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Speaker 1: Afro Tech executive Seattle. March twenty twenty three, Jessica Old Matthews,

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Speaker 1: CEO and founder of Uncharted stop by the conference to

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Speaker 1: discuss how we can have a sustainable future and harness

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Speaker 1: the power of AI. She's on the stage speaking with

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Speaker 1: Johnny Bradley, who was the responsible AI official for the

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Speaker 1: Department of Energy and senior program manager for the Artificial

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Speaker 1: Intelligence and Technology Office.

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Speaker 2: Hello, everyone, how are you? Yeah? Yeah, yeah, yeah, okay,

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Speaker 2: So let's get into it, right, yeah, all right, So

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Speaker 2: you know I love soccer and you made socket. So

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Speaker 2: I just have to start this conversation off by asking

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Speaker 2: one question, why did you go from hardware to data solutions?

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Speaker 3: So we have to go real deep into like the

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Speaker 3: technical stuff. But for hi, everybody, Hello, Hello, Hello, thank

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Speaker 3: you will dope, just consistent ass dude, man, how much

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Speaker 3: cursing is allowed?

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Speaker 4: Right?

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Speaker 5: It's always what I like to check.

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Speaker 3: I tend to check after my first one fucks falls out.

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Speaker 5: So listen, y'all.

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Speaker 3: I flew here from Harlem and I was like, like,

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Speaker 3: where does Morgan have me flying to you?

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Speaker 4: Now?

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Speaker 3: That's what I thought was on that plane. I was like,

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Speaker 3: Oh damn, Seattle, Seattle, okay.

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Speaker 5: And I was walking down the street and I was like,

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Speaker 5: where are all the black people?

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Speaker 6: Right?

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Speaker 5: And then but.

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Speaker 3: They're here, Hello, Hello, I'm gone gather them all. No, no,

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Speaker 3: so say, you know, it's a good question. So what

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Speaker 3: Joni's talk about here is that I started my career

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Speaker 3: making energy generating play products. So when I was nineteen

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Speaker 3: years old, I invented an energy generating soccer ball that

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Speaker 3: could harness the energy from play, the kinetic energy, and

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Speaker 3: store that power inside of the ball.

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Speaker 5: Yeah, it was pretty cool. You could play with it,

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Speaker 5: you could.

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Speaker 3: Roll, you know, as it rolled, it was generating energy,

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Speaker 3: and about an hour of play would give you three

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Speaker 3: hours of light fast forward. Now I'm thirty five. I

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Speaker 3: don't have problem saying it because, like, you know me,

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Speaker 3: black guys.

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Speaker 5: Hello, good right.

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Speaker 3: So, and and I run a data infrastructure company that

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Speaker 3: uses AI to help disadvantaged communities develop sustainable infrastructure with

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Speaker 3: more equity and efficiency than ever before. Right, So how

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Speaker 3: did I get from there to here? Well, one thing

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Speaker 3: is that my true north was always incredibly clear to me.

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Speaker 3: I'm a dual citizen of Nigeria and the United States. Always,

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Speaker 3: even in Seattle, where I'm being so messed up.

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Speaker 2: Yeah.

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Speaker 3: Yeah, the only thing I know about Seattle's Nirvana, right, So,

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Speaker 3: so I like and I love Nirvana. So I was like, shoah,

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Speaker 3: I wear my flannel. My mom was like oh se

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Speaker 3: ohse glennom so so for me, you know, it was

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Speaker 3: going back and forth between Nigeria and the United States,

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Speaker 3: whether it was for a weddings or funerals, and just

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Speaker 3: recognizing that there were some things that were so dope,

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Speaker 3: like everybody had a mobile phone. It became ubiquitous very quickly.

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Speaker 3: Oftentimes my cousins had a better cell phone than I did.

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Speaker 3: But also wondering like why is it that it doesn't

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Speaker 3: matter if we're in the village or if we're in

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Speaker 3: like the bustling city of Legos that were losing power

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Speaker 3: every single day.

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Speaker 5: And I knew it wasn't because.

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Speaker 3: Like there weren't the technologies that needed to exist to

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Speaker 3: make that happen. I knew it was an infrastructural issue immediately.

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Speaker 3: In fact, often times in places like Nigeria, people are

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Speaker 3: paying more per kilowatt hour than we pay here in

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Speaker 3: the United States.

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Speaker 5: I don't know if that's true anymore, because.

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Speaker 3: Like I was, the bills getting high i's your bills

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Speaker 3: get high. But at least before it was very much

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Speaker 3: like that. And you know, I believe that the first

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Speaker 3: step in innovation is the articulation of the problem. And

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Speaker 3: at the age of seventeen eighteen, I articulated the problem

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Speaker 3: to be that people like my cousins, who were trained engineers,

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Speaker 3: did not believe that there could be a world where

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Speaker 3: things could be different. They did not believe that this

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Speaker 3: problem could be solved by some innovation or some public

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Speaker 3: private partnership. They simply thought that the best way to

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Speaker 3: solve the problem was to pretend like it's not happening

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Speaker 3: and to get used to it. And so I wanted

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Speaker 3: to create something that would make them change that view,

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Speaker 3: that would make them see in the world not just

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Speaker 3: as it is, but as it could be. And soccer

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Speaker 3: is the most popular sport around the world. Yes, my

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Speaker 3: favorite for the most popular sport. And I'm assuming you're

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Speaker 3: probably really good. My cousins were not that good.

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Speaker 2: Teams went to the championship.

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Speaker 5: I was good.

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Speaker 3: Oh, you were so good that you were bad. Now,

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Speaker 3: my cousins were just My cousins were so average that

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Speaker 3: they were like, why are we playing this game?

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Speaker 5: This is awkward.

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Speaker 3: So but in that in that way though, seeing their

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Speaker 3: passion and seeing the way that they would play on

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Speaker 3: the field, I'm like, you know, the way that you

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Speaker 3: approach this game, knowing damn well that you are at

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Speaker 3: best average is the way you need to approach life.

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Speaker 3: It's the way you need to approach all of the

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Speaker 3: problems in our community and infrastructurally. And so my thought

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Speaker 3: was like, let me create something that would inspire them

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Speaker 3: to do it. And what ended up happening was that

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Speaker 3: long story short inspired.

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Speaker 5: Me to do it.

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Speaker 4: Got it?

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Speaker 2: Okay? Yeah.

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Speaker 3: It took a while to figure out exactly what technologies

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Speaker 3: would get there. First it was the play products, and

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Speaker 3: then different inventions, and then a couple of years ago,

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Speaker 3: right before the pandemic, I started to realize that the

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Speaker 3: common thread in every community wasn't needing like you know,

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Speaker 3: an energy generating speed bump or some cool thing here

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Speaker 3: or there that was fun but couldn't scale. The common

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Speaker 3: problem was data. It didn't matter if it was in

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Speaker 3: Nigeria or in the US. The data problems were kind

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Speaker 3: of compiling on each other, and that when governments were

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Speaker 3: operating in the right way, they were spending at least

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Speaker 3: half of their time tracking down, collecting, correcting, and sharing

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Speaker 3: SOILID information to make infrastructure decisions. They often did not

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Speaker 3: have the right information to know where to prioritize who

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Speaker 3: should be getting the solar or where should we be

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Speaker 3: replacing the lead pipes, And in the other half of

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Speaker 3: the time, they were just quote unquote shooting from the hip.

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Speaker 3: And so I was like, well, if we could solve

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Speaker 3: the data problem and improve the way that they're organizing

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Speaker 3: their data to make decisions to build sustainable infrastructure, we

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Speaker 3: can help them build it faster, we can help them

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Speaker 3: build it for less. And then as a result, we

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Speaker 3: can either help them make it more equitable or at

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Speaker 3: least make it very obvious when they're being fucked up,

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Speaker 3: like make it more obvious, like listen, that's what the

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Speaker 3: data say. If you just want to do that, you

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Speaker 3: can do that, but don't act like the data didn't

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Speaker 3: say that. And so that's how we came up with

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Speaker 3: our current products.

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Speaker 2: And we thank you, right, we thank you for that transition.

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Speaker 2: So we want to talk about demystifying AI. So when

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Speaker 2: we look at AI, I always think about that time,

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Speaker 2: and I don't I don't mind DAT myself either, fifty three. Listen, listen,

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Speaker 2: I don't mind that either. We should just talk about that, right,

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Speaker 2: this homegrown. So back when I was born, actually legislation

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Speaker 2: was passed on cigarettes. But as I grew up, you know,

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Speaker 2: cigarettes was just like whoa, it was a cool thing

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Speaker 2: to do. You had this slim lady smoking a cigarette, right,

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Speaker 2: and it was just like ayeing to me, right, And

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Speaker 2: for twenty years I smoked based off of that. But

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Speaker 2: what I will say is this, they demystified those cigarettes.

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Speaker 4: It was a.

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Speaker 2: Right and that's great, that's that's That's my exact point

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Speaker 2: is that during my time, it was cool, right, and

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Speaker 2: it was the thing to do. And as time went

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Speaker 2: by and the Surgeon General said, no, put these warnings

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Speaker 2: on each pack of these cigarettes and let people know

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Speaker 2: exactly what it does to you, and it would curb

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Speaker 2: and it just like demystified the coolness and the sexiness

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Speaker 2: of cigarettes. So my question is, how do we demystify

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Speaker 2: AI girl?

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Speaker 3: Oh yeah, listen, because we had a hole behind the

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Speaker 3: scenes conversation y'all that we're trying to, like, you know,

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Speaker 3: keep civil for the cameras and all that. So I

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Speaker 3: think that's part of why you guys are here, right,

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Speaker 3: everyone's talking about AI. Everyone's acting like it's the new

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Speaker 3: hot thing, and they're acting like it's this black box

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Speaker 3: and it's this scary monster that cannot be controlled and

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Speaker 3: things are just happening, But it's it's really not, you know,

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Speaker 3: like you should not be afraid of AI.

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Speaker 5: You should be afraid of the people who are building it.

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Speaker 5: That's it. So like what it comes down to, let

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Speaker 5: let's break this.

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Speaker 3: Down, right, AI Artificial intelligence, Right, and we discussed this.

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Speaker 5: It's it's kind of like a child. It's a child.

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Speaker 3: It's like a robot baby, right, chat GPT is at

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Speaker 3: best a sassy.

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Speaker 5: Seven year old.

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Speaker 3: And we all know, right, we all know that seven

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Speaker 3: year old that was born and like, you know, I

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Speaker 3: don't know whatever seven years ago was, like, you know,

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Speaker 3: like but like basically recently grew up with all all

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Speaker 3: the social media platforms and be out here talking to

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Speaker 3: you like they've grown, and.

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Speaker 5: You'd be like, well, damn girl, are you grown?

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Speaker 3: No? They just online? No, Like, do not have this

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Speaker 3: seven year old do your taxes?

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Speaker 5: It might go.

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Speaker 3: Well sometimes until it does not, right, But what it

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Speaker 3: ultimately then comes down to you have to ask yourself

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Speaker 3: is Okay, So if this is just this this code

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Speaker 3: that has a great capacity to learn, right, and the

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Speaker 3: way it's taught to learn, it's algorithms. Algorithms are just processes.

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Speaker 3: Like people like to use fancy words to scare us

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Speaker 3: and say, oh, that's a distant thing. But if you

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Speaker 3: have a process for anything, that's an algorithm, that's an algorithm.

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Speaker 3: You can call it that. Especially half y'all women, y'all

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Speaker 3: women look great in here. I know what you had

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Speaker 3: to do, whatever you had to do to get here

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Speaker 3: on time. That's a very efficient algorithm. That's a very

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Speaker 3: efficient algorithm. And all it actually comes down to is,

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Speaker 3: then how are you teaching that to an artificial intelligence?

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Speaker 3: How you're teaching that to this robot baby? Let's just

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Speaker 3: put it in that way so that it can start.

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Speaker 5: To do that for you.

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Speaker 3: So to that end, when we talk about demystifying it,

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Speaker 3: and we talked about this a lot, you have to

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Speaker 3: wonder who's doing the teaching.

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Speaker 5: You have to wonder who's doing the teaching and.

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Speaker 3: How are they framing the way that this child should

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Speaker 3: observe and respond to the world. So if someone is

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Speaker 3: not aware of their biases, if someone is not aware

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Speaker 3: of the fact, Like, can you imagine who's the guy

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Speaker 3: who's who does open Aiye, I'm not trying to start

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Speaker 3: no ship though, if this is going out to the world,

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Speaker 3: but let's just let's not use him specifically because you know,

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Speaker 3: but like, can you imagine can you imagine Elon Musk,

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Speaker 3: Like can you imagine Elon Musk being at trying to

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Speaker 3: teach AI how to help me do my hair?

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Speaker 5: I could imagine he thinks he can do it.

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Speaker 3: He thinks, of course, no problem. And that's a that's

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Speaker 3: a fun example, right right where So when we talk

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Speaker 3: about demystifying AI, it's it's really saying take the blame

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Speaker 3: away from the AI and start focusing on the people

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Speaker 3: who are training these models, right, and start focusing on

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Speaker 3: whether or not whether they are doing so intentionally or unintentionally,

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Speaker 3: if they're actually considering the vast globe of people and

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Speaker 3: all of their problems. Because all AI is really is

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Speaker 3: a tool. It's a tool to help us do more.

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Speaker 3: To help people do more, you need people to train

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Speaker 3: this AI, and trust, despite all the things that you're

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Speaker 3: hearing about AI taking jobs, thousands of jobs will be

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Speaker 3: created because of what this AI is doing.

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Speaker 5: The people who used to drive the carriage.

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Speaker 3: When they saw the cars, they were like, I don't know,

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Speaker 3: it's getting pretty scary out here.

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Speaker 5: That car don't even got no horses. They're like, this

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Speaker 5: is just wild, this is crazy. I don't trust this. Okay,

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Speaker 5: sure they found new jobs.

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Speaker 3: So I feel like when people start to say things like, oh, well,

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Speaker 3: be afraid of it. Oh it's gonna take your job,

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Speaker 3: what they're really trying to do is make you afraid

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Speaker 3: of going behind the veil and wonder, why can't I

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Speaker 3: be part of the team that's building this AI.

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Speaker 5: Why can't I be part of the crew that's raising

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Speaker 5: this baby?

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Speaker 3: You know, they say it takes a village, not just

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Speaker 3: some dude who doesn't blink in the corner.

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Speaker 5: So why are we allowing you?

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Speaker 2: I love the sassy seven year old, So now let's

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Speaker 2: talk about that sassy seven year old. All of you

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Speaker 2: know the landscape today, right, You have states that are

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Speaker 2: removing diversity and inclusion. You have states that are removing

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Speaker 2: African studies. You can't say the word gay. And you know,

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Speaker 2: I could go on and on right because I watched

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Speaker 2: the news all day every day, so and I don't

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Speaker 2: watch Fox. Sorry, but I will say this is that

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Speaker 2: what do those practitioners look like in the future. Now,

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Speaker 2: this sass seven year old has never had anyone tangling

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Speaker 2: with it that did not have diversity and inclusion, because

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Speaker 2: you have that today, right, you have African American studies today, Right,

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Speaker 2: you have gender equality, you have these things today. But

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Speaker 2: as the days go on, these things are being removed slowly.

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Speaker 2: So now you have people that are graduating college that

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Speaker 2: want to be an AI practitioner, but they did not

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Speaker 2: learn what discrimination was because they didn't believe in racism.

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Speaker 4: Right.

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Speaker 2: I had someone tell me yesterday when I was eating

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Speaker 2: She was like, my best friend said that there was

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Speaker 2: no racism.

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Speaker 5: Do I want that.

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Speaker 2: Person tangling with my sase seven year old AI? And

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Speaker 2: how does that look?

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Speaker 5: I think you know the answer.

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Speaker 3: I want to before I respond, I want to do

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Speaker 3: a quick show of hands. How many of you are

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Speaker 3: here because you're considering how to be more involved in

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Speaker 3: the AI industry?

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Speaker 5: Okay, okay.

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Speaker 3: How many of you are here because everyone's been talking

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Speaker 3: about AI and you're like, what what is this? And

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Speaker 3: you're just trying to understand what it actually is? Oh, okay, okay,

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Speaker 3: be proud.

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Speaker 5: Put your hand up on Okay, that's right.

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Speaker 3: Yeah, how many of you in some way already work

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Speaker 3: with AI or related to AI?

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Speaker 5: Okay, okay.

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Speaker 3: And so your concern then is really that you feel

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Speaker 3: like there's a lot of things happening around you that

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Speaker 3: you don't understand, or don't like or can't control. Okay,

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Speaker 3: So this is what what you're getting at. Indeed, we

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Speaker 3: we have to be concerned in general, and this goes

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Speaker 3: beyond AI that we're going to start to have generations

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Speaker 3: of people who are very much driving our economy, driving industries,

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Speaker 3: developing technologies that will have, from our perspective and our opinion,

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Speaker 3: are a skewed view of the world and how things work.

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Speaker 3: And they're not only going to teach their NI their

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Speaker 3: natural children, that they're going to teach the AI this.

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Speaker 3: But because of the rapid impact and the rapid scale,

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Speaker 3: it's it's not as bad as like, oh, those three

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Speaker 3: kids grew up in that racist sex as holl so

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Speaker 3: now they're racist and sexist. It's that AI was developed

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Speaker 3: by this person who has racist and sexist biases. And

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Speaker 3: because of how impactful AI can be, we now have

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Speaker 3: an army of racist sexists, or the very least incredibly

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Speaker 3: aloof right, like things happen right. Sospective is ultimately radical

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Speaker 3: self reliance. I can't help what's going on specifically Macro

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Speaker 3: in Florida in Texas, but I do know people who

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Speaker 3: live there who are saying, regardless of what they're teaching

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Speaker 3: my kids in school, here's what I'm going to teach

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Speaker 3: you at home. And so that's why I've been kind

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Speaker 3: of recently saying, please, please, please. The last thing you

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Speaker 3: should be is afraid of AI. This is now, more

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Speaker 3: than ever, the time where you need to be incredibly

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Speaker 3: excited about this tool. But you need to see this

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Speaker 3: as a battle, and you need to do everything you

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Speaker 3: can to get your hands on this weapon as well.

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Speaker 3: I'm gonna keep it super super real about this. So,

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Speaker 3: as I said earlier, I'm a thirty five year old woman.

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Speaker 3: I'm married, I love my husband. I'm getting ready. I'm

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Speaker 3: preparing myself to freeze my.

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Speaker 5: Eggs, and I'm gonna be keep it real.

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Speaker 3: Part of me is doing that because I learned that

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Speaker 3: the maternal mortality rate has gotten worse. It's twenty twenty three.

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Speaker 3: It is twenty twenty three in a developed country, and

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Speaker 3: you're telling me that over the last couple of years.

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Speaker 3: And it's not just because of COVID. If I get pregnant,

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Speaker 3: I have twelve patents and patents pending. I'm building all

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Speaker 3: these different things. And the thing that scares me the

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Speaker 3: most is having a baby and dying. But the people

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Speaker 3: are developing AI to do the wildest, most random shit possible.

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Speaker 3: But women are dying when when they get pregnant, because

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Speaker 3: not enough women and definitely not enough Black women are

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Speaker 3: sitting there saying, how can I use AI as a

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Speaker 3: tool to keep more of us alive?

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Speaker 2: Right?

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Speaker 3: And the only way that's gonna change is if more

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Speaker 3: of us say we're not afraid of AI. Regardless you

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Speaker 3: know what, y'all are gonna do what you want to

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Speaker 3: do with it. But here's what I'm gonna do with it.

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Speaker 3: Here's how I'm going to teach this child at home,

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Speaker 3: regardless of what you're doing.

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Speaker 5: And so that to me is the only answer.

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Speaker 3: It's it is not too late for us all to

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Speaker 3: recognize that this will not be done for us. This

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Speaker 3: will not be something where we can hope that the

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Speaker 3: right few people at the top are going to be

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Speaker 3: thinking about all the things that we need. We know

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Speaker 3: this or we would not have systemic issues. Right now,

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Speaker 3: we know this, so but what I now view though,

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Speaker 3: is that I believe the technology is one of the

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Speaker 3: best equalizers, one of the best democratizing tools, and that

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Speaker 3: to me is exciting. That to me is an opportunity.

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Speaker 3: So let's stop talking about being afraid. Let's stop talking

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Speaker 3: about it as a black box. There are several low

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Speaker 3: code and no code tools that you can use to

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Speaker 3: create something in AI if you want to. How do

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Speaker 3: we get people to see this as a playground versus

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Speaker 3: I don't know a mortuary?

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Speaker 4: Right right?

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Speaker 5: I'm also literally not kidden my I'm sorry.

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Speaker 3: My dad literally two days ago and was like, oh,

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Speaker 3: my grandkids are in the freezer.

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Speaker 5: I don't know when they're getting out.

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Speaker 3: And I was like, actually, Dad, Tiana, my older sister, Tiana's,

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Speaker 3: Tiana's uh grand kids are in the freezer.

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Speaker 5: Mine are about to be in the freezer. Just want

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Speaker 5: to confirm.

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Speaker 3: He's like, oh, when are they coming out of the freezer?

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Speaker 3: I'm like, this is what happens when you talk to

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Speaker 3: your mom. Your mom talks to your dad, your dad

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Speaker 3: talks to you. You don't know what's going on. So

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Speaker 3: but uh, it's it's real, and it's a it's it's

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Speaker 3: a real thing we have. We have a very small

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Speaker 3: group of people right now who are focusing AI and

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Speaker 3: their problems, and I do not blame them. Entrepreneurship is

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Speaker 3: problem solving without regard for resource, like science is the

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Speaker 3: study of life. Like these things should not be scary

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Speaker 3: big words. Uh, but when we silo ourselves, and I

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Speaker 3: say we, it's just like if you are anyone, if

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Speaker 3: you're if you're not affluent, if you are not a man,

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Speaker 3: if you're not there's so many things that actually most

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Speaker 3: of us are not that kind of that paradigm of

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Speaker 3: the person who's doing this, most of us.

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Speaker 5: But when you kind of like push something.

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Speaker 3: Away, you are disenfranchising yourself in so many ways. It

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Speaker 3: goes beyond It goes beyond anything we can imagine. The

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Speaker 3: thing that I'm most scared of is the number of

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Speaker 3: people who keep saying I'm afraid of what could literally

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Speaker 3: be the best thing they've ever put their hands on

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Speaker 3: in their entire lives.

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Speaker 7: All right, with that, we are privileged to have ten

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Speaker 7: more minutes with them, to have some Q and A.

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Speaker 7: So I'm sure there's some questions in the art ooh,

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Speaker 7: we got one already. I'm gonna come to you. Y'all

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Speaker 7: give another round of applause for that man. That was fantastic.

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Speaker 7: Please say your name. Hi, my name is Sidney.

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Speaker 5: Thank you. Oh I got it? No, okay, all right,

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Speaker 5: all right?

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Speaker 8: AnyWho I saw you know, I heard us talking about

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Speaker 8: like fear. I personally don't have fear if AI. Maybe

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Speaker 8: I should, I don't know, but that's not what you're saying.

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Speaker 8: So I shouldn't be afraid. But however, how do we

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Speaker 8: harness that you were sharing some ideas of there's some

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Speaker 8: low or no code ways of leveraging AI. Can you

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Speaker 8: tell us more about like how to like leverage it?

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Speaker 8: And yes, no, of course that's a really good question.

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Speaker 8: I actually think I'm gonna go ahead and maybe I

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Speaker 8: can talk to the afrotech people. I'm I'm gonna just

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Speaker 8: list like on my like LinkedIn just like seven seven platforms.

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Speaker 8: Some require you to know a little bit some of you,

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Speaker 8: some of them don't. Now there is the underlying issue of.

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Speaker 3: Kind of who's creating even that no code platform.

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Speaker 5: But at the end of the day, like you know, nothing's.

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Speaker 3: Ever going to be perfect, and we just want people

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Speaker 3: to get closer to something, and if your engagement, even

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Speaker 3: with these no code platforms, can better educate the sassy

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Speaker 3: seven year olds that are running it, So now all

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Speaker 3: of a sudden they're not just kind of operating with

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Speaker 3: whatever the hell. They're being told by the very specific

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Speaker 3: groups of people who are doing this it's a good thing.

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Speaker 3: And so there are several I don't want this necessarily

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Speaker 3: to be an advertisement for any one or the other,

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Speaker 3: but on Monday, I'm gonna post If you go to

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Speaker 3: my LinkedIn, I'm just Jessica, Oh Matthews, you'll see it.

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Speaker 3: I will post five to six that I've heard some

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Speaker 3: good things about. Because again I want to be very clear,

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Speaker 3: I study psychology and economics. I like to tell people

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Speaker 3: I have a PhD in Google, which pisses off people

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Speaker 3: with real PhDs, I find. But the main point is

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Speaker 3: that you know, I also have a granted patent for

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Speaker 3: wireless Mesh Energy Networks, which is an algorithm that essentially

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Speaker 3: considers the communication protocols for decentralized micro energy systems. And

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Speaker 3: I did that with a degree in psychology and economics

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Speaker 3: and a PhD in Google. So what I actually am

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Speaker 3: really trying to say is that you don't have to

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Speaker 3: go to school.

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Speaker 5: For this.

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Speaker 3: To do this, you do have to have quite a

439
00:24:27,680 --> 00:24:32,400
Speaker 3: ferocity for self learning, and again, I fate to say

440
00:24:32,440 --> 00:24:36,720
Speaker 3: a bit of self reliance in this. But with the

441
00:24:36,800 --> 00:24:40,760
Speaker 3: right tools and with that kind of interest in researching

442
00:24:40,800 --> 00:24:43,720
Speaker 3: as much as you can, you'd be surprised what you

443
00:24:43,760 --> 00:24:47,080
Speaker 3: can do, especially if you're comfortable with the prototype being

444
00:24:48,960 --> 00:24:51,199
Speaker 3: very much only a couple percentage points of what you

445
00:24:51,240 --> 00:24:54,880
Speaker 3: actually want. You talked about the socket earlier. My first

446
00:24:54,880 --> 00:24:58,119
Speaker 3: prototype for the socket was a shake the charge flashlight

447
00:24:58,160 --> 00:25:03,200
Speaker 3: and a hamster ball. So I will post that on

448
00:25:03,280 --> 00:25:05,520
Speaker 3: what's today Thursday. Got to get back to New York.

449
00:25:05,560 --> 00:25:07,320
Speaker 3: I'll post it on Monday. I promise.

450
00:25:07,400 --> 00:25:10,919
Speaker 7: I will perfect question over here and I'll come over there.

451
00:25:11,520 --> 00:25:13,600
Speaker 6: Yeah, my name is Evan poncelet's I'm with the Africa

452
00:25:13,640 --> 00:25:15,320
Speaker 6: Down Community Alanterest, and i just want to let you

453
00:25:15,359 --> 00:25:18,440
Speaker 6: know that black people are here in Seattle and mostly

454
00:25:18,440 --> 00:25:20,800
Speaker 6: concentrated in the Central district of Seattle, so let's all

455
00:25:20,840 --> 00:25:23,560
Speaker 6: learn a little geography about this. And so from the

456
00:25:23,600 --> 00:25:26,960
Speaker 6: Central District of Seattle, Ray Charles dropped his first studio album,

457
00:25:26,960 --> 00:25:29,760
Speaker 6: So it's not just Kirk Cobain also Jamie Hendrix is

458
00:25:29,760 --> 00:25:31,399
Speaker 6: from there. Shout out to my uncle who is like

459
00:25:31,480 --> 00:25:33,800
Speaker 6: a high school termer at Garfield High School, if anybody

460
00:25:33,800 --> 00:25:36,479
Speaker 6: from Garfield. And so what I wanted to say was

461
00:25:36,560 --> 00:25:39,159
Speaker 6: just that, you know, in addition to radical self reliance,

462
00:25:39,520 --> 00:25:43,840
Speaker 6: we also should be organizing around data and around artificial intelligence.

463
00:25:43,960 --> 00:25:46,840
Speaker 6: So one thing we're doing with Africa Town is building

464
00:25:46,960 --> 00:25:49,040
Speaker 6: programs so that you can be exposed to these sorts

465
00:25:49,040 --> 00:25:51,959
Speaker 6: of things. But I was wondering, my question really is

466
00:25:52,680 --> 00:25:55,359
Speaker 6: where can we get exposure to the data sets that

467
00:25:55,440 --> 00:25:58,840
Speaker 6: could help us solve with AI things like infant mortality

468
00:25:59,040 --> 00:26:02,679
Speaker 6: or a pregnant month mortality, mortality in the birthing scenarios.

469
00:26:03,320 --> 00:26:05,920
Speaker 6: So that because we work with universities that are studying,

470
00:26:06,000 --> 00:26:08,840
Speaker 6: you know, data and like things like coming up with

471
00:26:08,880 --> 00:26:11,960
Speaker 6: the language models for African American vernacular English and things

472
00:26:12,000 --> 00:26:14,720
Speaker 6: like this, And so right now we're about to start

473
00:26:14,720 --> 00:26:17,520
Speaker 6: a consortium where we're learning, well, what goals should we have,

474
00:26:17,520 --> 00:26:19,560
Speaker 6: what problems should we solve in, what strategy should we

475
00:26:19,560 --> 00:26:21,919
Speaker 6: we implement? And so I'm just trying to see where's

476
00:26:21,920 --> 00:26:24,720
Speaker 6: our best footing for that in terms of organizing, Jenny, I.

477
00:26:24,720 --> 00:26:26,920
Speaker 3: Think I have I can tell you where my company,

478
00:26:27,400 --> 00:26:31,760
Speaker 3: like when we really started looking at disadvantaged communities that

479
00:26:31,840 --> 00:26:34,720
Speaker 3: are black and Latino majority communities, and how do we

480
00:26:34,760 --> 00:26:38,040
Speaker 3: get the data to ensure that we're thinking through the

481
00:26:38,080 --> 00:26:40,520
Speaker 3: equitability of what's happening in this once in a generation

482
00:26:40,600 --> 00:26:44,120
Speaker 3: moment with our infrastructure, and how we started creating actual,

483
00:26:45,440 --> 00:26:49,760
Speaker 3: actual AI that could support that. But I'd love to

484
00:26:49,800 --> 00:26:53,600
Speaker 3: know what you think first. I can share our perspective,

485
00:26:53,640 --> 00:26:56,280
Speaker 3: but as someone who works with the government, you might

486
00:26:56,640 --> 00:26:57,439
Speaker 3: know a few more.

487
00:26:57,600 --> 00:27:03,879
Speaker 2: Peek so you know the government, you know that's a

488
00:27:03,920 --> 00:27:08,880
Speaker 2: beast by itself, right, we do have ways of putting

489
00:27:08,960 --> 00:27:12,080
Speaker 2: out actual where our data is stored, so I will

490
00:27:12,119 --> 00:27:14,399
Speaker 2: say that. Okay, So what my office does, I'm the

491
00:27:14,440 --> 00:27:17,760
Speaker 2: Artificial Intelligence and Technology office, right, and what we do

492
00:27:17,960 --> 00:27:20,840
Speaker 2: is every year we do an AI use case inventory.

493
00:27:21,080 --> 00:27:23,520
Speaker 2: And actually we're sitting here, but that's what's going on

494
00:27:23,640 --> 00:27:26,359
Speaker 2: back at home, is all the labs are putting together

495
00:27:26,640 --> 00:27:29,320
Speaker 2: in AI use case inventory. We're still turn into us

496
00:27:30,080 --> 00:27:33,480
Speaker 2: in mid April. Once that's turned into us, we will

497
00:27:33,520 --> 00:27:37,800
Speaker 2: put that inventory up on what's releasable to the public.

498
00:27:37,840 --> 00:27:40,480
Speaker 2: So let me say that because we're seventeen national laboratories,

499
00:27:40,520 --> 00:27:43,439
Speaker 2: so that way you know, everything's not releasable to the public.

500
00:27:43,720 --> 00:27:46,359
Speaker 2: But what is releasable to the public. We'll go up

501
00:27:46,359 --> 00:27:49,680
Speaker 2: on our website, right, it's the Artificial Intelligence and Technology Office.

502
00:27:49,800 --> 00:27:53,280
Speaker 2: You will see the inventory there. If they are listing

503
00:27:53,320 --> 00:27:55,919
Speaker 2: the code, it will be there, so you could actually

504
00:27:55,960 --> 00:27:58,520
Speaker 2: read what the name of the use case is. You'll

505
00:27:58,520 --> 00:28:00,840
Speaker 2: be able to see a description of that use case

506
00:28:01,040 --> 00:28:03,320
Speaker 2: if it matches anything that you're trying to do or

507
00:28:03,320 --> 00:28:05,919
Speaker 2: looking to do, and it says where the code is.

508
00:28:06,359 --> 00:28:07,440
Speaker 5: That's where the code is at.

509
00:28:07,560 --> 00:28:07,840
Speaker 7: Right.

510
00:28:08,080 --> 00:28:12,040
Speaker 2: If it's blank and you want us to find out,

511
00:28:12,200 --> 00:28:15,520
Speaker 2: that's Jason Tally. You want us to find out if

512
00:28:15,520 --> 00:28:18,080
Speaker 2: that code is available, just send us an email. Our

513
00:28:18,119 --> 00:28:20,359
Speaker 2: email address is on the website. You send it to

514
00:28:20,440 --> 00:28:22,640
Speaker 2: us and we'll get it for you. If it's available,

515
00:28:22,680 --> 00:28:25,480
Speaker 2: we'll get it for you. So that's from the government perspective.

516
00:28:25,200 --> 00:28:29,440
Speaker 3: Which matters, right, because I think for us we're often

517
00:28:29,480 --> 00:28:32,840
Speaker 3: looking at our data sources one from actual governmental context.

518
00:28:32,880 --> 00:28:36,399
Speaker 3: Like a lot of times people don't realize that almost

519
00:28:36,400 --> 00:28:37,480
Speaker 3: everything is available to you.

520
00:28:37,440 --> 00:28:38,479
Speaker 5: You just have to ask for it.

521
00:28:39,440 --> 00:28:41,320
Speaker 3: They're not going to make it clean and easy or

522
00:28:41,320 --> 00:28:44,080
Speaker 3: create an interface that makes it as simple as you know,

523
00:28:44,240 --> 00:28:47,640
Speaker 3: downloading photos from you know, from your whatever app you're

524
00:28:47,720 --> 00:28:51,160
Speaker 3: using for that, but you can reach out. The other

525
00:28:51,240 --> 00:28:53,440
Speaker 3: thing that's been interesting for us over the last two

526
00:28:53,520 --> 00:28:57,160
Speaker 3: years that, to be honest, was a bit surprising, was

527
00:28:57,320 --> 00:29:04,240
Speaker 3: connecting and partnering with journalists. Journalists are surprisingly good at

528
00:29:04,280 --> 00:29:07,920
Speaker 3: getting real hard data in the aggregate.

529
00:29:08,640 --> 00:29:12,240
Speaker 5: For example, it was the New York Times that.

530
00:29:12,280 --> 00:29:16,400
Speaker 3: Went and actually published with incredible support when you actually

531
00:29:16,440 --> 00:29:18,760
Speaker 3: go in and look at what they published and what

532
00:29:18,960 --> 00:29:23,360
Speaker 3: studies that they were pulling from the infant mortality rates

533
00:29:24,360 --> 00:29:26,120
Speaker 3: and I think some of you may have seen that

534
00:29:26,280 --> 00:29:31,040
Speaker 3: article and so, and that's happened before. We actually struggled

535
00:29:31,040 --> 00:29:33,800
Speaker 3: and looking at a lot of the things related to

536
00:29:33,920 --> 00:29:37,560
Speaker 3: justice forty and disadvantaged communities and this idea of forty

537
00:29:37,600 --> 00:29:41,520
Speaker 3: percent of the infrastructure funds that're actually meant to go

538
00:29:41,560 --> 00:29:45,840
Speaker 3: to disadvantaged communities across the United States, but we struggled

539
00:29:45,840 --> 00:29:51,960
Speaker 3: to understand how many of those disadvantaged communities were majority minority, right,

540
00:29:52,040 --> 00:29:54,560
Speaker 3: because you can work some things out there, and that

541
00:29:54,640 --> 00:29:57,000
Speaker 3: wasn't actually available through any government sources.

542
00:29:57,200 --> 00:29:59,239
Speaker 5: And so there was actually a journalist.

543
00:29:59,040 --> 00:30:02,920
Speaker 3: That had been doing work for about five years that

544
00:30:03,280 --> 00:30:07,240
Speaker 3: allowed us to actually see every city, township, and village

545
00:30:07,840 --> 00:30:12,600
Speaker 3: that was black majority Latino majority and black Latino majority,

546
00:30:13,480 --> 00:30:17,960
Speaker 3: and the data set was so massive and again readily available.

547
00:30:18,000 --> 00:30:21,880
Speaker 3: And so I think that because if you find truly

548
00:30:21,920 --> 00:30:27,960
Speaker 3: reputable news organizations that are pushing data because they are

549
00:30:27,960 --> 00:30:32,360
Speaker 3: often fearful of publishing a massive story that isn't backed up,

550
00:30:33,160 --> 00:30:35,760
Speaker 3: they've done their homework and you can dig there and

551
00:30:35,800 --> 00:30:37,920
Speaker 3: get their data sets, and when you combine those with

552
00:30:38,080 --> 00:30:40,720
Speaker 3: government data sets, you you can do some things that

553
00:30:40,720 --> 00:30:41,600
Speaker 3: are very very cool.

554
00:30:42,680 --> 00:30:42,760
Speaker 2: You.

555
00:30:43,800 --> 00:30:45,520
Speaker 5: Hi, everyone, my name is Asia.

556
00:30:45,880 --> 00:30:48,520
Speaker 4: First, I want to thank you for being so honest

557
00:30:48,560 --> 00:30:50,960
Speaker 4: and real and challenging all of us in this room

558
00:30:51,000 --> 00:30:53,480
Speaker 4: to do more with data and AI, I didn't have

559
00:30:53,520 --> 00:30:55,480
Speaker 4: a question, but I just wanted to tell you like

560
00:30:55,560 --> 00:30:58,280
Speaker 4: being a black woman and seeing you dominate this space

561
00:30:58,400 --> 00:30:59,320
Speaker 4: is just empowering.

562
00:31:00,240 --> 00:31:05,480
Speaker 7: Wow. Thank you should be the last one writing last one.

563
00:31:06,760 --> 00:31:06,880
Speaker 2: Hi.

564
00:31:07,160 --> 00:31:09,320
Speaker 9: My name is Erica Adams. I'm a grad student at

565
00:31:09,400 --> 00:31:12,840
Speaker 9: UDUB and I am in the Information School and I

566
00:31:12,920 --> 00:31:15,640
Speaker 9: sit on faculty committee, and we've been talking a lot

567
00:31:15,640 --> 00:31:19,040
Speaker 9: about student use with chat GPT. We're already using it,

568
00:31:19,120 --> 00:31:22,040
Speaker 9: most of us, but there's a lot of ambiguity around

569
00:31:23,680 --> 00:31:26,160
Speaker 9: I guess like cheating and stuff like that. So I'm

570
00:31:26,200 --> 00:31:29,800
Speaker 9: just curious if you have any advice on persuading older

571
00:31:30,600 --> 00:31:34,640
Speaker 9: academics on you know, like coming up with guidelines for use,

572
00:31:34,680 --> 00:31:37,360
Speaker 9: because I think it's a great tool for us to

573
00:31:37,440 --> 00:31:40,000
Speaker 9: continue to use and we shouldn't be using it with fear.

574
00:31:44,120 --> 00:31:45,520
Speaker 3: See I don't even know when you say older, do

575
00:31:45,520 --> 00:31:52,320
Speaker 3: you mean people like my age, because it, yeah, it

576
00:31:52,400 --> 00:31:53,800
Speaker 3: hits you real quick and all of a sudden, you

577
00:31:53,960 --> 00:31:55,680
Speaker 3: like when you go out and you're like, I'm not

578
00:31:55,720 --> 00:32:02,160
Speaker 3: the youngest person out here no more. Right, So, persuading

579
00:32:03,240 --> 00:32:06,040
Speaker 3: that's a that's a tricky one. That is a tricky

580
00:32:06,080 --> 00:32:11,560
Speaker 3: one because there has to be empathy for how long

581
00:32:12,560 --> 00:32:15,160
Speaker 3: they've existed and known certain things to be true that

582
00:32:15,200 --> 00:32:21,160
Speaker 3: are now becoming very much untrue. And I think I

583
00:32:21,240 --> 00:32:23,880
Speaker 3: think starting from that place of empathy is one. So

584
00:32:24,880 --> 00:32:26,960
Speaker 3: I think there's a couple of ways to see this,

585
00:32:27,040 --> 00:32:29,760
Speaker 3: if I'm going to be very just kind of direct

586
00:32:29,880 --> 00:32:30,360
Speaker 3: about it.

587
00:32:31,000 --> 00:32:33,200
Speaker 5: One is, you know, I don't.

588
00:32:33,040 --> 00:32:36,680
Speaker 3: Know what guidelines are in place right now. But obviously

589
00:32:36,760 --> 00:32:39,800
Speaker 3: if everyone goes to chat GPT and says, write me

590
00:32:39,840 --> 00:32:44,719
Speaker 3: a paper on the World War, and everyone submits a

591
00:32:44,760 --> 00:32:47,960
Speaker 3: similar paper, the teacher will say, oh, clearly there's some

592
00:32:48,000 --> 00:32:50,880
Speaker 3: sort of plagiarism, right, because like whether you did this

593
00:32:51,000 --> 00:32:54,000
Speaker 3: through something that you've googled or you've used chat GPT,

594
00:32:54,600 --> 00:32:57,800
Speaker 3: they can tell if you go to the effort of

595
00:32:57,880 --> 00:33:02,000
Speaker 3: engaging with that chat GPT interface such that what you

596
00:33:02,080 --> 00:33:05,080
Speaker 3: produce your professor cannot tell.

597
00:33:08,560 --> 00:33:10,880
Speaker 5: At this point, I don't really know what else to

598
00:33:10,960 --> 00:33:12,480
Speaker 5: tell me. You know, I mean, I'm not even I'm not.

599
00:33:12,720 --> 00:33:14,360
Speaker 5: And it's not about saying is this cheating or is

600
00:33:14,360 --> 00:33:15,040
Speaker 5: this not cheating.

601
00:33:15,240 --> 00:33:18,240
Speaker 3: This is about being realistic about the world that we're in.

602
00:33:18,920 --> 00:33:21,840
Speaker 3: Like if everyone, if you are lazy with this tool,

603
00:33:22,080 --> 00:33:23,200
Speaker 3: you will be found out.

604
00:33:23,080 --> 00:33:24,520
Speaker 5: To be lazy.

605
00:33:24,600 --> 00:33:30,680
Speaker 3: If you are innovative and proactive with this tool, you

606
00:33:30,720 --> 00:33:33,840
Speaker 3: will still rise above. I truly believe that there's always

607
00:33:33,880 --> 00:33:37,239
Speaker 3: still a way to rise above and still write the

608
00:33:37,280 --> 00:33:38,480
Speaker 3: best paper.

609
00:33:38,160 --> 00:33:40,400
Speaker 5: With chet GPT compared to everyone else.

610
00:33:40,880 --> 00:33:44,400
Speaker 3: And to be honest, if if college is meant to

611
00:33:44,400 --> 00:33:48,520
Speaker 3: prepare you for the real world, acting like these tools don't.

612
00:33:48,360 --> 00:33:51,320
Speaker 5: Exist when they do.

613
00:33:52,240 --> 00:33:54,240
Speaker 3: And I get it that if people, oh, we want

614
00:33:54,240 --> 00:33:55,479
Speaker 3: to make sure you can write a paper, we want

615
00:33:55,520 --> 00:33:55,840
Speaker 3: to make sure you.

616
00:33:55,800 --> 00:33:58,240
Speaker 5: Can do all those things. Yes, yes, guess what.

617
00:33:58,320 --> 00:34:02,120
Speaker 3: I also still don't know how to drive stick because

618
00:34:02,120 --> 00:34:05,640
Speaker 3: I didn't have to. So you know, we can lament

619
00:34:05,880 --> 00:34:08,560
Speaker 3: about this world of like, oh we hope people wish

620
00:34:08,640 --> 00:34:10,200
Speaker 3: they should. We want to make sure you still have

621
00:34:10,239 --> 00:34:13,600
Speaker 3: all these different things. Those who care about those skills

622
00:34:13,600 --> 00:34:16,560
Speaker 3: will get them. I believe my husband said he could

623
00:34:16,600 --> 00:34:18,560
Speaker 3: drive stick, but then recently, actually I was like, were

624
00:34:18,600 --> 00:34:21,799
Speaker 3: you were you lying? Because this is not I don't

625
00:34:21,800 --> 00:34:26,040
Speaker 3: think he'sposed to make those noises. You know, he's from Mississippi,

626
00:34:26,160 --> 00:34:28,279
Speaker 3: so he's a you know, so he was telling me

627
00:34:28,280 --> 00:34:31,280
Speaker 3: a whole of the storm Mississippi and Texas, so who knows.

628
00:34:31,320 --> 00:34:34,319
Speaker 3: But but to that end, right, like I think, but

629
00:34:34,360 --> 00:34:36,479
Speaker 3: he clearly felt that it was important that he said

630
00:34:36,880 --> 00:34:37,839
Speaker 3: that he could drive stick.

631
00:34:37,840 --> 00:34:39,480
Speaker 5: I was like, well, I could barely.

632
00:34:39,160 --> 00:34:41,520
Speaker 3: Do automatic at the time, honestly, Like I cannot wait

633
00:34:41,600 --> 00:34:43,040
Speaker 3: for driverless car.

634
00:34:43,239 --> 00:34:46,040
Speaker 5: So it's it's it's one of those.

635
00:34:45,960 --> 00:34:47,560
Speaker 3: Things where I would say so, I don't think it's

636
00:34:47,600 --> 00:34:53,719
Speaker 3: about persuasion. I think it's about recognizing that the entire

637
00:34:53,880 --> 00:34:58,160
Speaker 3: all the standards will shift, that you cannot restaur on

638
00:34:58,160 --> 00:35:00,960
Speaker 3: your laurels here like everyone keeps saying, I use chatchip

639
00:35:01,040 --> 00:35:02,719
Speaker 3: tea to create a marketing plan to do this and

640
00:35:02,760 --> 00:35:08,080
Speaker 3: do that, we will see certain similarities that will negate

641
00:35:08,200 --> 00:35:10,880
Speaker 3: that work if you do not still put your human

642
00:35:10,920 --> 00:35:13,960
Speaker 3: intellect on top of it. We're not there again, sassy

643
00:35:14,040 --> 00:35:17,600
Speaker 3: seven year old y'all, we're not there, and don't let

644
00:35:17,640 --> 00:35:20,000
Speaker 3: anyone make you think that that we are.

645
00:35:21,280 --> 00:35:23,720
Speaker 5: But yeah, so it really it sounds to me that like.

646
00:35:24,080 --> 00:35:27,120
Speaker 3: If your professors are like super not into it, you

647
00:35:27,200 --> 00:35:29,759
Speaker 3: might be able to save yourself some time and just what.

648
00:35:30,000 --> 00:35:33,920
Speaker 5: You gotta do be like with the chatchypt No, I

649
00:35:33,960 --> 00:35:34,960
Speaker 5: would never.

650
00:35:46,280 --> 00:35:49,040
Speaker 1: Black Deac Green Money is a production of Blavity, Afrotech

651
00:35:49,280 --> 00:35:52,480
Speaker 1: on the Black Effect Podcast Network and iHeart Media, and

652
00:35:52,520 --> 00:35:55,520
Speaker 1: it's produced by Morgan de Bonn and me Well Lucas,

653
00:35:55,800 --> 00:35:59,080
Speaker 1: with additional production support by Sarah Ergan and Rose McLucas.

654
00:36:00,160 --> 00:36:03,239
Speaker 1: Special thank you to Michael Davis of AANESSSA Sorano. Learn

655
00:36:03,239 --> 00:36:05,480
Speaker 1: more about my guests the other Tech. This reptisent innovators

656
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Speaker 1: at afrotech dot com. Enjoy your Black Tech Green Money,

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00:36:09,480 --> 00:36:12,360
Speaker 1: Share this with somebody, Go get your money.

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Speaker 6: Peace and love,