Machine learning for potion development at Hogwarts

TL;DR

This paper explores the use of deep neural networks to generate and classify potion recipes at Hogwarts, demonstrating that machine learning can produce potentially useful recipes for educational and research purposes.

Contribution

It introduces a novel application of machine learning to generate and classify magical potion recipes, bridging AI techniques with wizarding education.

Findings

Generated recipes mostly fall into psychoanaleptics and dermatologicals categories.

Predicted probabilities for classifications are often above 90%.

Some recipes are classified into multiple categories, indicating complexity.

Abstract

Objective: To determine whether machine learning methods can generate useful potion recipes for research and teaching at Hogwarts School of Witchcraft and Wizardry. Design: Using deep neural networks to classify generated recipes into a standard drug classification system. Setting: Hogwarts School of Witchcraft and Wizardry. Data sources: 72 potion recipes from the Hogwarts curriculum, extracted from the Harry Potter Wiki. Results: Most generated recipes fall into the categories of psychoanaleptics and dermatologicals. The number of recipes predicted for each category reflected the number of training recipes. Predicted probabilities were often above 90% but some recipes were classified into 2 or more categories with similar probabilities which complicates anticipating the predicted effects. Conclusions: Machine learning powered methods are able to generate potentially useful potion recipes for teaching and research at Hogwarts. This corresponds to similar efforts in the non-magical world where such methods have been applied to identify potentially effective drug combinations.