0:00

It is an interesting time these days

0:02

around AI. Over the weekend, the

0:05

board of directors for the nonprofit Open

0:08

Ai, which developed and operates

0:10

chat gpt, which is super

0:13

duper popular I use it a lot, they

0:15

abruptly fired the co founder

0:17

and now previous CEO,

0:20

Sam Aldman. So they did this

0:22

in like like thirty minutes before the markets

0:24

closed on Friday, which that

0:27

kind of thing only happens in the rarest

0:29

and most inflammatory circumstances

0:32

because it's such a market shifting

0:35

move to do that right before the markets

0:37

closed, especially on a Friday. So

0:39

this is big news in

0:41

AI as chat gpt has

0:44

the fastest growing user base

0:47

for consumer app in history. They

0:49

did one hundred million users in

0:51

two months. Now, to give you some perspective,

0:54

it took Facebook like four

0:57

years to hit one hundred million

0:59

users. It took Twitter

1:01

five years to get one hundred

1:03

million users. I think it took ig Instagram

1:06

of like two years. Open

1:08

ais chat gpt did

1:11

it in two months, and

1:13

almost as fast as they blew

1:16

up their incredible valuation,

1:19

they blew up their incredible valuation.

1:21

So it went from look, they

1:23

went from looking at like a ninety billion

1:26

dollar valuation to uncertainty

1:29

in the matter of hours and

1:32

days since the board gave Sam

1:34

the boot. In April this year, the

1:36

company was valued at twenty nine

1:38

billion dollars, and just a week

1:40

ago they were looking at a ninety

1:43

billion dollar valuation raising money at

1:45

a ninety billion dollar valuation.

1:48

From April's twenty nine billion

1:50

dollar valuation, they were on track to do a

1:52

billion dollars in revenue,

1:54

which shows you how fast chat

1:57

gpt was growing and with the future

2:00

looked like for them. If you recall

2:02

chat GPT, it

2:04

was just legit like launched ten months

2:06

ago, eleven months ago, So this is

2:09

incredible growth such

2:11

that AI is just about

2:14

all anybody is talking about these days,

2:16

whether they're building it, they're trying

2:18

to fund it, they are starting

2:20

an AI focused startup, or

2:22

they're afraid of it altogether. AI

2:25

is on many of our minds. At

2:28

the time this recording, a lot has happened.

2:30

Sam was out at

2:33

Open Ai as CEO. This

2:35

was just Friday. Then the

2:37

employees and investors revolted because

2:41

the way people believed in Sam

2:43

obviously was a surprise to the board, so

2:45

there were attempts to bring him back over

2:48

the weekend. Then that didn't work

2:50

out. Then today Microsoft

2:52

says they're bringing him over there to run

2:55

some AI projects, and undoubtedly

2:57

many necessary, highly

2:59

talented open ai employees who

3:02

were on his team will follow him in the

3:04

Microsoft. So who knows

3:07

what will happen an hour after I published

3:09

this episode, because this is all happening so

3:11

fast. Everything I just mentioned happened

3:13

in like the span of forty eight to seventy two hours.

3:17

This thing. Usually

3:19

things like this play out over weeks.

3:22

Now.

3:22

The reason I kick off this episode

3:25

with this news is because it's that significant.

3:27

Because we all, if I mean, if

3:30

you've embraced at some level AI

3:33

and you have tried chat GPT

3:35

at least once, there's no way you're

3:37

not hooked. It's so good at

3:41

figuring out how to say things in

3:43

the way you want to say it, by just you giving

3:45

it some simple commands. And

3:47

so I wanted to start

3:50

this episode off just to point

3:52

to make a point that things

3:54

are moving so quickly in

3:56

our society. This is a rallying

3:58

call, a rallying to you, my black

4:00

tech, green money family, to be bout

4:03

it. Nobody asked permission,

4:05

Certainly the board that open Ai

4:07

didn't do a poll of their employees

4:09

and investors to see if kicking out the

4:12

founder and CEO will be acceptable.

4:15

They just bust a move. So

4:18

we're living in the days where people who

4:20

beg forgiveness versus ask

4:22

permission are going to be in a

4:24

position to succeed. If

4:26

you're waiting for your work situation to

4:28

get right, or your kids to graduate,

4:31

or the weather to warm up in the sun to be out

4:34

again before you start

4:36

making your moves, you'll be at a disadvantage.

4:40

Things that used to take months are happening

4:42

now in hours. So I want

4:44

to bring you a conversation from afro

4:47

tech executive in Seattle between

4:50

Jessica Matthews Jessica Old Matthews,

4:52

who's CEO and founder of

4:54

Uncharted, and Johnny Bradley,

4:56

who was the responsible

4:59

AI official for the Department

5:01

of Energy and senior program manager for

5:03

the Artificial Intelligence and Technology

5:06

Office. Because this conversation

5:09

they're having is about demystifying

5:12

AI for our community. So

5:14

I hope you get something from this. Follow

5:16

along, find your way

5:19

to not necessarily you have to go build an

5:21

AI startup, but find your way to use

5:23

it, leverage it to

5:26

do what you want to do, and do what you're doing

5:28

even better, more efficiently, faster,

5:31

cheaper, and with a greater punch.

5:33

Okay, hello everyone, how

5:36

are you? Yeah?

5:39

Yeah, yes, yeah, okay,

5:43

So let's get into it, right, yeah,

5:45

all right. So you know I love soccer and

5:47

you made socket. So I just have to start this conversation

5:50

off by asking one question, why

5:54

did you go from hardware

5:56

to data solutions?

5:58

So we have to go real deep into like the

6:00

technical stuff, but for hi, everybody,

6:03

Hello, Hello, Hello, thank

6:06

you. Will Will's dope

6:09

just consistent. Ask dude, man, how

6:12

much cursing is allowed?

6:14

Right?

6:15

That's always what I like to check. I tend to check

6:17

after my first one fuck falls out. So

6:23

listen, y'all. I flew here from Harlem and I

6:25

was like here, like, where does Morgan have me flying

6:27

to you? Now? That's what I thought was on that plane. I

6:29

was like, oh damn Seattle, Seattle, okay.

6:33

And I was walking down the street and I was like, where

6:35

all the black people?

6:36

Right?

6:38

And then but then you're here, Hello, hello,

6:42

gathered them off? No,

6:45

no, So say, you know, it's a good

6:47

question. So, uh, what Jonie's

6:49

talk about here is that I started

6:52

my career making energy

6:54

generating play products. So when

6:57

I was nineteen years old, I invented

6:59

an energy generator eating soccer ball that

7:01

could harness the energy from play, the kinetic

7:03

energy, and store that power inside

7:06

of the ball. Yeah,

7:08

it was pretty cool. You could play with it, you could

7:10

roll, you know, as it rolled, it was generating energy,

7:13

and about an hour of play would give you three

7:16

hours of light. Fast

7:18

forward. Now I'm thirty five.

7:21

I don't have problem saying it because, like, you know,

7:23

me, black guys, good,

7:28

right, what I'm saying? So,

7:32

and and I run

7:35

a data infrastructure company that

7:37

uses AI to help disadvantaged

7:40

communities develop sustainable

7:42

infrastructure with more equity

7:45

and efficiency than ever before. Right,

7:49

So how did I get from there to

7:52

here? Well, one

7:54

thing is that my true north was always incredibly

7:57

clear to me. I'm a dual citizen

7:59

of Nigeria and States. Always,

8:04

even in Seattle, where I'm

8:06

being so messed up.

8:07

Yeah, yeah.

8:08

The only thing I know about Seattle's Nirvana.

8:11

Right, So, so I like, and I love Nirvana.

8:13

So I was like, sheah, I wear my flannel. My

8:16

mom was like oh s ohse glenno.

8:23

So so for me, you

8:26

know, it

8:28

was going back and forth between Nigeria and the United

8:31

States, whether it's for weddings

8:33

or funerals, and just recognizing that there

8:35

were some things that were so dope, like everybody

8:38

had a mobile phone. It became

8:40

ubiquitous very quickly. Oftentimes

8:43

my cousins had a better cell phone than I did.

8:46

But also wondering like why

8:48

is it that it doesn't matter if we're in the village

8:50

or if we're in like the bustling city of Legos that

8:53

were losing power every single day.

8:56

And I knew it wasn't because

8:59

like there were with the technologies that needed to

9:01

exist to make that happen. I

9:04

knew it was an infrastructural issue immediately.

9:06

In fact, oftentimes in places like

9:09

Nigeria, people are paying more per kilowatt

9:12

hour than we pay here in the United States. I don't know if

9:14

that's true anymore, because like I was, the

9:16

bills getting high, I was your bills getting

9:18

high. But at least before it

9:20

was very much like that. And

9:24

you know, I believe that the first step

9:27

in innovation is the articulation of the problem.

9:29

And at the age of seventeen eighteen,

9:32

I articulated the problem to be that

9:35

people like my cousins who were trained

9:37

engineers did not

9:40

believe that there could be a world where

9:42

things could be different. They did not believe

9:44

that this problem could be solved

9:47

by some innovation or some public private

9:49

partnership. They

9:51

simply thought that the best way to solve the problem

9:53

was to pretend like it's not happening and to get

9:56

used to it. And so I

9:58

wanted to create something that

10:00

would make them change that view,

10:03

that would make them see in

10:05

the world not just as it is, but as it could be.

10:08

And soccer is the most popular

10:10

sport around the world, my favorite

10:12

sport, the most popular sport. And

10:16

I'm assuming you're probably really good. My cousins

10:18

were not that good.

10:22

Teams to the championship.

10:24

I was good. Oh,

10:27

you were so good that you were bad.

10:29

Now my cousins were just My cousins were so average

10:32

that they were like, why are we playing this game? This

10:35

is awkward. So

10:37

but in that in that way though, seeing

10:39

their passion and seeing the way that they would play on

10:41

the field, I'm like, you know,

10:43

the way that you approach this game, knowing

10:46

damn well that you are at best average is

10:48

the way you need to approach life. It's

10:50

the way you need to approach all of the problems

10:52

in our community and infrastructurally, and

10:55

so my thought was, like, let

10:57

me create something that would inspere

11:00

hire them to do it. And what ended

11:02

up happening was that long

11:04

story short inspired me to do it.

11:06

Got it?

11:07

Okay?

11:07

Yeah, all right. It took a while to figure

11:10

out exactly what technologies would

11:12

get there. First it was the play

11:14

products, and then different inventions, and then

11:17

a couple of years ago, right before

11:19

the pandemic, I started to

11:22

realize that the common thread in every community

11:24

wasn't needing, like you know,

11:26

an energy generating speed bump or some cool

11:29

thing here or there that was fun but couldn't

11:31

scale. The common problem was

11:33

data. It didn't matter if it was in Nigeria

11:36

or in the US. The data

11:38

problems were kind

11:41

of compiling on each

11:43

other, and that when governments

11:45

were operating in the right way, they were

11:47

spending at least half of their time tracking

11:49

down, collecting, correcting, and sharing

11:52

SOILID information to make infrastructure

11:54

decisions. They often did not have

11:56

the right information to know where to prioritize

11:59

who should be getting the solar or

12:02

where should we be replacing the lead pipes,

12:04

And in the other half of the time, they were just quote unquote

12:06

shooting from the hip, and

12:08

so I was like, well, if we could solve the data

12:11

problem and improve the way that they're organizing

12:13

their data to make decisions to build

12:16

sustainable infrastructure, we can help

12:18

them build it faster, we can help them

12:20

build it for less. And then as

12:22

a result, we can either help them make

12:24

it more equitable or at least make it

12:26

very obvious when they're being fucked up,

12:29

like make it more obvious, like, listen, that's what the data

12:31

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

12:36

how we came up with our current products.

12:38

And we thank you, right,

12:42

we thank you for that transition.

12:45

So we want to.

12:46

Talk about demystifying

12:48

AI. So when we look

12:50

at AI, I always think about that time

12:53

and I don't I don't mind dating myself either. Fifty

12:55

three Listen, I

12:57

don't mind that either.

12:58

We should just talk about that.

13:00

I do, right,

13:03

this homegrown. So back

13:06

when I was born, actually

13:08

legislation was passed on cigarettes. But as

13:11

I grew up, you know, cigarettes was just

13:13

like whoa, it was a cool thing to do. You had this slim

13:15

lady smoking a cigarette, right, and it

13:17

was just like ayeing to me, right, And

13:19

for twenty years I smoked based off of that.

13:21

But what I will say is

13:23

this, they demystified those cigarettes.

13:26

It was not.

13:28

Right, and that's great, that's

13:31

that's that's my exact point

13:33

is that during my time, it was cool,

13:36

right, and it was the thing to do.

13:38

And as time went by and

13:41

the Surgeon General said, no,

13:43

put these warnings on each pack

13:45

of these cigarettes and let people know

13:48

exactly what it does to you,

13:50

and it would curb and it just like

13:53

demystified the coolness and the sexiness

13:56

of cigarettes. So my question is,

13:58

how do we demystify AI?

14:01

Girl? Oh yeah,

14:03

listen, because

14:06

we had a hole behind the scenes conversation y'all

14:08

that we're trying to like, you know, keep civil

14:10

for the cameras and all that. So

14:15

I think that's part of why you guys are here, right.

14:17

Everyone's talking about AI. Everyone's

14:19

acting like it's the new hot thing, and

14:22

they're acting like it's this black bots and it's this scary

14:25

monster that cannot be controlled and things

14:27

are just happening, but it's it's

14:29

really not, you know, like you

14:31

should not be afraid of AI. You

14:33

should be afraid of the people who are building it. That's

14:37

it. So, like what

14:39

it comes down to, let's break this down, right,

14:42

AI artificial intelligence,

14:44

right, and we discuss this. It's

14:47

it's kind of like a child. It's

14:50

a child. It's like a robot baby, right,

14:52

chat GPT is at best a

14:54

sassy seven year old. And we all know, right,

14:56

we all know that seven year old that was born in like you

14:59

know, I don't know whatever

15:01

seven years ago was like you know, like

15:04

but like basically recently grew

15:06

up with all the social media platforms

15:08

and be out here talking to you like they've grown, and

15:11

you'd be like, well, damn girl, you've grown. No,

15:13

they just online? No,

15:16

Like, do not have this seven year old do your taxes?

15:19

It might go well sometimes until it

15:21

does not, right, But

15:24

what it ultimately then comes down to you have

15:26

to ask yourself, is okay,

15:28

so if this is just this

15:31

this code that has a great capacity

15:33

to learn, right, and the way

15:35

it's taught to learn, it's algorithms.

15:38

Algorithms are just processes.

15:41

Like people like to use fancy words

15:43

to scare us and say, oh, that's a distant

15:45

thing. But if you have a process

15:48

for anything. That's an algorithm.

15:51

That's an algorithm. You can call it that. Especially

15:54

half y'all when y'all women look great in here, I

15:56

know what you had to do, whatever you had to do to

15:58

get here on time. That's a very efficient

16:00

algorithm. That's a very efficient

16:03

algorithm. And all it actually comes down to is,

16:05

then how are you teaching that to

16:09

an artificial intelligence?

16:11

How are you teaching that to this robot?

16:13

Baby? Let's just put it in that way so that it

16:15

can start to do that for you. So

16:19

to that end, when we talk about demystifying

16:21

it, and we talked about this a lot,

16:25

you have to wonder who's doing the teaching.

16:29

You have to wonder who's doing the

16:31

teaching and how are they

16:33

framing the

16:35

way that this child

16:38

should observe and

16:40

respond to the world. So

16:43

if someone is not aware of their biases, if

16:46

someone is not aware of the fact, like, can you

16:48

imagine who's

16:50

the guy who does open Aye, I'm not trying

16:52

to start no shit though, this is going out to the world.

16:54

But let's just let's not use him specifically, because

16:56

you know, but let's can you imagine

16:59

can you imagine an Elon Musk? Can

17:04

you imagine Elon Musk being

17:06

at trying to teach AI how

17:09

to help me do my hair. I

17:14

could imagine he thinks he

17:16

can do it. He thinks, of course,

17:18

no problem. And that's a that's a

17:20

fun example, right.

17:22

Where So when we talk about

17:24

demystifying AI, it's it's

17:27

really saying take the blame away

17:29

from the AI and start focusing

17:31

on the people who are training these models

17:34

right, and start focusing on whether or not whether

17:36

they are doing it so intentionally or on intentionally

17:39

if they're actually considering the vast

17:41

globe of people and

17:43

all of their problems. Because all AI

17:46

is really is a tool. It's

17:48

a tool to help us

17:50

do more. To help people do more, you

17:53

need people to train this AI

17:55

and trust despite all the

17:57

things that you're hearing about AI taking job,

18:01

the thousands of jobs will be created because

18:03

of what this AI is doing. The people

18:06

who used to drive the carriages, when they saw

18:08

the cars, they were like, I don't

18:10

know, it's getting pretty scary out here. That

18:13

car don't even got no horses. They're

18:15

like, this is just wild, this is crazy.

18:17

I don't trust this. Okay, Sure they

18:20

found new jobs. So there.

18:22

I feel like when people start to say things like, oh,

18:24

well be afraid of it. Oh it's

18:26

gonna take your job. What they're really trying

18:29

to do is make you afraid of

18:31

going behind the veil and

18:34

wonder why can't I

18:36

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

18:39

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

18:42

this baby. You know, they say

18:44

it takes a village, not just some dude who doesn't

18:46

blink in the corner. So

18:48

why are we allowing you?

18:49

I love the sasey seven year old.

18:52

So now let's talk about that sassy seven year old.

18:56

All of you know the landscape today, right,

18:58

you have states that are removed diversity

19:01

and inclusion. You have states that are removing

19:03

African studies. You can't say the word gay. And

19:06

you know I could go on and on, right because I watched the news

19:08

all day every day so and I don't

19:10

watch Fox, sorry, but I will

19:12

say this is that what

19:15

do those practitioners

19:17

look like in the future? Now, this sas

19:20

seven year old has never had anyone tangling

19:22

with it that did not have diversity

19:24

and inclusion. Because you have that today, right, you

19:27

have African American studies today, right, you

19:29

have gender equality, you have these things today,

19:32

but as the days go on, these things

19:34

are being removed slowly. So now

19:36

you have people that are graduating college

19:39

that want to be an AI practitioner,

19:42

but they did not learn what discrimination

19:45

was because they didn't believe in racism.

19:47

Right.

19:48

I had someone tell me yesterday when I was eating

19:50

She was like, my best friend said that there was no

19:52

racism.

19:53

Do I want that.

19:55

Person tangling

19:57

with my sassy seven year old?

20:00

And how does that look?

20:01

I think you know, you know, you know the answer, all right.

20:05

I want to before I respond, I want

20:07

to do a quick show of hands. How many of you are here

20:10

because you're considering how

20:12

to be more involved

20:14

in the AI industry? Okay,

20:17

okay? How many of you are

20:20

here because everyone's been talking about AI

20:23

and you're like, what what is

20:25

this? And you're just trying to understand what

20:27

it actually is? Oh

20:30

okay, okay, be proud, put your hand

20:32

up on Okay.

20:33

That's right. Yeah.

20:35

How many of you in some way

20:37

already work with AI

20:40

or related to AI? Okay,

20:43

okay. And so your

20:45

concern then is really that you

20:47

feel like there's a lot of things happening around

20:49

you that you don't

20:52

understand or don't like or can't control.

20:56

Okay, so this is

20:58

what what you're getting at. Indeed,

21:02

we have to be concerned in

21:04

general, and this goes beyond AI,

21:07

that we're going to start to have generations

21:10

of people who are very

21:12

much driving

21:14

our economy, driving industries,

21:17

developing technologies that

21:19

will have, from our perspective

21:21

and our opinion, a skewed view

21:24

of the world and how things work. And

21:27

they're not only going to teach their NI

21:29

their natural children, that they're

21:32

going to teach the AI this. But

21:35

because of the rapid impact

21:37

and a rapid scale, it's

21:40

it's not as bad as like, oh, those three

21:42

kids grew up in that racist sexist house, so now

21:44

they're racist and sexist. It's that

21:47

AI was developed by this

21:50

person who has racist and sexist biases.

21:53

And because of how impactful

21:55

AI can be, we now have an army

21:57

of racist, sexist or

22:00

the very least incredibly aloof right

22:02

like things happen

22:05

right. So my perspective

22:08

is ultimately radical

22:10

self reliance. I

22:13

can't help what's going on specifically

22:15

Macro in Florida, in Texas,

22:18

but I do know people who live there who are

22:20

saying, regardless of what they're teaching, my kids

22:22

in school. Here's what I'm going to teach you at home. And

22:25

so that's why I've been kind of recently

22:28

saying, please, please, please. The last

22:30

thing you should be is afraid of AI.

22:33

This is now, more than ever, the time where

22:35

you need to be incredibly excited about this

22:37

tool. But you need to see this as a battle

22:40

and you need to do everything you can to get your hands on

22:42

this weapon as well. I'm gonna keep

22:44

it super super real about this. So,

22:47

as I said earlier, I'm a thirty five year old woman, I'm

22:49

married, I love my husband. I'm getting

22:51

ready and preparing myself to freeze my eggs.

22:55

And.

22:57

I'm gonna be keep it real. Part of me is doing that because

23:00

I learned that the maternal

23:02

mortality rate has gotten worse. It's

23:06

twenty twenty three. It is

23:08

twenty twenty three in a developed country,

23:12

and you're telling me that over the last couple of years.

23:14

And it's not just because of COVID. If

23:17

I get pregnant, I have twelve patents

23:19

and patents pending. I'm building all these different

23:21

things, and the thing that scares me the most

23:24

is having a baby and dying. But

23:28

the people are developing AI to

23:30

do the wildest,

23:33

most random shit possible. But

23:36

women are dying when

23:39

when they get pregnant, because

23:42

not enough women and definitely not

23:44

enough Black women are sitting there saying, how

23:46

can I use AI as a tool to keep

23:48

more of us alive? And

23:51

the only way that's gonna change is if more

23:53

of us say we're not afraid of AI. Regardless

23:56

you know what, y'all are gonna do what you want to do with it, But here's

23:58

what I'm gonna do with it. Here's how I'm

24:01

going to teach this child on home, regardless

24:03

of what you're doing. And so that to me is

24:05

the only answer. It's it is not too

24:07

late for us all to

24:09

recognize that this

24:12

will not be done for us. This

24:14

will not be something where we can hope that

24:16

the right few people at the top are

24:19

going to be thinking about all the things that we

24:21

need. We know this, or we would not have systemic

24:23

issues right now. We know

24:25

this. So but what I

24:28

now view, though, is that I believe the technology

24:30

is one of the best equalizers, one of the

24:32

best democratizing tools,

24:36

and that to me is exciting.

24:39

That to me is an opportunity. So let's stop

24:41

talking about being afraid. Let's stop

24:43

talking about it as a black box. There

24:45

are several low code and no

24:48

code tools that you can use to

24:50

create something in AI if

24:52

you want to. How do we get

24:54

people to see this as a playground

24:57

versus I don't know more?

24:59

T ruary right right.

25:05

I'm also literally not kidden my

25:08

I'm sorry. My dad literally two days ago

25:10

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

25:12

I don't know when they're getting out. And

25:15

I was like, actually, Dad, Tiana,

25:18

my older sister, Tiana's, Tiana's

25:20

uh grand kids are in the freezer. Mine are about

25:22

to be in the freezer. Just want to confirm.

25:24

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

25:27

I'm like, this is what happens when you talk

25:29

to your mom. Your mom talks to your dad, your dad talks to

25:31

you. You don't know what's going on. So but

25:33

uh, it's it's real, and it's a

25:35

it's it's a real thing.

25:37

We have.

25:39

We have a very small group of people right

25:41

now who are focusing AI and

25:43

their problems, and I do

25:45

not blame them. Entrepreneurship

25:47

is problem solving without regard for resource, like

25:50

science is the study of life.

25:53

Like these things should not be scary big

25:55

words. Uh. But when

25:57

we silo ourselves and I say

26:00

we, it's just like if you are anyone, if you're

26:02

if you're not affluent, if you are

26:04

not a man, if you're not there's so many things

26:06

that actually most of us are

26:09

not that kind of that paradigm

26:11

of the person who's doing this, most of

26:13

us. But when you kind

26:15

of like push something away,

26:19

you are disenfranchising

26:23

yourself in so many ways. It goes

26:25

beyond, it goes beyond anything

26:27

we can imagine. The thing that I'm most scared

26:29

of is the

26:32

number of people who keep

26:34

saying I'm afraid of what

26:36

could literally be the best thing they've

26:38

ever put their hands on in their entire

26:41

lives.

26:43

All Right, with that, we

26:52

are we are privileged to have ten more minutes

26:54

with them, to have some Q and A. So I'm sure

26:57

there's some questions in the art. Ooh, we got one already.

26:59

I'm gonna come to you. You y'all

27:02

give another round of applause for that man that was

27:04

fantastic. Please

27:08

say your name.

27:10

Hi, my name is Sydney. Thank

27:13

you. Oh,

27:15

I got it?

27:16

No, okay, all right, all right, AnyWho

27:20

I saw you know I heard us talking about

27:22

like fear. I personally don't have fear

27:25

if AI. Maybe I should, I don't know, but

27:27

that's not what you're saying. So I shouldn't be afraid. But however,

27:29

how do we harness that you were sharing some ideas

27:32

of there's some low or no code ways

27:34

of leveraging AI. Can you tell us

27:36

more about like how to like leverage it? And

27:39

yes, no, of course that's a really good question.

27:41

I actually think I'm gonna go ahead and

27:43

maybe I can talk to the afrotech people.

27:46

I'm I'm gonna just list like on

27:48

my like LinkedIn just like seven seven

27:51

platforms. Some require you to know

27:53

a little bit some of you, some of them don't.

27:55

Now there is the underlying issue

27:58

of kind of who's creata even that no

28:01

code platform.

28:02

But at the end of the day, like

28:04

you know, nothing's ever going to be perfect, and we just

28:07

want people to get closer to something. And

28:09

if your engagement, even

28:12

with these no code platforms can

28:15

better educate the sassy

28:19

seven year olds that are running it, So now all

28:21

of a sudden they're not just kind

28:23

of operating with whatever the hell they're being told

28:26

by the very specific groups of people who are doing

28:28

this. It's a good thing, and so there

28:30

are several I don't want this necessarily

28:32

to be an advertisement for any one or the other.

28:34

But on

28:36

Monday, I'm gonna post If you go to

28:39

my LinkedIn, I'm just Jessica.

28:41

Oh Matthews, you'll see it. I will

28:43

post five to six that

28:45

I've heard some good things about. Because again

28:47

I want to be very clear, I

28:49

study psychology and economics. I

28:52

like to tell people I have a PhD in

28:54

Google, which pisses off people with

28:56

real PhDs, I find, But

28:59

the main point is that you know, I also have

29:01

a granted patent for wireless Mesh Energy

29:04

Networks, which is an algorithm

29:07

that essentially considers

29:10

the communication protocols for decentralized

29:14

micro energy systems. And

29:17

I did that with a degree

29:19

in psychology and economics and

29:21

a PhD in Google. So what I

29:24

actually am really trying to say is that you

29:27

don't have to go to school

29:31

for this. To do this, you

29:34

do have to have quite a ferocity for

29:36

self learning, and again

29:39

I fate to say a bit of self reliance in

29:41

this, but with

29:44

the right tools and

29:46

with that kind of interest in researching

29:48

as much as you can you'd be surprised

29:50

what you can do, especially if

29:52

you're comfortable with the prototype being

29:56

very much only a couple percentage points of

29:58

what you actually want. Talked about

30:00

the socket earlier. My first prototype

30:02

for the socket was a shake to charge

30:05

flashlight and a hamster ball. So

30:09

I will post that on what's today

30:11

Thursday. Got to get back to New York. I'll post

30:13

it on Monday. I promise I will.

30:16

Perfect question over here and I'll come over

30:18

there. Yeah, my name is Evan Poncels.

30:20

I'm with the Africa Down community of Alanterst and I just want to

30:22

let you know that black people are here in Seattle

30:25

and mostly concentrated in the Central district

30:27

of Seattle. So let's all learn a little geography about

30:29

this. So from the Central District

30:32

of.

30:32

Seattle, Ray Charles dropped his first studio album,

30:34

so it's not just Kirk Cobain. Also Jamie

30:36

Hendrix is from there. Shout out to my uncle high

30:39

school termer at Garfield High School of anybody from

30:41

Garfield. And so what I wanted to

30:43

say was just that, you know, in addition to radical

30:45

self reliance, we also should be

30:48

organizing around data and around

30:50

artificial intelligence. So one thing we're

30:52

doing with Africa Town is building programs

30:55

so that you can be exposed to these sorts of things. But

30:57

I was wondering, My question really is where

31:00

can we get exposure to the data sets that

31:02

could help us solve with AI things

31:05

like infant mortality or pregnant

31:07

mother mortality, mortality in the birthing

31:09

scenarios. So that because we

31:11

work with universities that are studying, you know,

31:13

data and like things like coming

31:16

up with the language models for African American vernacular

31:18

English and things like this. And so right

31:21

now we're about to start a consortium where we're

31:23

learning, well, what goals should we have, what problems

31:25

should we solve in, what strategy should we we implement?

31:28

And so I'm just trying to see where's our best footing

31:30

for that in terms of organizing, Jenny, I.

31:32

Think I have. I can tell you where my company,

31:34

like when we really started looking at disadvantaged

31:38

communities that are black and Latino majority

31:40

communities, and how do we get the data

31:43

to ensure that we're thinking through the

31:45

equitability of what's happening in this once in a

31:47

generation moment with our infrastructure, and

31:49

how we started creating actual actual

31:53

AI that could support that, But

31:56

I'd love to know what you think.

31:59

First. I can share our perspective, but as

32:02

someone who works with the government, you might know a

32:04

few more.

32:05

Peek so

32:08

you know the government, you know that's

32:11

a beast by itself, right, we

32:14

do have ways of putting

32:16

out actual where our data

32:18

is stored, so I will say that. Okay, So

32:20

what my office does. I'm the Artificial Intelligence

32:22

and Technology Office, right, and what

32:24

we do is every year we do an AI

32:27

use case inventory. And actually

32:29

we're sitting here, but that's what's going on back at

32:31

home is all the labs are putting

32:33

together in AI use case inventory

32:35

will still turn into us in

32:37

mid April. Once that's turned into

32:40

US, we will put that inventory

32:43

up on what's releasable to the public. So

32:45

let me say that because we're seventeen national laboratories,

32:48

so that way you know, everything's not releasable

32:50

to the public. But what is releasable

32:52

to the public. We'll go up on our website,

32:54

right, it's Artificial Intelligence and Technology Office.

32:57

You will see the inventory there. If

32:59

they are listing the code, it

33:01

will be there so you could actually read what

33:04

the name of the use case is you'll be able

33:06

to see a description of that use case if

33:08

it matches anything that you're trying to do or

33:10

looking to do, and it says where the

33:12

code is. That's where the code is at.

33:15

Right.

33:15

If it's blank and you want us

33:18

to find out, that's Jason Tally. You

33:20

want us to find out if

33:23

that code is available, just send us an

33:25

email. Our email address is on the website.

33:27

You send it to us and we'll get it for you.

33:29

If it's available, we'll get it for you. So that's from

33:31

the government perspective.

33:32

Which matters, right, because I think for

33:34

us we're often

33:37

looking at our data sources one from

33:39

actual governmental context. Like a lot of times people

33:41

don't realize that almost

33:43

everything is available to you. You just have to ask for it.

33:46

They're not going to make it clean and easy or create

33:49

an interface that makes it as simple as

33:51

a you know, downloading photos from you

33:53

know, from your whatever app you're using for that,

33:56

but you can reach out. The other thing

33:58

that's been interesting for us over the last two

34:01

years that, to be honest, was a

34:03

bit surprising, was connecting

34:05

and partnering with journalists. Journalists

34:08

are surprisingly good

34:11

at getting real hard data

34:14

in the aggregate. For example,

34:17

it was The New York Times that

34:19

went and actually published with

34:22

incredible support when you actually go in

34:24

and look at what they published and what

34:26

studies that they were pulling from the

34:29

infant mortality rates. And

34:32

I think some of you may have seen that article

34:35

and so, and that's happened before

34:36

we actually struggled and looking

34:38

at a lot of the things related

34:41

to justice forty and disadvantaged communities

34:43

and this idea of forty percent of the

34:46

infrastructure funds they're actually meant

34:48

to go to disadvantaged communities

34:50

across the United States. But we struggled

34:53

to understand how many of those disadvantaged

34:55

communities were majority

34:58

minority, right, because you

35:00

can work some things out there, and that wasn't actually

35:02

available through any government sources. And

35:04

so there was actually a journalist that had

35:06

been doing the work for about

35:09

five years that allowed

35:11

us to actually see every city, township,

35:14

and village that was black

35:16

majority Latino majority

35:18

and black Latino majority. And

35:21

the data set was so massive and

35:24

again readily available. And so I

35:26

think that because if you

35:28

find truly reputable news

35:31

organizations that are pushing data because

35:34

they are often fearful

35:36

of publishing a massive story

35:38

that isn't backed up. They've

35:41

done their homework and you can dig there

35:43

and get their data sets, and when you combine those

35:45

with government data sets, you can

35:47

do some things that are very very cool. You

35:50

know. Hi everyone, my name

35:52

is Asia.

35:53

First, I want to thank you for being

35:55

so honest and real and challenging all

35:57

of us in this room to do more with data.

35:59

And I didn't have a question, but I just

36:01

wanted to tell you, like being a black woman

36:04

is seeing you dominate this space is just

36:06

empowering.

36:07

Wow. Thank you.

36:10

Would be the last one, regular, last

36:12

one.

36:14

Hi.

36:14

My name is Erica Adams Immagrad student

36:16

at UDUB and I am in

36:18

the Information School and I sit on

36:20

faculty committee, and we've been talking

36:22

a lot about student use with ch hat GPT. We're

36:25

already using it most of us, but there's

36:27

a lot of ambiguity around,

36:31

I guess, like cheating and stuff like that. So

36:33

I'm just curious if you have any advice

36:35

on persuading older academics

36:39

on you know, like coming up with guidelines

36:41

for use, because I think it's a great tool for

36:43

us to continue to use and we shouldn't

36:46

be using it with fear.

36:51

See I don't even know when you say older, do you mean people like

36:53

my age,

36:58

because yeah, you're real quick

37:00

and all of a sudden, you like when you go out and you're like, I'm

37:03

not the youngest person out here no more. Right, So,

37:08

persuading that's

37:11

that's a that's a tricky one. That is a

37:13

tricky one because there

37:16

has to be empathy for how

37:18

long they've existed

37:21

and known certain things to be true that are now becoming

37:23

very much untrue. Uh,

37:26

And I I think I

37:28

think starting from that place of empathy is one. So

37:32

I think there's a couple of ways to see this

37:34

if I'm going to be very just kind of direct

37:37

about it. One is, you

37:39

know, I don't know what guidelines are in place

37:41

right now. But obviously

37:44

if everyone goes to chat GPT and

37:46

says, write me a paper on the World

37:48

War, and everyone

37:51

submits a similar paper, the

37:53

teacher will say, oh, clearly there's some sort of plagiarism,

37:56

right because like whether you did this through

37:58

something that you google or you use

38:00

chat GPT, they can tell if

38:04

you go to the effort of engaging with

38:06

that chat GPT interface

38:08

such that what you produce, your

38:11

professor cannot tell. At

38:16

this point I don't really know what else

38:18

to tell me. You No, I mean, I'm not even I'm not. And it's

38:20

not about saying is this cheating or is this not cheating?

38:22

This is about being realistic about the

38:24

world that we're in. Like if everyone,

38:27

if you are lazy with this tool, you

38:29

will be found out to be lazy.

38:32

If you are innovative

38:34

and proactive with this tool,

38:38

you will still rise above. I truly

38:40

believe that there's always still a way to rise

38:42

above and still write

38:44

the best paper with chat GPT compared

38:47

to everyone else. And to be

38:49

honest, if if college

38:51

is meant to prepare you for the real world, acting

38:55

like these tools don't exist when

38:58

they do. And I get

39:00

it that people, Oh, we want to make sure you can write a paper.

39:02

We want to make sure you can do all those things. Yes, yes,

39:05

guess what. I also still don't know how to drive

39:07

stick because I

39:09

didn't have to. So you

39:12

know, we can lament about this world

39:14

of like, oh, we hope people wish they should.

39:16

We want to make sure you still have all these different things.

39:19

Those who care about those skills will get

39:21

them. I believe my husband

39:23

said he could drive stick, but then recently actually I was

39:25

like, were you were you lying? Because this is

39:27

not I don't think he's spot

39:29

to make those noises. You

39:32

know, he's from Mississippi, so he's a

39:34

you know, so he was telling me a whole of the storm Mississippi

39:36

and Texas. So who knows, but

39:39

to that end, right, like I think, but

39:41

he clearly felt that it was important that he said

39:44

that he could drive stick. I was like, boy, I could barely

39:46

do automatic at the time, honestly, like I cannot

39:48

wait for driverless cars. So

39:50

it's it's it's one

39:53

of those things where I would say, so, I don't think it's about

39:56

persuasion. I think it's about recognizing

39:59

that the entire

40:01

all the standards will shift that

40:04

you cannot restaur on your laurels here, like

40:06

everyone keeps saying, I use chatchypet to create

40:09

a marketing plan to do this and do that. We

40:12

will see certain similarities

40:14

that will negate that work if

40:16

you do not still put your human intellect

40:18

on top of it. We're not there again,

40:21

sassy seven year old y'all, we're not

40:23

there, and don't let anyone make you think

40:26

that that we are. But

40:29

yeah, so it really it sounds to me that like,

40:31

if your professors are like super not into it,

40:34

you might be able to save yourself some time and just do

40:37

what you gotta do and be like with the chatchypt No,

40:41

I would never.

40:53

Black dec Green Money is a production of Blavity,

40:55

Afrotech, Black Effect Podcast

40:58

Network, and iHeart Media, and it's

41:00

produced by Morgan Debonne and me Well

41:02

Lucas, with additional production support

41:04

by Sarah Ergin and Rose McLucas. Special

41:08

thank you to Michael Davis of Vanessa Serrano. Learn

41:10

more about my guests the other tech This represent innovators

41:13

at afrotech dot com. Enjoy

41:15

your Black Tech Green Money. Share this

41:17

with somebody, Go get

41:19

your money. Peace and love,