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Speaker 1: I'm with Lucas and this is black tech, Green money.

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Speaker 1: Doctor Nashalie Cephas is the Applied Science Manager for the

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Speaker 1: Amazon AI team, focusing on fairness and AI. Based in Atlanta.

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Speaker 1: She formerly led the Amazon A nine visual search and

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Speaker 1: AR teams, which launched part Finder, the visual search tool

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Speaker 1: for replacement parts on the Amazon Shopping app, in twenty eighteen.

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Speaker 1: The team for Partfinder came from the Atlanta based startup

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Speaker 1: part Pick. In twenty sixteen. Shout out to Jewel Burks.

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Speaker 1: Nashally was CTO. Doctor Sephus received her PhD from the

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Speaker 1: School of Electrical and Computer Engineering at the Georgia Institute

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Speaker 1: of Technology in twenty fourteen. There's a lot of conversations

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Speaker 1: about AI happening in our general social discourse, and I

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Speaker 1: wonder what's being overlooked.

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Speaker 2: Yeah, So, I think we often talk about the limitations

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Speaker 2: and the fear of technology. We often talk about how

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Speaker 2: we don't feel like the technology was created for us

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Speaker 2: and people in our communities, and to some degree, you know,

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Speaker 2: I can definitely relate to that, but I do still

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Speaker 2: stress the importance of not letting that be an excuse

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Speaker 2: to not learn about the technology and not letting it

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Speaker 2: be an excuse for us to just dig in, get

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Speaker 2: your hands dirty, and see what you can do to

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Speaker 2: make it work for you, because all the while, if

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Speaker 2: we are continuously shine away from it, then we're just

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Speaker 2: getting further and further behind. And I literally see it

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Speaker 2: every day. I've been in the tech industry, in the

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Speaker 2: corporate and the startup industry world since twenty thirteen or so.

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Speaker 2: I started down this path when I started undergrad at

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Speaker 2: Mississippi State in two thousand and three, and Georgia Tech

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Speaker 2: I started in two thousand and eight, and so I

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Speaker 2: literally have seen it and lived it all of my

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Speaker 2: internships and all the companies you can imagine all of

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Speaker 2: the research projects over the past twenty years or so,

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Speaker 2: we are still left out of the conversation. We're still

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Speaker 2: left behind. We're still not taking advantage of the technology

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Speaker 2: and how it can work for us in our everyday lives.

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Speaker 2: So that's that's the message that I try to always

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Speaker 2: tell people about when I'm speaking about it.

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Speaker 1: So when I when listening to you talk there, it

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Speaker 1: gives me this idea that you know, when even when

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Speaker 1: I have conversations on this podcast, it's about there, there's

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Speaker 1: a great fear. You know, with AI, you know we

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Speaker 1: hear the news talks about this is going to take

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Speaker 1: jobs and not even go to restaurants now you know,

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Speaker 1: you go to a drive through when you can hear

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Speaker 1: AI talking to you.

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Speaker 2: These auto.

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Speaker 1: Order takers are taking orders now because it was for

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Speaker 1: a long time it was hard to get people to

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Speaker 1: fill those roles. So they true to a certain extent,

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Speaker 1: these are going to take jobs. And so when you

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Speaker 1: have communities of people that you deal with everyday, communities

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Speaker 1: that we live in, who do see this happening? What

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Speaker 1: is the offense we can play instead of just defense?

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Speaker 1: And how do we play that offense strategically and just tactically,

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Speaker 1: like what should we be doing?

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Speaker 2: Yeah, so it can be as simple as reading a

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Speaker 2: blog on AI. Letting that be a part of your

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Speaker 2: regular routine, staying in the know about the news and

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Speaker 2: what's new, what's to come. We recently saw that some

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Speaker 2: companies have released texts to video AI and generative AI.

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Speaker 2: They have now released instead of just in putting texts

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Speaker 2: into a chat boy, you can now input texts and

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Speaker 2: images to be able to create outputs. So they're becoming

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Speaker 2: what we call multimodal. There are also new business segments

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Speaker 2: that are coming out. I also talk about because people

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Speaker 2: have a concern about the technology and the data that

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Speaker 2: it's being trained on. We now need our and will

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Speaker 2: soon be mandated by policy and government to have special

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Speaker 2: companies come in and test your technology and do what

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Speaker 2: we call red teaming, intentionally try to break it and

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Speaker 2: see what the issues are. All these are new business

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Speaker 2: opportunities and who better to come up with business opportunities

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Speaker 2: to test for biases than those who are in the minority.

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Speaker 2: And so we have even at my role and working

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Speaker 2: as a principal AI scientist at Amazon, we have done

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Speaker 2: contrasts with several minority minority businesses to help get testing

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Speaker 2: data to help test our algorithms for bias, biases and

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Speaker 2: bias evaluation and other areas of responsible AI. On the

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Speaker 2: other hand, you look at the work that I've done

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Speaker 2: in the community, for example with bean Path in Jackson, Mississippi,

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Speaker 2: my hometown. We have and very intentional about going straight

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Speaker 2: to the community. This is literally what you can start

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Speaker 2: doing today. You can start using this for your business.

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Speaker 2: Here are the tools. We even have workshops set up

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Speaker 2: you can come in for free. You could be a

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Speaker 2: mom and pop business. You can be a startup company,

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Speaker 2: you can be a mid sized business, real estate company

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Speaker 2: that's been in operation for over you know, two three decades.

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Speaker 2: We have something for you. We also have a Senior

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Speaker 2: Citizen program where we teach senior systems how to come in,

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Speaker 2: get on the computer, not just check for their adoptor's

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Speaker 2: appointments and log in and get their lab results. They

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Speaker 2: are now able to engage in social media that now

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Speaker 2: are able to engage in how to build a website

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Speaker 2: for UH you know, a group like a Sunday school

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Speaker 2: class or something like that. We also impact the youth.

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Speaker 2: We have summer camps, we have UH spring camps, fill camps,

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Speaker 2: we have stem Saturdays where they come in and engage

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Speaker 2: in our maker space and able to use not just

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Speaker 2: AI at a computer, but tangible things you can touch,

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Speaker 2: like use AI to plan a guarden and use AI

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Speaker 2: to design a T shirt. And so we try to

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Speaker 2: make it as engaging and as culturally relevant as possible

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Speaker 2: to let people know that AI is for everyone and

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Speaker 2: this technology is for you. You belong here, even if

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Speaker 2: you're not going to major in this field and get

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Speaker 2: a computer science degree, which I want you to do,

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Speaker 2: but I understand everybody's not going to do that. There's

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Speaker 2: still a place for you.

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Speaker 1: I love that, and so I feel like you know

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Speaker 1: more and more as we have this AI conversation. You know,

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Speaker 1: when people say AI, it's like saying Africa, Like we

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Speaker 1: don't realize there's one hundred one hundred countries inside Africa

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Speaker 1: and people just say I'm going to Africa, Like okay,

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Speaker 1: where in Africa? It's like it's a whole continent. And

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Speaker 1: so if you can talk about different AI use cases,

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Speaker 1: different AI technologies so that we can start to part

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Speaker 1: or what is happening in the world today because you've

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Speaker 1: got chat botes, you got agi people are really afraid of.

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Speaker 1: But can you break down what these things are?

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Speaker 2: Right? So, what we like to do when just describing this,

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Speaker 2: we like to put it into two different categories. So

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Speaker 2: there's what we call traditional AI, excuse me, traditional AI,

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Speaker 2: which is what we normally would have thought about AI

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Speaker 2: before this last couple of years, where you're training models

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Speaker 2: like a computer to act, think, talk, respond, et cetera,

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Speaker 2: like a human based on previous data that it has

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Speaker 2: learned from. So now we can make decisions about things

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Speaker 2: in the future that we haven't seen before because of

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Speaker 2: the things that we've learned from in the past. In

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Speaker 2: this in this what we call an AI model, So

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Speaker 2: using it for predictions, using it for data analytics, using

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Speaker 2: it for it could be predicting what I'm saying in

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Speaker 2: speech in terms of natural language processing. It can be

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Speaker 2: in terms of programming or robot to move arms a

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Speaker 2: certain way according to the control system that for every

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Speaker 2: action is an opposite reaction. There's also we saw in

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Speaker 2: terms of recognizing things in images, recognizing things in video.

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Speaker 2: That's the traditional AI that we were talking about now today.

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Speaker 2: Most recently the last couple of years, we've seen generative

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Speaker 2: AI become really popular, and that basically means AI framework

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Speaker 2: that allows you to generate keyword genitive content based on

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Speaker 2: some previous data. So we can be generating a conversation

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Speaker 2: like in a chatbot, we can be generating images or

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Speaker 2: videos based on some prompt that you've given it. And

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Speaker 2: so this is the era of genitive AI, which a

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Speaker 2: lot of people use for things like marketing. If you're

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Speaker 2: trying to create pictures for your website, if you're trying

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Speaker 2: to get an understanding of something that you're trying to

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Speaker 2: search for it can respond in a way that's very

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Speaker 2: similar to a human by putting together the different uh

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Speaker 2: words and a sentence that most likely, according to statistics,

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Speaker 2: match the style that they think that you're looking for

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Speaker 2: based on the prompt that you gave it. And so

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Speaker 2: that's what we call genitive AI. And again, the use

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Speaker 2: cases are pretty much endless anything that you neique content for,

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Speaker 2: which is a lot of different things, Like I said, marketing,

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Speaker 2: website development, coming up with ideas on how to do

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Speaker 2: pretty much anything, create a vacation planner, or create a

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Speaker 2: middle plan, or even understand how can you go about, uh,

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Speaker 2: you know, building a dy project. Uh. Those are some

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Speaker 2: of the use cases that we've seen for generative AI.

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Speaker 1: So what's so funny about this is, you know, during

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Speaker 1: it's not funny, I shouldn't say that. It's so interesting

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Speaker 1: about this is like during COVID there was this human

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Speaker 1: among iss like freelancer movement, because people say, why am

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Speaker 1: I going to this job when I can just go

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Speaker 1: start my own thing. I can go start my own

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Speaker 1: marketing company, my own photography business, et cetera, et cetera.

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Speaker 1: And then you have these technologies that are coming out

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Speaker 1: and to your point, I can do my whole content

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Speaker 1: calendar for social media by type and a few prompts

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Speaker 1: in on chat GPT you know, four oh just came out,

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Speaker 1: you know for Omni just dropped in this by this

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Speaker 1: recording yesterday. And so I think about we kind of

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Speaker 1: got boxed into this, you know, new world that we

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Speaker 1: didn't even know, many of us didn't know we were

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Speaker 1: going to. So we've taken this leap in entrepreneurship and

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Speaker 1: now you have this thing coming to each at lunch.

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Speaker 1: And so how how do people who have taken that

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Speaker 1: leap of entrepreneurship make sure that they're, if not building,

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Speaker 1: leveraging it.

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Speaker 2: Absolutely. So I'll have to say this to my my MENTI.

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Speaker 2: I have some mentees, you know, people that I mentored,

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Speaker 2: and you know, this gen Z I'm a millennial, This

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Speaker 2: gen Z generation they're different, you know, and even after them,

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Speaker 2: the pandemic babies, they're different too. But I have to

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Speaker 2: explain to them, you know, you have to make yourself marketable.

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Speaker 2: I understand you not if you don't want to go

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Speaker 2: to college. I understand you don't think it's worth it

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Speaker 2: monetary wise, especially if you don't have financial aid. You know,

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Speaker 2: there are ways to make money. There are ways. They're

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Speaker 2: very successful people who didn't go to college, But you

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Speaker 2: have to figure out how to make yourself competitive in

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Speaker 2: today's world. The same is true for any large tech company.

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Speaker 2: The largest most successful tech companies are successful because they

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Speaker 2: figured out how to constantly adapt. They figure out how

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Speaker 2: to constantly move with the times and stay relevant and

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Speaker 2: stay competitive. And that's what you as an individual have

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Speaker 2: to do with your business too, regardless of what it is.

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Speaker 2: And so in terms of using AI, use it again,

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Speaker 2: find a way to use it to your advantage. Find

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Speaker 2: ways that nobody else is using it because we now

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Speaker 2: have this interface on top of this, like you said,

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Speaker 2: this technology. People may not have seen it coming, but

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Speaker 2: for example, we were using genati of AI in the

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Speaker 2: actual AI industry, you know, like years ago, and so

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Speaker 2: now many people can use it because these companies have

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Speaker 2: figured out, okay, let's make it easier for everybody to

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Speaker 2: interface to this, which I think was a great idea

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Speaker 2: because it helps level of playing field. The one thing

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Speaker 2: technology does is it's cold, right, it's computer programming code.

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Speaker 2: Once it's released into the world, you can't really get

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Speaker 2: it back. That could be a good thing that could

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Speaker 2: be a bad thing. But that's the one thing that

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Speaker 2: you can't take away from us and people in our community.

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Speaker 2: Once it's out there, is out there. So now we

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Speaker 2: have access to it. Now we have these interfaces, we

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Speaker 2: can now use it to our advantage. And like I said,

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Speaker 2: there are so many ways people are innovating every day

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Speaker 2: with these these new technologies and interfacing figuring out how

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Speaker 2: to use it for their business, whether it's on the

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Speaker 2: internal operation side, like helping make you more efficient, or

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Speaker 2: even on the external side, like generating content for other

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Speaker 2: people that are part of your freelance business. And so

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Speaker 2: it really just takes some sitting down. You have the tools,

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Speaker 2: you just have to let your mind take you to

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Speaker 2: that place of innovation so that you can make a

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Speaker 2: difference and you could be that competitive person in the market.

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Speaker 2: I would definitely start by just educating yourself. There's no

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Speaker 2: shortage of information out there, especially in today's information age.

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Speaker 2: There are so many tutorials out there, a lot of

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Speaker 2: them are free, and so if you're disciplined enough you

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Speaker 2: want to do that, then great. If you like to

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Speaker 2: enroll in a program or course or an academy of

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Speaker 2: some sort. I encourage you to do that too, but

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Speaker 2: just don't stop learning, and remember you have the tool

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Speaker 2: now to innovate.

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Speaker 1: It's particular the video on AI Generative AI. You know,

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Speaker 1: it's happening so fast and so one of the critiques

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Speaker 1: people have is, you know how you can tell you

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Speaker 1: can look at an image and tell, okay now because

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Speaker 1: the finger maybe it's eight fingers on one hand, like

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Speaker 1: it's something because there's crazy stuff. But a year ago

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Speaker 1: it couldn't even produce half of what is producing now.

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Speaker 1: But it's happening so fast and so and I'm not

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Speaker 1: going to give the negative spendity. I want you to

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Speaker 1: give the positive side of this. It is what excites

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Speaker 1: you most about the future we're walking into, specific to

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Speaker 1: how AI is going to impact our lives.

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Speaker 2: So I think it is really excited for me to

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Speaker 2: have a conversation like this with just about anybody, even

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Speaker 2: my grandmother in Jackson, Mississippi, because I tried for years

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Speaker 2: to have this conversation with people, and you know, nobody

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Speaker 2: knew what I was talking about. Nobody you know, like,

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Speaker 2: oh yeah, what's these are smart girls, that's not for me.

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Speaker 2: So finally we can sit at the table, we can

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Speaker 2: talk about these things. At least even if it's on

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Speaker 2: a high level, it's at least something. And so what

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Speaker 2: we've been trying to tell people, uh for the longest,

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Speaker 2: now that they're believing it, they're seeing it, and hopefully

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Speaker 2: they're able to take advantage of it. I think for

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Speaker 2: me in particular, I always say some of the most

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Speaker 2: primitive areas and industries that I think AI can impact

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Speaker 2: our healthcare and transportation. I love the applications that I'm

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Speaker 2: seeing come down the line, especially in terms of look

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Speaker 2: at the black community, especially black women, black women's health,

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Speaker 2: mental health, you know, physical health, even even amongst the

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Speaker 2: black male community, Like, there's so much innovation we can

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Speaker 2: do there. That's probably one of the most untapped areas

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Speaker 2: that if you have the data, you can do a

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Speaker 2: lot with AI and data science to be able to

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Speaker 2: help people, you know, feel better, help people you operate

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Speaker 2: better in their everyday lives. And I mentioned also transportation.

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Speaker 2: I'm still waiting on my my flying car so I

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Speaker 2: can fly with Tavvy in Atlanta, and so I just

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Speaker 2: hope that you know, someday, real soon, I could do that.

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Speaker 1: So how do we get more Nashally's in places like

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Speaker 1: you know, Jackson, Mississippi, because I think about what you're doing,

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Speaker 1: and there's others there's people who you know, I won't

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Speaker 1: mention names right now, but there's other people I know

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Speaker 1: who are gone back home, back to their original hometowns

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Speaker 1: and have recognized that it's not just about being in

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Speaker 1: Silicon Valley, it's not just about being in Miami, but

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Speaker 1: they're back there. The people back home need what the

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Speaker 1: future that they're being exposed to, And so how do

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Speaker 1: we get more people to recognize this opportunity? Like what

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Speaker 1: did you see or what called you? Pulled you back

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Speaker 1: home and said, you know what I'm doing big in

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Speaker 1: Atlanta and wherever else I'm at, but there's still work

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Speaker 1: for me to do here in Jackson.

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Speaker 2: Yeah. Well, first of all, all my family still in Jackson,

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Speaker 2: majority of all my family in Jackson and Mississippi area.

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Speaker 2: So never will forget home, never will forget my people,

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Speaker 2: my friends, my family. And then also it had so

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Speaker 2: much potential. I remember coming back home after being in

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Speaker 2: Silicon Valley, in New York City and Atlanta overseas even

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Speaker 2: as close as you know, Memphis and New Orleans, and

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Speaker 2: you're seeing so much progression happen all around you, But

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Speaker 2: in my hometown of Jacksonssissippi, I would see, you know,

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Speaker 2: just I didn't see the same rate of change that

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Speaker 2: I was seeing in other areas. And so I wanted

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Speaker 2: to be a part of that and at least contribute

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Speaker 2: in terms of STEM education, exposure and tech because that

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Speaker 2: was something I had been successful in. And so I

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Speaker 2: knew that I contributed that, you know, started the nonprofit

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Speaker 2: being Path and working on the real estate development to

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Speaker 2: do a lot more there in Jackson. And so I

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Speaker 2: knew that, you know, why not Jackson because it had

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Speaker 2: definitely has the most potential probably out of any city

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Speaker 2: I've seen this, and I think if we can do

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Speaker 2: it there, we can do it anywhere.

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Speaker 1: So a big conversation that people who are concerned about

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Speaker 1: AI have is where it's getting its data from. And

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Speaker 1: you mentioned this earlier, like, you know, it learns from

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Speaker 1: historical you know, facts, historical deta sets, et cetera. And

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Speaker 1: so I was listening to an episode of some podcasts

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Speaker 1: two weeks ago maybe, and it was talking about how,

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Speaker 1: you know, particularly like open AI and others have downloaded

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Speaker 1: like entire Hollywood movie, you know, libraries and entire music

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Speaker 1: catalogs by record labels and entire you know libraries from

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Speaker 1: you know, random housing, et cetera, to feed the model.

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Speaker 1: Some of that on some of that illegally because those

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Speaker 1: are copywritten you know materials and can you So there

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Speaker 1: they are deep ethical concerns. The point here and so

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Speaker 1: what concerns you when you think about how we're building

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Speaker 1: this so that you can work in your own way

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Speaker 1: to ensure that we're doing this in a way that's

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Speaker 1: still fair to people and creators.

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Speaker 2: Even yeah, it we I mean, I can't say it enough.

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Speaker 2: But so in the tech industry there's three percent. There's

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Speaker 2: a stat that says three percent of the tech industry

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Speaker 2: are black females, and that number has been the same

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Speaker 2: for I think they said last twenty or thirty years.

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Speaker 2: So that is a problem and I said black women,

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Speaker 2: but the stats are similar for black men and look

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Speaker 2: at any other BIPOD group. I think that until we

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Speaker 2: have diversity on the tech development teams, we will continue

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Speaker 2: to see a lot of these issues because that's where

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Speaker 2: a lot of this is flagged that you know, upper

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Speaker 2: level management. I think we're seeing a lot more black

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Speaker 2: and BIPOD CEOs of tech companies which is great. I'm

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Speaker 2: hoping that they can help steer, you know, more inclusion

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Speaker 2: in these emerging technologies. But the person who's actually doing

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Speaker 2: the coding, the person who's actually training the model, the

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Speaker 2: person who's actually gathering the data and making sure that

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Speaker 2: the data is good enough to go into the model.

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Speaker 2: That's where we need that extra set of eyes to

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Speaker 2: make sure that we're seeing the level of inclusion that

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Speaker 2: we want to see and also thinking of the right

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Speaker 2: bias testing that we should be adequately thinking about. Also

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Speaker 2: a lot of the data. I mean, in this country,

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Speaker 2: we have no policies in place. I mean they're a

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Speaker 2: lot closer than what we used to be just three

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Speaker 2: years ago, twenty nineteen, but we're very inching along in

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Speaker 2: terms of you know, what is considered in compliance, what

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Speaker 2: is not in compliance, what are how do we hold

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Speaker 2: companies accountable? How do we even put some standards in place?

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Speaker 2: And it's very difficult. So it's not a trivial thing,

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Speaker 2: even if everyone's hearts and minds were in the right place.

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Speaker 2: So it is something that we're constantly going to have

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Speaker 2: to work at, and it's a what we call a

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Speaker 2: shared responsibility model. Everybody has to play a part in this,

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Speaker 2: But ultimately, I would say again it boils down to,

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Speaker 2: you know, getting more diversity in the tech development roles.

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Speaker 1: What skills are going to be most valuable in a

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Speaker 1: market that's driven by largely AI models, Like what skills

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Speaker 1: when you talked about those kids who were like questioning

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Speaker 1: going to college, you know, what skills should they be

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Speaker 1: learning to ensure that they can still find a place

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Speaker 1: in the market.

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Speaker 2: They they should definitely be very hands on, uh and

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Speaker 2: at least somewhat tech savvy when it comes to these

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Speaker 2: emergent technologies. I again, it's so much information out there,

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Speaker 2: it's really no excuse to not learn. I mean, even

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Speaker 2: look at social media. We've been exposed. Look at how

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Speaker 2: fast something can go viral. Look at how fast a

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Speaker 2: trend can catch on across the entire world, or a

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Speaker 2: dance on TikTok and take off that in that same

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Speaker 2: vein these technical courses on how to learn how to

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Speaker 2: use your to AI for marketing, how to learn how

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Speaker 2: to use basic programming skills to help you solve problems,

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Speaker 2: and using different products on the market that you know,

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Speaker 2: learning how to evaluate products. Even like all these skill

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Speaker 2: sets are necessary and unfortunately a lot of our youth

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Speaker 2: are you know, not engaging with these things, you know,

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Speaker 2: at as they should be in order to be prepared.

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Speaker 2: I do whole hardly believe that people can be prepared

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Speaker 2: for this next wave. And just think about this is

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Speaker 2: only the beginning, Like have we barely scratched the surface

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Speaker 2: of because the technologies just now get into the hands

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Speaker 2: of people. It's just no getting into the hands of

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Speaker 2: the black community. So we barely scratched it. Serve there's

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Speaker 2: so much potential here and so I just hope that they,

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Speaker 2: you know, take the bull by the horns and just

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Speaker 2: run with them.

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Speaker 1: This is quote on your ex I wont say Twitter,

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Speaker 1: but your x account where it's just maybe a couple

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Speaker 1: of weeks ago, where you said tech is not black

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Speaker 1: or white, it's green as in money. That's why we

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Speaker 1: can't risk losing DEI diversity, equality inclusion in tech, furthering

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Speaker 1: the financial power gaps in communities, financial empower gaps in

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Speaker 1: the communities, from entrepreneurship to big tech. Find a way

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Speaker 1: to make tech work for you, your company, or your community.

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Speaker 1: So with that what you said there, you know, as

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Speaker 1: you and I know, there's only so few nationally, doctor doctor,

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Speaker 1: you know, walking around and you know, I'm sure you

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Speaker 1: walk into a lot of rooms where nobody looks like

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Speaker 1: you or is your gender, you know, especially in the

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Speaker 1: work that you do. And so to that last point

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Speaker 1: you said, find a way to make it work for you,

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Speaker 1: your company or your community. What was the way you

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Speaker 1: found to make this work for you where you added value?

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Speaker 2: I saw the need to, uh, you know, you know,

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Speaker 2: it's the saying, the biblical saying, teach a man how

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Speaker 2: to give him a man to fish, You feed him

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Speaker 2: for a day, teach him how to fish, feed him

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Speaker 2: for a lifetime. So I wanted to help people feed

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Speaker 2: themselves for a lifetime. So I started the nonprofit bean

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Speaker 2: Path in my hometown of Jackson, Mississippi, which has no

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Speaker 2: shortage of challenges. Are you gotta do is watch the

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Speaker 2: news or google Jackson, Mississippi to see all the challenges.

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Speaker 2: And but I knew that, uh, if you can teach

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Speaker 2: these people here, they will go on and teach other people,

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Speaker 2: and they will teach other people because that's the cycle.

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Speaker 2: And someone even showed me. I remember my eighth grade

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Speaker 2: teacher sent me to my first engineering camp when I

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Speaker 2: first realized what engineering actually was and if she had

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Speaker 2: not done that, I would have majored in music. I

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Speaker 2: probably was still really fine. But I think, you know,

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Speaker 2: I love music too, but that was a game changer.

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Speaker 2: So if I can change the game for somebody else,

434
00:25:28,320 --> 00:25:30,600
Speaker 2: hopefully they keep giving that cycle back and then the

435
00:25:30,600 --> 00:25:35,800
Speaker 2: whole community benefits from that, you know. I think that's

436
00:25:35,800 --> 00:25:40,239
Speaker 2: what did it for me. I mean everything else is

437
00:25:40,320 --> 00:25:42,800
Speaker 2: just kind of you know. I love my role and

438
00:25:42,880 --> 00:25:46,240
Speaker 2: as being a principal scientist and being able to interact

439
00:25:46,280 --> 00:25:48,520
Speaker 2: the cutting edge technology every day. I work with some

440
00:25:48,600 --> 00:25:53,960
Speaker 2: brilliant people from Amazon to startups that I advise to

441
00:25:54,160 --> 00:25:59,520
Speaker 2: other you know, techies, but the real passion comes from

442
00:25:59,640 --> 00:26:02,840
Speaker 2: teaching people how to fish when it comes to technology.

443
00:26:04,040 --> 00:26:07,560
Speaker 1: So I'm kind of position this question a little bit ago,

444
00:26:07,600 --> 00:26:09,199
Speaker 1: but you just brought it back up with you know,

445
00:26:09,240 --> 00:26:11,840
Speaker 1: the teachers that what you would have been in music

446
00:26:11,840 --> 00:26:14,720
Speaker 1: and et cetera. There had to be a conversation or

447
00:26:14,760 --> 00:26:17,480
Speaker 1: a moment or et cetera where somebody pointed out, you know,

448
00:26:17,600 --> 00:26:21,240
Speaker 1: this math and computer science area to you and it's

449
00:26:21,320 --> 00:26:23,720
Speaker 1: like it finally registered, like this is the path of

450
00:26:23,760 --> 00:26:25,480
Speaker 1: me or I don't know what your story is, but

451
00:26:25,480 --> 00:26:28,720
Speaker 1: there's had to be a moment where it just you

452
00:26:28,760 --> 00:26:31,280
Speaker 1: could have went left and you went right. What was that?

453
00:26:31,560 --> 00:26:35,760
Speaker 1: Can you tell more about that conversation that actually made

454
00:26:35,280 --> 00:26:40,920
Speaker 1: the switch flip, because what we're struggling enough already is

455
00:26:41,240 --> 00:26:44,600
Speaker 1: just having math principles in our community to be able

456
00:26:44,680 --> 00:26:47,520
Speaker 1: to take these paths. So can you talk about what

457
00:26:47,840 --> 00:26:51,360
Speaker 1: got your interest to the level where you're like, I'm

458
00:26:51,400 --> 00:26:52,199
Speaker 1: in that's for me.

459
00:26:54,200 --> 00:26:59,560
Speaker 2: Yeah, I knew that, I mean the way this particular.

460
00:27:00,240 --> 00:27:03,919
Speaker 2: So I've done math and science classes. Uh, you know,

461
00:27:03,920 --> 00:27:06,120
Speaker 2: I was I was excelling in math and science as

462
00:27:06,160 --> 00:27:08,679
Speaker 2: a kid. That was just kind of my thing. I

463
00:27:08,720 --> 00:27:11,480
Speaker 2: wasn't really big on on history and all these other subjects,

464
00:27:11,520 --> 00:27:13,720
Speaker 2: but that math and science was my thing. And so

465
00:27:14,359 --> 00:27:19,320
Speaker 2: I remember that eighth that camp after eighth grade, they

466
00:27:19,359 --> 00:27:21,879
Speaker 2: they just showed us, Hey, you type these letters and

467
00:27:21,960 --> 00:27:25,720
Speaker 2: numbers in the computer, you can control things around you.

468
00:27:26,440 --> 00:27:28,240
Speaker 2: And I was just like, oh, what hold on, let

469
00:27:28,280 --> 00:27:30,520
Speaker 2: me try this. And it was so fascinating to me.

470
00:27:30,600 --> 00:27:32,080
Speaker 2: And I was like, wow, I one day I'm gonna

471
00:27:32,080 --> 00:27:34,359
Speaker 2: be able to control the whole world. And that's really

472
00:27:34,359 --> 00:27:38,840
Speaker 2: what it really and so and little did I know

473
00:27:39,040 --> 00:27:41,600
Speaker 2: because this was you know, I mean, I graduated high

474
00:27:41,600 --> 00:27:45,520
Speaker 2: school and like in three and so back then, like

475
00:27:45,560 --> 00:27:49,320
Speaker 2: computers didn't control everything like it does now. Like you know,

476
00:27:49,359 --> 00:27:51,200
Speaker 2: I was, we went through the Y two K together

477
00:27:51,280 --> 00:27:52,879
Speaker 2: and all that, and so we didn't know it was

478
00:27:52,920 --> 00:27:56,480
Speaker 2: gonna be, you know, all this, and so I didn't

479
00:27:56,520 --> 00:27:58,960
Speaker 2: even know that and then let let alone. How I

480
00:27:59,000 --> 00:28:02,040
Speaker 2: ended up getting into A and knowing how that would take,

481
00:28:02,320 --> 00:28:05,240
Speaker 2: I never knew. I just was interested in it. And

482
00:28:05,520 --> 00:28:08,000
Speaker 2: I think for people, we just got to find a

483
00:28:08,040 --> 00:28:12,400
Speaker 2: way to show people in our community the possibilities that's

484
00:28:12,520 --> 00:28:15,240
Speaker 2: out there and how it relates to them according to

485
00:28:15,320 --> 00:28:18,760
Speaker 2: what they're interested in. I got into AI because I

486
00:28:18,800 --> 00:28:21,359
Speaker 2: was interested in the Shazam app and I thought that

487
00:28:21,400 --> 00:28:23,320
Speaker 2: was so cool. Because I was a musician, I was like, well,

488
00:28:23,320 --> 00:28:24,680
Speaker 2: I want to hear this song. I was like, how

489
00:28:24,760 --> 00:28:27,240
Speaker 2: is this thing working? This is so cool? And that

490
00:28:27,480 --> 00:28:31,159
Speaker 2: was the same techniques in AI and music. At the

491
00:28:31,200 --> 00:28:33,680
Speaker 2: time it was even called AI, it was machine learning

492
00:28:33,760 --> 00:28:37,520
Speaker 2: or pattern recognition or digital signal processing, all of that.

493
00:28:37,680 --> 00:28:40,640
Speaker 2: Those are the same techniques were using today in AI

494
00:28:40,840 --> 00:28:43,640
Speaker 2: and national language processing and jeergy of AI. And so

495
00:28:44,360 --> 00:28:46,280
Speaker 2: you know, you never know where things will take. You

496
00:28:46,320 --> 00:28:48,280
Speaker 2: just start with what you're interested in. So we present

497
00:28:48,320 --> 00:28:52,160
Speaker 2: it in a way that you know, shows people. You know, hey,

498
00:28:52,200 --> 00:28:55,000
Speaker 2: you now some people on the street, they've been doing

499
00:28:55,040 --> 00:28:58,120
Speaker 2: math for a long time. They're involved in some legal activities,

500
00:28:58,560 --> 00:29:01,920
Speaker 2: but they know math. You know, we just got to

501
00:29:01,960 --> 00:29:04,200
Speaker 2: make it relate to the things that they're used to,

502
00:29:04,240 --> 00:29:06,920
Speaker 2: the things that you know, excite them.

503
00:29:07,880 --> 00:29:10,440
Speaker 1: You know, you started being path you know, your nonprofit

504
00:29:10,560 --> 00:29:13,239
Speaker 1: to help along this route. And there's a lot of

505
00:29:13,560 --> 00:29:18,200
Speaker 1: nonprofit people, you know, executive directors and community organization folks

506
00:29:18,200 --> 00:29:21,080
Speaker 1: who will listen to this episode to hear you. And

507
00:29:21,240 --> 00:29:25,600
Speaker 1: what role do you see those community organizations and nonprofits

508
00:29:25,600 --> 00:29:27,960
Speaker 1: playing and making sure that we are educated and aware

509
00:29:28,120 --> 00:29:28,800
Speaker 1: of what's happening.

510
00:29:31,240 --> 00:29:35,600
Speaker 2: Yeah, the the organizations and the community, they play a

511
00:29:35,640 --> 00:29:39,000
Speaker 2: huge part in the ecosystem what I call the tech

512
00:29:39,040 --> 00:29:42,800
Speaker 2: ecosystem because I think takings really at the center of

513
00:29:43,160 --> 00:29:47,600
Speaker 2: it all. And people and so whereas you have the

514
00:29:47,640 --> 00:29:49,960
Speaker 2: colleges and universities, you know, they're going to educate the

515
00:29:49,960 --> 00:29:52,960
Speaker 2: people who are going to college. You have government, you

516
00:29:52,960 --> 00:29:55,680
Speaker 2: know they're going to take care of their constituents. You

517
00:29:55,840 --> 00:30:00,160
Speaker 2: have the large corporations, you have the startup companies. But

518
00:30:00,200 --> 00:30:02,959
Speaker 2: what about everybody else, which is the majority of people.

519
00:30:03,760 --> 00:30:06,479
Speaker 2: Those who are not in school, those who don't have

520
00:30:06,520 --> 00:30:09,080
Speaker 2: a startup company are working tech, and those who are

521
00:30:09,120 --> 00:30:11,600
Speaker 2: not in government. That's the majority of people. If we

522
00:30:11,760 --> 00:30:16,080
Speaker 2: touch those people, then we connect the gap in the ecosystem.

523
00:30:16,080 --> 00:30:18,560
Speaker 2: And that's what Being Path focuses on, and that's what

524
00:30:18,640 --> 00:30:21,480
Speaker 2: a lot of other nonprofits that are similar. They try

525
00:30:21,520 --> 00:30:23,920
Speaker 2: to focus on that gap in the community and make

526
00:30:23,960 --> 00:30:25,480
Speaker 2: sure that those people are not forgotten.

527
00:30:40,640 --> 00:30:43,000
Speaker 1: Black Tech Green Money is a production and Blavity afro

528
00:30:43,160 --> 00:30:46,520
Speaker 1: Tech on the Black Effect podcast Networking I Hire Media.

529
00:30:46,600 --> 00:30:49,920
Speaker 1: It's produced by Morgan Debond and me Well Lucas, with

530
00:30:50,000 --> 00:30:54,320
Speaker 1: additional production support by Sarah Ergon and Love Beach. Special

531
00:30:54,320 --> 00:30:57,160
Speaker 1: thank you to Michael Davis and Kate McDonald. Learn more

532
00:30:57,160 --> 00:30:59,240
Speaker 1: about my guests and other tech THIS'SPEP. It's to innovators

533
00:30:59,280 --> 00:31:02,640
Speaker 1: afro Tech that the video version of this episode will

534
00:31:02,680 --> 00:31:05,240
Speaker 1: drop the Black Tech Green Money on YouTube, So tap

535
00:31:05,320 --> 00:31:09,840
Speaker 1: in enjoying Black Tech Green Money. Shot up to somebody

536
00:31:11,240 --> 00:31:12,040
Speaker 1: look at your money.

537
00:31:12,960 --> 00:31:13,600
Speaker 2: Peace and love,