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November 21, 2023 41 mins

We are living in the days where the people who beg for forgiveness vs. ask for permission are going to be in position to succeed. If you’re waiting for the perfect time to start your thing, you’ll be at a disadvantage.

There's a lot going on in AI as of late. The stuff happening, both in the actual tech as well as in the ecoystem of AI, is at a fever pitch. Things that used to play out over months are now happening in a matter of hours.

It's time to bust a move if you're 'bout it.

Jessica O. Matthews, CEO and founder of Uncharted, stopped by the AfroTech Executive conference in Seattle 2023 to discuss how we can have a sustainable future and harness the power of AI. She’s on the stage speaking with Jonnie Bradley who is the Responsible AI Official for the Department of Energy and Sr. Program Manager for the Artificial Intelligence and Technology Office. 

Follow Will Lucas on Instagram at @willlucas

Learn more at AfroTech.com https://instagram.com/afro.tech

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Episode Transcript

Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
It is an interesting time these days around AI. Over
the weekend, the board of directors for the nonprofit Open Ai,
which developed and operates chat gpt, which is super duper
popular I use it a lot, they abruptly fired the
co founder and now previous CEO, Sam Aldman. So they

(00:22):
did this in like like thirty minutes before the markets
closed on Friday, which that kind of thing only happens
in the rarest and most inflammatory circumstances because it's such
a market shifting move to do that right before the
markets closed, especially on a Friday. So this is big
news in AI as chat gpt has the fastest growing

(00:46):
user base for consumer app in history. They did one
hundred million users in two months. Now, to give you
some perspective, it took Facebook like four years to hit
one hundred million users. It took Twitter five years to
get one hundred million users. I think it took ig
Instagram of like two years. Open ais chat gpt did

(01:11):
it in two months, and almost as fast as they
blew up their incredible valuation, they blew up their incredible valuation.
So it went from look, they went from looking at
like a ninety billion dollar valuation to uncertainty in the
matter of hours and days since the board gave Sam

(01:34):
the boot. In April this year, the company was valued
at twenty nine billion dollars, and just a week ago
they were looking at a ninety billion dollar valuation raising
money at a ninety billion dollar valuation. From April's twenty
nine billion dollar valuation, they were on track to do
a billion dollars in revenue, which shows you how fast

(01:57):
chat gpt was growing and with the future looked like
for them. If you recall chat GPT, it was just
legit like launched ten months ago, eleven months ago, So
this is incredible growth such that AI is just about
all anybody is talking about these days, whether they're building it,

(02:17):
they're trying to fund it, they are starting an AI
focused startup, or they're afraid of it altogether. AI is
on many of our minds. At the time this recording,
a lot has happened. Sam was out at Open Ai
as CEO. This was just Friday. Then the employees and

(02:38):
investors revolted because the way people believed in Sam obviously
was a surprise to the board, so there were attempts
to bring him back over the weekend. Then that didn't
work out. Then today Microsoft says they're bringing him over
there to run some AI projects, and undoubtedly many necessary,

(02:59):
highly talented open ai employees who were on his team
will follow him in the Microsoft. So who knows what
will happen an hour after I published this episode, because
this is all happening so fast. Everything I just mentioned
happened in like the span of forty eight to seventy
two hours. This thing. Usually things like this play out

(03:20):
over weeks. Now. The reason I kick off this episode
with this news is because it's that significant. Because we all,
if I mean, if you've embraced at some level AI
and you have tried chat GPT at least once, there's
no way you're not hooked. It's so good at figuring

(03:41):
out how to say things in the way you want
to say it, by just you giving it some simple commands.
And so I wanted to start this episode off just
to point to make a point that things are moving
so quickly in our society. This is a rallying call,
a rallying to you, my black tech, green money family,

(04:02):
to be bout it. Nobody asked permission, Certainly the board
that open Ai didn't do a poll of their employees
and investors to see if kicking out the founder and
CEO will be acceptable. They just bust a move. So
we're living in the days where people who beg forgiveness
versus ask permission are going to be in a position

(04:25):
to succeed. If you're waiting for your work situation to
get right, or your kids to graduate, or the weather
to warm up in the sun to be out again
before you start making your moves, you'll be at a disadvantage.
Things that used to take months are happening now in hours.
So I want to bring you a conversation from afro

(04:47):
tech executive in Seattle between Jessica Matthews Jessica Old Matthews,
who's CEO and founder of Uncharted, and Johnny Bradley, who
was the responsible AI official for the Department of Energy
and senior program manager for the Artificial Intelligence and Technology Office.

(05:07):
Because this conversation they're having is about demystifying AI for
our community. So I hope you get something from this.
Follow along, find your way to not necessarily you have
to go build an AI startup, but find your way
to use it, leverage it to do what you want
to do, and do what you're doing even better, more efficiently, faster, cheaper,

(05:32):
and with a greater punch. Okay, hello everyone, how are you? Yeah? Yeah, yes, yeah, okay,
So let's get into it, right, yeah, all right. So
you know I love soccer and you made socket. So
I just have to start this conversation off by asking
one question, why did you go from hardware to data solutions?

(05:58):
So we have to go real deep into like the
technical stuff, but for hi, everybody, Hello, Hello, Hello, thank you.
Will Will's dope just consistent. Ask dude, man, how much
cursing is allowed? Right? That's always what I like to check.
I tend to check after my first one fuck falls out.

(06:21):
So listen, y'all. I flew here from Harlem and I
was like here, like, where does Morgan have me flying
to you? Now? That's what I thought was on that plane.
I was like, oh damn Seattle, Seattle, okay. And I
was walking down the street and I was like, where
all the black people? Right? And then but then you're here, Hello, hello,

(06:42):
gathered them off? No, no, So say, you know, it's
a good question. So, uh, what Jonie's talk about here
is that I started my career making energy generating play products.
So when I was nineteen years old, I invented an
energy generator eating soccer ball that could harness the energy

(07:02):
from play, the kinetic energy, and store that power inside
of the ball. Yeah, it was pretty cool. You could
play with it, you could roll, you know, as it rolled,
it was generating energy, and about an hour of play
would give you three hours of light. Fast forward. Now
I'm thirty five. I don't have problem saying it because, like,

(07:22):
you know, me, black guys, good, right, what I'm saying? So,
and and I run a data infrastructure company that uses
AI to help disadvantaged communities develop sustainable infrastructure with more

(07:44):
equity and efficiency than ever before. Right, So how did
I get from there to here? Well, one thing is
that my true north was always incredibly clear to me.
I'm a dual citizen of Nigeria and States. Always, even

(08:04):
in Seattle, where I'm being so messed up. Yeah, yeah.
The only thing I know about Seattle's Nirvana. Right, So,
so I like, and I love Nirvana. So I was like, sheah,
I wear my flannel. My mom was like oh s
ohse glenno. So so for me, you know, it was

(08:29):
going back and forth between Nigeria and the United States,
whether it's for weddings or funerals, and just recognizing that
there were some things that were so dope, like everybody
had a mobile phone. It became ubiquitous very quickly. Oftentimes
my cousins had a better cell phone than I did.
But also wondering like why is it that it doesn't

(08:49):
matter if we're in the village or if we're in
like the bustling city of Legos that were losing power
every single day. And I knew it wasn't because like
there were with the technologies that needed to exist to
make that happen. I knew it was an infrastructural issue immediately.
In fact, oftentimes in places like Nigeria, people are paying

(09:10):
more per kilowatt hour than we pay here in the
United States. I don't know if that's true anymore, because
like I was, the bills getting high, I was your
bills getting high. But at least before it was very
much like that. And you know, I believe that the
first step in innovation is the articulation of the problem.
And at the age of seventeen eighteen, I articulated the

(09:33):
problem to be that people like my cousins who were
trained engineers did not believe that there could be a
world where things could be different. They did not believe
that this problem could be solved by some innovation or
some public private partnership. They simply thought that the best
way to solve the problem was to pretend like it's

(09:54):
not happening and to get used to it. And so
I wanted to create something that would make them change
that view, that would make them see in the world
not just as it is, but as it could be.
And soccer is the most popular sport around the world,
my favorite sport, the most popular sport. And I'm assuming

(10:17):
you're probably really good. My cousins were not that good
teams to the championship. I was good. Oh, you were
so good that you were bad. Now my cousins were
just My cousins were so average that they were like,
why are we playing this game? This is awkward. So

(10:37):
but in that in that way though, seeing their passion
and seeing the way that they would play on the field,
I'm like, you know, the way that you approach this game,
knowing damn well that you are at best average is
the way you need to approach life. It's the way
you need to approach all of the problems in our
community and infrastructurally, and so my thought was, like, let

(10:57):
me create something that would inspere hire them to do it.
And what ended up happening was that long story short
inspired me to do it. Got it? Okay? Yeah, all right.
It took a while to figure out exactly what technologies
would get there. First it was the play products, and
then different inventions, and then a couple of years ago,

(11:18):
right before the pandemic, I started to realize that the
common thread in every community wasn't needing, like you know,
an energy generating speed bump or some cool thing here
or there that was fun but couldn't scale. The common
problem was data. It didn't matter if it was in
Nigeria or in the US. The data problems were kind

(11:41):
of compiling on each other, and that when governments were
operating in the right way, they were spending at least
half of their time tracking down, collecting, correcting, and sharing
SOILID information to make infrastructure decisions. They often did not
have the right information to know where to prioritize who
should be getting the solar or where should we be

(12:02):
replacing the lead pipes, And in the other half of
the time, they were just quote unquote shooting from the hip,
and so I was like, well, if we could solve
the data problem and improve the way that they're organizing
their data to make decisions to build sustainable infrastructure, we
can help them build it faster, we can help them
build it for less. And then as a result, we

(12:23):
can either help them make it more equitable or at
least make it very obvious when they're being fucked up,
like make it more obvious, like, listen, that's what the
data say. If you just want to do that, you
can do that, but don't act like that that data didn't
say that. And so that's how we came up with
our current products. And we thank you, right, we thank
you for that transition. So we want to talk about

(12:47):
demystifying AI. So when we look at AI, I always
think about that time and I don't I don't mind
dating myself either. Fifty three Listen, I don't mind that either.
We should just talk about that. I do, right, this homegrown.
So back when I was born, actually legislation was passed

(13:10):
on cigarettes. But as I grew up, you know, cigarettes
was just like whoa, it was a cool thing to do.
You had this slim lady smoking a cigarette, right, and
it was just like ayeing to me, right, And for
twenty years I smoked based off of that. But what
I will say is this, they demystified those cigarettes. It
was not right, and that's great, that's that's that's my

(13:32):
exact point is that during my time, it was cool, right,
and it was the thing to do. And as time
went by and the Surgeon General said, no, put these
warnings on each pack of these cigarettes and let people
know exactly what it does to you, and it would
curb and it just like demystified the coolness and the

(13:54):
sexiness of cigarettes. So my question is, how do we
demystify AI? Girl? Oh yeah, listen, because we had a
hole behind the scenes conversation y'all that we're trying to like,
you know, keep civil for the cameras and all that.
So I think that's part of why you guys are here, right.

(14:17):
Everyone's talking about AI. Everyone's acting like it's the new
hot thing, and they're acting like it's this black bots
and it's this scary monster that cannot be controlled and
things are just happening, but it's it's really not, you know,
like you should not be afraid of AI. You should
be afraid of the people who are building it. That's it. So,

(14:38):
like what it comes down to, let's break this down, right,
AI artificial intelligence, right, and we discuss this. It's it's
kind of like a child. It's a child. It's like
a robot baby, right, chat GPT is at best a
sassy seven year old. And we all know, right, we
all know that seven year old that was born in

(14:58):
like you know, I don't know whatever seven years ago
was like you know, like but like basically recently grew
up with all the social media platforms and be out
here talking to you like they've grown, and you'd be like, well,
damn girl, you've grown. No, they just online? No, Like,
do not have this seven year old do your taxes?

(15:19):
It might go well sometimes until it does not, right,
But what it ultimately then comes down to you have
to ask yourself, is okay, so if this is just
this this code that has a great capacity to learn, right,
and the way it's taught to learn, it's algorithms. Algorithms
are just processes. Like people like to use fancy words

(15:43):
to scare us and say, oh, that's a distant thing.
But if you have a process for anything. That's an algorithm.
That's an algorithm. You can call it that. Especially half
y'all when y'all women look great in here, I know
what you had to do, whatever you had to do
to get here on time. That's a very efficient algorithm.
That's a very efficient algorithm. And all it actually comes

(16:04):
down to is, then how are you teaching that to
an artificial intelligence? How are you teaching that to this robot? Baby?
Let's just put it in that way so that it
can start to do that for you. So to that end,
when we talk about demystifying it, and we talked about
this a lot, you have to wonder who's doing the teaching.

(16:29):
You have to wonder who's doing the teaching and how
are they framing the way that this child should observe
and respond to the world. So if someone is not
aware of their biases, if someone is not aware of
the fact, like, can you imagine who's the guy who

(16:51):
does open Aye, I'm not trying to start no shit though,
this is going out to the world. But let's just
let's not use him specifically, because you know, but let's
can you imagine can you imagine an Elon Musk? Can
you imagine Elon Musk being at trying to teach AI
how to help me do my hair. I could imagine

(17:15):
he thinks he can do it. He thinks, of course,
no problem. And that's a that's a fun example, right.
Where So when we talk about demystifying AI, it's it's
really saying take the blame away from the AI and
start focusing on the people who are training these models right,
and start focusing on whether or not whether they are

(17:37):
doing it so intentionally or on intentionally if they're actually
considering the vast globe of people and all of their problems.
Because all AI is really is a tool. It's a
tool to help us do more. To help people do more,
you need people to train this AI and trust despite

(17:57):
all the things that you're hearing about AI taking job,
the thousands of jobs will be created because of what
this AI is doing. The people who used to drive
the carriages, when they saw the cars, they were like,
I don't know, it's getting pretty scary out here. That
car don't even got no horses. They're like, this is
just wild, this is crazy. I don't trust this. Okay,

(18:19):
Sure they found new jobs. So there. I feel like
when people start to say things like, oh, well be
afraid of it. Oh it's gonna take your job. What
they're really trying to do is make you afraid of
going behind the veil and wonder why can't I be
part of the team that's building this AI. Why can't

(18:40):
I be part of the crew that's raising this baby.
You know, they say it takes a village, not just
some dude who doesn't blink in the corner. So why
are we allowing you? I love the sasey seven year old.
So now let's talk about that sassy seven year old.
All of you know the landscape today, right, you have
states that are removed diversity and inclusion. You have states

(19:02):
that are removing African studies. You can't say the word gay.
And you know I could go on and on, right
because I watched the news all day every day so
and I don't watch Fox, sorry, but I will say
this is that what do those practitioners look like in
the future? Now, this sas seven year old has never
had anyone tangling with it that did not have diversity

(19:24):
and inclusion. Because you have that today, right, you have
African American studies today, right, you have gender equality, you
have these things today, but as the days go on,
these things are being removed slowly. So now you have
people that are graduating college that want to be an
AI practitioner, but they did not learn what discrimination was

(19:45):
because they didn't believe in racism. Right. I had someone
tell me yesterday when I was eating She was like,
my best friend said that there was no racism. Do
I want that person tangling with my sassy seven year old?
And how does that look? I think you know, you know,
you know the answer, all right. I want to before

(20:06):
I respond, I want to do a quick show of hands.
How many of you are here because you're considering how
to be more involved in the AI industry? Okay, okay?
How many of you are here because everyone's been talking
about AI and you're like, what what is this? And
you're just trying to understand what it actually is? Oh okay, okay,

(20:31):
be proud, put your hand up on Okay, that's right. Yeah.
How many of you in some way already work with
AI or related to AI? Okay, okay. And so your
concern then is really that you feel like there's a
lot of things happening around you that you don't understand

(20:52):
or don't like or can't control. Okay, so this is
what what you're getting at. Indeed, we have to be
concerned in general, and this goes beyond AI, that we're
going to start to have generations of people who are
very much driving our economy, driving industries, developing technologies that

(21:19):
will have, from our perspective and our opinion, a skewed
view of the world and how things work. And they're
not only going to teach their NI their natural children,
that they're going to teach the AI this. But because
of the rapid impact and a rapid scale, it's it's

(21:40):
not as bad as like, oh, those three kids grew
up in that racist sexist house, so now they're racist
and sexist. It's that AI was developed by this person
who has racist and sexist biases. And because of how
impactful AI can be, we now have an army of racist,
sexist or the very least incredibly aloof right like things

(22:04):
happen right. So my perspective is ultimately radical self reliance.
I can't help what's going on specifically Macro in Florida,
in Texas, but I do know people who live there
who are saying, regardless of what they're teaching, my kids
in school. Here's what I'm going to teach you at home.

(22:25):
And so that's why I've been kind of recently saying, please, please, please.
The last thing you should be is afraid of AI.
This is now, more than ever, the time where you
need to be incredibly excited about this tool. But you
need to see this as a battle and you need
to do everything you can to get your hands on
this weapon as well. I'm gonna keep it super super
real about this. So, as I said earlier, I'm a

(22:48):
thirty five year old woman, I'm married, I love my husband.
I'm getting ready and preparing myself to freeze my eggs,
and I'm gonna be keep it real. Part of me
is doing that because I learned that the maternal mortality
rate has gotten worse. It's twenty twenty three. It is
twenty twenty three in a developed country, and you're telling

(23:12):
me that over the last couple of years. And it's
not just because of COVID. If I get pregnant, I
have twelve patents and patents pending. I'm building all these
different things, and the thing that scares me the most
is having a baby and dying. But the people are
developing AI to do the wildest, most random shit possible.

(23:35):
But women are dying when when they get pregnant, because
not enough women and definitely not enough Black women are
sitting there saying, how can I use AI as a
tool to keep more of us alive? And the only
way that's gonna change is if more of us say
we're not afraid of AI. Regardless you know what, y'all

(23:57):
are gonna do what you want to do with it,
But here's what I'm gonna do with it. Here's how
I'm going to teach this child on home, regardless of
what you're doing. And so that to me is the
only answer. It's it is not too late for us
all to recognize that this will not be done for us.
This will not be something where we can hope that
the right few people at the top are going to

(24:19):
be thinking about all the things that we need. We
know this, or we would not have systemic issues right now.
We know this. So but what I now view, though,
is that I believe the technology is one of the
best equalizers, one of the best democratizing tools, and that
to me is exciting. That to me is an opportunity.

(24:40):
So let's stop talking about being afraid. Let's stop talking
about it as a black box. There are several low
code and no code tools that you can use to
create something in AI if you want to. How do
we get people to see this as a playground versus
I don't know more? T ruary right right. I'm also

(25:05):
literally not kidden my I'm sorry. My dad literally two
days ago and was like, oh, my grandkids are in
the freezer. I don't know when they're getting out. And
I was like, actually, Dad, Tiana, my older sister, Tiana's,
Tiana's uh grand kids are in the freezer. Mine are
about to be in the freezer. Just want to confirm.
He's like, oh, when are they coming out of the freezer?

(25:27):
I'm like, this is what happens when you talk to
your mom. Your mom talks to your dad, your dad
talks to you. You don't know what's going on. So
but uh, it's it's real, and it's a it's it's
a real thing. We have. We have a very small
group of people right now who are focusing AI and
their problems, and I do not blame them. Entrepreneurship is

(25:48):
problem solving without regard for resource, like science is the
study of life. Like these things should not be scary
big words. Uh. But when we silo ourselves and I
say we, it's just like if you are anyone, if
you're if you're not affluent, if you are not a man,
if you're not there's so many things that actually most

(26:08):
of us are not that kind of that paradigm of
the person who's doing this, most of us. But when
you kind of like push something away, you are disenfranchising
yourself in so many ways. It goes beyond, it goes
beyond anything we can imagine. The thing that I'm most

(26:29):
scared of is the number of people who keep saying
I'm afraid of what could literally be the best thing
they've ever put their hands on in their entire lives.
All Right, with that, we are we are privileged to

(26:54):
have ten more minutes with them, to have some Q
and A. So I'm sure there's some questions in the art. Ooh,
we got one already. I'm gonna come to you. You
y'all give another round of applause for that man that
was fantastic. Please say your name. Hi, my name is Sydney.
Thank you. Oh, I got it? No, okay, all right,

(27:16):
all right, AnyWho I saw you know I heard us
talking about like fear. I personally don't have fear if AI.
Maybe I should, I don't know, but that's not what
you're saying. So I shouldn't be afraid. But however, how
do we harness that you were sharing some ideas of
there's some low or no code ways of leveraging AI.
Can you tell us more about like how to like

(27:37):
leverage it? And yes, no, of course that's a really
good question. I actually think I'm gonna go ahead and
maybe I can talk to the afrotech people. I'm I'm
gonna just list like on my like LinkedIn just like
seven seven platforms. Some require you to know a little
bit some of you, some of them don't. Now there
is the underlying issue of kind of who's creata even

(28:00):
that no code platform. But at the end of the day,
like you know, nothing's ever going to be perfect, and
we just want people to get closer to something. And
if your engagement, even with these no code platforms can
better educate the sassy seven year olds that are running it,

(28:21):
So now all of a sudden they're not just kind
of operating with whatever the hell they're being told by
the very specific groups of people who are doing this.
It's a good thing, and so there are several I
don't want this necessarily to be an advertisement for any
one or the other. But on Monday, I'm gonna post
If you go to my LinkedIn, I'm just Jessica. Oh Matthews,

(28:42):
you'll see it. I will post five to six that
I've heard some good things about. Because again I want
to be very clear, I study psychology and economics. I
like to tell people I have a PhD in Google,
which pisses off people with real PhDs, I find, But
the main point is that you know, I also have
a granted patent for wireless Mesh Energy Networks, which is

(29:05):
an algorithm that essentially considers the communication protocols for decentralized
micro energy systems. And I did that with a degree
in psychology and economics and a PhD in Google. So
what I actually am really trying to say is that

(29:27):
you don't have to go to school for this. To
do this, you do have to have quite a ferocity
for self learning, and again I fate to say a
bit of self reliance in this, but with the right
tools and with that kind of interest in researching as

(29:48):
much as you can you'd be surprised what you can do,
especially if you're comfortable with the prototype being very much
only a couple percentage points of what you actually want.
Talked about the socket earlier. My first prototype for the
socket was a shake to charge flashlight and a hamster ball.
So I will post that on what's today Thursday. Got

(30:12):
to get back to New York. I'll post it on Monday.
I promise I will perfect question over here and I'll
come over there. Yeah, my name is Evan Poncels. I'm
with the Africa Down community of Alanterst and I just
want to let you know that black people are here
in Seattle and mostly concentrated in the Central district of Seattle.
So let's all learn a little geography about this. So
from the Central District of Seattle, Ray Charles dropped his

(30:33):
first studio album, so it's not just Kirk Cobain. Also
Jamie Hendrix is from there. Shout out to my uncle
high school termer at Garfield High School of anybody from Garfield.
And so what I wanted to say was just that,
you know, in addition to radical self reliance, we also
should be organizing around data and around artificial intelligence. So
one thing we're doing with Africa Town is building programs

(30:55):
so that you can be exposed to these sorts of things.
But I was wondering, My question really is where can
we get exposure to the data sets that could help
us solve with AI things like infant mortality or pregnant
mother mortality, mortality in the birthing scenarios. So that because
we work with universities that are studying, you know, data
and like things like coming up with the language models

(31:17):
for African American vernacular English and things like this. And
so right now we're about to start a consortium where
we're learning, well, what goals should we have, what problems
should we solve in, what strategy should we we implement?
And so I'm just trying to see where's our best
footing for that in terms of organizing, Jenny, I think
I have. I can tell you where my company, like
when we really started looking at disadvantaged communities that are

(31:39):
black and Latino majority communities, and how do we get
the data to ensure that we're thinking through the equitability
of what's happening in this once in a generation moment
with our infrastructure, and how we started creating actual actual
AI that could support that, But I'd love to know
what you think. First. I can share our perspective, but

(32:02):
as someone who works with the government, you might know
a few more peek so you know the government, you
know that's a beast by itself, right, we do have
ways of putting out actual where our data is stored,
so I will say that. Okay, So what my office does.
I'm the Artificial Intelligence and Technology Office, right, and what

(32:24):
we do is every year we do an AI use
case inventory. And actually we're sitting here, but that's what's
going on back at home is all the labs are
putting together in AI use case inventory will still turn
into us in mid April. Once that's turned into US,
we will put that inventory up on what's releasable to
the public. So let me say that because we're seventeen

(32:47):
national laboratories, so that way you know, everything's not releasable
to the public. But what is releasable to the public.
We'll go up on our website, right, it's Artificial Intelligence
and Technology Office. You will see the inventory there. If
they are listing the code, it will be there so
you could actually read what the name of the use
case is you'll be able to see a description of

(33:07):
that use case if it matches anything that you're trying
to do or looking to do, and it says where
the code is. That's where the code is at. Right.
If it's blank and you want us to find out,
that's Jason Tally. You want us to find out if
that code is available, just send us an email. Our
email address is on the website. You send it to

(33:27):
us and we'll get it for you. If it's available,
we'll get it for you. So that's from the government perspective,
which matters, right, because I think for us we're often
looking at our data sources one from actual governmental context.
Like a lot of times people don't realize that almost
everything is available to you. You You just have to ask
for it. They're not going to make it clean and

(33:48):
easy or create an interface that makes it as simple
as a you know, downloading photos from you know, from
your whatever app you're using for that, but you can
reach out. The other thing that's been interesting for us
over the last two years that, to be honest, was
a bit surprising, was connecting and partnering with journalists. Journalists

(34:08):
are surprisingly good at getting real hard data in the aggregate.
For example, it was The New York Times that went
and actually published with incredible support when you actually go
in and look at what they published and what studies
that they were pulling from the infant mortality rates. And

(34:32):
I think some of you may have seen that article
and so, and that's happened before we actually struggled and
looking at a lot of the things related to justice
forty and disadvantaged communities and this idea of forty percent
of the infrastructure funds they're actually meant to go to
disadvantaged communities across the United States. But we struggled to

(34:53):
understand how many of those disadvantaged communities were majority minority, right,
because you can work some things out there, and that
wasn't actually available through any government sources. And so there
was actually a journalist that had been doing the work
for about five years that allowed us to actually see
every city, township, and village that was black majority Latino

(35:17):
majority and black Latino majority. And the data set was
so massive and again readily available. And so I think
that because if you find truly reputable news organizations that
are pushing data because they are often fearful of publishing
a massive story that isn't backed up. They've done their

(35:41):
homework and you can dig there and get their data sets,
and when you combine those with government data sets, you
can do some things that are very very cool. You know.
Hi everyone, my name is Asia. First, I want to
thank you for being so honest and real and challenging
all of us in this room to do more with data.
And I didn't have a question, but I just wanted

(36:02):
to tell you, like being a black woman is seeing
you dominate this space is just empowering. Wow. Thank you.
Would be the last one, regular, last one. Hi. My
name is Erica Adams Immagrad student at UDUB and I
am in the Information School and I sit on faculty committee,
and we've been talking a lot about student use with

(36:24):
ch hat GPT. We're already using it most of us,
but there's a lot of ambiguity around, I guess, like
cheating and stuff like that. So I'm just curious if
you have any advice on persuading older academics on you know,
like coming up with guidelines for use, because I think
it's a great tool for us to continue to use

(36:45):
and we shouldn't be using it with fear. See I
don't even know when you say older, do you mean
people like my age, because yeah, you're real quick and
all of a sudden, you like when you go out
and you're like, I'm not the youngest person out here
no more. Right, So, persuading that's that's a that's a

(37:11):
tricky one. That is a tricky one because there has
to be empathy for how long they've existed and known
certain things to be true that are now becoming very
much untrue. Uh, And I I think I think starting
from that place of empathy is one. So I think

(37:32):
there's a couple of ways to see this if I'm
going to be very just kind of direct about it.
One is, you know, I don't know what guidelines are
in place right now. But obviously if everyone goes to
chat GPT and says, write me a paper on the
World War, and everyone submits a similar paper, the teacher

(37:54):
will say, oh, clearly there's some sort of plagiarism, right
because like whether you did this through something that you
google or you use chat GPT, they can tell if
you go to the effort of engaging with that chat
GPT interface such that what you produce, your professor cannot tell.

(38:16):
At this point I don't really know what else to
tell me. You No, I mean, I'm not even I'm not.
And it's not about saying is this cheating or is
this not cheating? This is about being realistic about the
world that we're in. Like if everyone, if you are
lazy with this tool, you will be found out to
be lazy. If you are innovative and proactive with this tool,

(38:38):
you will still rise above. I truly believe that there's
always still a way to rise above and still write
the best paper with chat GPT compared to everyone else.
And to be honest, if if college is meant to
prepare you for the real world, acting like these tools
don't exist when they do. And I get it that people, Oh,

(39:01):
we want to make sure you can write a paper.
We want to make sure you can do all those things. Yes, yes,
guess what. I also still don't know how to drive
stick because I didn't have to. So you know, we
can lament about this world of like, oh, we hope
people wish they should. We want to make sure you
still have all these different things. Those who care about
those skills will get them. I believe my husband said

(39:23):
he could drive stick, but then recently actually I was like,
were you were you lying? Because this is not I
don't think he's spot to make those noises. You know,
he's from Mississippi, so he's a you know, so he
was telling me a whole of the storm Mississippi and Texas.
So who knows, but to that end, right, like I think,
but he clearly felt that it was important that he
said that he could drive stick. I was like, boy,

(39:46):
I could barely do automatic at the time, honestly, like
I cannot wait for driverless cars. So it's it's it's
one of those things where I would say, so, I
don't think it's about persuasion. I think it's about recognizing
that the entire all the standards will shift that you
cannot restaur on your laurels here, like everyone keeps saying,

(40:07):
I use chatchypet to create a marketing plan to do
this and do that. We will see certain similarities that
will negate that work if you do not still put
your human intellect on top of it. We're not there again,
sassy seven year old y'all, we're not there, and don't
let anyone make you think that that we are. But yeah,

(40:29):
so it really it sounds to me that like, if
your professors are like super not into it, you might
be able to save yourself some time and just do
what you gotta do and be like with the chatchypt No,
I would never Black dec Green Money is a production

(40:55):
of Blavity, Afrotech, Black Effect Podcast Network, and iHeart Media,
and it's produced by Morgan Debonne and me Well Lucas,
with additional production support by Sarah Ergin and Rose McLucas.
Special thank you to Michael Davis of Vanessa Serrano. Learn
more about my guests the other tech This represent innovators
at afrotech dot com. Enjoy your Black Tech Green Money.

(41:17):
Share this with somebody, Go get your money. Peace and love,
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