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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Transcript
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.
Speaker 2 (03:22):
Now.
Speaker 1 (03:22):
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 out how to
say things in the way you want to say it,
(03:44):
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, to be
bout it. Nobody asked permission, Certainly the board that open
(04:07):
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 to succeed.
If you're waiting for your work situation to get right,
(04:29):
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
tech executive in Seattle between Jessica Matthews Jessica Old Matthews,
(04:52):
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.
Because this conversation they're having is about demystifying AI for
(05:13):
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,
and with a greater punch.
Speaker 3 (05:33):
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?
Speaker 4 (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?
Speaker 5 (06:14):
Right?
Speaker 4 (06:15):
That's always what I like to check. I tend to
check after my first one fuck falls out. 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?
Speaker 3 (06:36):
Right?
Speaker 4 (06:38):
And then but then you're here, Hello, hello, 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
(07:00):
ball that could harness the energy 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
(07:21):
have problem saying it because, like, 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
(07:42):
sustainable infrastructure with more 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,
(08:04):
even in Seattle, where I'm being so messed up.
Speaker 6 (08:07):
Yeah, yeah.
Speaker 4 (08:08):
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.
Speaker 3 (10:22):
Teams to the championship.
Speaker 4 (10:24):
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 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
(10:46):
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 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.
Speaker 5 (11:06):
Got it?
Speaker 3 (11:07):
Okay?
Speaker 4 (11:07):
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, 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
(11:28):
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 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,
(11:51):
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 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
(12:12):
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 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
(12:33):
data didn't say that. And so that's how we came
up with our current products.
Speaker 3 (12:38):
And we thank you, right, we thank you for that transition.
Speaker 4 (12:45):
So we want to.
Speaker 3 (12:46):
Talk about 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.
Speaker 4 (12:58):
We should just talk about that.
Speaker 3 (13:00):
I do, right, this homegrown. So back when I was born,
actually legislation was passed 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.
(13:21):
But what I will say is this, they demystified those cigarettes.
Speaker 2 (13:26):
It was not.
Speaker 3 (13:28):
Right, and that's great, that's that's that's my 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
(13:52):
and it just like demystified the coolness and the sexiness
of cigarettes. So my question is, how do we demystify AI?
Speaker 4 (14:01):
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.
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
(14:24):
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,
like what it comes down to, let's break this down, right,
AI artificial intelligence, right, and we discuss this. It's it's
(14:47):
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
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
(15:09):
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?
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
(15:30):
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
to scare us and say, oh, that's a distant thing.
But if you have a process for anything. That's an algorithm.
(15:51):
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
down to is, then how are you teaching that to
an artificial intelligence? How are you teaching that to this robot? Baby?
(16:14):
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.
You have to wonder who's doing the teaching and how
are they framing the way that this child should observe
(16:40):
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
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
(17:04):
you imagine Elon Musk being at trying to teach AI
how to help me do my hair. I could imagine
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
(17:27):
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
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
(17:48):
tool to help us do more. To help people do more,
you need people to train this AI and trust despite
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,
(18:10):
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, 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
(18:32):
veil and wonder why can't I be part of the
team that's building this AI. Why can't 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?
Speaker 3 (18:49):
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 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
(19:10):
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 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
(19:32):
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 because they didn't believe in racism.
Speaker 5 (19:47):
Right.
Speaker 3 (19:48):
I had someone tell me yesterday when I was eating
She was like, my best friend said that there was
no racism.
Speaker 4 (19:53):
Do I want that.
Speaker 3 (19:55):
Person tangling with my sassy seven year old? And how
does that look?
Speaker 4 (20:01):
I think you know, you know, you know the answer,
all right. I want to before 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
(20:23):
you're like, what what is this? And you're just trying
to understand what it actually is? Oh okay, okay, be proud,
put your hand up on Okay.
Speaker 3 (20:33):
That's right. Yeah.
Speaker 4 (20:35):
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
or don't like or can't control. Okay, so this is
(20:58):
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.
Speaker 7 (22:55):
And.
Speaker 4 (22:57):
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 me
that over the last couple of years. And it's not
just because of COVID. If I get pregnant, I have
(23:18):
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. But women
are dying when when they get pregnant, because not enough
(23:43):
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 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.
(24:04):
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 be thinking
about all the things that we need. We know this,
or we would not have systemic issues right now. We
(24:25):
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. So let's
stop talking about being afraid. Let's stop talking about it
as a black box. There are several low code and
(24:47):
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 literally not
kidden my I'm sorry. My dad literally two days ago
(25:10):
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? 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
(25:31):
know what's going on. So but uh, it's it's real,
and it's a it's it's a real thing.
Speaker 8 (25:37):
We have.
Speaker 4 (25:39):
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 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
(26:00):
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 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.
(26:24):
It goes beyond, it goes beyond anything we can imagine.
The thing that I'm most 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.
Speaker 1 (26:43):
All Right, with that, we are we are privileged to
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
(27:04):
was fantastic. Please say your name.
Speaker 4 (27:10):
Hi, my name is Sydney. Thank you. Oh, I got it?
Speaker 8 (27:16):
No, okay, all right, 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
(27:37):
how to like 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
(27:59):
creata even that no code platform.
Speaker 4 (28:02):
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, So now
all of a sudden they're not just kind of operating
(28:24):
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, you'll
see it. I will post five to six that I've
(28:46):
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 an
(29:06):
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 you
(29:27):
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 much as you
(29:48):
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
(30:09):
I will post that on what's today Thursday. Got to
get back to New York. I'll post it on Monday.
I promise I will.
Speaker 6 (30:16):
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.
Speaker 2 (30:32):
Seattle, Ray Charles dropped his 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
(30:52):
with Africa Town is building programs 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
(31:15):
coming up with the language models 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.
Speaker 4 (31:32):
Think I have. I can tell you where my company,
like when we really started looking at disadvantaged communities that
are 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
(31:52):
actual AI that could support that, But I'd love to
know what you think. First. I can share our perspective,
but as someone who works with the government, you might
know a few more.
Speaker 3 (32:05):
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 we do
(32:25):
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 national laboratories,
(32:48):
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 that use case
(33:08):
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.
Speaker 2 (33:15):
Right.
Speaker 3 (33:15):
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
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.
Speaker 4 (33:32):
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 just have to ask for it.
They're not going to make it clean and easy or
create an interface that makes it as simple as a
you know, downloading photos from you know, from your whatever
(33:54):
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 are surprisingly good
at getting real hard data in the aggregate. For example,
(34:17):
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 I think
some of you may have seen that article and so,
and that's happened before we actually struggled and looking at
(34:39):
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 understand how
many of those disadvantaged communities were majority minority, right, because
(35:00):
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 majority and black
Latino majority. And the data set was so massive and
(35:24):
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 homework and you
can dig there and get their data sets, and when
(35:44):
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.
Speaker 5 (35:53):
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 to tell you, like being a
black woman is seeing you dominate this space is just empowering.
Speaker 4 (36:07):
Wow. Thank you.
Speaker 1 (36:10):
Would be the last one, regular, last one.
Speaker 4 (36:14):
Hi.
Speaker 7 (36:14):
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 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
(36:39):
you know, like coming up with guidelines for use, because
I think it's a great tool for us to continue
to use and we shouldn't be using it with fear.
Speaker 4 (36:51):
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 tricky one. That is a tricky one because
(37:16):
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 there's a couple of ways to see this
if I'm going to be very just kind of direct
(37:37):
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 will say, oh, clearly there's some sort of plagiarism,
right because like whether you did this through something that
(37:59):
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. At this point I don't really know what
else to tell me. You No, I mean, I'm not
(38:19):
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, you will still rise above. I
(38:39):
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, we want to make
(39:01):
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 he could
(39:24):
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.
Speaker 1 (40:53):
Black dec Green Money is a production 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.
(41:14):
Enjoy your Black Tech Green Money. Share this with somebody,
Go get your money. Peace and love,
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.
Speaker 2 (03:22):
Now.
Speaker 1 (03:22):
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 out how to
say things in the way you want to say it,
(03:44):
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, to be
bout it. Nobody asked permission, Certainly the board that open
(04:07):
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 to succeed.
If you're waiting for your work situation to get right,
(04:29):
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
tech executive in Seattle between Jessica Matthews Jessica Old Matthews,
(04:52):
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.
Because this conversation they're having is about demystifying AI for
(05:13):
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,
and with a greater punch.
Speaker 3 (05:33):
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?
Speaker 4 (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?
Speaker 5 (06:14):
Right?
Speaker 4 (06:15):
That's always what I like to check. I tend to
check after my first one fuck falls out. 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?
Speaker 3 (06:36):
Right?
Speaker 4 (06:38):
And then but then you're here, Hello, hello, 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
(07:00):
ball that could harness the energy 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
(07:21):
have problem saying it because, like, 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
(07:42):
sustainable infrastructure with more 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,
(08:04):
even in Seattle, where I'm being so messed up.
Speaker 6 (08:07):
Yeah, yeah.
Speaker 4 (08:08):
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.
Speaker 3 (10:22):
Teams to the championship.
Speaker 4 (10:24):
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 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
(10:46):
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 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.
Speaker 5 (11:06):
Got it?
Speaker 3 (11:07):
Okay?
Speaker 4 (11:07):
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, 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
(11:28):
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 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,
(11:51):
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 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
(12:12):
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 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
(12:33):
data didn't say that. And so that's how we came
up with our current products.
Speaker 3 (12:38):
And we thank you, right, we thank you for that transition.
Speaker 4 (12:45):
So we want to.
Speaker 3 (12:46):
Talk about 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.
Speaker 4 (12:58):
We should just talk about that.
Speaker 3 (13:00):
I do, right, this homegrown. So back when I was born,
actually legislation was passed 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.
(13:21):
But what I will say is this, they demystified those cigarettes.
Speaker 2 (13:26):
It was not.
Speaker 3 (13:28):
Right, and that's great, that's that's that's my 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
(13:52):
and it just like demystified the coolness and the sexiness
of cigarettes. So my question is, how do we demystify AI?
Speaker 4 (14:01):
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.
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
(14:24):
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,
like what it comes down to, let's break this down, right,
AI artificial intelligence, right, and we discuss this. It's it's
(14:47):
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
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
(15:09):
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?
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
(15:30):
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
to scare us and say, oh, that's a distant thing.
But if you have a process for anything. That's an algorithm.
(15:51):
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
down to is, then how are you teaching that to
an artificial intelligence? How are you teaching that to this robot? Baby?
(16:14):
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.
You have to wonder who's doing the teaching and how
are they framing the way that this child should observe
(16:40):
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
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
(17:04):
you imagine Elon Musk being at trying to teach AI
how to help me do my hair. I could imagine
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
(17:27):
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
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
(17:48):
tool to help us do more. To help people do more,
you need people to train this AI and trust despite
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,
(18:10):
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, 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
(18:32):
veil and wonder why can't I be part of the
team that's building this AI. Why can't 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?
Speaker 3 (18:49):
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 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
(19:10):
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 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
(19:32):
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 because they didn't believe in racism.
Speaker 5 (19:47):
Right.
Speaker 3 (19:48):
I had someone tell me yesterday when I was eating
She was like, my best friend said that there was
no racism.
Speaker 4 (19:53):
Do I want that.
Speaker 3 (19:55):
Person tangling with my sassy seven year old? And how
does that look?
Speaker 4 (20:01):
I think you know, you know, you know the answer,
all right. I want to before 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
(20:23):
you're like, what what is this? And you're just trying
to understand what it actually is? Oh okay, okay, be proud,
put your hand up on Okay.
Speaker 3 (20:33):
That's right. Yeah.
Speaker 4 (20:35):
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
or don't like or can't control. Okay, so this is
(20:58):
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.
Speaker 7 (22:55):
And.
Speaker 4 (22:57):
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 me
that over the last couple of years. And it's not
just because of COVID. If I get pregnant, I have
(23:18):
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. But women
are dying when when they get pregnant, because not enough
(23:43):
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 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.
(24:04):
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 be thinking
about all the things that we need. We know this,
or we would not have systemic issues right now. We
(24:25):
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. So let's
stop talking about being afraid. Let's stop talking about it
as a black box. There are several low code and
(24:47):
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 literally not
kidden my I'm sorry. My dad literally two days ago
(25:10):
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? 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
(25:31):
know what's going on. So but uh, it's it's real,
and it's a it's it's a real thing.
Speaker 8 (25:37):
We have.
Speaker 4 (25:39):
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 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
(26:00):
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 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.
(26:24):
It goes beyond, it goes beyond anything we can imagine.
The thing that I'm most 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.
Speaker 1 (26:43):
All Right, with that, we are we are privileged to
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
(27:04):
was fantastic. Please say your name.
Speaker 4 (27:10):
Hi, my name is Sydney. Thank you. Oh, I got it?
Speaker 8 (27:16):
No, okay, all right, 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
(27:37):
how to like 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
(27:59):
creata even that no code platform.
Speaker 4 (28:02):
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, So now
all of a sudden they're not just kind of operating
(28:24):
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, you'll
see it. I will post five to six that I've
(28:46):
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 an
(29:06):
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 you
(29:27):
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 much as you
(29:48):
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
(30:09):
I will post that on what's today Thursday. Got to
get back to New York. I'll post it on Monday.
I promise I will.
Speaker 6 (30:16):
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.
Speaker 2 (30:32):
Seattle, Ray Charles dropped his 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
(30:52):
with Africa Town is building programs 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
(31:15):
coming up with the language models 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.
Speaker 4 (31:32):
Think I have. I can tell you where my company,
like when we really started looking at disadvantaged communities that
are 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
(31:52):
actual AI that could support that, But I'd love to
know what you think. First. I can share our perspective,
but as someone who works with the government, you might
know a few more.
Speaker 3 (32:05):
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 we do
(32:25):
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 national laboratories,
(32:48):
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 that use case
(33:08):
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.
Speaker 2 (33:15):
Right.
Speaker 3 (33:15):
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
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.
Speaker 4 (33:32):
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 just have to ask for it.
They're not going to make it clean and easy or
create an interface that makes it as simple as a
you know, downloading photos from you know, from your whatever
(33:54):
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 are surprisingly good
at getting real hard data in the aggregate. For example,
(34:17):
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 I think
some of you may have seen that article and so,
and that's happened before we actually struggled and looking at
(34:39):
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 understand how
many of those disadvantaged communities were majority minority, right, because
(35:00):
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 majority and black
Latino majority. And the data set was so massive and
(35:24):
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 homework and you
can dig there and get their data sets, and when
(35:44):
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.
Speaker 5 (35:53):
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 to tell you, like being a
black woman is seeing you dominate this space is just empowering.
Speaker 4 (36:07):
Wow. Thank you.
Speaker 1 (36:10):
Would be the last one, regular, last one.
Speaker 4 (36:14):
Hi.
Speaker 7 (36:14):
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 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
(36:39):
you know, like coming up with guidelines for use, because
I think it's a great tool for us to continue
to use and we shouldn't be using it with fear.
Speaker 4 (36:51):
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 tricky one. That is a tricky one because
(37:16):
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 there's a couple of ways to see this
if I'm going to be very just kind of direct
(37:37):
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 will say, oh, clearly there's some sort of plagiarism,
right because like whether you did this through something that
(37:59):
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. At this point I don't really know what
else to tell me. You No, I mean, I'm not
(38:19):
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, you will still rise above. I
(38:39):
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, we want to make
(39:01):
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 he could
(39:24):
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.
Speaker 1 (40:53):
Black dec Green Money is a production 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.
(41:14):
Enjoy your Black Tech Green Money. Share this with somebody,
Go get your money. Peace and love,
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