Deep Learning Algorithm Rewrites Traditional Recipes for New Regions, Ingredients

Imagine your favorite go-to recipe mutated to conform to the traditional methods and ingredients of any number of diverse regional food cultures. Consider, say, lasagne, but a sort of lasagne that's instead a naturally occurring part of Japanese or Ethiopian cuisine. Not "fusion," but something deeper--a whole rewriting of what a lasagne even is according to the culinary traditions of some other place. It's not necessarily an easy or natural thing to do, but a new machine learning algorithm developed by a team of French, American, and Japanese researchers offers an automated solution based on neural networks and large amounts of food data. The result, which is described in a paper published this month to the arXiv preprint server (via I Programmer), is a system that can take a given recipe and shift it into an alternative dietary style--sushi lasagne, say--as well as parse a recipe for its underlying style components.