Credit: MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)
Duckietown: Where Self-Driving Cars Meet Rubber Duckies

Creators: Jay Weiss-Curry, Sam Daitzman, Micah Reid

This studio was about taking human decisions and solving them using computer friendly algorithms. This was often counter-intuitive, making simple to solve problems into processes with several steps and variables. We wanted to make them thorough so they considered all factors, and we wanted to prompt the user for info with a simple and user-friendly website. 

The issue we’ve tried to address is that it is very difficult to find restaurants to eat at when one has multiple or severe allergies. We decided to create a website that takes in a list of allergens to avoid, as well as cuisine types that the user prefers, and returns a list of restaurants in order of most to least likely to work. To do this it takes into account ingredients lists for individual dishes, as well as kitchen contaminations for more severe allergies. It also considers type of food and Yelp rating.

We designed a simple input system for restaurants that could be sent to them en masse, allowing us to uptake and update our restaurant data more effectively than alternatives like AllergyEats that rely on users submitting data. This strategy of 100% user-submitted information works well for major chains like McDonalds, but fails to give users information for smaller restaurants. We didn’t have time to code this restaurant-facing interface, but we wrote a proof-of-concept for the restaurant ranking which is entirely functional. We also wanted to add options for different types of food, selectable with checkboxes. This would make our algorithm more complex, but we’ve included it in our mockups.

Creators: Christine Bourdeau, Sofia Cabral, Jack Saperstone

The goal of the studio was to create a website that would act as a decision aid to help users answer complicated questions. 

Often times people are unable to decide what they should eat that is nutritious and healthy for them. We created a website that acts as a decision aid to help you decide what to eat as a healthy meal or snack based on your days previous meals and the recommended daily value of different nutrients.

In order to offer food suggestions, our website takes information from three sources: 1. Sliders that allow the user to indicate approximately what types of nutrients they have consumed. 2. A checkbox that indicates what meal they wish to eat next and hence how many nutrients they need from the food suggestions. 3. A database of food items with their associated nutrients. The nutrients our website considers is protein, fiber, sugar, fat, sodium vitamin A(IU), vitamin c, calcium, iron, magnesium, and potassium. The decision making algorithm starts by computing the remaining nutrients needed by computing the difference between the amount of nutrients they have consumed and the amount of nutrients they need for the next meal. Once this difference is found a search starts which explores a branching tree. Each branch of the tree represents a food item from the database that the user could consume. Each node in the tree represents the remaining nutrients they need to eat. The search stops when the remaining nutrients at a particular node is negative, indicating they have eaten enough. The returned answer is the set of foods associated with the branching path from the root of the tree to the node with zero remaining nutrients.