Speaker 1: It is an interesting time these days around AI. Over
Speaker 1: the weekend, the board of directors for the nonprofit Open Ai,
Speaker 1: which developed and operates chat gpt, which is super duper
Speaker 1: popular I use it a lot, they abruptly fired the
Speaker 1: co founder and now previous CEO, Sam Aldman. So they
Speaker 1: did this in like like thirty minutes before the markets
Speaker 1: closed on Friday, which that kind of thing only happens
Speaker 1: in the rarest and most inflammatory circumstances because it's such
Speaker 1: a market shifting move to do that right before the
Speaker 1: markets closed, especially on a Friday. So this is big
Speaker 1: news in AI as chat gpt has the fastest growing
Speaker 1: user base for consumer app in history. They did one
Speaker 1: hundred million users in two months. Now, to give you
Speaker 1: some perspective, it took Facebook like four years to hit
Speaker 1: one hundred million users. It took Twitter five years to
Speaker 1: get one hundred million users. I think it took ig
Speaker 1: Instagram of like two years. Open ais chat gpt did
Speaker 1: it in two months, and almost as fast as they
Speaker 1: blew up their incredible valuation, they blew up their incredible valuation.
Speaker 1: So it went from look, they went from looking at
Speaker 1: like a ninety billion dollar valuation to uncertainty in the
Speaker 1: matter of hours and days since the board gave Sam
Speaker 1: the boot. In April this year, the company was valued
Speaker 1: at twenty nine billion dollars, and just a week ago
Speaker 1: they were looking at a ninety billion dollar valuation raising
Speaker 1: money at a ninety billion dollar valuation. From April's twenty
Speaker 1: nine billion dollar valuation, they were on track to do
Speaker 1: a billion dollars in revenue, which shows you how fast
Speaker 1: chat gpt was growing and with the future looked like
Speaker 1: for them. If you recall chat GPT, it was just
Speaker 1: legit like launched ten months ago, eleven months ago, So
Speaker 1: this is incredible growth such that AI is just about
Speaker 1: all anybody is talking about these days, whether they're building it,
Speaker 1: they're trying to fund it, they are starting an AI
Speaker 1: focused startup, or they're afraid of it altogether. AI is
Speaker 1: on many of our minds. At the time this recording,
Speaker 1: a lot has happened. Sam was out at Open Ai
Speaker 1: as CEO. This was just Friday. Then the employees and
Speaker 1: investors revolted because the way people believed in Sam obviously
Speaker 1: was a surprise to the board, so there were attempts
Speaker 1: to bring him back over the weekend. Then that didn't
Speaker 1: work out. Then today Microsoft says they're bringing him over
Speaker 1: there to run some AI projects, and undoubtedly many necessary,
Speaker 1: highly talented open ai employees who were on his team
Speaker 1: will follow him in the Microsoft. So who knows what
Speaker 1: will happen an hour after I published this episode, because
Speaker 1: this is all happening so fast. Everything I just mentioned
Speaker 1: happened in like the span of forty eight to seventy
Speaker 1: two hours. This thing. Usually things like this play out
Speaker 1: over weeks.
Speaker 2: Now.
Speaker 1: The reason I kick off this episode with this news
Speaker 1: is because it's that significant. Because we all, if I mean,
Speaker 1: if you've embraced at some level AI and you have
Speaker 1: tried chat GPT at least once, there's no way you're
Speaker 1: not hooked. It's so good at figuring out how to
Speaker 1: say things in the way you want to say it,
Speaker 1: by just you giving it some simple commands. And so
Speaker 1: I wanted to start this episode off just to point
Speaker 1: to make a point that things are moving so quickly
Speaker 1: in our society. This is a rallying call, a rallying
Speaker 1: to you, my black tech, green money family, to be
Speaker 1: bout it. Nobody asked permission, Certainly the board that open
Speaker 1: Ai didn't do a poll of their employees and investors
Speaker 1: to see if kicking out the founder and CEO will
Speaker 1: be acceptable. They just bust a move. So we're living
Speaker 1: in the days where people who beg forgiveness versus ask
Speaker 1: permission are going to be in a position to succeed.
Speaker 1: If you're waiting for your work situation to get right,
Speaker 1: or your kids to graduate, or the weather to warm
Speaker 1: up in the sun to be out again before you
Speaker 1: start making your moves, you'll be at a disadvantage. Things
Speaker 1: that used to take months are happening now in hours.
Speaker 1: So I want to bring you a conversation from afro
Speaker 1: tech executive in Seattle between Jessica Matthews Jessica Old Matthews,
Speaker 1: who's CEO and founder of Uncharted, and Johnny Bradley, who
Speaker 1: was the responsible AI official for the Department of Energy
Speaker 1: and senior program manager for the Artificial Intelligence and Technology Office.
Speaker 1: Because this conversation they're having is about demystifying AI for
Speaker 1: our community. So I hope you get something from this.
Speaker 1: Follow along, find your way to not necessarily you have
Speaker 1: to go build an AI startup, but find your way
Speaker 1: to use it, leverage it to do what you want
Speaker 1: to do, and do what you're doing even better, more efficiently, faster, cheaper,
Speaker 1: and with a greater punch.
Speaker 3: Okay, hello everyone, how are you? Yeah? Yeah, yes, yeah, okay,
Speaker 3: So let's get into it, right, yeah, all right. So
Speaker 3: you know I love soccer and you made socket. So
Speaker 3: I just have to start this conversation off by asking
Speaker 3: one question, why did you go from hardware to data solutions?
Speaker 4: So we have to go real deep into like the
Speaker 4: technical stuff, but for hi, everybody, Hello, Hello, Hello, thank you.
Speaker 4: Will Will's dope just consistent. Ask dude, man, how much
Speaker 4: cursing is allowed?
Speaker 5: Right?
Speaker 4: That's always what I like to check. I tend to
Speaker 4: check after my first one fuck falls out. So listen, y'all.
Speaker 4: I flew here from Harlem and I was like here, like,
Speaker 4: where does Morgan have me flying to you? Now? That's
Speaker 4: what I thought was on that plane. I was like,
Speaker 4: oh damn Seattle, Seattle, okay. And I was walking down
Speaker 4: the street and I was like, where all the black people?
Speaker 3: Right?
Speaker 4: And then but then you're here, Hello, hello, gathered them off? No, no,
Speaker 4: So say, you know, it's a good question. So, uh,
Speaker 4: what Jonie's talk about here is that I started my
Speaker 4: career making energy generating play products. So when I was
Speaker 4: nineteen years old, I invented an energy generator eating soccer
Speaker 4: ball that could harness the energy from play, the kinetic energy,
Speaker 4: and store that power inside of the ball. Yeah, it
Speaker 4: was pretty cool. You could play with it, you could roll,
Speaker 4: you know, as it rolled, it was generating energy, and
Speaker 4: about an hour of play would give you three hours
Speaker 4: of light. Fast forward. Now I'm thirty five. I don't
Speaker 4: have problem saying it because, like, you know, me, black guys, good, right,
Speaker 4: what I'm saying? So, and and I run a data
Speaker 4: infrastructure company that uses AI to help disadvantaged communities develop
Speaker 4: sustainable infrastructure with more equity and efficiency than ever before. Right,
Speaker 4: So how did I get from there to here? Well,
Speaker 4: one thing is that my true north was always incredibly
Speaker 4: clear to me. I'm a dual citizen of Nigeria and States. Always,
Speaker 4: even in Seattle, where I'm being so messed up.
Speaker 6: Yeah, yeah.
Speaker 4: The only thing I know about Seattle's Nirvana. Right, So,
Speaker 4: so I like, and I love Nirvana. So I was like, sheah,
Speaker 4: I wear my flannel. My mom was like oh s
Speaker 4: ohse glenno. So so for me, you know, it was
Speaker 4: going back and forth between Nigeria and the United States,
Speaker 4: whether it's for weddings or funerals, and just recognizing that
Speaker 4: there were some things that were so dope, like everybody
Speaker 4: had a mobile phone. It became ubiquitous very quickly. Oftentimes
Speaker 4: my cousins had a better cell phone than I did.
Speaker 4: But also wondering like why is it that it doesn't
Speaker 4: matter if we're in the village or if we're in
Speaker 4: like the bustling city of Legos that were losing power
Speaker 4: every single day. And I knew it wasn't because like
Speaker 4: there were with the technologies that needed to exist to
Speaker 4: make that happen. I knew it was an infrastructural issue immediately.
Speaker 4: In fact, oftentimes in places like Nigeria, people are paying
Speaker 4: more per kilowatt hour than we pay here in the
Speaker 4: United States. I don't know if that's true anymore, because
Speaker 4: like I was, the bills getting high, I was your
Speaker 4: bills getting high. But at least before it was very
Speaker 4: much like that. And you know, I believe that the
Speaker 4: first step in innovation is the articulation of the problem.
Speaker 4: And at the age of seventeen eighteen, I articulated the
Speaker 4: problem to be that people like my cousins who were
Speaker 4: trained engineers did not believe that there could be a
Speaker 4: world where things could be different. They did not believe
Speaker 4: that this problem could be solved by some innovation or
Speaker 4: some public private partnership. They simply thought that the best
Speaker 4: way to solve the problem was to pretend like it's
Speaker 4: not happening and to get used to it. And so
Speaker 4: I wanted to create something that would make them change
Speaker 4: that view, that would make them see in the world
Speaker 4: not just as it is, but as it could be.
Speaker 4: And soccer is the most popular sport around the world,
Speaker 4: my favorite sport, the most popular sport. And I'm assuming
Speaker 4: you're probably really good. My cousins were not that good.
Speaker 3: Teams to the championship.
Speaker 4: I was good. Oh, you were so good that you
Speaker 4: were bad. Now my cousins were just My cousins were
Speaker 4: so average that they were like, why are we playing
Speaker 4: this game? This is awkward. So but in that in
Speaker 4: that way though, seeing their passion and seeing the way
Speaker 4: that they would play on the field, I'm like, you know,
Speaker 4: the way that you approach this game, knowing damn well
Speaker 4: that you are at best average is the way you
Speaker 4: need to approach life. It's the way you need to
Speaker 4: approach all of the problems in our community and infrastructurally,
Speaker 4: and so my thought was, like, let me create something
Speaker 4: that would inspere hire them to do it. And what
Speaker 4: ended up happening was that long story short inspired me
Speaker 4: to do it.
Speaker 5: Got it?
Speaker 3: Okay?
Speaker 4: Yeah, all right. It took a while to figure out
Speaker 4: exactly what technologies would get there. First it was the
Speaker 4: play products, and then different inventions, and then a couple
Speaker 4: of years ago, right before the pandemic, I started to
Speaker 4: realize that the common thread in every community wasn't needing,
Speaker 4: like you know, an energy generating speed bump or some
Speaker 4: cool thing here or there that was fun but couldn't scale.
Speaker 4: The common problem was data. It didn't matter if it
Speaker 4: was in Nigeria or in the US. The data problems
Speaker 4: were kind of compiling on each other, and that when
Speaker 4: governments were operating in the right way, they were spending
Speaker 4: at least half of their time tracking down, collecting, correcting,
Speaker 4: and sharing SOILID information to make infrastructure decisions. They often
Speaker 4: did not have the right information to know where to
Speaker 4: prioritize who should be getting the solar or where should
Speaker 4: we be replacing the lead pipes, And in the other
Speaker 4: half of the time, they were just quote unquote shooting
Speaker 4: from the hip, and so I was like, well, if
Speaker 4: we could solve the data problem and improve the way
Speaker 4: that they're organizing their data to make decisions to build
Speaker 4: sustainable infrastructure, we can help them build it faster, we
Speaker 4: can help them build it for less. And then as
Speaker 4: a result, we can either help them make it more
Speaker 4: equitable or at least make it very obvious when they're
Speaker 4: being fucked up, like make it more obvious, like, listen,
Speaker 4: that's what the data say. If you just want to
Speaker 4: do that, you can do that, but don't act like that
Speaker 4: data didn't say that. And so that's how we came
Speaker 4: up with our current products.
Speaker 3: And we thank you, right, we thank you for that transition.
Speaker 4: So we want to.
Speaker 3: Talk about demystifying AI. So when we look at AI,
Speaker 3: I always think about that time and I don't I
Speaker 3: don't mind dating myself either. Fifty three Listen, I don't
Speaker 3: mind that either.
Speaker 4: We should just talk about that.
Speaker 3: I do, right, this homegrown. So back when I was born,
Speaker 3: actually legislation was passed on cigarettes. But as I grew up,
Speaker 3: you know, cigarettes was just like whoa, it was a
Speaker 3: cool thing to do. You had this slim lady smoking
Speaker 3: a cigarette, right, and it was just like ayeing to me, right,
Speaker 3: And for twenty years I smoked based off of that.
Speaker 3: But what I will say is this, they demystified those cigarettes.
Speaker 2: It was not.
Speaker 3: Right, and that's great, that's that's that's my exact point
Speaker 3: is that during my time, it was cool, right, and
Speaker 3: it was the thing to do. And as time went
Speaker 3: by and the Surgeon General said, no, put these warnings
Speaker 3: on each pack of these cigarettes and let people know
Speaker 3: exactly what it does to you, and it would curb
Speaker 3: and it just like demystified the coolness and the sexiness
Speaker 3: of cigarettes. So my question is, how do we demystify AI?
Speaker 4: Girl? Oh yeah, listen, because we had a hole behind
Speaker 4: the scenes conversation y'all that we're trying to like, you know,
Speaker 4: keep civil for the cameras and all that. So I
Speaker 4: think that's part of why you guys are here, right.
Speaker 4: Everyone's talking about AI. Everyone's acting like it's the new
Speaker 4: hot thing, and they're acting like it's this black bots
Speaker 4: and it's this scary monster that cannot be controlled and
Speaker 4: things are just happening, but it's it's really not, you know,
Speaker 4: like you should not be afraid of AI. You should
Speaker 4: be afraid of the people who are building it. That's it. So,
Speaker 4: like what it comes down to, let's break this down, right,
Speaker 4: AI artificial intelligence, right, and we discuss this. It's it's
Speaker 4: kind of like a child. It's a child. It's like
Speaker 4: a robot baby, right, chat GPT is at best a
Speaker 4: sassy seven year old. And we all know, right, we
Speaker 4: all know that seven year old that was born in
Speaker 4: like you know, I don't know whatever seven years ago
Speaker 4: was like you know, like but like basically recently grew
Speaker 4: up with all the social media platforms and be out
Speaker 4: here talking to you like they've grown, and you'd be like, well,
Speaker 4: damn girl, you've grown. No, they just online? No, Like,
Speaker 4: do not have this seven year old do your taxes?
Speaker 4: It might go well sometimes until it does not, right,
Speaker 4: But what it ultimately then comes down to you have
Speaker 4: to ask yourself, is okay, so if this is just
Speaker 4: this this code that has a great capacity to learn, right,
Speaker 4: and the way it's taught to learn, it's algorithms. Algorithms
Speaker 4: are just processes. Like people like to use fancy words
Speaker 4: to scare us and say, oh, that's a distant thing.
Speaker 4: But if you have a process for anything. That's an algorithm.
Speaker 4: That's an algorithm. You can call it that. Especially half
Speaker 4: y'all when y'all women look great in here, I know
Speaker 4: what you had to do, whatever you had to do
Speaker 4: to get here on time. That's a very efficient algorithm.
Speaker 4: That's a very efficient algorithm. And all it actually comes
Speaker 4: down to is, then how are you teaching that to
Speaker 4: an artificial intelligence? How are you teaching that to this robot? Baby?
Speaker 4: Let's just put it in that way so that it
Speaker 4: can start to do that for you. So to that end,
Speaker 4: when we talk about demystifying it, and we talked about
Speaker 4: this a lot, you have to wonder who's doing the teaching.
Speaker 4: You have to wonder who's doing the teaching and how
Speaker 4: are they framing the way that this child should observe
Speaker 4: and respond to the world. So if someone is not
Speaker 4: aware of their biases, if someone is not aware of
Speaker 4: the fact, like, can you imagine who's the guy who
Speaker 4: does open Aye, I'm not trying to start no shit though,
Speaker 4: this is going out to the world. But let's just
Speaker 4: let's not use him specifically, because you know, but let's
Speaker 4: can you imagine can you imagine an Elon Musk? Can
Speaker 4: you imagine Elon Musk being at trying to teach AI
Speaker 4: how to help me do my hair. I could imagine
Speaker 4: he thinks he can do it. He thinks, of course,
Speaker 4: no problem. And that's a that's a fun example, right.
Speaker 4: Where So when we talk about demystifying AI, it's it's
Speaker 4: really saying take the blame away from the AI and
Speaker 4: start focusing on the people who are training these models right,
Speaker 4: and start focusing on whether or not whether they are
Speaker 4: doing it so intentionally or on intentionally if they're actually
Speaker 4: considering the vast globe of people and all of their problems.
Speaker 4: Because all AI is really is a tool. It's a
Speaker 4: tool to help us do more. To help people do more,
Speaker 4: you need people to train this AI and trust despite
Speaker 4: all the things that you're hearing about AI taking job,
Speaker 4: the thousands of jobs will be created because of what this
Speaker 4: AI is doing. The people who used to drive the carriages,
Speaker 4: when they saw the cars, they were like, I don't know,
Speaker 4: it's getting pretty scary out here. That car don't even
Speaker 4: got no horses. They're like, this is just wild, this
Speaker 4: is crazy. I don't trust this. Okay, Sure they found
Speaker 4: new jobs. So there. I feel like when people start
Speaker 4: to say things like, oh, well be afraid of it.
Speaker 4: Oh it's gonna take your job. What they're really trying
Speaker 4: to do is make you afraid of going behind the
Speaker 4: veil and wonder why can't I be part of the
Speaker 4: team that's building this AI. Why can't I be part
Speaker 4: of the crew that's raising this baby. You know, they
Speaker 4: say it takes a village, not just some dude who
Speaker 4: doesn't blink in the corner. So why are we allowing you?
Speaker 3: I love the sasey seven year old. So now let's
Speaker 3: talk about that sassy seven year old. All of you
Speaker 3: know the landscape today, right, you have states that are
Speaker 3: removed diversity and inclusion. You have states that are removing
Speaker 3: African studies. You can't say the word gay. And you
Speaker 3: know I could go on and on, right because I
Speaker 3: watched the news all day every day so and I
Speaker 3: don't watch Fox, sorry, but I will say this is
Speaker 3: that what do those practitioners look like in the future? Now,
Speaker 3: this sas seven year old has never had anyone tangling
Speaker 3: with it that did not have diversity and inclusion. Because
Speaker 3: you have that today, right, you have African American studies today, right,
Speaker 3: you have gender equality, you have these things today, but
Speaker 3: as the days go on, these things are being removed slowly.
Speaker 3: So now you have people that are graduating college that
Speaker 3: want to be an AI practitioner, but they did not
Speaker 3: learn what discrimination was because they didn't believe in racism.
Speaker 5: Right.
Speaker 3: I had someone tell me yesterday when I was eating
Speaker 3: She was like, my best friend said that there was
Speaker 3: no racism.
Speaker 4: Do I want that.
Speaker 3: Person tangling with my sassy seven year old? And how
Speaker 3: does that look?
Speaker 4: I think you know, you know, you know the answer,
Speaker 4: all right. I want to before I respond, I want
Speaker 4: to do a quick show of hands. How many of
Speaker 4: you are here because you're considering how to be more
Speaker 4: involved in the AI industry? Okay, okay? How many of
Speaker 4: you are here because everyone's been talking about AI and
Speaker 4: you're like, what what is this? And you're just trying
Speaker 4: to understand what it actually is? Oh okay, okay, be proud,
Speaker 4: put your hand up on Okay.
Speaker 3: That's right. Yeah.
Speaker 4: How many of you in some way already work with
Speaker 4: AI or related to AI? Okay, okay. And so your
Speaker 4: concern then is really that you feel like there's a
Speaker 4: lot of things happening around you that you don't understand
Speaker 4: or don't like or can't control. Okay, so this is
Speaker 4: what what you're getting at. Indeed, we have to be
Speaker 4: concerned in general, and this goes beyond AI, that we're
Speaker 4: going to start to have generations of people who are
Speaker 4: very much driving our economy, driving industries, developing technologies that
Speaker 4: will have, from our perspective and our opinion, a skewed
Speaker 4: view of the world and how things work. And they're
Speaker 4: not only going to teach their NI their natural children,
Speaker 4: that they're going to teach the AI this. But because
Speaker 4: of the rapid impact and a rapid scale, it's it's
Speaker 4: not as bad as like, oh, those three kids grew
Speaker 4: up in that racist sexist house, so now they're racist
Speaker 4: and sexist. It's that AI was developed by this person
Speaker 4: who has racist and sexist biases. And because of how
Speaker 4: impactful AI can be, we now have an army of racist,
Speaker 4: sexist or the very least incredibly aloof right like things
Speaker 4: happen right. So my perspective is ultimately radical self reliance.
Speaker 4: I can't help what's going on specifically Macro in Florida,
Speaker 4: in Texas, but I do know people who live there
Speaker 4: who are saying, regardless of what they're teaching, my kids
Speaker 4: in school. Here's what I'm going to teach you at home.
Speaker 4: And so that's why I've been kind of recently saying, please, please, please.
Speaker 4: The last thing you should be is afraid of AI.
Speaker 4: This is now, more than ever, the time where you
Speaker 4: need to be incredibly excited about this tool. But you
Speaker 4: need to see this as a battle and you need
Speaker 4: to do everything you can to get your hands on
Speaker 4: this weapon as well. I'm gonna keep it super super
Speaker 4: real about this. So, as I said earlier, I'm a
Speaker 4: thirty five year old woman, I'm married, I love my husband.
Speaker 4: I'm getting ready and preparing myself to freeze my eggs.
Speaker 7: And.
Speaker 4: I'm gonna be keep it real. Part of me is
Speaker 4: doing that because I learned that the maternal mortality rate
Speaker 4: has gotten worse. It's twenty twenty three. It is twenty
Speaker 4: twenty three in a developed country, and you're telling me
Speaker 4: that over the last couple of years. And it's not
Speaker 4: just because of COVID. If I get pregnant, I have
Speaker 4: twelve patents and patents pending. I'm building all these different things,
Speaker 4: and the thing that scares me the most is having
Speaker 4: a baby and dying. But the people are developing AI
Speaker 4: to do the wildest, most random shit possible. But women
Speaker 4: are dying when when they get pregnant, because not enough
Speaker 4: women and definitely not enough Black women are sitting there saying,
Speaker 4: how can I use AI as a tool to keep
Speaker 4: more of us alive? And the only way that's gonna
Speaker 4: change is if more of us say we're not afraid
Speaker 4: of AI. Regardless you know what, y'all are gonna do
Speaker 4: what you want to do with it, But here's what
Speaker 4: I'm gonna do with it. Here's how I'm going to
Speaker 4: teach this child on home, regardless of what you're doing.
Speaker 4: And so that to me is the only answer. It's
Speaker 4: it is not too late for us all to recognize
Speaker 4: that this will not be done for us. This will
Speaker 4: not be something where we can hope that the right
Speaker 4: few people at the top are going to be thinking
Speaker 4: about all the things that we need. We know this,
Speaker 4: or we would not have systemic issues right now. We
Speaker 4: know this. So but what I now view, though, is
Speaker 4: that I believe the technology is one of the best equalizers,
Speaker 4: one of the best democratizing tools, and that to me
Speaker 4: is exciting. That to me is an opportunity. So let's
Speaker 4: stop talking about being afraid. Let's stop talking about it
Speaker 4: as a black box. There are several low code and
Speaker 4: no code tools that you can use to create something
Speaker 4: in AI if you want to. How do we get
Speaker 4: people to see this as a playground versus I don't
Speaker 4: know more? T ruary right right. I'm also literally not
Speaker 4: kidden my I'm sorry. My dad literally two days ago
Speaker 4: and was like, oh, my grandkids are in the freezer.
Speaker 4: I don't know when they're getting out. And I was like, actually, Dad, Tiana,
Speaker 4: my older sister, Tiana's, Tiana's uh grand kids are in
Speaker 4: the freezer. Mine are about to be in the freezer.
Speaker 4: Just want to confirm. He's like, oh, when are they
Speaker 4: coming out of the freezer? I'm like, this is what
Speaker 4: happens when you talk to your mom. Your mom talks
Speaker 4: to your dad, your dad talks to you. You don't
Speaker 4: know what's going on. So but uh, it's it's real,
Speaker 4: and it's a it's it's a real thing.
Speaker 8: We have.
Speaker 4: We have a very small group of people right now
Speaker 4: who are focusing AI and their problems, and I do
Speaker 4: not blame them. Entrepreneurship is problem solving without regard for resource,
Speaker 4: like science is the study of life. Like these things
Speaker 4: should not be scary big words. Uh. But when we
Speaker 4: silo ourselves and I say we, it's just like if
Speaker 4: you are anyone, if you're if you're not affluent, if
Speaker 4: you are not a man, if you're not there's so
Speaker 4: many things that actually most of us are not that
Speaker 4: kind of that paradigm of the person who's doing this,
Speaker 4: most of us. But when you kind of like push
Speaker 4: something away, you are disenfranchising yourself in so many ways.
Speaker 4: It goes beyond, it goes beyond anything we can imagine.
Speaker 4: The thing that I'm most scared of is the number
Speaker 4: of people who keep saying I'm afraid of what could
Speaker 4: literally be the best thing they've ever put their hands
Speaker 4: on in their entire lives.
Speaker 1: All Right, with that, we are we are privileged to
Speaker 1: have ten more minutes with them, to have some Q
Speaker 1: and A. So I'm sure there's some questions in the art. Ooh,
Speaker 1: we got one already. I'm gonna come to you. You
Speaker 1: y'all give another round of applause for that man that
Speaker 1: was fantastic. Please say your name.
Speaker 4: Hi, my name is Sydney. Thank you. Oh, I got it?
Speaker 8: No, okay, all right, all right, AnyWho I saw you
Speaker 8: know I heard us talking about like fear. I personally
Speaker 8: don't have fear if AI. Maybe I should, I don't know,
Speaker 8: but that's not what you're saying. So I shouldn't be afraid.
Speaker 8: But however, how do we harness that you were sharing
Speaker 8: some ideas of there's some low or no code ways
Speaker 8: of leveraging AI. Can you tell us more about like
Speaker 8: how to like leverage it? And yes, no, of course
Speaker 8: that's a really good question. I actually think I'm gonna
Speaker 8: go ahead and maybe I can talk to the afrotech people.
Speaker 8: I'm I'm gonna just list like on my like LinkedIn
Speaker 8: just like seven seven platforms. Some require you to know
Speaker 8: a little bit some of you, some of them don't.
Speaker 8: Now there is the underlying issue of kind of who's
Speaker 8: creata even that no code platform.
Speaker 4: But at the end of the day, like you know,
Speaker 4: nothing's ever going to be perfect, and we just want
Speaker 4: people to get closer to something. And if your engagement,
Speaker 4: even with these no code platforms can better educate the
Speaker 4: sassy seven year olds that are running it, So now
Speaker 4: all of a sudden they're not just kind of operating
Speaker 4: with whatever the hell they're being told by the very
Speaker 4: specific groups of people who are doing this. It's a
Speaker 4: good thing, and so there are several I don't want
Speaker 4: this necessarily to be an advertisement for any one or
Speaker 4: the other. But on Monday, I'm gonna post If you
Speaker 4: go to my LinkedIn, I'm just Jessica. Oh Matthews, you'll
Speaker 4: see it. I will post five to six that I've
Speaker 4: heard some good things about. Because again I want to
Speaker 4: be very clear, I study psychology and economics. I like
Speaker 4: to tell people I have a PhD in Google, which
Speaker 4: pisses off people with real PhDs, I find, But the
Speaker 4: main point is that you know, I also have a
Speaker 4: granted patent for wireless Mesh Energy Networks, which is an
Speaker 4: algorithm that essentially considers the communication protocols for decentralized micro
Speaker 4: energy systems. And I did that with a degree in
Speaker 4: psychology and economics and a PhD in Google. So what
Speaker 4: I actually am really trying to say is that you
Speaker 4: don't have to go to school for this. To do this,
Speaker 4: you do have to have quite a ferocity for self learning,
Speaker 4: and again I fate to say a bit of self
Speaker 4: reliance in this, but with the right tools and with
Speaker 4: that kind of interest in researching as much as you
Speaker 4: can you'd be surprised what you can do, especially if
Speaker 4: you're comfortable with the prototype being very much only a
Speaker 4: couple percentage points of what you actually want. Talked about
Speaker 4: the socket earlier. My first prototype for the socket was
Speaker 4: a shake to charge flashlight and a hamster ball. So
Speaker 4: I will post that on what's today Thursday. Got to
Speaker 4: get back to New York. I'll post it on Monday.
Speaker 4: I promise I will.
Speaker 6: Perfect question over here and I'll come over there. Yeah,
Speaker 6: my name is Evan Poncels. I'm with the Africa Down
Speaker 6: community of Alanterst and I just want to let you
Speaker 6: know that black people are here in Seattle and mostly
Speaker 6: concentrated in the Central district of Seattle. So let's all
Speaker 6: learn a little geography about this. So from the Central
Speaker 6: District of.
Speaker 2: Seattle, Ray Charles dropped his first studio album, so it's
Speaker 2: not just Kirk Cobain. Also Jamie Hendrix is from there.
Speaker 2: Shout out to my uncle high school termer at Garfield
Speaker 2: High School of anybody from Garfield. And so what I
Speaker 2: wanted to say was just that, you know, in addition
Speaker 2: to radical self reliance, we also should be organizing around
Speaker 2: data and around artificial intelligence. So one thing we're doing
Speaker 2: with Africa Town is building programs so that you can
Speaker 2: be exposed to these sorts of things. But I was wondering,
Speaker 2: My question really is where can we get exposure to
Speaker 2: the data sets that could help us solve with AI
Speaker 2: things like infant mortality or pregnant mother mortality, mortality in
Speaker 2: the birthing scenarios. So that because we work with universities
Speaker 2: that are studying, you know, data and like things like
Speaker 2: coming up with the language models for African American vernacular
Speaker 2: English and things like this. And so right now we're
Speaker 2: about to start a consortium where we're learning, well, what
Speaker 2: goals should we have, what problems should we solve in,
Speaker 2: what strategy should we we implement? And so I'm just
Speaker 2: trying to see where's our best footing for that in
Speaker 2: terms of organizing, Jenny, I.
Speaker 4: Think I have. I can tell you where my company,
Speaker 4: like when we really started looking at disadvantaged communities that
Speaker 4: are black and Latino majority communities, and how do we
Speaker 4: get the data to ensure that we're thinking through the
Speaker 4: equitability of what's happening in this once in a generation
Speaker 4: moment with our infrastructure, and how we started creating actual
Speaker 4: actual AI that could support that, But I'd love to
Speaker 4: know what you think. First. I can share our perspective,
Speaker 4: but as someone who works with the government, you might
Speaker 4: know a few more.
Speaker 3: Peek so you know the government, you know that's a
Speaker 3: beast by itself, right, we do have ways of putting
Speaker 3: out actual where our data is stored, so I will
Speaker 3: say that. Okay, So what my office does. I'm the
Speaker 3: Artificial Intelligence and Technology Office, right, and what we do
Speaker 3: is every year we do an AI use case inventory.
Speaker 3: And actually we're sitting here, but that's what's going on
Speaker 3: back at home is all the labs are putting together
Speaker 3: in AI use case inventory will still turn into us
Speaker 3: in mid April. Once that's turned into US, we will
Speaker 3: put that inventory up on what's releasable to the public.
Speaker 3: So let me say that because we're seventeen national laboratories,
Speaker 3: so that way you know, everything's not releasable to the public.
Speaker 3: But what is releasable to the public. We'll go up
Speaker 3: on our website, right, it's Artificial Intelligence and Technology Office.
Speaker 3: You will see the inventory there. If they are listing
Speaker 3: the code, it will be there so you could actually
Speaker 3: read what the name of the use case is you'll
Speaker 3: be able to see a description of that use case
Speaker 3: if it matches anything that you're trying to do or
Speaker 3: looking to do, and it says where the code is.
Speaker 3: That's where the code is at.
Speaker 2: Right.
Speaker 3: If it's blank and you want us to find out,
Speaker 3: that's Jason Tally. You want us to find out if
Speaker 3: that code is available, just send us an email. Our
Speaker 3: email address is on the website. You send it to
Speaker 3: us and we'll get it for you. If it's available,
Speaker 3: we'll get it for you. So that's from the government perspective.
Speaker 4: Which matters, right, because I think for us we're often
Speaker 4: looking at our data sources one from actual governmental context.
Speaker 4: Like a lot of times people don't realize that almost
Speaker 4: everything is available to you. You just have to ask for it.
Speaker 4: They're not going to make it clean and easy or
Speaker 4: create an interface that makes it as simple as a
Speaker 4: you know, downloading photos from you know, from your whatever
Speaker 4: app you're using for that, but you can reach out.
Speaker 4: The other thing that's been interesting for us over the
Speaker 4: last two years that, to be honest, was a bit surprising,
Speaker 4: was connecting and partnering with journalists. Journalists are surprisingly good
Speaker 4: at getting real hard data in the aggregate. For example,
Speaker 4: it was The New York Times that went and actually
Speaker 4: published with incredible support when you actually go in and
Speaker 4: look at what they published and what studies that they
Speaker 4: were pulling from the infant mortality rates. And I think
Speaker 4: some of you may have seen that article and so,
Speaker 4: and that's happened before we actually struggled and looking at
Speaker 4: a lot of the things related to justice forty and
Speaker 4: disadvantaged communities and this idea of forty percent of the
Speaker 4: infrastructure funds they're actually meant to go to disadvantaged communities
Speaker 4: across the United States. But we struggled to understand how
Speaker 4: many of those disadvantaged communities were majority minority, right, because
Speaker 4: you can work some things out there, and that wasn't
Speaker 4: actually available through any government sources. And so there was
Speaker 4: actually a journalist that had been doing the work for
Speaker 4: about five years that allowed us to actually see every city, township,
Speaker 4: and village that was black majority Latino majority and black
Speaker 4: Latino majority. And the data set was so massive and
Speaker 4: again readily available. And so I think that because if
Speaker 4: you find truly reputable news organizations that are pushing data
Speaker 4: because they are often fearful of publishing a massive story
Speaker 4: that isn't backed up. They've done their homework and you
Speaker 4: can dig there and get their data sets, and when
Speaker 4: you combine those with government data sets, you can do
Speaker 4: some things that are very very cool. You know. Hi everyone,
Speaker 4: my name is Asia.
Speaker 5: First, I want to thank you for being so honest
Speaker 5: and real and challenging all of us in this room
Speaker 5: to do more with data. And I didn't have a question,
Speaker 5: but I just wanted to tell you, like being a
Speaker 5: black woman is seeing you dominate this space is just empowering.
Speaker 4: Wow. Thank you.
Speaker 1: Would be the last one, regular, last one.
Speaker 4: Hi.
Speaker 7: My name is Erica Adams Immagrad student at UDUB and
Speaker 7: I am in the Information School and I sit on
Speaker 7: faculty committee, and we've been talking a lot about student
Speaker 7: use with ch hat GPT. We're already using it most
Speaker 7: of us, but there's a lot of ambiguity around, I guess,
Speaker 7: like cheating and stuff like that. So I'm just curious
Speaker 7: if you have any advice on persuading older academics on
Speaker 7: you know, like coming up with guidelines for use, because
Speaker 7: I think it's a great tool for us to continue
Speaker 7: to use and we shouldn't be using it with fear.
Speaker 4: See I don't even know when you say older, do
Speaker 4: you mean people like my age, because yeah, you're real
Speaker 4: quick and all of a sudden, you like when you
Speaker 4: go out and you're like, I'm not the youngest person
Speaker 4: out here no more. Right, So, persuading that's that's a
Speaker 4: that's a tricky one. That is a tricky one because
Speaker 4: there has to be empathy for how long they've existed
Speaker 4: and known certain things to be true that are now
Speaker 4: becoming very much untrue. Uh, And I I think I
Speaker 4: think starting from that place of empathy is one. So
Speaker 4: I think there's a couple of ways to see this
Speaker 4: if I'm going to be very just kind of direct
Speaker 4: about it. One is, you know, I don't know what
Speaker 4: guidelines are in place right now. But obviously if everyone
Speaker 4: goes to chat GPT and says, write me a paper
Speaker 4: on the World War, and everyone submits a similar paper,
Speaker 4: the teacher will say, oh, clearly there's some sort of plagiarism,
Speaker 4: right because like whether you did this through something that
Speaker 4: you google or you use chat GPT, they can tell
Speaker 4: if you go to the effort of engaging with that
Speaker 4: chat GPT interface such that what you produce, your professor
Speaker 4: cannot tell. At this point I don't really know what
Speaker 4: else to tell me. You No, I mean, I'm not
Speaker 4: even I'm not. And it's not about saying is this
Speaker 4: cheating or is this not cheating? This is about being
Speaker 4: realistic about the world that we're in. Like if everyone,
Speaker 4: if you are lazy with this tool, you will be
Speaker 4: found out to be lazy. If you are innovative and
Speaker 4: proactive with this tool, you will still rise above. I
Speaker 4: truly believe that there's always still a way to rise
Speaker 4: above and still write the best paper with chat GPT
Speaker 4: compared to everyone else. And to be honest, if if
Speaker 4: college is meant to prepare you for the real world,
Speaker 4: acting like these tools don't exist when they do. And
Speaker 4: I get it that people, Oh, we want to make
Speaker 4: sure you can write a paper. We want to make
Speaker 4: sure you can do all those things. Yes, yes, guess what.
Speaker 4: I also still don't know how to drive stick because
Speaker 4: I didn't have to. So you know, we can lament
Speaker 4: about this world of like, oh, we hope people wish
Speaker 4: they should. We want to make sure you still have
Speaker 4: all these different things. Those who care about those skills
Speaker 4: will get them. I believe my husband said he could
Speaker 4: drive stick, but then recently actually I was like, were
Speaker 4: you were you lying? Because this is not I don't
Speaker 4: think he's spot to make those noises. You know, he's
Speaker 4: from Mississippi, so he's a you know, so he was
Speaker 4: telling me a whole of the storm Mississippi and Texas.
Speaker 4: So who knows, but to that end, right, like I think,
Speaker 4: but he clearly felt that it was important that he
Speaker 4: said that he could drive stick. I was like, boy,
Speaker 4: I could barely do automatic at the time, honestly, like
Speaker 4: I cannot wait for driverless cars. So it's it's it's
Speaker 4: one of those things where I would say, so, I
Speaker 4: don't think it's about persuasion. I think it's about recognizing
Speaker 4: that the entire all the standards will shift that you
Speaker 4: cannot restaur on your laurels here, like everyone keeps saying,
Speaker 4: I use chatchypet to create a marketing plan to do
Speaker 4: this and do that. We will see certain similarities that
Speaker 4: will negate that work if you do not still put
Speaker 4: your human intellect on top of it. We're not there again,
Speaker 4: sassy seven year old y'all, we're not there, and don't
Speaker 4: let anyone make you think that that we are. But yeah,
Speaker 4: so it really it sounds to me that like, if
Speaker 4: your professors are like super not into it, you might
Speaker 4: be able to save yourself some time and just do
Speaker 4: what you gotta do and be like with the chatchypt No,
Speaker 4: I would never.
Speaker 1: Black dec Green Money is a production of Blavity, Afrotech,
Speaker 1: Black Effect Podcast Network, and iHeart Media, and it's produced
Speaker 1: by Morgan Debonne and me Well Lucas, with additional production
Speaker 1: support by Sarah Ergin and Rose McLucas. Special thank you
Speaker 1: to Michael Davis of Vanessa Serrano. Learn more about my
Speaker 1: guests the other tech This represent innovators at afrotech dot com.
Speaker 1: Enjoy your Black Tech Green Money. Share this with somebody,
Speaker 1: Go get your money. Peace and love,