Episode 27 – Calla Lee (Dark Futures 3/5)

This special episode is the third of 5 talks delivered on stage at StartWell's Event Space on King St W in downtown Toronto on November 28, 2019 for a globally roaming annual series called Dark Futures. This talk was presented by Calla Lee (https://www.linkedin.com/in/callalee) and is titled "Tinder" *Dark Futures is presented by globally renowned Futurist, speaker, researcher, and author Nikolas Badminton. (https://nikolasbadminton.com/) [expand title="Podcast Transcript"] Qasim Virjee 0:27 Welcome back to this the 27th episode of the start well podcast. As always, I'm your host start Will's founder and CEO Qasim Virjee. And for this special episode, we've got the third talk that was presented a dark futures y, y, z in our event space on King Street West in downtown Toronto. This talk is about Tinder, and it was presented by calla Lee. Calla Lee 0:57 Hi, everyone, this is good. Alright, so Tinder, it's a pretty loaded topic. And I'm gonna say whether you've actually used it or not, it's kind of undeniable that Tinder has actually changed the way that we actually meet people and the way we date and how we interact with each other even. So when Tinder first launched, it launched with an algorithm that was actually called the ELO rating system. So if you've never used Tinder, it basically means that if you, it serves you up a stream of people. And if you like them, you swipe right. And if you don't, and if you don't like them, you swipe left to Nope. And then basically, if two people swipe right on each other, it's a match. And it's really that simple. But it's actually underneath that stream of people that get where it gets really interesting and complex. So this ELO rating system, it's actually the same system that they use to rank chess players with. So basically, so what you see in that stream of people are a collection of people who have all received a similar number of swipe rights as you have. So the kind of cluster you and what they call groups of that are based around your desirability score. So that was our first algorithm. And their second algorithm actually launched earlier this year. So they basically said that, now that they have enough sufficient data, they can actually start writing their own algorithm. So their new one will adjust the potential matches you see, each and every time your profile is liked, or note. And any changes to that order of your potential matches are reflected within 24 hours or so. So what does this basically tell us? It basically tells us that within 24 hours, like, it'll basically keep turning, and it'll keep collecting data on you. So Tinder cubed, what we're actually looking for is, will one day Tinder be able to actually tell us if, based on two dimensional experiences, so all the things that make us look really great on paper? Will those actually translate into three dimensional, long lasting human interactions? Calla Lee 3:16 So we actually have all the things that we need today to actually say, Yeah, Tinder can do that. And there's actually four things, I'm going to walk you through those right now. So the first one is actually big data. It's kind of everywhere. And it's this invisible layer that just captures everything that we do. So whether it's how long you what words, you search for it, the places you go, the people you meet, how long you spend taking selfies, how long what you purchase, and how frequently you purchase them, you name it, we actually track that. So one of the best examples that I know is actually the time that target was actually able to figure out that a teenage girl was pregnant before her father did. And the way they did that is they took they took a look at the data and they said, What do expectant mothers purchase and they were actually able to figure that out. So once you figure that out, then you're basically at a