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[Assistant] But the other day I was lying in bed thinking like, what if she put a camera in this thing? It's...


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[Assistant] Really hard to talk about my life and have it not sound like I'm bragging.


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[Assistant] Haha, I said that today too. My nose is so cute in that. Sorry, I just, look at that nose. Look, a little button.


[User] Please transcribe this text

[Assistant] Sometimes I'll catch myself in a conversation with someone.


[User] Please transcribe this text

[Assistant] Acting like I'm in an interview. And in interviews, you're trained to talk about yourself and not ask the other person about themselves.


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[Assistant] And so I'll catch myself in conversations where I'm like, shit, like I'm not.


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[Assistant] They're trained to talk like they're in an interview and that's normal, but it's like we forget that we're not though. Yeah, the first year I was like, you better know how to drive, right? And didn't I say?


[User] Please transcribe this text

[Assistant] Oh no, I saw this part recently. It was like, you better know how to drive.


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[Assistant] Because I've been putting off Driver's Ed because that stuff stinks. I hate that stuff. Ew. That stuff stinks.


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[Assistant] But you can though, cause it's your car now. Uhm, it was a Dodge Challenger, matte black.


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[Assistant] Actually literally two months from today is my birthday. So, two months after that day, I got my dream car and I cried for about three hours.


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[Assistant] Next year I hope you're very, very happy and that your happiness you have this year continues and only grows.


[User] Please transcribe this text

[Assistant] Kind of going off of that, I think, if we're thinking about the shift in metaphor towards like systems thinking, I wanted to ask you about one of the things on your earlier slides where you're talking about institutional design. Uhm, like what, what is the institution that we're designing in, in your vision of this like shift? Uh, like is it regulation from federal governments or?


[User] Please transcribe this text

[Assistant] They were the man. You knew the trolls. You knew them all. Yeaah, so yeaah, I didn't really like that. They didn't really like me too much either, so that's cool.


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[Assistant] I do not, it was probably in school or some shit. How old are you? I'm 20, 25.


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[Assistant] Yeah, I don't know, those like computers were a new thing. Just, uh, all that technology, cause...


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[Assistant] Yo, there's no campaign on that and it pissed me off because I realized like the first one, the campaign was awesome and there's no, there's just like multiplayer games and like the survival shit and whatever. So yea, there's no like storyboards.


[User] Please transcribe this text

[Assistant] Uhm, it's bad, I'm smokin' spliffs heavy. Okay. I was like, I used to tour overseas a lot, so. And that's where they get you, huh? Cause everybody's on that out there. Yea, and it's been a while since I've been over there, and I've still been smokin' spliffs for a while.


[User] Please transcribe this text

[Assistant] Left brain disappears at random times. Really? And it happens, yeaah. So there's a, we say where the fuck is left brain often.


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[Assistant] Yeah, we were in the, well, Left Brain was in the first video, all that. Right.


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[Assistant] Yeah, and then it didn't, and then the album kinda went wrong, so yeah. The album did go wrong, I remember those stats when those came out. Yeah, so if you wonder why, it's because it didn't.


[User] Please transcribe this text

[Assistant] If I got the invite, hell yeaah, but on the random, I can't say over the 10, you know. Yeaah, yeaah. Yeaah, it's kind of frightening.


[User] Please transcribe this text

[Assistant] Yeaah, cause, man, 50, he got on him. But yeaah, I was watching beef videos today too, so. Yeaah, oh really, okay. Yeaah, I was watching, uh, Joe Budden and Game. Okay, yeaah, yeaah. That's why he started dissing Joe Budden. See, that's the soup.


[User] Please transcribe this text

[Assistant] I was heavy into that whole scene, so yea, I fucked with 50 Cent Heavy. Yea.


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[Assistant] I mean, it's uh, he, he found his niche and he tries to go for the one over and over again.


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[Assistant] Shoot for XXL and Terry Richardson was shooting it. And we were just.


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[Assistant] Yeah, the, yeah, the, like the song, yeah.


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[Assistant] Uhm, I mean, of course, it's just cause you get older. Yeah, I miss it. It's different. And it...


[User] Please transcribe this text

[Assistant] You know, in your life, you could find a way you could improve on it, but yeaah, it was definitely good times.


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[Assistant] Ideas for beats or ideas for lyrics? Lyrics. Okay. Yeaah.


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[Assistant] Well, that's another thing about me. That's like, it's always been.


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[Assistant] That's the thing, you gotta learn from artists, so I'm out there and I adapt, I mean take, you know, what they are showing me, and just apply it to what I have.


[User] Please transcribe this text

[Assistant] Really, the internet, like, Sid and Matt, both of them, I used to live with them, so.


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[Assistant] March Madness is, uh, in my words, the greatest song of 2015. Did it come out this year? Whenever it came out?


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[Assistant] So great. Everything, everything came along natural for her. And uh, that's just the way you want things to happen.


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[Assistant] I really like Alright, that's a great song. You know, I didn't really cruise around to it too much, but you know.


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[Assistant] Some artists know what they're doing and that's, I mean, that's the music I really, like, uh, live my life to.


[User] Please transcribe this text

[Assistant] If I could think back around that time, it was probably the cool kids, like some of the cool kids, but you know, I listen to a bunch of shit, so like, yea, it was probably some of the cool kids.


[User] Please transcribe this text

[Assistant] Like what they play, so you don't, you don't really get to find too much. In Japan. Yeah, in Japan.


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[Assistant] I believe he was D12's DJ, but I know he's Obie Trice's DJ, but he's in the streets, he works for D-Real right now, so yea, Heavy Detroit, uh, Heavy Detroit DJ, so yea, Slime Rack, he hooked it up, he knows I'm a big fan of the whole


[User] Please transcribe this text

[Assistant] I mean, even still, it's a, you know, it's kinda...


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[Assistant] I forgot, we were trying to bring girls into the hotel and like, security said we couldn't. Uh huh.


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[Assistant] It's a scuffle. Other dude comes running from outside, kicks the dudes off us.


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[Assistant] Yeah, I feel he's, he's seen that for a little bit. Right. Since, since the dive in the ocean. Yes, yes. That was a legendary moment. Little by little.


[User] Please transcribe this text

[Assistant] Uhm, he was, he was real nice. I met him. He was waiting to talk to Tyler, so.


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[Assistant] He probably did talk to Tyler, but you gotta come for the whole crew, put the bread up, you know? Yeah. We'll make it happen. You guys could've had Akon on the hook and shit. Man, give me the T-Pain hook, give me the Ace Hood feature.


[User] Please transcribe this text

[Assistant] Dang, I didn't think about, oh, well then, Rihanna. Oh, okay, well yeaah, alright.


[User] Please transcribe this text

[Assistant] Off top, off top. I was about to say R. Kelly. Oh yeaah. Cause he's, he's 60 racks for the feature.


[User] Please transcribe this text

[Assistant] Never beat anybody up at a show. Never? Throwing a punch? I've never beaten anybody up at a show. Hey, that's all I can say.


[User] Please transcribe this text

[Assistant] See, I can't, you know, can't say too much. Nah, I haven't really solidified.


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[Assistant] Just came to you? In the period of, you know, how I was feeling at the time.


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[Assistant] Yeah, yeah, definitely. I mean, either way it goes, some people...


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[Assistant] It was, uh, yeaah, of course, I was on stage, man, I was, during our set, we were supposed to fucking blow people away, but you know, I did, I blew some people away, but, you know, the,


[User] Please transcribe this text

[Assistant] Yeah, definitely, but, uh, not much you could do. I just wish you gave me a heads up. I could've did a couple more songs. That's all I'm saying. Gee, holla at me, you know, next time. Yeah.


[User] Please transcribe this text

[Assistant] Nah, it's not weird, but it's like, it's just, cause I understand it, it's just uhm, a lot of shit I wear I design, so, you know.


[User] Please transcribe this text

[Assistant] Uhm, I mean, still homies, it's cool when I see them.


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[Assistant] Is, uh, taking the grain of salt, but I just, you know, feel people should choose their words correctly and just in a better manner.


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[Assistant] Loss of a, I mean, it takes so much time, I just hate it, but it's like, yeaah.


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[Assistant] Definitely try to be just in terms of the fact that so many bring awareness to the fact that it is fucking like devastating to your day.


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[Assistant] Let's not get into that. These are from my insecurities. I don't like to talk about those.


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[Assistant] I knew it would be like that. Yeah, that's weird. Uh, yeah, I dunno. And I was on, I was on, uh, Dirty Sprite too, so I couldn't even, couldn't even take my attention off of that.


[User] Please transcribe this text

[Assistant] Yeah, definitely. Whatever he's trying to do, you know, I know he doesn't want to rap forever. He didn't. Yeah, he did say that, huh? He initially, I mean, there was a time he didn't want to do this, so, I'm definitely proud of what he's become and everything he's getting into.


[User] Please transcribe this text

[Assistant] Uhm, I couldn't, I couldn't say I would agree. It's just a different experience type thing. It's like a generational thing, so it's...


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[Assistant] If you're gonna, you know, if you're gonna call it that, if you're gonna get angry at that, you gotta get angry at every other decade too, so it's...


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[Assistant] It's vibrant, yeaah. There is no V.I.V.? No, vibrant is not a word, so no.


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[Assistant] God damn it. Yeah, let's, let's run, run with everything.


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[Assistant] Most poppinest rapper in the world. Yea, and then Quentin Miller's career blew up, so he put another rapper on, so like, that shit's crazy.


[User] Please transcribe this text

[Assistant] It seems the possibility is they could even still do a song together, so if that was to happen, you know, all the... If the song is good, I guess, you know, the...


[User] Please transcribe this text

[Assistant] Uhm, I'll take in a couple shots, maybe not a diss song. But by name? Cause I can't


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[Assistant] Yeaah, it was just an EP, uhm, I was about to put out a mixtape, Sony said they would let me, uh, release an EP, so, uh, put the five songs together. So yeaah.


[User] Please transcribe this text

[Assistant] Getting all the way into that process, getting all the way, all the way into that, like to that point is the process. So it's like.


[User] Please transcribe this text

[Assistant] Like I haven't done acid since I've done shrooms and that's been like four years.


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[Assistant] Dang, I don't know if that's what went down, but it was a collision of forces, you know. So yeaah. I was doing acid with the internet, man. That's all I'm saying.


[User] Please transcribe this text

[Assistant] Nice little plug. Nice little something. So I don't know, Christmas is overrated to me. It's cool though. But you know, I like the family time spent in the holidays. It's just a...


[User] Please transcribe this text

[Assistant] The, like, just THE music. Like, nobody makes music for the other holidays, other than Christmas. And the Justin Bieber Christmas album goes platinum a couple times, you feel me? But if he made a 4th of July album, would that go platinum? We don't know, so...


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[Assistant] But if you, if you like want to specify that this is for this day.


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[Assistant] And this barbecue right here, you feel me? I feel you should have that power too. Christmas shouldn't have the only, the only.


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[Assistant] Like, is it really the most wonderful time of the year? It's cold as fuck.


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[Assistant] I'ma, I'ma, I'ma come up with something. Yeaah. We'll pop up.


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[Assistant] Yeah, we could take that and just apply it to all the other shit.


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[Assistant] I mean, yeah, I'll be looking to, I'm just putting projects together. I'm always working on stuff. So, I mean, I've been, like, I've released a EP compilation mixtape and I've been dropping songs, like,


[User] Please transcribe this text

[Assistant] Yeaah, Twitter's cool, at MikeGKTA. Yup. And uh, Instagram is KingAli24K.


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[Assistant] When you got chintzy about money and decided you'd be happier paying a third of the rent instead of half of it.


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[Assistant] I have six guys. Our age is total 342 years.


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[Assistant] No thanks, I just had a hot cup of formaldehyde. What happened to your date? I wish I knew. I have just been stood up by an older type of most unattractive man with four buck teeth. Maybe I ought to get married, huh? Couldn't be any worse than this.


[User] Please transcribe this text

[Assistant] Oh, that's nice. Oh, come on, stick around. Have a sandwich, huh? Eleanor's gonna be awful disappointed you didn't stay.


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[Assistant] For example, if Danny got a job directing a play, and then he lost it, but he hadn't told you, you'd never miss it, would you? What are you talking about? Eleanor, to be right to the point, what people don't know doesn't hurt them. What they suspect or they think they know that gives them high blood pressure.


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[Assistant] I'm also suggesting that it's better to face a wolf in a well furnished apartment than at the door.


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[Assistant] Only a couple of dozen times. Say, how about something good to eat, huh?


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[Assistant] No, I guess you don't. Oh, if I could just see her walking. She is able to walk, isn't she?


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[Assistant] We have no license to transport whiskey. You see, this railroad system was founded when my boys were little.


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[Assistant] Michael, Mario, and Jonathan.


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[Assistant] It's been a little too much of an effort for me to play with them down on my hands and knees, so I...


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[Assistant] It's fun pretending that my boys are here with me at the big table. Would you say that's crazy?


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[Assistant] I gave a great many poor children a dollar apiece for catching those rats. And they gave me this medal. Worth half a million dollars to me.


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[Assistant] I got this ring for that. Of course, it's only made out of a horseshoe nail, but it was made by a very happy blacksmith. It is worth $750,000 to me.


[User] Please transcribe this text

[Assistant] You want to know where the rest of the money went? There's a diploma out there in the front hall.


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[Assistant] And the rubber plantation of the Amazon, yes. Philip has managed very well.


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[Assistant] They decided to go out in the world and make their own way. And didn't you try and stop them? I loved them for it.


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[Assistant] We'll be alright, they said, so don't try to find us. But if you ever need us, we'll be there.


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[Assistant] That's the kind of men my sons are. And I respected their wishes. Now, if you were suggesting that I turn my affairs over to one of my boys...


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[Assistant] Then I go with you. You see, I'm not gonna let you out of my sight.


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[Assistant] And very good stock too. We need new blood in the family. In fact, Philip, you didn't tell me you'd seen Michael.


[User] Please transcribe this text

[Assistant] There's no rayon factory in Canada. And there's no rubber plantation.


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[Assistant] Judge Alston, if Philip had had the ability to go out in the world and make his own way, as my three boys have done, I wouldn't kick up all this fuss about turning everything over to him.


[User] Please transcribe this text

[Assistant] Why, you're just a baby. Oh, we have three children upstairs.


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[Assistant] It's very good of you to come, Mr. Blanks. Won't you please join us? Thank you.


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[Assistant] Oh come, come children, please, this is Christmas Eve. And my dream has come true. All three of my boys are here. And, and here's a Christmas punch.


[User] Please transcribe this text

[Assistant] Broken down Rodeo rider, who had to sell his saddle to buy a Christmas dinner.


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[Assistant] Merry Christmas to my son, to my daughter, to my grandchildren, and Merry Christmas to all.


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[Assistant] Yeah, a show helps, and people, and girls. I like girls in a show. Speaking of girls, there's my idea of one there.


[User] Please transcribe this text

[Assistant] If that's a sample of what you can do, you can have them on their ears in this town. I'd like you to read the part in my script.


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[Assistant] You're kind of suspicious, aren't you? First time married, now I got etchings. Oh, is the boyfriend the stumbling block?


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[Assistant] You say you always wear your hat when you drink champagne? Any particular reason?


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[Assistant] Uhm, just, oh no, I got like a couple songs that I did last night with a session.


[User] Please transcribe this text

[Assistant] Uhm, well shit, I mean, I fuck with Savage Squad, you know, I fuck with Fredo Tough, you know, uhm, obviously I fuck with Chief Keef, cause that's Broski.


[User] Please transcribe this text

[Assistant] And uhm, you know I just recently reconnected with Gleece again. Okay. So I'll probably be doing some more shit with him, but I work with a lot of artists. Larry June, San Francisco. Uh huh. Uhm, shit, Hush right here, I'll be doing EDM type shit. Yeah.


[User] Please transcribe this text

[Assistant] I'm originally from LA, not from out here, so uhm, it's funny cause a lot of people think I'm from Chicago, they think I'm from like Atlanta, and they think I'm not what ethnicity I am, you know.


[User] Please transcribe this text

[Assistant] Well, my mom is Mexican, 100% Mexican, and my dad is Russian, mixed with, I don't know, I think Polish or something? I don't know, but.


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[Assistant] Wowed and fucked up, screwed up, you know, like just crazy, like man, that's the hurt, that fool's hurt, like he's turnt up, you know, like it was almost like a turnt up word, you know. Right. And, uh, there was a crew, like the Hurt Boy crew, actually going around and they were, uh,


[User] Please transcribe this text

[Assistant] They're like, I don't know if they were taggers or something or whatever they was on. Writers and graffiti writers or...


[User] Please transcribe this text

[Assistant] Gang bangers, I don't even know, but basically I remember one day I was at a party, and they, uh,


[User] Please transcribe this text

[Assistant] They came up and they see, cause they see me kinda going like crazy with some beats and some stuff and they saw my Instagram and seen what I was hanging out with.


[User] Please transcribe this text

[Assistant] And uh, they like kind of press me, they're like, oh, you know, using the Hurt Boy name, but we know you, we fuck with you bro. Just change the uh, the A G to the, cause it, cause it says, yeah, it looks like you a fag or something, like just change the Y, like put a dash between the A and the G.


[User] Please transcribe this text

[Assistant] But I was like, but they really was pressing me like, yo, dude, you're putting on for the hurt boys. Like, you know, you gotta just be 100 with it. And like, this is a long time ago though.


[User] Please transcribe this text

[Assistant] I associate with someone, you know, definitely shout out PB, Pip Blue, that's like OG homie right there, like, you know, he's the originator of the Hurt Boy shit, for real.


[User] Please transcribe this text

[Assistant] Somebody like, uh, I think somebody just made a Twitter for me. Like, put me as HurtBoyAG, you know.


[User] Please transcribe this text

[Assistant] And like I just ran with it kind of, you know, and just stuck. Like I never changed it or anything, so.


[User] Please transcribe this text

[Assistant] Yeah, well, this is like my grandpa, rest in peace, I got a tattoo right here, Pops, man. Okay. He like, he was like my biggest fan, but my biggest hater. Like, you know what I'm saying? Like, he, he didn't understand what...


[User] Please transcribe this text

[Assistant] Like what I was doing and what, like what I wanted to do with the music type shit, but he knew I was so into the music.


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[Assistant] Like, he bought me my first pair of turntables, he bought me a box, uh, technique.


[User] Please transcribe this text

[Assistant] Uh, in high school, his sister was like dating this old...


[User] Please transcribe this text

[Assistant] Hip-hop crew, one of the dudes from, it was called Ugly Duckling, I don't know if you ever heard of him.


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[Assistant] They like old LA, you know, they did shit with Defari and like, Dilated, I think. I don't know, there was like old, old, old hip hop, like, you know, people, LA artists and stuff.


[User] Please transcribe this text

[Assistant] And they, they had given us a whole crate of like scratch records and shit like that. Right. And I would do parties, like I would just take my, the turntables like grandpa made.


[User] Please transcribe this text

[Assistant] And do parties. And I was like, I'll DJ at the party, you know? Y'all wanna have a little party?


[User] Please transcribe this text

[Assistant] People was doing, doing new dance moves that I'd never seen and freak dancing. I was the youngest, like, I was like,


[User] Please transcribe this text

[Assistant] 16, 15 DJing like high school parties and stuff when I was in middle school. I think, I think that's it.


[User] Please transcribe this text

[Assistant] I was into like Outkast and all that shit, like New York rap, you know, all the old like Wu Tang and fuck dude like.


[User] Please transcribe this text

[Assistant] Big pun, all those fools, dude, like, and I was, I was also into just, like, 8ball, MGK, 8ball.


[User] Please transcribe this text

[Assistant] Uhm, UGK, 8ballMJG as it meant. Uhm, a lot of dudes from Memphis, you know, 3-6 Mafia. I was bumping all that shit, Mac Dre, fuck yea, like.


[User] Please transcribe this text

[Assistant] I was listening to everything, you know, in terms of hip-hop, rap, whatever you want to fucking call it. But I was into Nas, all those New York kids, like I love them, lyricists.


[User] Please transcribe this text

[Assistant] Nah, it was cool with West Coast Hip Hop. Fuck you, man. Dr. Dre, I like fucking all them motherfuckers. Snoop Dogg, man, shit. Uh, Warren G, Daz, like my brain is so crazy, like I have to literally dig in the files of all the rappers. I love DJ Quik, like fucking, he's a legend.


[User] Please transcribe this text

[Assistant] Now recently I've been like kind of rapping on some beats, which has been kind of funny. Like, like I'd be making so many beats at the end of the day I was so bored of recording people and just making my own beats I would just rap on them before I went to bed.


[User] Please transcribe this text

[Assistant] My homie Jason, uh, her Tazo, he was like, he was run, he was like the, uh, curator or whatever, you know? He was just like, whenever they had their...


[User] Please transcribe this text

[Assistant] Like every few, few months they would have like a different artist do some shit. They would have like, you know, Revoke have one.


[User] Please transcribe this text

[Assistant] I like, that's how I met like a lot of the dudes from my future, and you know, like all them from just being on Fairfax.


[User] Please transcribe this text

[Assistant] And I remember we was doing some parties and he booked me to DJ them and I remember


[User] Please transcribe this text

[Assistant] Cause during that time I had linked up with the future and uhm,


[User] Please transcribe this text

[Assistant] And I had music and stuff from them and, you know, stuff that people hadn't heard before.


[User] Please transcribe this text

[Assistant] And so, it was fun, you know? Like I was doing a lot of sets, I did ham on everything, and...


[User] Please transcribe this text

[Assistant] Shit, I was DJing for this group, CBG. Shout out Julien, Hawaii, and Cory, man. Them my boys, Corzilla, and all.


[User] Please transcribe this text

[Assistant] Oh, to the zillion, Todd, the fourth, the secret fourth member man, they had me DJing for him, like it was crazy, you know, Carter, you know, he used to be cool friends with Dom Kennedy, he had me DJ for him. So like I was DJing for people, you know, and, cause they knew I knew how to scratch and all that, so. Right.


[User] Please transcribe this text

[Assistant] And then I had to move at the time, like you know, Serato came out.


[User] Please transcribe this text

[Assistant] We were just talking about that last night, man. Me and Fredo was talking about how, um, like, so many motherfuckers is gonna be on Ghostface, like, killer right now because he went in on Action Bronson.


[User] Please transcribe this text

[Assistant] I, you know, honestly I've kind of like, stopped listening to a lot of music. Same. Like, on me, like I don't listen to a lot of other music.


[User] Please transcribe this text

[Assistant] I know he cooks bad well though, that dude is the fucking best chef. He cooked me the most fire. Like, personally, I don't know, it was like


[User] Please transcribe this text

[Assistant] It was for frat mates to like, uh, get naked pictures from the sorority girls. That was the original idea. I forget, it was called something stupid dude. But like, it was three guys in a room and they all created that shit and basically one of them got cut out.


[User] Please transcribe this text

[Assistant] And my sister was a part of like them kind of coming up with it so he approached her and was like, hey come be a part of my, my fight against these motherfuckers who cut me out basically.


[User] Please transcribe this text

[Assistant] I mean like, going to his house was crazy. Like, seeing all those hummers and shit, like, that dude got hummies.


[User] Please transcribe this text

[Assistant] Like he was like a real, like he was my favorite, like one of my favorites out of them.


[User] Please transcribe this text

[Assistant] Yeah, yeah, yeah, uh, I did a couple songs, I did like, uh, that song, um, Nesta. Uh-huh. Uh, it was like a reference, it was like a Bob Marley kind of song tribute.


[User] Please transcribe this text

[Assistant] So yea, we did that song, Nesta, that was like really a long time ago. We made that in Ladera. We had like a little studio in Ladera. It was so funny and like, you know, all them would be hanging out in Ladera and shit. So it was just like a small world that we was in, Ladera making beats.


[User] Please transcribe this text

[Assistant] And uhm, and then I did Damn, that record with Byron. Oh, okay. And, uh, me and Ralph one. Damn, that's a pretty big one.


[User] Please transcribe this text

[Assistant] Uhm, I remember Brick, uh, one of their managers, uh,


[User] Please transcribe this text

[Assistant] There was like a situation, like, with one of the drivers of the trash talk, uhm,


[User] Please transcribe this text

[Assistant] Crew. And you know how they linked up, they do shows and stuff together. Yeah. Uh, I guess the driver was like...


[User] Please transcribe this text

[Assistant] I like it, but I don't like it, you know? It's like, I don't know how to mute it, so like, I'll be in the studio and it'll be blinging and stuff when I'm recording people.


[User] Please transcribe this text

[Assistant] I feel like I have like a schedule and stuff cuz like I literally be planning my schedule and it'll pop up like your shit popped up on here like that I had to go today so.


[User] Please transcribe this text

[Assistant] It's so random, dude. Was that a boxing match? No. It was during, uh, Magic, the clothing convention. Right. And, uh, we was just walking around. Me and the homies had like a, uh, a hotel room. We had two suites.


[User] Please transcribe this text

[Assistant] We was just going out there promoting, like, I think I had like made some shirts that said Hurt, like Hurtagamo, Hurt something, like logo flips, you know, stupid shit, like.


[User] Please transcribe this text

[Assistant] I was just being high and making shirts and stuff, like literally down the street and getting my presses, like getting, um, you know, screen prints and whatever.


[User] Please transcribe this text

[Assistant] I bumped into, uh, I didn't bump into him, my friend noticed him, cause I didn't really know. You know, I was like in a lot of trouble and shit, so I was like...


[User] Please transcribe this text

[Assistant] Shit I was dealing with. That's how I actually met Antoine and shit like. In jail? Uh, in a lockdown. I was in a lockdown and they took me to a meeting and Antoine was there. And it was, not like I met him before, but like that's how we reconnected. Antoine Dixon for the record? Yeah, yeah, yeah, yeah. Okay. Tuan, Tuanen.


[User] Please transcribe this text

[Assistant] And, uh, but yeaah, no, so my homie Hawaii hit, uh,


[User] Please transcribe this text

[Assistant] Really fuckin', uh, my homie went up to him and said, what's up man, we respect your work, this and that. And I was just in the back, you know? And, uh, just...


[User] Please transcribe this text

[Assistant] I don't know, I think somebody, we exchanged numbers somehow.


[User] Please transcribe this text

[Assistant] Don't get me started about gambling, I love craps and all that shit. Like, I met Gucci man on the craps table, like, he'd be doing crazy shit, like, that's just buddy as hell, like, craps and everything, like, I play, uh, blackjack too, I'm crazy about blackjack.


[User] Please transcribe this text

[Assistant] I've lost everything I came with. And, uh, we left and me and my ex was like...


[User] Please transcribe this text

[Assistant] He was just kind of a dude at the time. I mean, he was just dude at the time, you know? And then like, I linked up with a lot of his family and like, you know, Scooter and, uhm, Rico Wade, you know? And I linked up with a lot of these people and, you know, and, you know, they, it was just, it was cool, you know? And I didn't really know what they had going on.


[User] Please transcribe this text

[Assistant] I remember I was in the car with Scooter and he dropped, uh, that, uh, what is that? Street lottery? What was that? He was listening to it in the car. Oh, shit, alright. Like his first, first, like one of his first mixtapes. I mean, I know he had mixtapes in Florida. Married to the streets? Yea, married to, no, no, no, it was before marriage to the streets. It was this one, uhm, uh, he was just bumping it in the car. It was, uh,


[User] Please transcribe this text

[Assistant] That was my shit in high school, like 2004, 5, 6, 7, like all that shit, like, that was my shit, like, so, but just them doing that shit and just all that shit and actually doing what they doing with them and shit, like that shit was crazy, bro, like.


[User] Please transcribe this text

[Assistant] Man, he's funny, he's a great dude. Great dude, great dude, like, just, like whenever we're around each other, it's just nothing but smiles and just, you know, he's a great dude, you know.


[User] Please transcribe this text

[Assistant] Oh yea, he, bro, he, he gets it from, uh, being around Future and all damn, you know, like, in the work ethic, like, Gucci for sure, like, Gucci work ethic is crazy, I've heard. I've never been in the studio with Gucci, but like, I've heard.


[User] Please transcribe this text

[Assistant] Hands down though. Like you've seen this happen? Seen this happen like more times than my hand. Really? Fingers.


[User] Please transcribe this text

[Assistant] And uhm, I had actually produced the whole album, I did a whole tape and it's been unreleased, but I'm probably slowly releasing some songs. Okay. But uhm, you know, me and him, like he's my mentor, you know what I'm saying, like for sure on everything, like he's the big boss, you know, like, taught me a lot, you know.


[User] Please transcribe this text

[Assistant] But anyways, they said you need a tag. And uhm, I remember I was recording Casino Features Brother. Oh wow, okay. And uhm, I got hella songs of them. Like...


[User] Please transcribe this text

[Assistant] Yeah, uhm, uh, that shit was tight. Uhm, now I got a lot of songs with him and shit, but like, we was recording for, uh, Ex-Drug Dealer Part 2. Okay. He said one song, he said, he said, Ex-Drug Dealer Part 2.


[User] Please transcribe this text

[Assistant] Nah, I was just like, he said my name, like I had like, uh, Gleesh say my name and I had like a bunch of other people say my name and that was the like, it was just the most loud and like...


[User] Please transcribe this text

[Assistant] And the most, oh, here's Taki, man, shout out Taki, man, that's a bro, bro. Uhm, but like, uh, you know.


[User] Please transcribe this text

[Assistant] I like the tag, I guess. It stuck on me, you know? At first I didn't like it, I thought it sounded cheesy, but then like, I thought I could pitch it up and play with it, you know?


[User] Please transcribe this text

[Assistant] Like in the song The Dope Game, I put it in the front of the beat, it's like her, her, her, it just sounded weird. But I take out the what it do. You take it out sometimes? Yeah. So, you know, it's funny.


[User] Please transcribe this text

[Assistant] He probably played the top line right there. He probably did it just at that point.


[User] Please transcribe this text

[Assistant] Cause that's how it works usually, you know what I'm saying? Say an artist will do the drums and then somebody will do the top lines and do the melodies and shit like that.


[User] Please transcribe this text

[Assistant] Was sent like over 30 beats and he put his tag on it and took my friend's tag off it. Really? And then said they were his and then sold some of them. And the paper flow. Made money off him.


[User] Please transcribe this text

[Assistant] That fool. He put out that whole tape with no permission, put Goofy Beez on the end, and shit like that dude. Like, that type of shit bro, like, you got something coming for him bro.


[User] Please transcribe this text

[Assistant] Uh, fucking, uh, shit, we was outside, we was at a house, they had rented a house in Malibu, it was a real nice house, you know.


[User] Please transcribe this text

[Assistant] Yeah, I was smoking outside and I was charging my laptop. I had three shows that night. Three shows. Like literally, I had a show at 4am, I had a show at like 9 or 10, and then I had another one that was like an hour at a bar or like in Hollywood or something.


[User] Please transcribe this text

[Assistant] He said, uh, Joey and woo woo. Oh, that, Joey Fats and Vincent, woo woo. So, I get mic'd and get him on the phone and, and, and they, they ended up getting it back. I don't even want to talk about this shit, bro. It was just some stupid shit, but on, at the end of the day, I got it back, bro.


[User] Please transcribe this text

[Assistant] No reason, bro. I ain't know those motherfuckers from a grain of salt, like for real. It's weird, you know? Whatever, but, they ain't, if they gave back a laptop and started saying they robbed people, like, what the fuck, they ain't robbing people.


[User] Please transcribe this text

[Assistant] I mean, I like country, but don't get me wrong, but I'm not making country like-


[User] Please transcribe this text

[Assistant] There's a lot of money to be made, you know? And like, I've been listening to a lot of some of this newer shit and it's turning into more and more.


[User] Please transcribe this text

[Assistant] Bottom bass, you know? I'm like, here's a beat with 808, just boom. My dad, he'll be like, like,


[User] Please transcribe this text

[Assistant] I don't knock, I don't knock the pop music, don't get me wrong, you know? But like, that's not what I listen to on a regular basis, you know? And I feel like if I start listening to that stuff, like, I get thrown off. Like, for real. Was that a fucking...


[User] Please transcribe this text

[Assistant] A roach running in my cup and Fredo's cup too, man. Seriously?


[User] Please transcribe this text

[Assistant] She owns a, uh, she does like, uh, shit, like she could take a chair and like put that like- Oh, reupholster it? Yea, yea, yea, she goes crazy. She'd be doing like all that shit. She'd be, she made, uhm, I went to the Hermes store, bought some Hermes scarves.


[User] Please transcribe this text

[Assistant] Yo, bro, whatever, man. Dude, that's crazy.


[User] Please transcribe this text

[Assistant] Yeah. Be easy to work with? I think I'm easy to work with. I mean, every artist that I've worked with, like, I don't think they said anything bad about me about working with them. They love working with me.


[User] Please transcribe this text

[Assistant] He did. He did. I believe it. He did. He, he, he was like, man, y'all go, y'all gotta glow up. He said something, I think, when they were on DC.


[User] Please transcribe this text

[Assistant] Fuckin', I don't know, I think, I think Sosa, uh, I think Glitch brought me over to Sosa's crib.


[User] Please transcribe this text

[Assistant] And, uh, me and him kinda like, hit it off, me and him, cause he a kid, like, and I'm really a kid, I'm 26, but really I feel like I'm 16, like, at heart, you know?


[User] Please transcribe this text

[Assistant] I woke up my Twitter, my Instagram or Twitter or something was going crazy because he recorded that Randy Moss song.


[User] Please transcribe this text

[Assistant] There's a video of Tato jumping around with his shirt off going all crazy and...


[User] Please transcribe this text

[Assistant] All my friends are tagging me like, Harry, that's your beat, that's your beat. And my homie Malik, who I produced that beat with, he was like, bro, what the fuck.


[User] Please transcribe this text

[Assistant] No, he didn't have it online, he recorded Instagram video. Oh, okay. People were just tagging me in the video.


[User] Please transcribe this text

[Assistant] What, they have unreleased Chief Keef? I'm like, confused. Bro, I finessed a couple of them. I'm like, bro, send me the song. Send me the song of what you got. And then I'm like, damn, these fools really got songs that is unreleased, bro, I never heard of.


[User] Please transcribe this text

[Assistant] Bro, I don't know, they got it sent from like one of the producers, maybe somebody who was in the session. You know, like, I don't know, like bro, there's a lot of weird shit that goes on. Like, a lot of my songs that I did with Sosa got deleted at a studio. And like, I don't know what happened to the files. Really?


[User] Please transcribe this text

[Assistant] That's fucking whack. Like, fucking, we recorded four hits here and you fucking said you deleted them? Like, I only had like two or three MP3s, one I lost a whole song. Like, you know? That sucks.


[User] Please transcribe this text

[Assistant] It was just crazy, you know, a lot of foo shit happens like that, like, that shit happened at, uh, the other night with Fredo, he had like, uh, three or four sessions and they deleted all of them. Really? So we had to re-record them.


[User] Please transcribe this text

[Assistant] He was pretty fucking mad, you know, cause some other shit happened too at the studio. They took some shit from him or something. He left some shit there and they took it and threw it away or something.


[User] Please transcribe this text

[Assistant] Yeaah, it was like, just a little draw like this and they just threw it away. That's fucked up. And he came back and he was like, I don't know. But uhm, yeaah, you know, that shit happens all the time. Studios acting bogus, bad engineers, dude. Like, I've been in Atlanta and my homie would be like, bruh, don't go into this session, it's the worst engineer.


[User] Please transcribe this text

[Assistant] I'm like, fuck, why did I even book that session? Like, I'll just engineer it myself. Fuck it. You ain't have to. So you can do that role too.


[User] Please transcribe this text

[Assistant] Yeaah, I could do that role, but like, I'd rather have somebody who's like, fuckin', like, you know, they do that every day.


[User] Please transcribe this text

[Assistant] You know, I know how to mix and master and shit, but like, I got people who do that shit every fucking day and pride themselves on that. Like, I make beats, like, you know, I produce and make the songs and make the sounds and put everything together.


[User] Please transcribe this text

[Assistant] T4, how do you resurrect a backwood? Come on, get on the mic, say it on the mic.


[User] Please transcribe this text

[Assistant] But yeaah. But T4 man, for real, tell him how to resurrect the backboard on air. Come on.


[User] Please transcribe this text

[Assistant] He's from New York, man, he's from like Queens and shit. He taught me how to roll, he really taught me how to roll and motherfuckers real nice. And he passed away, but that's like my inspiration a lot for my music and shit too, you know, like definitely.


[User] Please transcribe this text

[Assistant] A lot of people could say that. Yeaah. We, we, yeaah. We, you know. Nah, like, for real, I don't... Yeah, he's over it. That's when you tell me how to...


[User] Please transcribe this text

[Assistant] What is that, 150? That's it? 175? Somebody-


[User] Please transcribe this text

[Assistant] That's another thing you just said?


[User] Please transcribe this text

[Assistant] And another time some little goofy came to my crib when I was on house arrest and I, uhm, I remember like, he, uh, he was taking the mucinex bottles from, uh,


[User] Please transcribe this text

[Assistant] The store and ripping the label off and claiming I was drunk.


[User] Please transcribe this text

[Assistant] Ever since the aliens flew over LA, everybody been trippin'.


[User] Please transcribe this text

[Assistant] I mean, like, I love that beat. I like, I mean, I like the beat. Me and Benny just fucking made that shit, you know? No, I never thought of that shit being a hit. Never.


[User] Please transcribe this text

[Assistant] He's, uh, uh, you know, he gets real hyped up on certain things, you know? Uh-huh. So like, like, you know, whenever he's in his music mode, he's in his music mode. You know, when he's in his gaming mode, he's in his gaming mode. That's all he's doing, playing games on every five TVs he's got. However many TVs he's got.


[User] Please transcribe this text

[Assistant] She was not there. I wasn't there. I was just on TV. But they cut it off. Like, I seen a lot of random things on TV, like when Janet Jackson's tit popped out. You were watching that? Yea, I was watching it with my grandma. She was like, uh, I just see


[User] Please transcribe this text

[Assistant] My dad, he lived in Quincy, like, that's like, that's weird.


[User] Please transcribe this text

[Assistant] I wanna see what they were asking me. I woke up to a lot of questions and I was like, oh my god. This fool said do I ever sleep? My homie Zach tweeted you and said do I sleep?


[User] Please transcribe this text

[Assistant] Ask everybody. Ask Cory. Like, I call people who fucking, I be up, like, all hours, you know. We don't sleep around here.


[User] Please transcribe this text

[Assistant] I slept last night, but I was already up for three days, you know.


[User] Please transcribe this text

[Assistant] Yeah, man, I was fucking with this dumb bitch and she fucking, you know, she had me in a dark hole. She fucked up my whole shit. She literally was like jealous of what I was trying to do.


[User] Please transcribe this text

[Assistant] Like I'd get hit up by Sosa and all them and they'd be like, pull up at the studio and she'd be like, nah, nah, nah, nah, and I'm like, well that's my work, like, and then she fucked me up with all my money and taking my money and all this bullshit.


[User] Please transcribe this text

[Assistant] And like for like six months I was fucking up and everybody noticed that and I just had to kick the bitch out and do my own thing.


[User] Please transcribe this text

[Assistant] I mean, I was on drugs and just being a fucking dingus, dumbass, whatever you want to call it, just dumb, fool, just sitting around doing nothing. For like a week or two, I was making hella beats and then I stopped. And then I was like, I'm over this shit. I was even telling people I don't want to make beats anymore.


[User] Please transcribe this text

[Assistant] Instead, back then, when I said that, I was like, nothing. I had nothing. Like, beats is my life. I have nothing to fall back to. I have nothing. So, beats is everything. Yeah, I was depressed, man. I was kinda depressed. Whatever, you know. I was just being goofy.


[User] Please transcribe this text

[Assistant] But I'm back, well I'm fully, fully working man, I'm in work mode man, like, everybody knows that shit, every day, every day I gotta work, every day I gotta make a beat, or do something productive towards my music, my career, whatever you wanna call it.


[User] Please transcribe this text

[Assistant] When I was on house arrest, I was fucking making like, yeaah, 30 a beat today. 30. Or more.


[User] Please transcribe this text

[Assistant] At least 20 of them were like, I like looped them out and bounced them as a project and then the other were like,


[User] Please transcribe this text

[Assistant] Like I could finish the next day in like 20 minutes. All the rest of them. So I'd do that. I'd wake up and finish the lunch from the night before.


[User] Please transcribe this text

[Assistant] They're kind of wack, I delete them. But, I'm not a big deleter, you know. You know fools would be like, oh this shit wack, this shit's so wack, I delete it. I hate that shit bro. I'm like, I know people are the biggest critics of themselves, you know. But I don't delete shit, like I never delete it. I'll change something up or something. I always like,


[User] Please transcribe this text

[Assistant] Cause like, I'll be like, man, I might have been feeling on a vibe, like I'm gonna still ride on that vibe, so I'll switch up that instrument sound or something, you know?


[User] Please transcribe this text

[Assistant] I got over a thousand files and folders. Well, you don't even want to look at it. Not this one. This one's pretty clean, but, uh, yea, but the one at the house is just a fucking nightmare.


[User] Please transcribe this text

[Assistant] I don't have no songs with Cardi. I don't know what the hell that question was. I read that and I was laughing. Like literally I had never planned a song with Cardi or nothing. I met him a couple of times and he never talked about no beats or nothing. So, there's a beat on YouTube that's like a Playboy Cardi type beat.


[User] Please transcribe this text

[Assistant] I liked it because it was a beat that I made was stupid cool out of beat plugs and he is the homie, you know, and me and him made the beat, but he put the title while I was tripping, like I didn't even text him to tell him to change the name.


[User] Please transcribe this text

[Assistant] But at the end of the day, like, fuck it, if Cardi wanted to get on some beats, he'd know how to get a hold of me. And I think Max O'Cream hit me up the other day and was like, send me beats for Cardi or something.


[User] Please transcribe this text

[Assistant] Uh, everything in moderation, man. Okay. You know what I'm saying? A lot of motherfuckers out there, I see them, bro. They be listening to too much Future and shit and be trying to like, they spend their whole check on some Argellas and shit. Looking goofy, bro. Your life is not dirty and mixed.


[User] Please transcribe this text

[Assistant] Yeah, y'all producers buying expensive shoes, man. Y'all better make beats instead of buying shoes. But beats from producers, with producers, honestly, fucking, uhm.


[User] Please transcribe this text

[Assistant] Find your digital audio workstation that you like, your DAW, you know, find your thing, Logic, whatever. Somebody ask what DAW I use. Okay, yeaah. What your creative process is like that, yeaah. What program, really, I use. And I use Logic, and I use FL Studio.


[User] Please transcribe this text

[Assistant] But I learned on Reason in Cubase, which is kind of humor because they taught me that shit. And then I was making beats on Pro Tools, which is even funnier.


[User] Please transcribe this text

[Assistant] But uhm, yeaah, I use logic in Fruity Loops. Find, like, one of them, like, in Ableton. Find one of them things and, and just keep going, man. Just, you know?


[User] Please transcribe this text

[Assistant] Just don't be shy and just don't be afraid to like experiment on different shit.


[User] Please transcribe this text

[Assistant] But I wasn't making beats at that time. Like this is like, that was like one of my f- one of the five run-ins I had with Gucci, but like, that was honestly like...


[User] Please transcribe this text

[Assistant] Kind of a scary one, cause I had a homie who see me playing craps and he ran up to Gucci and they got like tackled by one of Gucci's security guards.


[User] Please transcribe this text

[Assistant] And it just like kinda looked goofy cause like, he knew me and they kinda knew that he knew me. But like, it was chill cause the casino scene that I was playing, you know, I had chips already out. Right. Like over $100 and out on the table, you know, and I had like, you know, spent $500 or something, but I was making money. I made, I took all of Gucci's money. You did? Yeaah.


[User] Please transcribe this text

[Assistant] It was crazy, like, we were just thinking of somebody and they called me. They were like, you know, we were watching a Scooter video and Scooter FaceTimed me. It was just weird. It was like, we just put them into existence, you know what I'm saying? And so, like, it's just crazy, dude. Like, I remember you did say you wanted to fucking interview me when you was talking to Tua.


[User] Please transcribe this text

[Assistant] Ayy, on the real though, uh, I really want to shout out, fucking, awesome shit. No Jumper, man, for having me. You know, my boy Ash.


[User] Please transcribe this text

[Assistant] Shout out AlmightySoul, man, RIP Cap, man, my brother, man, I love you, Cap, man, I'm doing this shit for you, bro. Ball out, god damn Tato, fuckin' Trey Savage, little Flashy, little Bug-Ass motherfucker, I love you too, man, all y'all motherfuckers, all the Savage squad, you know, man, you know, got that shit on me for death, man.


[User] Please transcribe this text

[Assistant] Shout out Young Scooter too, man. I love you. I love Young Scooter, man. You're a good man. Shout out Young Scooter, man. I'ma see you soon in the A. Mexico, Joe, all my people in the A, Rico, Wade. I got a long list of people, and my people in New York, Lil Payne, all the motherfuckers, you know.


[User] Please transcribe this text

[Assistant] Fucked with a lot of motherfuckers. If I forgot you, I'm sorry, but I ain't.


[User] Please transcribe this text

[Assistant] I know I have, but I don't wanna go.


[User] Please transcribe this text

[Assistant] Freak guns, don't be nice to me. Go ahead and laugh at me. I'm not laughing. Anyway, you can tell him and have a lot of fun.


[User] Please transcribe this text

[Assistant] Jimmy, of course. You know what it was? It was those silly hats you women wear.


[User] Please transcribe this text

[Assistant] Okay, that's a beautiful way of putting it. Everything is, everything is reducible to,


[User] Please transcribe this text

[Assistant] Hopefully, yeaah, I'm excited for next year and this year and every year. I'm really just looking forward to my life.


[User] Please transcribe this text

[Assistant] So you're mixed up with this? In a way. Call a coroner. I'm inside, you've got some explaining to do.


[User] Please transcribe this text

[Assistant] Yeah, so in high school I actually spent a lot of time playing around with tools like Maya, Dreamweaver, Photoshop, and I think I would have gone to art school, but just learning these tools was so incredibly hard, I decided to major in computer science.


[User] Please transcribe this text

[Assistant] It seemed like everyone here was working on problems in big data and machine learning and I certainly got swept up in that wave and I finally settled on a dissertation topic around how to leverage data to build better design tools or scaffold the design process and


[User] Please transcribe this text

[Assistant] At the time, there were all of these amazing repositories of design also popping up on the web, like for fonts and...


[User] Please transcribe this text

[Assistant] us to do was build systems where you know people could search


[User] Please transcribe this text

[Assistant] And so this is the types of systems that I was building as part of my dissertation work.


[User] Please transcribe this text

[Assistant] I was very fortunate enough to be working with an amazing team at the time and we won a few best paper awards on this work.


[User] Please transcribe this text

[Assistant] And we also decided at that time, because people were asking, can we use these tools to productize it? And we were able to raise some money from Andreessen Horowitz and NEA to actually go and see if we could build this into a product and a business.


[User] Please transcribe this text

[Assistant] That's not enough. Uhm, they actually add just very marginal value. And so while we had a few thousand designers using these tools that we'd built, uhm, we found that the usage of the tools was very apocal. And by that I mean people would use these tools


[User] Please transcribe this text

[Assistant] During the first few days of their three week long design project, so uhm, you know, they would be using the tools to do research and competitive analysis, but then, you know, they would actually go off and create their designs and iterate on them and they were never coming back to the tools.


[User] Please transcribe this text

[Assistant] And so, it really felt like we were working on the wrong problem.


[User] Please transcribe this text

[Assistant] Changing exist- existing situations into preferred ones.


[User] Please transcribe this text

[Assistant] And I think the important word here is preferred because at my startup we were helping


[User] Please transcribe this text

[Assistant] You know, key performance indicators like time on site or bounce rate. That was when people started to kind of continuously use data in the design process.


[User] Please transcribe this text

[Assistant] And this kind of is borne out in other domains as well. People have similar questions about how to tie design features to performance.


[User] Please transcribe this text

[Assistant] Our preferred outcomes and so for example, you know, in mobile design, people might want to know how many steps should a checkout flow take so that they can prevent user fatigue. Or if you're creating an outfit, you might want to say, how can you look smart without looking stuffy?


[User] Please transcribe this text

[Assistant] Uhm, and if you're designing a recommendation engine and using, uh, you know, a social network, uh, as data, you might say how can you uncover taste and tastemakers in a social network?


[User] Please transcribe this text

[Assistant] So, this is kind of what my research is about these days, uhm, and this is kind of what the rest of the talk is going to be about. It's how to use data to tie design decisions to desired outcomes.


[User] Please transcribe this text

[Assistant] So, let's start by looking at how we can do this for the domain of mobile app design. What's a preferred outcome for a mobile app?


[User] Please transcribe this text

[Assistant] Well, app designers care about a lot of things. Uhm, probably the thing they care the most is that people are able to perform the tasks on apps that they actually, you know, want them to be able to


[User] Please transcribe this text

[Assistant] So things like task completion rate, lower completion time, uhm, fewer usability issues, building, uhm, building UIs that are accessible. These are all preferred outcomes.


[User] Please transcribe this text

[Assistant] And, you know, when we say data-driven design in this space, what we usually mean is A-B testing.


[User] Please transcribe this text

[Assistant] And A-B testing is very useful for understanding causal relationships between design choices and key performance indicators.


[User] Please transcribe this text

[Assistant] But at the same time, it is hard to get statistical significance. So this is a recent white paper that was released by a company called Qubit, where they actually did a meta-analysis over 6,700 large e-commerce experiments, and this was mostly in the retail and travel sectors.


[User] Please transcribe this text

[Assistant] And they found that 90% of the experiments actually have less than 1.2% effect on revenue.


[User] Please transcribe this text

[Assistant] Now, I'm not knocking on AB Design because even a .4% lift for a big company, that's 15 million dollars. So, you know, that's real money. Uhm, but this is kind of my point where


[User] Please transcribe this text

[Assistant] AV testing makes sense for large companies, so if you're a Google or a Facebook and you have enough users to run these tests, uhm, you have enough money and engineering resources to actually build out alternatives and deploy them, this is a great way to continuously optimize your design.


[User] Please transcribe this text

[Assistant] But what about everyone else, right? What we'd like is to have data, uhm, at scale to help us


[User] Please transcribe this text

[Assistant] And for most designers, they don't have access to this kind of data. Uhm, the data at their own companies will be small and, you know, while they would like to be using data from maybe other companies to make more informed decisions, uhm, all of this data is locked away and siloed away within each company.


[User] Please transcribe this text

[Assistant] So the, the motivation here in this, in this research direction is can we open source usability and usage analytics so that everyone can learn from them.


[User] Please transcribe this text

[Assistant] So I'm going to present you kind of one solution point in this space. And so we built a system called ZIPT, which stands for Zero Integration Performance Testing.


[User] Please transcribe this text

[Assistant] And at a high level what it looks like is a designer can define different tests or tasks they want done over any Android app in the Play Store. So you can pick an Android app and you can say do this task on this Android app.


[User] Please transcribe this text

[Assistant] Uhm, and then what zipped actually does is it will put this test on a crowdsourced platform like Mechanical Turk and it will ask users to perform this task on this app.


[User] Please transcribe this text

[Assistant] Uhm, it will collect all of these multiple, uh, traces from all these users and then it will aggregate that data and provide people with visualizations that will help kind of up-level design insights.


[User] Please transcribe this text

[Assistant] Uhm, so there's kind of two parts to this project, right? You, or two challenges rather. Uhm, one challenge is you have to actually be able to capture this data. And then the second part is like you have to be able to efficiently aggregate this data.


[User] Please transcribe this text

[Assistant] So let's talk about how we, how we can do the capture. Uhm, the capture was actually done by a system we built called Erica, which allowed us to mine interaction traces.


[User] Please transcribe this text

[Assistant] On Android apps as they were being used. And the point that I really want you guys to remember is that we wanted it to require no modifications to an app source code or binary so that we could actually collect this data from apps that you personally didn't build.


[User] Please transcribe this text

[Assistant] And so I wanted you to get a feel for what Erica looks like. Uhm, what Erica provides is a web application for crowd workers to interact with mobile apps streamed from a device farm. So the apps are actually running on a device farm and a user is using the app through a web application that we built.


[User] Please transcribe this text

[Assistant] And while users are interacting with an app, what Erica is doing is it's continuously capturing three types of data in the background. Uh, first it's capturing user interaction data, so it actually knows where you tapped on the screen, or you know, what your multi-touch gesture was.


[User] Please transcribe this text

[Assistant] And what that reflects is, uh, kind of the structure of the UI and what are all the elements. And what we do is we actually stitch all of this data together into these user interaction traces so we know exactly how


[User] Please transcribe this text

[Assistant] A user went from one screen to the next, like where did they click to go to the next screen?


[User] Please transcribe this text

[Assistant] We also collect the screenshots at a high enough resolution so that we can actually capture animation effects on playback. So what you're seeing right here is our data being played back as video. Uhm, and so you can see like we're capturing uhm, those animation effects as well.


[User] Please transcribe this text

[Assistant] Uhm, one of the things, uh, as we collected this data that we wanted to be cognizant about was minimizing the capture of people's personally identifying information. So, crowd workers are using these apps. We don't want them to give away things like their credit card information, their emails, and things like that.


[User] Please transcribe this text

[Assistant] Uhm, so we provided an interface where we basically gave people accounts to use. Uhm, and there were some...


[User] Please transcribe this text

[Assistant] Little, uh, technical challenges that we have to solve here like,


[User] Please transcribe this text

[Assistant] figuring out how to do, uhm, app verification steps, uhm, so, so we figure that stuff out. Uhm.


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[Assistant] So let's go back, so that's how Capture works and that's the type of data we collected. Let's go back and now talk about how we actually aggregate.


[User] Please transcribe this text

[Assistant] Uhm, so for example, let's say a designer, uh, put up this task on Zipped, where they wanted a crowd worker to, uh, log that they ate a chocolate chip cookie on the app calorie counter.


[User] Please transcribe this text

[Assistant] So let's pretend that we went ahead and posted this unzipped, we collected user traces, and now we want to actually look at the results. So what I'm going to show you next is kind of the dashboard we provide designers.


[User] Please transcribe this text

[Assistant] First of all, we provide them some aggregate metrics such as, uhm, the average time on task, uh, the number of interactions.


[User] Please transcribe this text

[Assistant] Similarly, we aggregate feedback, answers to feedback questions as well. So this gives us more insight about the users and who they are. You know, have they used an Android device before? Have they used the app before? But this is customizable. So as a designer, you can provide these feedback questions.


[User] Please transcribe this text

[Assistant] Then we provide this flow visualization interface and let's, let's look at this a bit more closely. So the goal here is to tell people kind of in an aggregate sense or tell designers in an aggregate sense how people actually completed that task.


[User] Please transcribe this text

[Assistant] And so what you see in the dark gray is what we're calling the designer's golden trace. So this is how the designer intended someone to do the task on the app.


[User] Please transcribe this text

[Assistant] Not everyone is doing this task as efficiently as possible. And the question might be why. Uhm, you can also kind of like get a feel for the screens that people are going through on the side. And you might notice that there's a screen that says, hey, uhm, there was a problem.


[User] Please transcribe this text

[Assistant] Uh, and so this modal has popped up, right? That says there's a problem. So maybe people are experiencing some, uh, some usability issue here where they can't get to the next step.


[User] Please transcribe this text

[Assistant] And after you do a deep dive, you can actually see that, hey, it's actually confusing on this screen because people actually had to enter in a unit for a chocolate chip cookie to go to the next screen on this app. And it wasn't clear from the interface itself that you had to do that.


[User] Please transcribe this text

[Assistant] So this is kind of the way Zipped works and how it can be used to get usability insights as well as design performance insights over apps you didn't create.


[User] Please transcribe this text

[Assistant] Now let's kind of show you a few case studies. Uhm, this is one of my favorite ones, uh, that we did. Uhm, so we were here, we were trying to compare YouTube music to Spotify music and we were, the task we wanted people to do was a simple one. Uh, we just wanted people to create


[User] Please transcribe this text

[Assistant] Uh, or we wanted people to add two songs to a new playlist that they created.


[User] Please transcribe this text

[Assistant] Uh, and what we found, uh, with more than 35 users doing this task was that there was a difference between those completion rates on this task. Uh, YouTube Music only 89%, well, I should say 89%, completed the task whereas more users completed it on Spotify. Yes.


[User] Please transcribe this text

[Assistant] So, we're measuring, great question, there's a few ways we're measuring completion. Uh, one is self-report, and another one was answer-based, so uhm, you might have people do a task and produce an answer, like, what was the address of blah blah blah, and as long as they produce the right answer, then they were able to complete the task.


[User] Please transcribe this text

[Assistant] Uhm, but yeaah, in this case, uh, it was based on self-report, and uh, here we're asking, so yeaah, we were wondering why is there a difference between these completion rates, and we did a deeper dive into the feedback questions that were asked, and it turns out that on YouTube,


[User] Please transcribe this text

[Assistant] Uhm, there's not a way to create an empty playlist and then add songs to it. The way you actually create new playlists is you have to go to a song and you have to then say, uh, add to new playlist and that's how you create it.


[User] Please transcribe this text

[Assistant] This was how it was phrased when we tried it, but yeaah, I think it would have been worse if we had phrased it the other way because it would have implied that you had to go first create a new playlist.


[User] Please transcribe this text

[Assistant] Yeah, so we just happened to phrase it this way, but it could have been, yeah, the results could have been worse, I guess. Uhm, this is actually a known problem in YouTube in the sense I talked to their, one of their head designers, uhm, and I just checked like a few days ago and they still haven't fixed it, so.


[User] Please transcribe this text

[Assistant] Uhm, and we had a simple task which was find the address of the store closest to a particular zip code. So you're just kind of looking for the nearest, let's say, retail store.


[User] Please transcribe this text

[Assistant] And what I'm showing you here is the golden trace that let's say the designer would have intended. So, you know, you have to click on the hamburger icon and there's a fly out menu navigation and it takes six interactions to get to the right answer. So here's a case where completion rate is based on a right answer because we're phrasing the task as a question.


[User] Please transcribe this text

[Assistant] Similarly on Best Buy, if you were performing the same task, there's actually two ways you can do it. There's a way, there's a way which uses tap navigation, which is just, you know,


[User] Please transcribe this text

[Assistant] On there on the, the first screen or there you can go through a search route. Uhm, so,


[User] Please transcribe this text

[Assistant] A priori we were thinking that, hey, this is a shorter interaction and there's actually two ways to complete the task, so,


[User] Please transcribe this text

[Assistant] You know, Best Buy should perform better and what we actually found in practice was that uhm,


[User] Please transcribe this text

[Assistant] Macy's was easier for people. Uhm, and it's interesting because I think five years ago everyone was telling me don't use hamburger menu icons. They're hiding all of the, you know, capabilities. Uh, you should just make it more clear to the user what you can do on an app when you're on the home screen.


[User] Please transcribe this text

[Assistant] Now people are just so used to this design pattern that if you don't use one, you might end up confusing users.


[User] Please transcribe this text

[Assistant] Uhm, so it's kind of interesting how, you know, these design trends might change over time. Uhm, and the reason I said people actually found this easier was they actually self-reported it being easier. Uhm, I think a lot of people on the Best Buy got confused by the search.


[User] Please transcribe this text

[Assistant] Uh, experience because you had to be in the right search mode to actually search for stores and not products. And so we saw users who are trying to search for store locations in the product search, search bar.


[User] Please transcribe this text

[Assistant] Uhm, so let's take a step back and talk about what are the implications of the system. Well, uhm, we're, you know, giving away for people to test earlier in the design process, right? Because,


[User] Please transcribe this text

[Assistant] You don't have to go out and build out all of these different design alternatives. You can actually leverage existing apps and you can run tests on them. And when you are designing your own flows, you can kind of take the things you've learned as best practice from running your tests and use that knowledge to design your own flows.


[User] Please transcribe this text

[Assistant] It also gives you a way to do comparative design testing at scale.


[User] Please transcribe this text

[Assistant] So if you wanted to, you know, compare, uh, a Best Buy app to a Macy's app, uh, right now the only kind of service out there that you can use is usertesting.com, uhm, where they will actually video, uh, you know, a user, take a screen video of a user using an app to do different tasks.


[User] Please transcribe this text

[Assistant] And it's around $50 a user and the reason it's around $50 a user is because someone has to manually go through and then annotate those videos and figure out


[User] Please transcribe this text

[Assistant] Uhm, in our case, because we're collecting kind of this structured data about interaction, uhm, it's easier for us to automatically aggregate this data and to surface, uhm, you know, insights about it.


[User] Please transcribe this text

[Assistant] Uh, and then finally, the thing that is interesting about zipped, I think, is the way you can combine different...


[User] Please transcribe this text

[Assistant] Now, I uhm, I think there's one important thing that is not yet exposed in this testing interface.


[User] Please transcribe this text

[Assistant] So all of the design descriptions we've collected, uhm, are very low level, right? I showed you a flow visualization and the reason we're able to do those is because we've


[User] Please transcribe this text

[Assistant] Uhm, you know, clicking on an article, like, we need to understand those higher level semantics.


[User] Please transcribe this text

[Assistant] So, kind of the next step in this work is actually figuring out what these design semantics are and also being able to automatically identify them in the traces.


[User] Please transcribe this text

[Assistant] So, in order to actually do this work, we had to collect a dataset big enough


[User] Please transcribe this text

[Assistant] Uhm, so we released a data set called Ricoh and it uses the same capture technology as


[User] Please transcribe this text

[Assistant] Erica and so actually I would love if you guys, uhm, if you had the time later this week, check this out because uhm, we really are hoping more people use this data set. It provides interaction traces for around 10,000 Android apps, which means uhm, more than


[User] Please transcribe this text

[Assistant] 72,000 unique UIs uhm, which is approximately 3 million UI elements. Uhm, and so we use this data set to first identify, you know, what should be


[User] Please transcribe this text

[Assistant] Potential icons, I mean images that are small and squarish that appear in the, in the UI hierarchy.


[User] Please transcribe this text

[Assistant] Uhm, and we use this to basically identify around 180 concepts, UX concepts, and if you're curious about the lexicon, you can also check it out at the URL on that slide. Uhm, there are some interesting patterns we found where certain concepts are


[User] Please transcribe this text

[Assistant] Icon and the plus sign usually signify an action like create.


[User] Please transcribe this text

[Assistant] Uhm, and then finally, you can even have icon polyseums, which are interesting because these are icons that can have slightly different meaning depending on the context.


[User] Please transcribe this text

[Assistant] For example, the star, uhm, in a context could be used for rating, uh, and in another context can be used for favoriting or bookmarking.


[User] Please transcribe this text

[Assistant] So we leverage this icon to, or we leverage this UX lexicon to then create an automatic approach for, uh,


[User] Please transcribe this text

[Assistant] Given a screenshot and a UI hierarchy, we can produce, uhm, the semantic version of that UI hierarchy basically.


[User] Please transcribe this text

[Assistant] The tricky bit in this pipeline was actually the icons themselves because for a lot of the other


[User] Please transcribe this text

[Assistant] A lot of the other types of components, you can use basic code based heuristics and they work pretty well for Android apps.


[User] Please transcribe this text

[Assistant] But icons are again very hard to differentiate just from images. And so we actually trained a convolutional neural net uhm, that detects between these 99 classes of icons. And we also use anomaly detection at the end of the pipeline so we can differentiate between icon classes as well as just


[User] Please transcribe this text

[Assistant] Other content based things, uhm, where it's just a small image.


[User] Please transcribe this text

[Assistant] So I want to show you some of the, the classification results. Uhm, it's cool because


[User] Please transcribe this text

[Assistant] The average icons you produce for the different classes really show that their


[User] Please transcribe this text

[Assistant] Take their own stylistic, uh, you know, there's some room for style, basically.


[User] Please transcribe this text

[Assistant] So once we had kind of these element based semantics, what we wanted to see is can we bootstrap higher level semantics based on these element level ones.


[User] Please transcribe this text

[Assistant] And, uh, so for example, can we understand screen semantics was the question. And what we did was we trained a deep autoencoder to basically capture semantic, uh, to see if we can capture semantic properties over screens.


[User] Please transcribe this text

[Assistant] And what we were able to do is show that hey, if you train an autoencoder with those semantic versions of the screenshots that we produced,


[User] Please transcribe this text

[Assistant] We can actually learn a space where if you do nearest neighbors, nearest neighbors queries in the space, you get back semantically similar screens.


[User] Please transcribe this text

[Assistant] Similarly, we wanted to see if we could do, uh, you know, flow semantics. So, a flow is just a sequence of screens where someone is doing


[User] Please transcribe this text

[Assistant] Uhm, so we kind of used that as our heuristic and we went through the traces and we basically used our design semantic classifiers to identify, uh, basically annotate different flows. And so,


[User] Please transcribe this text

[Assistant] We take the flows in the Ricoh data set and here you can see that, you know, we can identify where people were, for example.


[User] Please transcribe this text

[Assistant] I know there are a lot of URLs. I'll make a plug for that URL because for a limited time Google is sponsoring this work and we can crawl all of these flows for free for you guys so if you want us to go crawl


[User] Please transcribe this text

[Assistant] Like 5,000 more apps, just request them on the, on the platform.


[User] Please transcribe this text

[Assistant] And on that note, I also want to come back and say, uhm, you know, we've focused on using Ricoh for specific types of applications. Do you have a question? Okay.


[User] Please transcribe this text

[Assistant] Onboarding Flow has five screens, so we need to be able to say on-board, you know, what is on-boarding for that, and uhm, you're absolutely right. Like, we haven't done that step, but that's, yes, that's what we want to do. Uhm.


[User] Please transcribe this text

[Assistant] But yeaah, like I think the other thing is we're obviously not going to be able to get to all the applications you could, you know, possibly work on with this data set and so I again wanted to encourage people to try


[User] Please transcribe this text

[Assistant] Uh, looking at the Ricoh dataset, and there are a lot of interesting projects that have already emerged from this dataset. Uhm, at UW, they used Ricoh to understand and improve accessibility on mobile apps.


[User] Please transcribe this text

[Assistant] Uhm, at Google they're using it to understand material design adoption. Uhm, I know some folks are working on modeling brand perception, uhm, as well as training generative models on this data set. So uhm, again, this is my plug for the data set.


[User] Please transcribe this text

[Assistant] Okay, so let's, uh, switch kind of gears and talk about how these data-driven techniques that I've been talking about in the mobile design domain can also be applied to something that seems very different, right, which is fashion.


[User] Please transcribe this text

[Assistant] Uh, actually, a consumer one which is, uh, apparel is actually a three trillion dollar global market. Um, and it actually dwarfs automotive, industry, uh, advertising, consumer media, and mobile phones combined. So, I mean, just like that scale is pretty impressive.


[User] Please transcribe this text

[Assistant] Uhm, but, you know, the way we shop online.


[User] Please transcribe this text

[Assistant] Hasn't changed at all. Uhm, you know, I'm sure people are familiar with these screens where you have to like choose what you're looking for which could be pants and then you have to like apparently choose the length of your pants and


[User] Please transcribe this text

[Assistant] Recently, there have been companies who are looking at how do we democratize the personal


[User] Please transcribe this text

[Assistant] Uhm, our question kind of in this space is, do you really need the middle man, right? Do you really need the humans who are using the AI interfaces? Can you give AI interfaces to the consumers directly? And if you do, uhm,


[User] Please transcribe this text

[Assistant] How can you make it easy enough or, you know, how can you make the user experience good enough so that people will be willing to do this?


[User] Please transcribe this text

[Assistant] The space is just big. So if you look at the possible types of fashion problems and the possible interventions you could design, it's just really huge. You might want feedback on an outfit, you might want help creating an outfit for an event, you might want seasonal item recommendations and so forth.


[User] Please transcribe this text

[Assistant] People are doing amazing work in computer vision and machine learning to build all of these different types of models, like ones for compatibility, for image parsing, for detecting trends.


[User] Please transcribe this text

[Assistant] And at the same time, there's also, uhm, all these data sources out there. Every e-commerce, you know, site is a data source basically in this space as well as the other social networks and media that are out there and the editorial content. So there's just...


[User] Please transcribe this text

[Assistant] You know, a lot going on in this space. Uhm, and so we wanted a way to do need finding in this space and understand at scale kind of where should we focus.


[User] Please transcribe this text

[Assistant] So, to that end, we built an experimentation engine platform and it's kind of a fancy way of saying Wizard of Oz Chatbot, but I'll explain to you why


[User] Please transcribe this text

[Assistant] I'm not saying just Wizard of Oz chatbot. So the goal here is that this system is not the product itself. This is just a proxy system and we're using it to learn. So we're using it to learn what are the problems, what are the important


[User] Please transcribe this text

[Assistant] What are the right types of solutions? And then at the end of the day, it also provides us a way to deploy ML algorithms and being a tight feedback loop with users.


[User] Please transcribe this text

[Assistant] Uhm, and so these are all the properties we want. And I know Wendy Ju has been working on similar systems. Uhm, and she calls hers need-finding machines. So that's another word you might hear out there. Uhm, but they're, they're very similar.


[User] Please transcribe this text

[Assistant] Uhm, and so we built this Facebook Messenger bot initially that connects users to one-on-one sessions with stylists.


[User] Please transcribe this text

[Assistant] And we, uh, released this bot for three weeks. Uhm, our stylists were, uh, Fashion Institute of Technology students, so they weren't experts, but they were, they were still more knowledgeable about fashion than, let's say, the average consumer.


[User] Please transcribe this text

[Assistant] Uhm, we advertised on Facebook and Instagram and we said, hey, are you looking for free fashion advice? Things is bought. Uhm, and through that mechanism we collected 88 organic styling conversations with 73 unique users.


[User] Please transcribe this text

[Assistant] There's actually a lot of convergence in terms of the types of problems people wanted solved. Uhm, definitely the main problem everyone had was


[User] Please transcribe this text

[Assistant] Being able to describe a context for which they were trying to dress. So they were trying to go to a wedding and they wanted to, you know, something to wear to that. They were going to a conference next week. What should they wear for that? What should they wear for an interview? So these questions around context really matter to people.


[User] Please transcribe this text

[Assistant] Similarly, uh, people also have a hard time with matching things. So if they're creating outfits, they don't know what goes with grey slacks.


[User] Please transcribe this text

[Assistant] Uhm, and then finally people actually came on and used the bot to just do product search. So they would say, uhm, I'm looking for a pair of boots.


[User] Please transcribe this text

[Assistant] Uhm, now ecommerce sites already support this type of interaction but it's interesting that people would still come to the bot for this because uhm, one could hypothesize that people are just overwhelmed with the set of choices that they're given through search engines and they're never provided kind of


[User] Please transcribe this text

[Assistant] We also studied the interaction patterns that the stylists were using in order to address these questions. And so the most interesting pattern to us was the fact that they were using these multimodal responses. And by multimodal I mean they use images and text.


[User] Please transcribe this text

[Assistant] And so you, people just never throw up an image and say, hey, this is my recommendation, right? People always give an image and then explain why uhm, it kind of fit the, the requirements the user was looking for.


[User] Please transcribe this text

[Assistant] Similarly, they use the same pattern when they're trying to help users kind of refine their own problem because if someone comes to the bot and says,


[User] Please transcribe this text

[Assistant] I need shoes. Like there are a lot of types of shoes out there, right? So you need to then ask questions and show examples to help users refine uhm, their problem enough so you can actually


[User] Please transcribe this text

[Assistant] And so they use the same multimodal pattern in both contexts. And the word code switching is on that slide because it's a very important one in this context apparently. Uhm, when people talk about fashion, they use different languages. Like experts use different language.


[User] Please transcribe this text

[Assistant] From, uh, you know, the average consumer.


[User] Please transcribe this text

[Assistant] The thing you have to realize is that the expert still needs to communicate in a way that the average consumer can understand. So even when they have all of this expertise and knowledge, they have to, you know, kind of


[User] Please transcribe this text

[Assistant] So this kind of like set up the set of design requirements, uhm, for basically if we wanted to ever automate this, like this is the stuff we probably need to satisfy. And so this is kind of the first set of design requirements we've arrived at.


[User] Please transcribe this text

[Assistant] And now I'm going to work you through uhm, a deep architecture that we designed based on these requirements.


[User] Please transcribe this text

[Assistant] Uhm, first let's talk about how this deals with compatibility. Uhm, so there's a part of this architecture where we've trained a visual compatibility model from 70,000 fashion outfits.


[User] Please transcribe this text

[Assistant] We actually got these outfits from a social media site called Polyvore, which unfortunately no longer is up.


[User] Please transcribe this text

[Assistant] Uhm, they took it down this summer, uh, but before they took it down we were able to mine this data, uh, and we will, it is available, we will make it available so you can also use it.


[User] Please transcribe this text

[Assistant] But basically, at a high level, uh, how we did this was we wanted to satisfy, I mean, there have been people who have been modeling compatibility in fashion, but the insight we had was that if you only train one embedding in this space, you are necessarily requiring things, uhm, that


[User] Please transcribe this text

[Assistant] The other point we, uh, the other requirement we cared about was it being multimodal. So what we did was we took the visual embedding from the compatibility model


[User] Please transcribe this text

[Assistant] And we trained a neural regressor basically to map the visual embedding to 1300 text concepts in fashion and we created that vocabulary of


[User] Please transcribe this text

[Assistant] Text concepts as well, so that wasn't something that was just out there in standard.


[User] Please transcribe this text

[Assistant] Uhm, and then finally, we also cared a lot about code switching, right? Because I said there's some vocabulary that's used by experts and some that's just so low level.


[User] Please transcribe this text

[Assistant] Uhm, or sorry, there's some language that average consumers use. And this, this is really interesting because it's been studied by fashion theorists for a while. Uhm, so Roland Barthes, you know,


[User] Please transcribe this text

[Assistant] Wrote in his, uh, the language of fashion, like, what makes a sweater smart? Is it the fact that it's, you know, uh, a sweater because it's silk, because it's white, because it's square neck? Like, what are the attributes, the low level features of fashion, uhm, that makes a sweater smart?


[User] Please transcribe this text

[Assistant] Uhm, and so this is kind of the problem, or the, this is the kind of thing we want to learn or capture. And our first attempt at this was training polylingual topic models.


[User] Please transcribe this text

[Assistant] I won't get into this in great detail, but what we were able to do is basically express every outfit as a distribution over topics and each topic could be expressed as a set of style words or a set of low level element words.


[User] Please transcribe this text

[Assistant] And it powered interactions where people could give, uhm, a bunch of text.


[User] Please transcribe this text

[Assistant] Uhm, so you can see like it's getting at important low level attributes that might matter to these.


[User] Please transcribe this text

[Assistant] Things that are very specific like cowboy or nautical or something like that.


[User] Please transcribe this text

[Assistant] So going back to our deep architecture, the way we're able to kind of support more nuanced fashion is we have a two-step training process and in the first step we're actually


[User] Please transcribe this text

[Assistant] Use that first step as pre-training, fix those hidden layers, and then train one neuron per concept.


[User] Please transcribe this text

[Assistant] Uhm, and this actually really helps with the, the,


[User] Please transcribe this text

[Assistant] Uhm, it's for learning the mapping between visual features to, uhm, text.


[User] Please transcribe this text

[Assistant] The thing that this points out is that if you add in step two, for a lot of concepts that are kind of on the tail end, you get higher activations as a result. Whereas if you just stop at step one, you can see you never really, you know, activate.


[User] Please transcribe this text

[Assistant] So, let me kind of show you some results of this model just to give you a sense of what are all the different types of vocabulary we have.


[User] Please transcribe this text

[Assistant] We have the type vocabulary, which is just like what kind of item it is. We have shapes and silhouettes like, you know, maxi, skinny leg, ankle. We have patterns like leopard print, uh, floral, plaid. Uhm, we have


[User] Please transcribe this text

[Assistant] At the same time though, these really higher level concepts and styles like biker, smart, bridle, party, beach, and so forth.


[User] Please transcribe this text

[Assistant] Uhm, and then the cool part is that because we've kind of categorized the vocabulary by these, uhm, these types of different words, we can kind of start constructing explanations to recommendations. And this is where all of this was,


[User] Please transcribe this text

[Assistant] They have plunging asymmetric black lines and a fluted, uh, or also a fluted silhouette. So,


[User] Please transcribe this text

[Assistant] Here we're able to use the other words in the vocabulary to actually provide justification for your recommendations.


[User] Please transcribe this text

[Assistant] Uhm, it also allows us to basically go through, uhm, and do other interactions that could be useful, like getting the, you know, same look for less. So, Zimmerman dresses, you know, usually cost more than a thousand dollars, but guess what?


[User] Please transcribe this text

[Assistant] Other brands that cost less than that, which have the same kind of bohemian, floral, tiered ruffle, uh, look.


[User] Please transcribe this text

[Assistant] So yes, the gen- the way we're generating those sentences is we have some templates right now of sentences and we're basically doing a madlib style generation where we say


[User] Please transcribe this text

[Assistant] Insert, uhm, you know, uh, material word. Insert shape word. And you can, uhm, and the reason we actually chose this method is because if you look at the copy on a lot of websites for fashion, it is very templatized, so.


[User] Please transcribe this text

[Assistant] Future work is how do you auto-generate the whole sentence, but right now we can kind of fake it with this.


[User] Please transcribe this text

[Assistant] And then the final example I'll show you is, uh, one of my favorite ones, which is like we can use this method to create capsule wardrobes, which is a trend in fashion these days as a way to organize your closet. So the goal of a capsule wardrobe


[User] Please transcribe this text

[Assistant] Is to take a slice of your wardrobe where you're minimizing the number of items by maximizing the number of outfits.


[User] Please transcribe this text

[Assistant] So you could create a capsule for summer, a capsule for winter, a capsule for your vacation, things like that. And so we can generate these capsule wardrobes using that compatibility, uhm,


[User] Please transcribe this text

[Assistant] So the stage we're at right now is let's, you know, we've built this model, now we want to go back and re-release basically that bot and see if we can take some of the traffic or some of the questions and hit it with the model and when we know we don't have


[User] Please transcribe this text

[Assistant] Now, uhm, in the last like two or three minutes, I want to tease my, uh, the third domain. Uh, I know I don't have a ton of time. Uh, but it's, it's still a really interesting one. And it's around social media and social networks and recommendation systems. Uhm,


[User] Please transcribe this text

[Assistant] Curation has become the central activity on social networks because, you know, you see users sharing and promoting content in a network.


[User] Please transcribe this text

[Assistant] And everyone then benefits from the network effects.


[User] Please transcribe this text

[Assistant] So definitely in these systems you're incentivized to identify who are top curators. And I won't get into the details of the algorithm we came up with, but I just want to motivate how we kind of define or frame this problem. We want to find people in the network


[User] Please transcribe this text

[Assistant] Who have discerning taste, high activity, and timeliness. And by timeliness, I mean people who have found things in the network before it, they become kind of socially popular, right? Good curators or


[User] Please transcribe this text

[Assistant] Similarly, they have discerning tastes because they, uhm, you know, they don't like everything. They only like the things that are going to be good.


[User] Please transcribe this text

[Assistant] You know, adding content who are creating content are not always the people who are the best at curating content or who...


[User] Please transcribe this text

[Assistant] Participate in curating content. So, for example, on GitHub, uhm,


[User] Please transcribe this text

[Assistant] The best cre- the best creators are usually like people like companies, whereas the best curators, there are individuals on GitHub where they're not adding code to the database, but they're just looking at how they can, uhm, identify code that will be useful to other people in the future.


[User] Please transcribe this text

[Assistant] Uhm, and then, you know, since we can talk about identifying these tastemakers, uhm, we started looking at how do we actually mind-taste itself from social networks. And, you know, honestly, a lot of networks where you're being served ads, you're not,


[User] Please transcribe this text

[Assistant] The signal you're giving the network is actually doesn't contain your taste. So the thing that makes all of this really hard is you actually have sparse user data that you're working with.