented are an overview of the course, its objectives, and its content. Two important, over-arching elements of the course strategy were to (first) ground students in some established foundational elements of A.I. and (second) to expose students to relatively mundane applications of AI that they are likely to see in their future workplaces. These two elements have the effect of fostering realistic expectations regarding what AI can do and improving students’ ability to make significant contributions in their future workplaces. Excerpts of student feedback for the course are presented and discussed. Next steps in course / curriculum development are also discussed.
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 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 | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».