Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to create novel foods within a structured design space. Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design.

翻译：饮食选择深刻影响着人类与地球的健康；然而，设计出兼具美味、营养与可持续性的食品仍是一项挑战。本研究证明，生成式人工智能能够直接从大规模人类生成的食谱数据中学习人类味觉的结构，从而在结构化设计空间内创造新型食品。以汉堡为模型系统，生成式人工智能在无明确监督的情况下重新发现了经典巨无霸汉堡，并生成了针对美味度、可持续性或营养进行优化的新型汉堡。在餐厅环境中对101名参与者进行的盲测感官评估中，其生成的美味汉堡在总体喜好度、风味与口感上均达到或超越了巨无霸汉堡的水平；其蘑菇汉堡的环境影响评分降低了一个数量级以上；其豆类汉堡的营养评分则接近巨无霸的两倍。这些结果共同确立了生成式人工智能作为一种量化框架，能够学习人类味觉并在原则性食品设计中驾驭复杂的权衡关系。