{"id":16901,"date":"2016-11-18T00:10:44","date_gmt":"2016-11-18T05:10:44","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/chopchop-smart-cooking-backed-by-artificial-intelligence\/"},"modified":"2016-11-18T00:14:22","modified_gmt":"2016-11-18T05:14:22","slug":"chopchop-smart-cooking-backed-by-artificial-intelligence","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/chopchop-smart-cooking-backed-by-artificial-intelligence\/","title":{"rendered":"CHOPCHOP \u2013 Smart Cooking Backed by Artificial Intelligence"},"content":{"rendered":"

In the beginning\u2026<\/strong><\/p>\n

Around 2 years ago, my girlfriend and I were driving along Australia\u2019s Great Ocean Road in Victoria. During our drive we placed our trust in the car\u2019s GPS to guide us to our destination, which allowed us to better enjoy the experience and take in the stunning coastal views. This got us thinking about other life events where we wish we could outsource the thinking to maximize our enjoyment in the moment. Being food lovers, our thoughts drifted to cooking and dinner parties. What if we could be guided through the process of cooking multiple dishes, without the stress of potentially making mistakes on sequencing and timing, so we could better enjoy cooking and eat perfectly cooked meals? That was the spark. Since then, we\u2019ve developed CHOPCHOP \u2013 a dynamic task management app for cooking \u2013 essentially, a\u00a0GPS for cooking[1]<\/a>.<\/p>\n

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Cooking up value<\/strong><\/p>\n

The problem with traditional recipes is that they\u2019re static and specific to only one dish with a fixed serving size. As we\u2019ve all experienced when cooking, timings often change, we usually prepare more than one dish, and group sizes vary. \u00a0CHOPCHOP\u2019s value comes from providing users with a customized \u201cgameplan\u201d (step-by-step guide), optimized for their chosen dishes and serving quantities, that dynamically adjusts based on the user\u2019s individual cooking pace. Additional services include a shopping list aggregator linked to supermarkets for home delivery, and RSVP management tools to communicate with guests.<\/p>\n

At the core of CHOPCHOP, is a cooking algorithm \u2013 developed by two team members who are PhD quantum physicists from Imperial College in London \u2013 that replicates how trained chefs determine the optimal sequence of steps to cook a meal. This is done by drawing on a database of \u201ctime curves\u201d for hundreds of ingredient-action combinations and employing ideas from particle physics and biological optimization. \u201cTime curves\u201d describe how the time required to perform a specific action (e.g., chopping) scale as the number of ingredient items (e.g., onions) increase. These curves are continually refined each time users cook with CHOPCHOP to help improve the algorithm\u2019s accuracy for all users[2]<\/a>.<\/p>\n

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The ability to combine discrete recipes into one optimized sequence is what sets CHOPCHOP apart from competitors. Current competitors fall into one of two buckets:<\/p>\n