Katherine Olives
With Demo Day coming soon, wed like to introduce you to some of our startups competing for seed funding totaling $150,000. Some of our startups are using cool technologies like AR/VR, machine learning, and big data to empower everyday entrepreneurs. Thats what Fare, an app designed to help hardworking cab drivers better predict demand, is all about.
I sat down with two members of the team to talk about how they came up with this idea, and what they think the future will look like (we talked robots) in this blog. Fare is made up electrical engineering students King Crawford & Michael McIntosh and computer engineering student Sarena Tran (whos father is a yellow cab driver). This is how they got started:
Michael McIntosh: It started out as a concept that I had thought about during an AI class. We were studying bio-inspired algorithms and particles swarm optimization. Its all about how flocks of birds find food. Initially I thought the algorithm would be able to locate and distribute taxi cabs in a city. I wondered how would you distribute them in a space so theyd all have the most chance of meeting demand.
At first it was just an idea, but then I started to think how can we actually do that? They say autonomous vehicles are still 15 years out, but this would be a great technology for them. But it can help the current taxi industry. Taxi drivers have been hurt a lot by services like Uber and Lyft. How can we create something that helps them, and also work with all of this data?
King Crawford: So he approached me and told me about this idea. It was first for senior design–it seemed timely and it was interesting material to explore on the technology side of it. Plus, I started working at Bustrek around that time. Its a special group at the MTA that writes the software the bus drivers use to route busses in the city, so I had some experience in the area of transportation. We write the software that the bus drivers use to route busses and how they run.
MM: We had taken this idea into senior design, and when Lindsay came and spoke to our class, I realized that the Zahn Center was the resource that we needed to get this off the ground, build it, and understand it. Thats when we applied to the competition. It was the perfect fit.
Katherine Olives: Thats awesome–it sounds like you really fell in love with a problem.
MM: We definitely did. And we love learning more about this problem, but its so hard to talk to people sometimes–it can be really nerve-wracking. Weve spoken to a bunch of Uber drivers and yellow cab drivers, and we even went to a garage a couple of weeks ago. A couple of the drivers were skeptical and not so open to talking to us in the beginning, but once we spoke to some of the dispatchers and well-respected people there, they all saw that they could trust us.
You definitely have to approach it a certain way, say were engineers working on tools, we know things, were students, so they know we can talk to about this problem and we want to work on it. Theres definitely a wall at first. We were using that garage as a practice, but they quickly let us in, and they gave us a lot of contacts.
KC: But there were definitely some surprises–or validations wrapped in surprises. When we asked them questions about what they need and what they were looking for, theyd almost give us our idea. But then their imagination went a little further, and they started talking about things that we couldnt deliver based on current technology. What it did prove was their willingness to accept a technological solution for this problem.
KO: Is that a major concern? That drivers just wont want to use technology to solve this issue?
MM: Its definitely something we think about. Some drivers say that they wouldnt need this because there are people everywhere. But then when they start talking, they say things like they never know whether they should turn left or right. They want to know where the customers are and if theyre there. They want us to predict with 100% accuracy. We know that the problem is now real, and we didnt just make it up. Now were thinking of how wed market it.
KC: We also need to know how we can build that trust and show them this tool is useful to them so that they have a desire for it. Its more than just a tech challenge.
MM: Right now were thinking well have to market it to new and younger drivers who dont have any specific habits yet.
KC: And maybe years down the line, after building relationships with drivers, wed break into the autonomous market. But right now were concerned about making this working for people.
MM: Exactly. If we can figure out a way to take what we have and empower medallion owners to get into the autonomous game–because people are going to be responsible for these autonomous vehicles, theyll need trainers, garages still–and help them through this process of transition, thats our main goal. Of course, should the autonomous market come in to play.
KO: Right. We definitely grew up imagining flying cars–but well have to see how soon autonomous vehicles actually become a normal part of life.
KC: Beneath it all, the main idea is to enhance human life.
KO: Im hoping robots will do just that!
KC: Hahaha maybe!
KO: Before we get into a discussion about the future of robots, Ill let you both get back to work. Were all super excited about Demo Day. Is there anything you want readers to know before we see you there?
MM: By Demo Day we hope to have users testing out the platform. We want to have our business plan formalised and in a deliverable form, and were still looking for interviews. We want to talk to as many people as possible and get yellow cab drivers. So if you know anyone, send them to us!
You can reach the Fare team at fare@zahncenternyc.com . Dont miss them at Demo Day–make sure to RSVP here!