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Speaker 1: It is an interesting time these days around AI. Over

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Speaker 1: the weekend, the board of directors for the nonprofit Open Ai,

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Speaker 1: which developed and operates chat gpt, which is super duper

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Speaker 1: popular I use it a lot, they abruptly fired the

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Speaker 1: co founder and now previous CEO, Sam Aldman. So they

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Speaker 1: did this in like like thirty minutes before the markets

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Speaker 1: closed on Friday, which that kind of thing only happens

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Speaker 1: in the rarest and most inflammatory circumstances because it's such

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Speaker 1: a market shifting move to do that right before the

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Speaker 1: markets closed, especially on a Friday. So this is big

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Speaker 1: news in AI as chat gpt has the fastest growing

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Speaker 1: user base for consumer app in history. They did one

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Speaker 1: hundred million users in two months. Now, to give you

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Speaker 1: some perspective, it took Facebook like four years to hit

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Speaker 1: one hundred million users. It took Twitter five years to

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Speaker 1: get one hundred million users. I think it took ig

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Speaker 1: Instagram of like two years. Open ais chat gpt did

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Speaker 1: it in two months, and almost as fast as they

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Speaker 1: blew up their incredible valuation, they blew up their incredible valuation.

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Speaker 1: So it went from look, they went from looking at

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Speaker 1: like a ninety billion dollar valuation to uncertainty in the

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Speaker 1: matter of hours and days since the board gave Sam

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Speaker 1: the boot. In April this year, the company was valued

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Speaker 1: at twenty nine billion dollars, and just a week ago

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Speaker 1: they were looking at a ninety billion dollar valuation raising

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Speaker 1: money at a ninety billion dollar valuation. From April's twenty

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Speaker 1: nine billion dollar valuation, they were on track to do

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Speaker 1: a billion dollars in revenue, which shows you how fast

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Speaker 1: chat gpt was growing and with the future looked like

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Speaker 1: for them. If you recall chat GPT, it was just

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Speaker 1: legit like launched ten months ago, eleven months ago, So

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Speaker 1: this is incredible growth such that AI is just about

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Speaker 1: all anybody is talking about these days, whether they're building it,

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Speaker 1: they're trying to fund it, they are starting an AI

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Speaker 1: focused startup, or they're afraid of it altogether. AI is

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Speaker 1: on many of our minds. At the time this recording,

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Speaker 1: a lot has happened. Sam was out at Open Ai

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Speaker 1: as CEO. This was just Friday. Then the employees and

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Speaker 1: investors revolted because the way people believed in Sam obviously

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Speaker 1: was a surprise to the board, so there were attempts

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Speaker 1: to bring him back over the weekend. Then that didn't

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Speaker 1: work out. Then today Microsoft says they're bringing him over

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Speaker 1: there to run some AI projects, and undoubtedly many necessary,

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Speaker 1: highly talented open ai employees who were on his team

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Speaker 1: will follow him in the Microsoft. So who knows what

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Speaker 1: will happen an hour after I published this episode, because

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Speaker 1: this is all happening so fast. Everything I just mentioned

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Speaker 1: happened in like the span of forty eight to seventy

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Speaker 1: two hours. This thing. Usually things like this play out

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Speaker 1: over weeks.

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Speaker 2: Now.

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Speaker 1: The reason I kick off this episode with this news

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Speaker 1: is because it's that significant. Because we all, if I mean,

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Speaker 1: if you've embraced at some level AI and you have

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Speaker 1: tried chat GPT at least once, there's no way you're

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Speaker 1: not hooked. It's so good at figuring out how to

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Speaker 1: say things in the way you want to say it,

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Speaker 1: by just you giving it some simple commands. And so

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Speaker 1: I wanted to start this episode off just to point

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Speaker 1: to make a point that things are moving so quickly

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Speaker 1: in our society. This is a rallying call, a rallying

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Speaker 1: to you, my black tech, green money family, to be

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Speaker 1: bout it. Nobody asked permission, Certainly the board that open

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Speaker 1: Ai didn't do a poll of their employees and investors

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Speaker 1: to see if kicking out the founder and CEO will

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Speaker 1: be acceptable. They just bust a move. So we're living

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Speaker 1: in the days where people who beg forgiveness versus ask

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Speaker 1: permission are going to be in a position to succeed.

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Speaker 1: If you're waiting for your work situation to get right,

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Speaker 1: or your kids to graduate, or the weather to warm

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Speaker 1: up in the sun to be out again before you

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Speaker 1: start making your moves, you'll be at a disadvantage. Things

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Speaker 1: that used to take months are happening now in hours.

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Speaker 1: So I want to bring you a conversation from afro

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Speaker 1: tech executive in Seattle between Jessica Matthews Jessica Old Matthews,

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Speaker 1: who's CEO and founder of Uncharted, and Johnny Bradley, who

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Speaker 1: was the responsible AI official for the Department of Energy

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Speaker 1: and senior program manager for the Artificial Intelligence and Technology Office.

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Speaker 1: Because this conversation they're having is about demystifying AI for

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Speaker 1: our community. So I hope you get something from this.

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Speaker 1: Follow along, find your way to not necessarily you have

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Speaker 1: to go build an AI startup, but find your way

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Speaker 1: to use it, leverage it to do what you want

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Speaker 1: to do, and do what you're doing even better, more efficiently, faster, cheaper,

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Speaker 1: and with a greater punch.

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Speaker 3: Okay, hello everyone, how are you? Yeah? Yeah, yes, yeah, okay,

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Speaker 3: So let's get into it, right, yeah, all right. So

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Speaker 3: you know I love soccer and you made socket. So

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Speaker 3: I just have to start this conversation off by asking

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Speaker 3: one question, why did you go from hardware to data solutions?

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Speaker 4: So we have to go real deep into like the

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Speaker 4: technical stuff, but for hi, everybody, Hello, Hello, Hello, thank you.

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Speaker 4: Will Will's dope just consistent. Ask dude, man, how much

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Speaker 4: cursing is allowed?

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Speaker 5: Right?

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Speaker 4: That's always what I like to check. I tend to

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Speaker 4: check after my first one fuck falls out. So listen, y'all.

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Speaker 4: I flew here from Harlem and I was like here, like,

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Speaker 4: where does Morgan have me flying to you? Now? That's

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Speaker 4: what I thought was on that plane. I was like,

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Speaker 4: oh damn Seattle, Seattle, okay. And I was walking down

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Speaker 4: the street and I was like, where all the black people?

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Speaker 3: Right?

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Speaker 4: And then but then you're here, Hello, hello, gathered them off? No, no,

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Speaker 4: So say, you know, it's a good question. So, uh,

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Speaker 4: what Jonie's talk about here is that I started my

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Speaker 4: career making energy generating play products. So when I was

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Speaker 4: nineteen years old, I invented an energy generator eating soccer

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Speaker 4: ball that could harness the energy from play, the kinetic energy,

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Speaker 4: and store that power inside of the ball. Yeah, it

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Speaker 4: was pretty cool. You could play with it, you could roll,

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Speaker 4: you know, as it rolled, it was generating energy, and

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Speaker 4: about an hour of play would give you three hours

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Speaker 4: of light. Fast forward. Now I'm thirty five. I don't

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Speaker 4: have problem saying it because, like, you know, me, black guys, good, right,

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Speaker 4: what I'm saying? So, and and I run a data

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Speaker 4: infrastructure company that uses AI to help disadvantaged communities develop

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Speaker 4: sustainable infrastructure with more equity and efficiency than ever before. Right,

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Speaker 4: So how did I get from there to here? Well,

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Speaker 4: one thing is that my true north was always incredibly

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Speaker 4: clear to me. I'm a dual citizen of Nigeria and States. Always,

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Speaker 4: even in Seattle, where I'm being so messed up.

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Speaker 6: Yeah, yeah.

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Speaker 4: The only thing I know about Seattle's Nirvana. Right, So,

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Speaker 4: so I like, and I love Nirvana. So I was like, sheah,

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Speaker 4: I wear my flannel. My mom was like oh s

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Speaker 4: ohse glenno. So so for me, you know, it was

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Speaker 4: going back and forth between Nigeria and the United States,

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Speaker 4: whether it's for weddings or funerals, and just recognizing that

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Speaker 4: there were some things that were so dope, like everybody

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Speaker 4: had a mobile phone. It became ubiquitous very quickly. Oftentimes

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Speaker 4: my cousins had a better cell phone than I did.

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Speaker 4: But also wondering like why is it that it doesn't

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Speaker 4: matter if we're in the village or if we're in

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Speaker 4: like the bustling city of Legos that were losing power

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Speaker 4: every single day. And I knew it wasn't because like

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Speaker 4: there were with the technologies that needed to exist to

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Speaker 4: make that happen. I knew it was an infrastructural issue immediately.

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Speaker 4: In fact, oftentimes in places like Nigeria, people are paying

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Speaker 4: more per kilowatt hour than we pay here in the

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Speaker 4: United States. I don't know if that's true anymore, because

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Speaker 4: like I was, the bills getting high, I was your

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Speaker 4: bills getting high. But at least before it was very

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Speaker 4: much like that. And you know, I believe that the

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Speaker 4: first step in innovation is the articulation of the problem.

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Speaker 4: And at the age of seventeen eighteen, I articulated the

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Speaker 4: problem to be that people like my cousins who were

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Speaker 4: trained engineers did not believe that there could be a

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Speaker 4: world where things could be different. They did not believe

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Speaker 4: that this problem could be solved by some innovation or

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Speaker 4: some public private partnership. They simply thought that the best

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Speaker 4: way to solve the problem was to pretend like it's

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Speaker 4: not happening and to get used to it. And so

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Speaker 4: I wanted to create something that would make them change

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Speaker 4: that view, that would make them see in the world

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Speaker 4: not just as it is, but as it could be.

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Speaker 4: And soccer is the most popular sport around the world,

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Speaker 4: my favorite sport, the most popular sport. And I'm assuming

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Speaker 4: you're probably really good. My cousins were not that good.

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Speaker 3: Teams to the championship.

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Speaker 4: I was good. Oh, you were so good that you

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Speaker 4: were bad. Now my cousins were just My cousins were

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Speaker 4: so average that they were like, why are we playing

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Speaker 4: this game? This is awkward. So but in that in

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Speaker 4: that way though, seeing their passion and seeing the way

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Speaker 4: that they would play on the field, I'm like, you know,

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Speaker 4: the way that you approach this game, knowing damn well

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Speaker 4: that you are at best average is the way you

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Speaker 4: need to approach life. It's the way you need to

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Speaker 4: approach all of the problems in our community and infrastructurally,

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Speaker 4: and so my thought was, like, let me create something

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Speaker 4: that would inspere hire them to do it. And what

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Speaker 4: ended up happening was that long story short inspired me

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Speaker 4: to do it.

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Speaker 5: Got it?

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Speaker 3: Okay?

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Speaker 4: Yeah, all right. It took a while to figure out

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Speaker 4: exactly what technologies would get there. First it was the

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Speaker 4: play products, and then different inventions, and then a couple

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Speaker 4: of years ago, right before the pandemic, I started to

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Speaker 4: realize that the common thread in every community wasn't needing,

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Speaker 4: like you know, an energy generating speed bump or some

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Speaker 4: cool thing here or there that was fun but couldn't scale.

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Speaker 4: The common problem was data. It didn't matter if it

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Speaker 4: was in Nigeria or in the US. The data problems

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Speaker 4: were kind of compiling on each other, and that when

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Speaker 4: governments were operating in the right way, they were spending

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Speaker 4: at least half of their time tracking down, collecting, correcting,

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Speaker 4: and sharing SOILID information to make infrastructure decisions. They often

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Speaker 4: did not have the right information to know where to

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Speaker 4: prioritize who should be getting the solar or where should

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Speaker 4: we be replacing the lead pipes, And in the other

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Speaker 4: half of the time, they were just quote unquote shooting

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Speaker 4: from the hip, and so I was like, well, if

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Speaker 4: we could solve the data problem and improve the way

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Speaker 4: that they're organizing their data to make decisions to build

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Speaker 4: sustainable infrastructure, we can help them build it faster, we

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Speaker 4: can help them build it for less. And then as

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Speaker 4: a result, we can either help them make it more

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Speaker 4: equitable or at least make it very obvious when they're

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Speaker 4: being fucked up, like make it more obvious, like, listen,

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Speaker 4: that's what the data say. If you just want to

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Speaker 4: do that, you can do that, but don't act like that

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Speaker 4: data didn't say that. And so that's how we came

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Speaker 4: up with our current products.

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Speaker 3: And we thank you, right, we thank you for that transition.

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Speaker 4: So we want to.

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Speaker 3: Talk about demystifying AI. So when we look at AI,

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Speaker 3: I always think about that time and I don't I

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Speaker 3: don't mind dating myself either. Fifty three Listen, I don't

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Speaker 3: mind that either.

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Speaker 4: We should just talk about that.

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Speaker 3: I do, right, this homegrown. So back when I was born,

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Speaker 3: actually legislation was passed on cigarettes. But as I grew up,

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Speaker 3: you know, cigarettes was just like whoa, it was a

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Speaker 3: cool thing to do. You had this slim lady smoking

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Speaker 3: a cigarette, right, and it was just like ayeing to me, right,

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Speaker 3: And for twenty years I smoked based off of that.

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Speaker 3: But what I will say is this, they demystified those cigarettes.

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Speaker 2: It was not.

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Speaker 3: Right, and that's great, that's that's that's my exact point

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Speaker 3: is that during my time, it was cool, right, and

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Speaker 3: it was the thing to do. And as time went

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Speaker 3: by and the Surgeon General said, no, put these warnings

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Speaker 3: on each pack of these cigarettes and let people know

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Speaker 3: exactly what it does to you, and it would curb

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Speaker 3: and it just like demystified the coolness and the sexiness

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Speaker 3: of cigarettes. So my question is, how do we demystify AI?

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Speaker 4: Girl? Oh yeah, listen, because we had a hole behind

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Speaker 4: the scenes conversation y'all that we're trying to like, you know,

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Speaker 4: keep civil for the cameras and all that. So I

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Speaker 4: think that's part of why you guys are here, right.

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Speaker 4: Everyone's talking about AI. Everyone's acting like it's the new

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Speaker 4: hot thing, and they're acting like it's this black bots

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Speaker 4: and it's this scary monster that cannot be controlled and

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Speaker 4: things are just happening, but it's it's really not, you know,

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Speaker 4: like you should not be afraid of AI. You should

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Speaker 4: be afraid of the people who are building it. That's it. So,

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Speaker 4: like what it comes down to, let's break this down, right,

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Speaker 4: AI artificial intelligence, right, and we discuss this. It's it's

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Speaker 4: kind of like a child. It's a child. It's like

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Speaker 4: a robot baby, right, chat GPT is at best a

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Speaker 4: sassy seven year old. And we all know, right, we

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Speaker 4: all know that seven year old that was born in

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Speaker 4: like you know, I don't know whatever seven years ago

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Speaker 4: was like you know, like but like basically recently grew

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Speaker 4: up with all the social media platforms and be out

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Speaker 4: here talking to you like they've grown, and you'd be like, well,

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Speaker 4: damn girl, you've grown. No, they just online? No, Like,

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Speaker 4: do not have this seven year old do your taxes?

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Speaker 4: It might go well sometimes until it does not, right,

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Speaker 4: But what it ultimately then comes down to you have

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Speaker 4: to ask yourself, is okay, so if this is just

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Speaker 4: this this code that has a great capacity to learn, right,

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Speaker 4: and the way it's taught to learn, it's algorithms. Algorithms

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Speaker 4: are just processes. Like people like to use fancy words

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Speaker 4: to scare us and say, oh, that's a distant thing.

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Speaker 4: But if you have a process for anything. That's an algorithm.

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Speaker 4: That's an algorithm. You can call it that. Especially half

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Speaker 4: y'all when y'all women look great in here, I know

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Speaker 4: what you had to do, whatever you had to do

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Speaker 4: to get here on time. That's a very efficient algorithm.

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Speaker 4: That's a very efficient algorithm. And all it actually comes

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Speaker 4: down to is, then how are you teaching that to

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Speaker 4: an artificial intelligence? How are you teaching that to this robot? Baby?

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Speaker 4: Let's just put it in that way so that it

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Speaker 4: can start to do that for you. So to that end,

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Speaker 4: when we talk about demystifying it, and we talked about

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Speaker 4: this a lot, you have to wonder who's doing the teaching.

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Speaker 4: You have to wonder who's doing the teaching and how

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Speaker 4: are they framing the way that this child should observe

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Speaker 4: and respond to the world. So if someone is not

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Speaker 4: aware of their biases, if someone is not aware of

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Speaker 4: the fact, like, can you imagine who's the guy who

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Speaker 4: does open Aye, I'm not trying to start no shit though,

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Speaker 4: this is going out to the world. But let's just

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Speaker 4: let's not use him specifically, because you know, but let's

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Speaker 4: can you imagine can you imagine an Elon Musk? Can

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Speaker 4: you imagine Elon Musk being at trying to teach AI

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Speaker 4: how to help me do my hair. I could imagine

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Speaker 4: he thinks he can do it. He thinks, of course,

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Speaker 4: no problem. And that's a that's a fun example, right.

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Speaker 4: Where So when we talk about demystifying AI, it's it's

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Speaker 4: really saying take the blame away from the AI and

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Speaker 4: start focusing on the people who are training these models right,

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Speaker 4: and start focusing on whether or not whether they are

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Speaker 4: doing it so intentionally or on intentionally if they're actually

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Speaker 4: considering the vast globe of people and all of their problems.

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Speaker 4: Because all AI is really is a tool. It's a

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Speaker 4: tool to help us do more. To help people do more,

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Speaker 4: you need people to train this AI and trust despite

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Speaker 4: all the things that you're hearing about AI taking job,

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Speaker 4: the thousands of jobs will be created because of what this

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Speaker 4: AI is doing. The people who used to drive the carriages,

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Speaker 4: when they saw the cars, they were like, I don't know,

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Speaker 4: it's getting pretty scary out here. That car don't even

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Speaker 4: got no horses. They're like, this is just wild, this

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Speaker 4: is crazy. I don't trust this. Okay, Sure they found

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Speaker 4: new jobs. So there. I feel like when people start

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Speaker 4: to say things like, oh, well be afraid of it.

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Speaker 4: Oh it's gonna take your job. What they're really trying

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Speaker 4: to do is make you afraid of going behind the

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Speaker 4: veil and wonder why can't I be part of the

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Speaker 4: team that's building this AI. Why can't I be part

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Speaker 4: of the crew that's raising this baby. You know, they

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Speaker 4: say it takes a village, not just some dude who

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Speaker 4: doesn't blink in the corner. So why are we allowing you?

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Speaker 3: I love the sasey seven year old. So now let's

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Speaker 3: talk about that sassy seven year old. All of you

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Speaker 3: know the landscape today, right, you have states that are

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Speaker 3: removed diversity and inclusion. You have states that are removing

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Speaker 3: African studies. You can't say the word gay. And you

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Speaker 3: know I could go on and on, right because I

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Speaker 3: watched the news all day every day so and I

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Speaker 3: don't watch Fox, sorry, but I will say this is

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Speaker 3: that what do those practitioners look like in the future? Now,

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Speaker 3: this sas seven year old has never had anyone tangling

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Speaker 3: with it that did not have diversity and inclusion. Because

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Speaker 3: you have that today, right, you have African American studies today, right,

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Speaker 3: you have gender equality, you have these things today, but

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Speaker 3: as the days go on, these things are being removed slowly.

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Speaker 3: So now you have people that are graduating college that

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Speaker 3: want to be an AI practitioner, but they did not

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Speaker 3: learn what discrimination was because they didn't believe in racism.

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Speaker 5: Right.

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Speaker 3: I had someone tell me yesterday when I was eating

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Speaker 3: She was like, my best friend said that there was

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Speaker 3: no racism.

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Speaker 4: Do I want that.

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Speaker 3: Person tangling with my sassy seven year old? And how

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Speaker 3: does that look?

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Speaker 4: I think you know, you know, you know the answer,

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Speaker 4: all right. I want to before I respond, I want

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Speaker 4: to do a quick show of hands. How many of

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Speaker 4: you are here because you're considering how to be more

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Speaker 4: involved in the AI industry? Okay, okay? How many of

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Speaker 4: you are here because everyone's been talking about AI and

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Speaker 4: you're like, what what is this? And you're just trying

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Speaker 4: to understand what it actually is? Oh okay, okay, be proud,

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Speaker 4: put your hand up on Okay.

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Speaker 3: That's right. Yeah.

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Speaker 4: How many of you in some way already work with

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Speaker 4: AI or related to AI? Okay, okay. And so your

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Speaker 4: concern then is really that you feel like there's a

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Speaker 4: lot of things happening around you that you don't understand

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Speaker 4: or don't like or can't control. Okay, so this is

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Speaker 4: what what you're getting at. Indeed, we have to be

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Speaker 4: concerned in general, and this goes beyond AI, that we're

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Speaker 4: going to start to have generations of people who are

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Speaker 4: very much driving our economy, driving industries, developing technologies that

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Speaker 4: will have, from our perspective and our opinion, a skewed

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Speaker 4: view of the world and how things work. And they're

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Speaker 4: not only going to teach their NI their natural children,

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Speaker 4: that they're going to teach the AI this. But because

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Speaker 4: of the rapid impact and a rapid scale, it's it's

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Speaker 4: not as bad as like, oh, those three kids grew

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Speaker 4: up in that racist sexist house, so now they're racist

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Speaker 4: and sexist. It's that AI was developed by this person

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Speaker 4: who has racist and sexist biases. And because of how

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Speaker 4: impactful AI can be, we now have an army of racist,

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Speaker 4: sexist or the very least incredibly aloof right like things

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Speaker 4: happen right. So my perspective is ultimately radical self reliance.

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Speaker 4: I can't help what's going on specifically Macro in Florida,

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Speaker 4: in Texas, but I do know people who live there

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Speaker 4: who are saying, regardless of what they're teaching, my kids

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Speaker 4: in school. Here's what I'm going to teach you at home.

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Speaker 4: And so that's why I've been kind of recently saying, please, please, please.

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Speaker 4: The last thing you should be is afraid of AI.

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Speaker 4: This is now, more than ever, the time where you

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Speaker 4: need to be incredibly excited about this tool. But you

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Speaker 4: need to see this as a battle and you need

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Speaker 4: to do everything you can to get your hands on

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Speaker 4: this weapon as well. I'm gonna keep it super super

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Speaker 4: real about this. So, as I said earlier, I'm a

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Speaker 4: thirty five year old woman, I'm married, I love my husband.

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Speaker 4: I'm getting ready and preparing myself to freeze my eggs.

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Speaker 7: And.

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Speaker 4: I'm gonna be keep it real. Part of me is

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Speaker 4: doing that because I learned that the maternal mortality rate

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Speaker 4: has gotten worse. It's twenty twenty three. It is twenty

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Speaker 4: twenty three in a developed country, and you're telling me

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Speaker 4: that over the last couple of years. And it's not

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Speaker 4: just because of COVID. If I get pregnant, I have

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Speaker 4: twelve patents and patents pending. I'm building all these different things,

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Speaker 4: and the thing that scares me the most is having

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Speaker 4: a baby and dying. But the people are developing AI

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Speaker 4: to do the wildest, most random shit possible. But women

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Speaker 4: are dying when when they get pregnant, because not enough

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Speaker 4: women and definitely not enough Black women are sitting there saying,

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Speaker 4: how can I use AI as a tool to keep

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Speaker 4: more of us alive? And the only way that's gonna

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Speaker 4: change is if more of us say we're not afraid

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Speaker 4: of AI. Regardless you know what, y'all are gonna do

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Speaker 4: what you want to do with it, But here's what

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Speaker 4: I'm gonna do with it. Here's how I'm going to

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Speaker 4: teach this child on home, regardless of what you're doing.

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Speaker 4: And so that to me is the only answer. It's

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Speaker 4: it is not too late for us all to recognize

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Speaker 4: that this will not be done for us. This will

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Speaker 4: not be something where we can hope that the right

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Speaker 4: few people at the top are going to be thinking

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Speaker 4: about all the things that we need. We know this,

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Speaker 4: or we would not have systemic issues right now. We

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Speaker 4: know this. So but what I now view, though, is

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Speaker 4: that I believe the technology is one of the best equalizers,

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Speaker 4: one of the best democratizing tools, and that to me

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Speaker 4: is exciting. That to me is an opportunity. So let's

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Speaker 4: stop talking about being afraid. Let's stop talking about it

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Speaker 4: as a black box. There are several low code and

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Speaker 4: no code tools that you can use to create something

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Speaker 4: in AI if you want to. How do we get

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Speaker 4: people to see this as a playground versus I don't

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Speaker 4: know more? T ruary right right. I'm also literally not

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Speaker 4: kidden my I'm sorry. My dad literally two days ago

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Speaker 4: and was like, oh, my grandkids are in the freezer.

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Speaker 4: I don't know when they're getting out. And I was like, actually, Dad, Tiana,

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Speaker 4: my older sister, Tiana's, Tiana's uh grand kids are in

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Speaker 4: the freezer. Mine are about to be in the freezer.

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Speaker 4: Just want to confirm. He's like, oh, when are they

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Speaker 4: coming out of the freezer? I'm like, this is what

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Speaker 4: happens when you talk to your mom. Your mom talks

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Speaker 4: to your dad, your dad talks to you. You don't

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Speaker 4: know what's going on. So but uh, it's it's real,

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Speaker 4: and it's a it's it's a real thing.

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Speaker 8: We have.

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Speaker 4: We have a very small group of people right now

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Speaker 4: who are focusing AI and their problems, and I do

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Speaker 4: not blame them. Entrepreneurship is problem solving without regard for resource,

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Speaker 4: like science is the study of life. Like these things

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Speaker 4: should not be scary big words. Uh. But when we

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Speaker 4: silo ourselves and I say we, it's just like if

432
00:26:00,880 --> 00:26:04,360
Speaker 4: you are anyone, if you're if you're not affluent, if

433
00:26:04,400 --> 00:26:06,320
Speaker 4: you are not a man, if you're not there's so

434
00:26:06,400 --> 00:26:09,800
Speaker 4: many things that actually most of us are not that

435
00:26:09,920 --> 00:26:12,480
Speaker 4: kind of that paradigm of the person who's doing this,

436
00:26:12,960 --> 00:26:16,639
Speaker 4: most of us. But when you kind of like push

437
00:26:16,680 --> 00:26:24,879
Speaker 4: something away, you are disenfranchising yourself in so many ways.

438
00:26:24,920 --> 00:26:28,280
Speaker 4: It goes beyond, it goes beyond anything we can imagine.

439
00:26:28,320 --> 00:26:32,399
Speaker 4: The thing that I'm most scared of is the number

440
00:26:32,440 --> 00:26:36,679
Speaker 4: of people who keep saying I'm afraid of what could

441
00:26:36,720 --> 00:26:40,240
Speaker 4: literally be the best thing they've ever put their hands

442
00:26:40,240 --> 00:26:41,399
Speaker 4: on in their entire lives.

443
00:26:43,400 --> 00:26:54,000
Speaker 1: All Right, with that, we are we are privileged to

444
00:26:54,040 --> 00:26:55,919
Speaker 1: have ten more minutes with them, to have some Q

445
00:26:56,080 --> 00:26:58,560
Speaker 1: and A. So I'm sure there's some questions in the art. Ooh,

446
00:26:58,600 --> 00:27:00,199
Speaker 1: we got one already. I'm gonna come to you. You

447
00:27:02,119 --> 00:27:04,240
Speaker 1: y'all give another round of applause for that man that

448
00:27:04,400 --> 00:27:09,440
Speaker 1: was fantastic. Please say your name.

449
00:27:10,560 --> 00:27:15,880
Speaker 4: Hi, my name is Sydney. Thank you. Oh, I got it?

450
00:27:16,080 --> 00:27:21,159
Speaker 8: No, okay, all right, all right, AnyWho I saw you

451
00:27:21,160 --> 00:27:23,600
Speaker 8: know I heard us talking about like fear. I personally

452
00:27:23,800 --> 00:27:26,840
Speaker 8: don't have fear if AI. Maybe I should, I don't know,

453
00:27:27,000 --> 00:27:29,280
Speaker 8: but that's not what you're saying. So I shouldn't be afraid.

454
00:27:29,280 --> 00:27:31,720
Speaker 8: But however, how do we harness that you were sharing

455
00:27:31,760 --> 00:27:34,600
Speaker 8: some ideas of there's some low or no code ways

456
00:27:34,640 --> 00:27:37,080
Speaker 8: of leveraging AI. Can you tell us more about like

457
00:27:37,080 --> 00:27:40,320
Speaker 8: how to like leverage it? And yes, no, of course

458
00:27:40,320 --> 00:27:42,840
Speaker 8: that's a really good question. I actually think I'm gonna

459
00:27:42,840 --> 00:27:46,040
Speaker 8: go ahead and maybe I can talk to the afrotech people.

460
00:27:46,119 --> 00:27:48,960
Speaker 8: I'm I'm gonna just list like on my like LinkedIn

461
00:27:49,160 --> 00:27:53,119
Speaker 8: just like seven seven platforms. Some require you to know

462
00:27:53,200 --> 00:27:55,560
Speaker 8: a little bit some of you, some of them don't.

463
00:27:55,600 --> 00:27:59,680
Speaker 8: Now there is the underlying issue of kind of who's

464
00:27:59,720 --> 00:28:02,639
Speaker 8: creata even that no code platform.

465
00:28:02,840 --> 00:28:05,159
Speaker 4: But at the end of the day, like you know,

466
00:28:05,240 --> 00:28:07,280
Speaker 4: nothing's ever going to be perfect, and we just want

467
00:28:07,280 --> 00:28:11,880
Speaker 4: people to get closer to something. And if your engagement,

468
00:28:12,359 --> 00:28:17,560
Speaker 4: even with these no code platforms can better educate the

469
00:28:19,040 --> 00:28:21,399
Speaker 4: sassy seven year olds that are running it, So now

470
00:28:21,440 --> 00:28:24,359
Speaker 4: all of a sudden they're not just kind of operating

471
00:28:24,400 --> 00:28:26,639
Speaker 4: with whatever the hell they're being told by the very

472
00:28:26,680 --> 00:28:29,280
Speaker 4: specific groups of people who are doing this. It's a

473
00:28:29,280 --> 00:28:32,080
Speaker 4: good thing, and so there are several I don't want

474
00:28:32,080 --> 00:28:34,280
Speaker 4: this necessarily to be an advertisement for any one or

475
00:28:34,280 --> 00:28:38,760
Speaker 4: the other. But on Monday, I'm gonna post If you

476
00:28:38,800 --> 00:28:42,240
Speaker 4: go to my LinkedIn, I'm just Jessica. Oh Matthews, you'll

477
00:28:42,240 --> 00:28:45,960
Speaker 4: see it. I will post five to six that I've

478
00:28:46,000 --> 00:28:48,160
Speaker 4: heard some good things about. Because again I want to

479
00:28:48,200 --> 00:28:52,360
Speaker 4: be very clear, I study psychology and economics. I like

480
00:28:52,400 --> 00:28:55,480
Speaker 4: to tell people I have a PhD in Google, which

481
00:28:55,520 --> 00:29:00,080
Speaker 4: pisses off people with real PhDs, I find, But the

482
00:29:00,120 --> 00:29:02,000
Speaker 4: main point is that you know, I also have a

483
00:29:02,040 --> 00:29:06,120
Speaker 4: granted patent for wireless Mesh Energy Networks, which is an

484
00:29:06,160 --> 00:29:14,520
Speaker 4: algorithm that essentially considers the communication protocols for decentralized micro

485
00:29:14,720 --> 00:29:19,520
Speaker 4: energy systems. And I did that with a degree in

486
00:29:19,560 --> 00:29:23,760
Speaker 4: psychology and economics and a PhD in Google. So what

487
00:29:23,880 --> 00:29:27,640
Speaker 4: I actually am really trying to say is that you

488
00:29:27,720 --> 00:29:32,520
Speaker 4: don't have to go to school for this. To do this,

489
00:29:33,520 --> 00:29:37,080
Speaker 4: you do have to have quite a ferocity for self learning,

490
00:29:38,040 --> 00:29:40,520
Speaker 4: and again I fate to say a bit of self

491
00:29:40,520 --> 00:29:46,400
Speaker 4: reliance in this, but with the right tools and with

492
00:29:46,480 --> 00:29:48,880
Speaker 4: that kind of interest in researching as much as you

493
00:29:48,920 --> 00:29:52,360
Speaker 4: can you'd be surprised what you can do, especially if

494
00:29:52,360 --> 00:29:57,360
Speaker 4: you're comfortable with the prototype being very much only a

495
00:29:57,400 --> 00:30:00,520
Speaker 4: couple percentage points of what you actually want. Talked about

496
00:30:00,520 --> 00:30:03,880
Speaker 4: the socket earlier. My first prototype for the socket was

497
00:30:04,240 --> 00:30:07,959
Speaker 4: a shake to charge flashlight and a hamster ball. So

498
00:30:09,160 --> 00:30:12,240
Speaker 4: I will post that on what's today Thursday. Got to

499
00:30:12,240 --> 00:30:14,000
Speaker 4: get back to New York. I'll post it on Monday.

500
00:30:14,320 --> 00:30:15,440
Speaker 4: I promise I will.

501
00:30:16,040 --> 00:30:19,240
Speaker 6: Perfect question over here and I'll come over there. Yeah,

502
00:30:19,280 --> 00:30:21,280
Speaker 6: my name is Evan Poncels. I'm with the Africa Down

503
00:30:21,280 --> 00:30:22,800
Speaker 6: community of Alanterst and I just want to let you

504
00:30:22,880 --> 00:30:25,920
Speaker 6: know that black people are here in Seattle and mostly

505
00:30:25,960 --> 00:30:28,320
Speaker 6: concentrated in the Central district of Seattle. So let's all

506
00:30:28,360 --> 00:30:31,440
Speaker 6: learn a little geography about this. So from the Central

507
00:30:31,720 --> 00:30:32,240
Speaker 6: District of.

508
00:30:32,200 --> 00:30:34,760
Speaker 2: Seattle, Ray Charles dropped his first studio album, so it's

509
00:30:34,800 --> 00:30:37,640
Speaker 2: not just Kirk Cobain. Also Jamie Hendrix is from there.

510
00:30:37,720 --> 00:30:40,440
Speaker 2: Shout out to my uncle high school termer at Garfield

511
00:30:40,520 --> 00:30:43,080
Speaker 2: High School of anybody from Garfield. And so what I

512
00:30:43,120 --> 00:30:45,200
Speaker 2: wanted to say was just that, you know, in addition

513
00:30:45,240 --> 00:30:49,240
Speaker 2: to radical self reliance, we also should be organizing around

514
00:30:49,440 --> 00:30:52,720
Speaker 2: data and around artificial intelligence. So one thing we're doing

515
00:30:52,760 --> 00:30:55,560
Speaker 2: with Africa Town is building programs so that you can

516
00:30:55,600 --> 00:30:58,320
Speaker 2: be exposed to these sorts of things. But I was wondering,

517
00:30:58,360 --> 00:31:01,600
Speaker 2: My question really is where can we get exposure to

518
00:31:01,600 --> 00:31:04,360
Speaker 2: the data sets that could help us solve with AI

519
00:31:04,880 --> 00:31:09,160
Speaker 2: things like infant mortality or pregnant mother mortality, mortality in

520
00:31:09,200 --> 00:31:12,720
Speaker 2: the birthing scenarios. So that because we work with universities

521
00:31:12,720 --> 00:31:15,720
Speaker 2: that are studying, you know, data and like things like

522
00:31:15,800 --> 00:31:18,800
Speaker 2: coming up with the language models for African American vernacular

523
00:31:18,840 --> 00:31:21,480
Speaker 2: English and things like this. And so right now we're

524
00:31:21,480 --> 00:31:24,280
Speaker 2: about to start a consortium where we're learning, well, what

525
00:31:24,320 --> 00:31:25,960
Speaker 2: goals should we have, what problems should we solve in,

526
00:31:26,000 --> 00:31:28,440
Speaker 2: what strategy should we we implement? And so I'm just

527
00:31:28,480 --> 00:31:30,920
Speaker 2: trying to see where's our best footing for that in

528
00:31:31,000 --> 00:31:32,200
Speaker 2: terms of organizing, Jenny, I.

529
00:31:32,200 --> 00:31:34,440
Speaker 4: Think I have. I can tell you where my company,

530
00:31:34,920 --> 00:31:39,240
Speaker 4: like when we really started looking at disadvantaged communities that

531
00:31:39,360 --> 00:31:42,240
Speaker 4: are black and Latino majority communities, and how do we

532
00:31:42,280 --> 00:31:45,560
Speaker 4: get the data to ensure that we're thinking through the

533
00:31:45,600 --> 00:31:48,000
Speaker 4: equitability of what's happening in this once in a generation

534
00:31:48,120 --> 00:31:51,600
Speaker 4: moment with our infrastructure, and how we started creating actual

535
00:31:52,920 --> 00:31:57,280
Speaker 4: actual AI that could support that, But I'd love to

536
00:31:57,320 --> 00:32:01,120
Speaker 4: know what you think. First. I can share our perspective,

537
00:32:01,160 --> 00:32:03,760
Speaker 4: but as someone who works with the government, you might

538
00:32:04,120 --> 00:32:05,080
Speaker 4: know a few more.

539
00:32:05,080 --> 00:32:11,360
Speaker 3: Peek so you know the government, you know that's a

540
00:32:11,400 --> 00:32:16,400
Speaker 3: beast by itself, right, we do have ways of putting

541
00:32:16,480 --> 00:32:19,600
Speaker 3: out actual where our data is stored, so I will

542
00:32:19,600 --> 00:32:21,880
Speaker 3: say that. Okay, So what my office does. I'm the

543
00:32:21,960 --> 00:32:25,240
Speaker 3: Artificial Intelligence and Technology Office, right, and what we do

544
00:32:25,440 --> 00:32:28,320
Speaker 3: is every year we do an AI use case inventory.

545
00:32:28,600 --> 00:32:31,040
Speaker 3: And actually we're sitting here, but that's what's going on

546
00:32:31,120 --> 00:32:33,840
Speaker 3: back at home is all the labs are putting together

547
00:32:34,120 --> 00:32:36,880
Speaker 3: in AI use case inventory will still turn into us

548
00:32:37,600 --> 00:32:41,000
Speaker 3: in mid April. Once that's turned into US, we will

549
00:32:41,040 --> 00:32:45,280
Speaker 3: put that inventory up on what's releasable to the public.

550
00:32:45,320 --> 00:32:48,000
Speaker 3: So let me say that because we're seventeen national laboratories,

551
00:32:48,000 --> 00:32:50,959
Speaker 3: so that way you know, everything's not releasable to the public.

552
00:32:51,200 --> 00:32:53,840
Speaker 3: But what is releasable to the public. We'll go up

553
00:32:53,880 --> 00:32:57,200
Speaker 3: on our website, right, it's Artificial Intelligence and Technology Office.

554
00:32:57,280 --> 00:33:00,760
Speaker 3: You will see the inventory there. If they are listing

555
00:33:00,800 --> 00:33:03,440
Speaker 3: the code, it will be there so you could actually

556
00:33:03,440 --> 00:33:06,000
Speaker 3: read what the name of the use case is you'll

557
00:33:06,040 --> 00:33:08,360
Speaker 3: be able to see a description of that use case

558
00:33:08,520 --> 00:33:10,800
Speaker 3: if it matches anything that you're trying to do or

559
00:33:10,840 --> 00:33:13,400
Speaker 3: looking to do, and it says where the code is.

560
00:33:13,840 --> 00:33:14,920
Speaker 3: That's where the code is at.

561
00:33:15,080 --> 00:33:15,320
Speaker 2: Right.

562
00:33:15,600 --> 00:33:19,480
Speaker 3: If it's blank and you want us to find out,

563
00:33:19,720 --> 00:33:23,040
Speaker 3: that's Jason Tally. You want us to find out if

564
00:33:23,040 --> 00:33:25,600
Speaker 3: that code is available, just send us an email. Our

565
00:33:25,640 --> 00:33:27,840
Speaker 3: email address is on the website. You send it to

566
00:33:27,920 --> 00:33:30,160
Speaker 3: us and we'll get it for you. If it's available,

567
00:33:30,200 --> 00:33:33,000
Speaker 3: we'll get it for you. So that's from the government perspective.

568
00:33:32,680 --> 00:33:36,960
Speaker 4: Which matters, right, because I think for us we're often

569
00:33:37,000 --> 00:33:40,320
Speaker 4: looking at our data sources one from actual governmental context.

570
00:33:40,400 --> 00:33:43,840
Speaker 4: Like a lot of times people don't realize that almost

571
00:33:43,880 --> 00:33:46,000
Speaker 4: everything is available to you. You just have to ask for it.

572
00:33:46,960 --> 00:33:48,800
Speaker 4: They're not going to make it clean and easy or

573
00:33:48,840 --> 00:33:51,360
Speaker 4: create an interface that makes it as simple as a

574
00:33:51,440 --> 00:33:54,680
Speaker 4: you know, downloading photos from you know, from your whatever

575
00:33:54,720 --> 00:33:58,080
Speaker 4: app you're using for that, but you can reach out.

576
00:33:58,360 --> 00:34:00,360
Speaker 4: The other thing that's been interesting for us over the

577
00:34:00,400 --> 00:34:03,840
Speaker 4: last two years that, to be honest, was a bit surprising,

578
00:34:04,560 --> 00:34:11,000
Speaker 4: was connecting and partnering with journalists. Journalists are surprisingly good

579
00:34:11,600 --> 00:34:16,800
Speaker 4: at getting real hard data in the aggregate. For example,

580
00:34:17,360 --> 00:34:20,800
Speaker 4: it was The New York Times that went and actually

581
00:34:20,840 --> 00:34:24,239
Speaker 4: published with incredible support when you actually go in and

582
00:34:24,239 --> 00:34:27,200
Speaker 4: look at what they published and what studies that they

583
00:34:27,200 --> 00:34:32,239
Speaker 4: were pulling from the infant mortality rates. And I think

584
00:34:32,280 --> 00:34:35,400
Speaker 4: some of you may have seen that article and so,

585
00:34:35,480 --> 00:34:39,040
Speaker 4: and that's happened before we actually struggled and looking at

586
00:34:39,040 --> 00:34:42,239
Speaker 4: a lot of the things related to justice forty and

587
00:34:42,320 --> 00:34:45,799
Speaker 4: disadvantaged communities and this idea of forty percent of the

588
00:34:46,600 --> 00:34:50,920
Speaker 4: infrastructure funds they're actually meant to go to disadvantaged communities

589
00:34:50,960 --> 00:34:54,120
Speaker 4: across the United States. But we struggled to understand how

590
00:34:54,160 --> 00:34:59,920
Speaker 4: many of those disadvantaged communities were majority minority, right, because

591
00:35:00,280 --> 00:35:02,319
Speaker 4: you can work some things out there, and that wasn't

592
00:35:02,360 --> 00:35:05,120
Speaker 4: actually available through any government sources. And so there was

593
00:35:05,160 --> 00:35:08,640
Speaker 4: actually a journalist that had been doing the work for

594
00:35:08,719 --> 00:35:13,960
Speaker 4: about five years that allowed us to actually see every city, township,

595
00:35:14,080 --> 00:35:19,040
Speaker 4: and village that was black majority Latino majority and black

596
00:35:19,120 --> 00:35:24,040
Speaker 4: Latino majority. And the data set was so massive and

597
00:35:24,120 --> 00:35:28,600
Speaker 4: again readily available. And so I think that because if

598
00:35:28,640 --> 00:35:32,920
Speaker 4: you find truly reputable news organizations that are pushing data

599
00:35:33,640 --> 00:35:38,760
Speaker 4: because they are often fearful of publishing a massive story

600
00:35:38,800 --> 00:35:42,360
Speaker 4: that isn't backed up. They've done their homework and you

601
00:35:42,400 --> 00:35:44,440
Speaker 4: can dig there and get their data sets, and when

602
00:35:44,480 --> 00:35:47,759
Speaker 4: you combine those with government data sets, you can do

603
00:35:47,840 --> 00:35:51,960
Speaker 4: some things that are very very cool. You know. Hi everyone,

604
00:35:52,080 --> 00:35:52,920
Speaker 4: my name is Asia.

605
00:35:53,360 --> 00:35:56,040
Speaker 5: First, I want to thank you for being so honest

606
00:35:56,080 --> 00:35:58,439
Speaker 5: and real and challenging all of us in this room

607
00:35:58,520 --> 00:36:01,440
Speaker 5: to do more with data. And I didn't have a question,

608
00:36:01,480 --> 00:36:03,400
Speaker 5: but I just wanted to tell you, like being a

609
00:36:03,400 --> 00:36:06,799
Speaker 5: black woman is seeing you dominate this space is just empowering.

610
00:36:07,719 --> 00:36:08,680
Speaker 4: Wow. Thank you.

611
00:36:10,200 --> 00:36:13,240
Speaker 1: Would be the last one, regular, last one.

612
00:36:14,239 --> 00:36:14,439
Speaker 4: Hi.

613
00:36:14,640 --> 00:36:18,000
Speaker 7: My name is Erica Adams Immagrad student at UDUB and

614
00:36:18,280 --> 00:36:20,759
Speaker 7: I am in the Information School and I sit on

615
00:36:20,880 --> 00:36:23,760
Speaker 7: faculty committee, and we've been talking a lot about student

616
00:36:23,880 --> 00:36:26,839
Speaker 7: use with ch hat GPT. We're already using it most

617
00:36:26,880 --> 00:36:31,520
Speaker 7: of us, but there's a lot of ambiguity around, I guess,

618
00:36:31,520 --> 00:36:34,440
Speaker 7: like cheating and stuff like that. So I'm just curious

619
00:36:34,560 --> 00:36:39,600
Speaker 7: if you have any advice on persuading older academics on

620
00:36:39,880 --> 00:36:42,480
Speaker 7: you know, like coming up with guidelines for use, because

621
00:36:42,520 --> 00:36:45,360
Speaker 7: I think it's a great tool for us to continue

622
00:36:45,400 --> 00:36:47,520
Speaker 7: to use and we shouldn't be using it with fear.

623
00:36:51,640 --> 00:36:53,000
Speaker 4: See I don't even know when you say older, do

624
00:36:53,040 --> 00:37:00,440
Speaker 4: you mean people like my age, because yeah, you're real

625
00:37:00,520 --> 00:37:01,840
Speaker 4: quick and all of a sudden, you like when you

626
00:37:01,880 --> 00:37:03,839
Speaker 4: go out and you're like, I'm not the youngest person

627
00:37:03,920 --> 00:37:11,400
Speaker 4: out here no more. Right, So, persuading that's that's a

628
00:37:11,560 --> 00:37:14,160
Speaker 4: that's a tricky one. That is a tricky one because

629
00:37:16,280 --> 00:37:21,080
Speaker 4: there has to be empathy for how long they've existed

630
00:37:21,120 --> 00:37:22,960
Speaker 4: and known certain things to be true that are now

631
00:37:23,040 --> 00:37:28,719
Speaker 4: becoming very much untrue. Uh, And I I think I

632
00:37:28,719 --> 00:37:31,400
Speaker 4: think starting from that place of empathy is one. So

633
00:37:32,400 --> 00:37:34,440
Speaker 4: I think there's a couple of ways to see this

634
00:37:34,520 --> 00:37:37,280
Speaker 4: if I'm going to be very just kind of direct

635
00:37:37,400 --> 00:37:40,680
Speaker 4: about it. One is, you know, I don't know what

636
00:37:40,719 --> 00:37:44,800
Speaker 4: guidelines are in place right now. But obviously if everyone

637
00:37:45,040 --> 00:37:47,880
Speaker 4: goes to chat GPT and says, write me a paper

638
00:37:47,920 --> 00:37:53,240
Speaker 4: on the World War, and everyone submits a similar paper,

639
00:37:53,719 --> 00:37:56,319
Speaker 4: the teacher will say, oh, clearly there's some sort of plagiarism,

640
00:37:56,520 --> 00:37:59,160
Speaker 4: right because like whether you did this through something that

641
00:37:59,200 --> 00:38:02,719
Speaker 4: you google or you use chat GPT, they can tell

642
00:38:03,840 --> 00:38:06,399
Speaker 4: if you go to the effort of engaging with that

643
00:38:06,520 --> 00:38:11,840
Speaker 4: chat GPT interface such that what you produce, your professor

644
00:38:11,880 --> 00:38:18,120
Speaker 4: cannot tell. At this point I don't really know what

645
00:38:18,160 --> 00:38:19,360
Speaker 4: else to tell me. You No, I mean, I'm not

646
00:38:19,440 --> 00:38:21,279
Speaker 4: even I'm not. And it's not about saying is this

647
00:38:21,360 --> 00:38:23,600
Speaker 4: cheating or is this not cheating? This is about being

648
00:38:23,760 --> 00:38:27,440
Speaker 4: realistic about the world that we're in. Like if everyone,

649
00:38:27,600 --> 00:38:30,040
Speaker 4: if you are lazy with this tool, you will be

650
00:38:30,080 --> 00:38:34,960
Speaker 4: found out to be lazy. If you are innovative and

651
00:38:35,360 --> 00:38:39,880
Speaker 4: proactive with this tool, you will still rise above. I

652
00:38:39,960 --> 00:38:42,560
Speaker 4: truly believe that there's always still a way to rise

653
00:38:42,560 --> 00:38:46,440
Speaker 4: above and still write the best paper with chat GPT

654
00:38:46,840 --> 00:38:50,719
Speaker 4: compared to everyone else. And to be honest, if if

655
00:38:51,200 --> 00:38:53,520
Speaker 4: college is meant to prepare you for the real world,

656
00:38:54,600 --> 00:39:00,080
Speaker 4: acting like these tools don't exist when they do. And

657
00:38:59,840 --> 00:39:01,880
Speaker 4: I get it that people, Oh, we want to make

658
00:39:01,920 --> 00:39:03,120
Speaker 4: sure you can write a paper. We want to make

659
00:39:03,120 --> 00:39:05,759
Speaker 4: sure you can do all those things. Yes, yes, guess what.

660
00:39:05,840 --> 00:39:09,600
Speaker 4: I also still don't know how to drive stick because

661
00:39:09,640 --> 00:39:13,120
Speaker 4: I didn't have to. So you know, we can lament

662
00:39:13,400 --> 00:39:16,120
Speaker 4: about this world of like, oh, we hope people wish

663
00:39:16,160 --> 00:39:17,719
Speaker 4: they should. We want to make sure you still have

664
00:39:17,760 --> 00:39:21,040
Speaker 4: all these different things. Those who care about those skills

665
00:39:21,120 --> 00:39:24,040
Speaker 4: will get them. I believe my husband said he could

666
00:39:24,080 --> 00:39:26,080
Speaker 4: drive stick, but then recently actually I was like, were

667
00:39:26,080 --> 00:39:29,319
Speaker 4: you were you lying? Because this is not I don't

668
00:39:29,320 --> 00:39:32,840
Speaker 4: think he's spot to make those noises. You know, he's

669
00:39:32,840 --> 00:39:35,520
Speaker 4: from Mississippi, so he's a you know, so he was

670
00:39:35,520 --> 00:39:37,480
Speaker 4: telling me a whole of the storm Mississippi and Texas.

671
00:39:38,040 --> 00:39:41,000
Speaker 4: So who knows, but to that end, right, like I think,

672
00:39:41,719 --> 00:39:43,560
Speaker 4: but he clearly felt that it was important that he

673
00:39:43,719 --> 00:39:46,040
Speaker 4: said that he could drive stick. I was like, boy,

674
00:39:46,080 --> 00:39:48,279
Speaker 4: I could barely do automatic at the time, honestly, like

675
00:39:48,320 --> 00:39:52,919
Speaker 4: I cannot wait for driverless cars. So it's it's it's

676
00:39:52,920 --> 00:39:54,440
Speaker 4: one of those things where I would say, so, I

677
00:39:54,480 --> 00:39:58,800
Speaker 4: don't think it's about persuasion. I think it's about recognizing

678
00:39:59,040 --> 00:40:04,719
Speaker 4: that the entire all the standards will shift that you

679
00:40:04,800 --> 00:40:07,719
Speaker 4: cannot restaur on your laurels here, like everyone keeps saying,

680
00:40:07,719 --> 00:40:10,000
Speaker 4: I use chatchypet to create a marketing plan to do

681
00:40:10,040 --> 00:40:14,759
Speaker 4: this and do that. We will see certain similarities that

682
00:40:14,800 --> 00:40:17,799
Speaker 4: will negate that work if you do not still put

683
00:40:17,840 --> 00:40:20,840
Speaker 4: your human intellect on top of it. We're not there again,

684
00:40:21,120 --> 00:40:24,960
Speaker 4: sassy seven year old y'all, we're not there, and don't

685
00:40:25,000 --> 00:40:29,200
Speaker 4: let anyone make you think that that we are. But yeah,

686
00:40:29,200 --> 00:40:31,680
Speaker 4: so it really it sounds to me that like, if

687
00:40:31,719 --> 00:40:34,839
Speaker 4: your professors are like super not into it, you might

688
00:40:34,840 --> 00:40:37,239
Speaker 4: be able to save yourself some time and just do

689
00:40:37,360 --> 00:40:40,000
Speaker 4: what you gotta do and be like with the chatchypt No,

690
00:40:41,360 --> 00:40:42,400
Speaker 4: I would never.

691
00:40:53,800 --> 00:40:56,520
Speaker 1: Black dec Green Money is a production of Blavity, Afrotech,

692
00:40:57,040 --> 00:41:00,560
Speaker 1: Black Effect Podcast Network, and iHeart Media, and it's produced

693
00:41:00,560 --> 00:41:04,320
Speaker 1: by Morgan Debonne and me Well Lucas, with additional production

694
00:41:04,400 --> 00:41:08,279
Speaker 1: support by Sarah Ergin and Rose McLucas. Special thank you

695
00:41:08,280 --> 00:41:11,200
Speaker 1: to Michael Davis of Vanessa Serrano. Learn more about my

696
00:41:11,200 --> 00:41:14,400
Speaker 1: guests the other tech This represent innovators at afrotech dot com.

697
00:41:14,960 --> 00:41:18,480
Speaker 1: Enjoy your Black Tech Green Money. Share this with somebody,

698
00:41:19,080 --> 00:41:21,600
Speaker 1: Go get your money. Peace and love,