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“The goal of the Natural Language Processing (NLP) is to design and build software that will analyze, understand, and generate languages that humans use naturally.”
We want to create a way in which natural language processing and machine learning could improve an already existing task from a design perspective. We studied numerous examples of these processes being used in all types of Internet-based industries. What stood out to us most in comparing the different types of machine learning tasks (from synthesis to summarization to search, etc.) was their common thread:
"Performing actions based on datasets that are too large for humans to parse through."
Ultimately, an expert human will understand their own preferences enough to make the right decision; however, given large amounts of options and potential conclusions, it can be very difficult for anyone to make sense of the information that’s presented to us.
Therefore, how can AI help people?
A Real Life Problem
It's easy to think of Yelp when I am thinking about large data sets and language. Once in awhile, I check Yelp to see if there are any good restaurants to try with friends.
Figuring out where to eat isn’t just time-consuming, it’s also inefficient. Between two people, making plans can be a huge hassle, because there are too many steps involved with making a decision.
Planning a meet-up occurs in a single conversation, but virtually, it involves at least three separate app environments, including messaging, calling, navigating, using Yelp, and more. Furthermore, if these apps don't communicate with each other, there is often overlap in the data entered.
Therefore, we want to design something that can access all the information you need to get from planning to doing by simply having one conversation.
To begin with, I first sent out surveys to see if people were having problems scheduling a meeting and how would they use Yelp in this process.I got 45 responses, most from college students