An Artificial Intelligence Based Method for Bartending

In recent years, artificial intelligence (AI) has made significant advancements, but its application in precision-demanding fields like bartending remains limited. This paper presents an AI-based method for bartending that integrates computer vision and natural language processing (NLP) for precise ingredient identification and action generation. Using the YOLOv8 model, the system achieves real-time detection of bartending tools and ingredients, while Generative Adversarial Networks (GANs) are employed to generate customized bartending actions based on user preferences. This method enhances adaptability in dynamic bar environments, addressing gaps in current semi-automated bartending systems. The proposed system significantly improves operational efficiency, precision, and user satisfaction, showcasing the potential for advanced commercial AI applications in hospitality.