Session 2.7: AI Careers

0:13 What? Why is it? Hello everybody. Why is it not showing my my video? Dear Lord Jesus. Okay, thank you. Hello everyone on on YouTube. Hello. Good to see you. Thank you so much for joining me. H I just want to welcome the people on on Instagram and and then we get the show going today. We're starting a little bit late. I'm in Johannesburg and so things don't always move smoothly in Johannesburg but

1:00 but here I am. H we are starting right now. Thank you so much for joining me today. I'm speaking to a young woman who who um started after metric without thinking that she'll end up with a career in um in AI and data science. and now she's in AI and data science and I'm talking to her about how she pivoted into data science and AI. Uh it's an important conversation because some of you may be thinking about um getting into AI and maybe you're wondering how does one get into AI and Dell who I'm speaking with today will talk to us about how she went in and also she's doing work to um encourage people to get

1:46 into AI and um and uh there's a the there's a way in which she does it. She'll she'll help us understand she will help us understand um uh uh you know what's what's the best way you didn't study computer science maybe you didn't study mathematics and and now you want to get into AI is it possible and and if so how do you do it how do you do it in a in in a way that can be useful some for someone who's already in another career so I will welcome D in D I'm hoping that you're online I I didn't see your I didn't see your your your Instagram um handle. So So I don't know if you're

2:33 online, Dell, please say hello if you're online. Dell Dell Maro, please tell me if you're online and and um ask to be welcomed onto this and so that I can have you online. Um yes, I can see you. I can see you D. So you're not asking to be um welcome in. So can you can you ask for me to get let you in? Dell, can you press the plus on your thing and ask to be let in? Is it possible for you to do that? Okay, there. Otherwise, I cannot invite you there. There. Okay, there we go. I've just sent you an invitation, Dell, to join me. Erh, I don't want to be talking about Dell's employer, but I will say to you

3:20 that Dell is employed by a telecommunications company and h she works for that telecommunications company where she's doing a a a data science and AI and um we're not going to talk about the company. We're talking about Dell because I don't have permission from the company and I don't want to market the company without them their their permission and so on. Dino, thank you so much for joining me. Really appreciate you making the time. I apologize for for all this, you know, when when you don't have a settled place yet, this happens, you know, because you're struggling with internet and then and then you're struggling and then I drive and then I forgot the lights and then I forgot the laptop and then I forgot this. So, but here we are and I

4:07 really appreciate you making the time. So, Dell, give us an idea of the of the kind of work that you do today without mentioning your employer. I don't want to get into trouble, but I don't want to get you into trouble either. Yeah. Yeah. All right. Cool. No, thank you so much. I don't know if you guys can hear me clearly. I can hear you so they can hear you. Perfect. Great. Um, yeah. So I think what I would just like to share in terms of like what data science is um so it's basically in a normal term just the science of using data to make datadriven um decisions. So to solve all problems. So this doesn't span um specific sectors. This can be used from sc um from health science from you know finance um education also I think we

4:56 were having a conversation um last week with prof you know how this is changing and actually then driving transformation in the educational space. So basically it's just using this data to actually make decisions um out of that and I think you know on a day-to-day basis we actually use data to make decisions without maybe at some point not necessarily like thinking in the back of our head that we may be doing data science you know when you wake up and you want to go to work you already know that there's traffic on a certain route that you want to take for example so you start thinking okay fine maybe I should actually you know leave a little bit early when a lot of people are traveling to work. You know, you see you incorporating now data in your daytoday global

5:41 way of work to make a decision. So that's practically what we do. Okay. Okay. And and how does AI fit into all of this? Because you're talking about using data in our daytoday decision making. H how does AI come into play in this regard? So now I think we spoke about like you know having all the different data sources um right to make your decisions but like now how AI comes into space is that you have vast amount um amount of data. So now some of the things that you want to do with that data is to actually for it to start learning patterns. Um so now you want to say because I've seen that like the example that I gave earlier with traffic you want to say because I've seen that this route has um

6:28 high traffic at the point in time now you start calculating um traffic from different points getting whether there's there's an accident on that road. So it's like the integration of that and the learning behind that and also using it to either you know predict the future actually um in terms of what is most likely to to happen next. So that's also what AI thinking and also in the new trend of AI the ways of work is like integrating all the different components um using data machine learning um generated AI then to make it autonomous as well. So yes so so so basically AI helps uses that data to help you sort of in the decision making process the

7:13 decision making process. Yes. Okay. But but Dell, you are from Bul in Liu. Yes. Did you know anything about AI before you went to vets? I mean, you went to vets years ago before AI became a thing and you registered for a BSC degree and from interacting and working with you, you I know that you had never used a computer in in the rest of your basic education. Basically you you are from a lowincome family, single mother in a rural village and then boom you go to vets because of your top resulting metric you register for the BSC. What courses did you take? Um yeah so I think just to also um to give like a little

8:00 bit of background with regard to that as well is that um so because like you said I come from a very low income school you don't have career guidance you don't like you're literally just going to school most of the time like you have to go to school and you go to school and that's mostly just about it um but I think I was fortunate enough in that when I was in grade 10 I had a semester teacher who was from sim and from that she was like no I actually identified you as one of the students who worked at Google and um there's this career you know called um acturial science which I think would fit well with with mathematics um so and I didn't know what it was I just going on science be you know an actual scientist at some point and um when I passed my trick I had go

8:46 to v when I went to v initially the plan was like okay sh I'm still going to do science but when I got there um when I on the queue to register. Then I start finding out that you know like oh it's very difficult to actually you know get through actual science and I was like yo I'm just a little girl from Jame. Yeah. And so the the other guys were like, you know, there's actually like a way you can get it. First, you can first year pure maths um computational applied maths and then economies with accounting, then transition into science the following year. Ah, so you were basically going for a tural science then

9:33 you got scared then they were advised that you should go this way of maths computational and applied maths and economics and then you will you pivot into a science in the second year they were advising you that maybe you go into science in the second year it might be manageable for you. It might be magical because at least maybe I would have transitioned and understood the big lights and the big buildings. Yeah. So, and I think when I got there to register um I spoke to one of my supervisors. She's actually has been my supervisor my whole entire life. Yeah. That day. So, I met her and was like and then I met her and then she she said you can even add computer science. like I

10:20 have never touched a computer. I've never I've never touched anything on a computer and there's computer science. I'm not even going to be able to do this. I was like, but it's fine. You know what? This is how I got here. Um I wanted to do well and I will try to do this even here. I'll try my best. then it computer science um um so then that was math um computational and applied mathematics computer science and um and eos um yeah but the plan was still we're going to back but yeah let let's let's be honest I mean I always say to young people a BC degree is a very difficult degree by all standards but if you choose to take computational and applied maths maths um and computer science and EOS. I mean,

11:07 this is a killer combination. Combination. Yeah. Because you're taking you're taking all the difficult subjects and there you are. Boom. And you you majored in computational and applied maths. Yes. Yeah. Um so, so basically you went that route and then you completed your first degree. You know, you didn't know what these subjects are, but you went into them. You completed your first degree with computer science and computation and applied maths. after you got your degree, your first degree, BSc degree, then what? Yeah. Um, I think at that point I still wasn't sure in terms of where this degrees can take me. By the way, Dale, here's another thing. I say a BSc degree is difficult because people get into it.

11:52 But you are not a scientist when you have a BSc degree. You are just a graduate of science. Graduate of science. Yes. You can only be called a scientist when you have a PhD. you are just a graduate of science. So you didn't know what to do with the with the junior degree. And then what happened? Yeah. And then from there because I don't have enough information in terms of where to go. Then I did an honors. Okay. Um computational and applied maths. So I was still fully on campus. Um and even from there I was still looking in terms of what is what like people that have left after the degree are doing and um they had gone into like you know your quan space and I was like I still need to make a decision. I'm not quite sure if that's the route I want to take. Yeah. Um and then even after that you know you still feel like there's

12:38 still more that needs to be done. And then that's when I went into a master's degree. Okay. I'm still trying to figure out this um this journey like what do I do with all the science that I've studied. But what did you do for the masters? What what in in honors you went with computational and applied maths? You did an honors in computational applied math and then in masters what what what did you go with? I continued with computational and applied math as well. Okay. Yeah. Yeah. So um and then I think when I was doing my masters um that's also when I started like getting some out of academic into research space. I went to study at a um I mean to to work as an intern in a research um institution that

13:23 had partnered with V at that time. Um and I think from then that's when I started getting into like the background of um machine learning, data science but from a research um point of view. Right. Yeah. And I think those ones we can give them a a a name because you know they IBM invested a lot of money in this institute advance and so it gave a lot of students opportunity and you were one of them. I was one of the an intern in that IBM institute. What where was what was it called by the way? The place um it was the the research lab the one that Yeah. In Brafontain it was like Africa. Yes. Yeah. Africa think lab. Africa think lab in in Brafontaine that's

14:10 funded by the by by IBM and students went in there to get internships. So that's where you went. And what happened when you were in there? Um yeah so I think that that is when I started realizing that the space that I have been in which is like then just the computational applied maths and mathematics um is actually the foundation you know of machine learning and I didn't know all of it like it just became an aha moment for me I'm like so it's like the the whole time I was been led to this thing you know because I walked in without knowing a computer then I'm doing computer science Now I'm here. So now it's like just understanding that. So from IBM then that's when we're doing like a little bit of research with health companies

14:56 and we're still using like machine learning. Um that time we're doing research in TV. So basically how can we understand TV from its DNA data and we're using we were using AI for that. So that was like quite eye opening. Um and then I think that is when after that I realized that this actually the background of all of this is what I had studied. So it's just like a matter of now applications um to different industries. Okay. And it's at that time then I remember that's that's when you pivoted now you you pivoted to a PhD in was it in data science or was it in machine learning or AI? What was it? Um the PhD was still in computational um and Okay. All right. So now you are in this

15:44 space where your knowledge of computational applied maths is needed. You can see that that form the base but then it's getting practical. You are now you you are you are exploring problems of the world and then how how does it happen that you find yourself in in the AI world and data science and you can even get a job in that space in data science. Yeah. So when we were there at IBM one of the things that we used to realize that coming from um you know a research full background the the application side of things was not there especially if the machine learning point of view um so what they did was to put us onto some courses. So IBM at that point had created a course um that all

16:29 the interns were required to do which had um learning which had most of the data science component deep learning as well inde um so that's where I studied it and over and above that there was like that we did during that internship as well course yes yes yeah that um yeah was was from IBM as well so We did that and I think from there that's when the full transition actually started. But what was the name of the course on course that you did? Was it AI? Was it Yes. So it was we we did all most of the company. So it was like a full on course on um machine learning. So we started from the foundations um basic statistic,

17:15 descriptive statistics um all the way to now deep learning at that time. Okay. But there was someone who was guiding you. these are the courses that you should take these courses and yes there was full guidance on that okay now Dell tell me so you and I are talking about this you studied mathematics that's why actually you and I get got to know each other but but here's the thing there are many people who have not studied mathematics and they are out there and they're saying I want to get into AI is it possible at all to get into data science or AI if you've never studied mathematics or statistics Yeah. Um actually it's very possible um at this point in time. So I think one of the things that even makes it easier

18:00 even the the developments that we have in AI right now but um one of the things that we actually do like maybe just to give also as like a background for that in terms of some of the things that we do even in the company that um for we have people who are coming all the way from spaces that are not data science or mathematics backgrounds. Um and they they come from HR, they come from you know um finance, they come from customer value management. Some of them and most of them have never even done mathematics or some of them haven't done computer science, they haven't done programming to start with. So um it's definitely possible via like one of the things that we actually do like one of the things is the cost itself. They go through some

18:46 costs. Um so when you say they come to you, they come to your company. Um so this is people within the company. So people within the company or so the company in your space would recruit people from different disciplines and they will join your unit which functions like the program but your unit functions like a consulting kind of unit in the company. Yes. Yes. Yes. Yeah. So what happens is like they would come in from their spaces and what they do is they go through the program the six month program and um through the six month program they fundamentals you know of data science um and they would learn at the point they also go to deep learning and generative AI but the whole point is

19:33 usually just to get through the fundamentals and what I have seen through the journey like it's also like the people that succeed thenition from the training program to actually being parents or um citizens within data science program who just have skills in terms of um problem solving. So I think all of us we do have that right. Yeah. Hence I said in the beginning anyone can get into the space. So they have problem solving skills. Um they are very curious and they always want to grow. So if you have a curious mind because we have vast amount you always need to ask yourself the why. Why are we doing this? What are we trying to

20:20 and you ask the data the questions that you want. how does this variable you know um make have an impact on the other variable. So it's like you just need to have like mindset of want to learn and want to to grow you know that's from the software side of things um but then from that it's just technicality programming and which now with what we have in terms of AI you can you can learn programming very fast and have yeah we have now thing that you call co-pilots for example. So you can literally be working side by side with an AI that can help you programming. So that's there's also that that's fantastic. That's I see someone called

21:06 Malopee who's also talking about this and maybe at some stage weo D and I will allow you to join us but I just want to continue with D a little bit because we is also explaining her journeys. She says she's in the tech space as a data analyst. She doesn't have mathematical sciences or stats or programming background. She did a digital skills program in 2023 and has since transitioned you know so so it's exactly what you are saying that you're working with people who are transitioning h and since then she says she's done business and information technology data analytics business analytics but if I'm not in your company how do I start I maybe I have a degree in sociology or in business management I'm frustrated I'm not getting a job I don't know what to

21:52 do and I'm interested in AI and I'm curious. I mean, you said things that are important. If you are curious and you have an inquisitive mind, you problem solving skills, then this is the space for you. Let's say I'm that person. I've got a business management degree. Um, you know, I'm struggling to get into an honors or I don't have funding, whatever. I want to get into AI. Where do I start now? What's the quickest, the easiest, or the most accessible way to go in? Yeah. So um with that there are also many programs out there that are free. So what I would just um probably like advise there would be to do some online courses um in terms of data um and data science and the cool

22:38 thing about that is that data is easily now um data science or the space itself it's becoming very um democratized in a way. So there platforms where you can get data already that you can you can let's say you are in a particular space like you said business management um I'm sure whatever space that um the individual would be in there's a lot of data that's been collected on a dayto-day basis so whatever space you work in you can go to a platform like kegle and ask for data in your in your space let's say I want to say I want to actually um maybe just give an example let's say I I would like I education I want to now start helping um learners in schools and I want I want to understand what the problems are in a school

23:24 setting why the learners are not passing or something like that. So you can get data like a quick data set ongo or you can even create data set now and take data sets um besides learning some of the skills there of data science online and try to start solving problems practically you know create a problem that you want to solve um let's say like I said with the example of a teacher maybe you're a teacher who just finished and you don't have work right now to go but what you can do is you can build a problem around that and solve it using data So like data sets that you can already start using in your space but online there's online courses that we I think we will attach to this that people can go learn um for Udacity

24:12 um and from also like a South African point of view as well even if you don't want to do online something called explore AI um what happens that the African um apply They give you funding for that as well for you to go through a certain program for like um a year or so and they even place you on top of that in you to start doing work which is quite amazing. So they they you if you register with explore AI is it free though? Um they do provide funding yes for some people. Yeah, I think there's a qualifying criteria. So, we can just attach all the details here for um the viewers who would love to transition to also um just get information on that. But they do um some people but the big

24:59 deal is that if you go through their course, the explore AI program, they even place you which means they find a job for you. They find they help you find a job. Okay. So, so there are opportunities here. Yeah. Go ahead. Yes. Um what I also wanted to say is that over and above maybe thinking of just being placed in a company and stuff like that, one can also start thinking about what products can they build and actually sell out there, right? Um we have a lot of problems in South Africa that can be solved with data. So maybe you can also just you know pivot to entrepreneurship but like from a data point you find a problem where you get data once you you you have established your name in terms of the data science platforms and learning then you can even

25:46 start solving like being um an independent consultant you know just build that that you your CV up so that you they can already start seeing that you can apply data science um you have solved practical problems as well. So that's why I'm saying like just the the the data sets and solving problems after you've went through the courses it's very important go through the course but also do the practical side so that um people are all actually able to see that you have applied learning to an actual problem. This this is very interesting weo welcome. Hi we thank you so much for agreeing to join us. Um I want to hear you say something so that we I can hear that we

26:31 can hear you. Um thank you very much for inviting me to be part of this live. So we tell us when how did you get into into AI or data science? How did it start? What did you study something? Did you pivot? Did you get invited? Were you sitting at home unemployed? Because you unlike Dell who studied mathematics and computation and applied maths is a hardcore mathematician. this one. You didn't study maths at all. How did you get in? Um, so basically I I was studying at a university in the Free State. Uhhuh. And I had initially dropped out due to to funds. Uhhuh. Um, so by the time this

27:17 happened 2023, there's an institute called EBL Institute of Business and Technology. Mhm. Um it's based in Bloomfontain and it basically offers digital skills for digital jobs program in the free state, northern Cape, Eastern Cape, Western Cape and Head. So like I mentioned, I I don't have any mathematical or programming background. I applied for this particular program. By then we we were studying business and information technology by the UF business school. Mhm. Um and basically taught us about AI, machine learning, big data and this is data governance.

28:03 The the EBL got you into the business school to do this course. Yes. So it it's partnered with MTN foundation. Okay. Um and then by then it was working along with the UFS business school. Ah, okay. So, it's a it's you don't pay to be part of the program. You basically just apply for it. Um, and then they select 100 students per five provinces that I mentioned. So, in total provinces, the provinces are three states. Northern Cape, Eastern Cape, Western Cape, and Quazulunat. Okay. Okay. And where do people get this information on this EBL so that people know where to go to apply? Is it online?

28:51 LinkedIn, Facebook, Instagram. It's EBL Institute of Business and Technology. Okay. Or by the website www.blinstitute.com. Okay. Okay. So then you you did this course. How long did it take you to do this course? It was eight months. Eight months. And when you dropped out at UFS because of fees, how far were you? You what? What degree were you doing? A BA? I was doing a a BA. So, and you did it for three years or for two years or for one year? You dropped out after how much? So, before I was doing marketing. Okay. And then I I changed into BA. Okay. Okay. And and and so by the time I dropped out, I was left with my last

29:38 year. Wow. And then in eight months you were you had completed h the digital program that EBL was offering. It is okay. Does digital this um EBL do like D is saying do they place you or do you then hustle after that for a job? Um so at the moment they are SA accredited. Okay. Um and they do work along with Harambe. So they do they don't necessarily place you but they lead you towards having access to markets. Ah okay. Okay. So you're not necessarily guaranteed a job but you're guaranteed to get opportunities and to see what's available for you out there. So what do you mean you're not guaranteed a job but an opportunity? What does an opportunity

30:25 look like? So an opportunity would be that on Harambe they list the the available job opportunities that are that are there throughout the country and you would still need to go through the process of applying and going through a job interview. Okay. So you apply through Harambe you get so you did all of that. So like I mentioned when I did it in 2023 it was business and information technology via business school. they were not sea accredited by then and then last year they changed and became a SA accredited institute. Okay. So that's when they they are offering data analytics cyber security and digital marketing. Okay. So basically we can channel people can say to people to try

31:11 that by the way we're doing this for people so that people don't you know people sit there with billow and dell graduates and they say oh I don't have a job there's nothing to do hey whatever what nobody's going to give you money for free so this information is very useful because you you learn a skill and there's a possibility of also getting jobs so there's a lot that that actually through sittas our government is doing to make opportunities available and our corporates are also doing to make opportunities available. Um, so now you have a permanent job with I'm self-employed. So tell us about that's what D was talking about. That's exactly what D was talking about.

31:57 In my comment, I mentioned data analytics and business analytics. So how that came about, I was the top student amongst the 500 students. Wow. Um and because of that I was awarded an opportunity to further my studies. So that's when I studied data analytics with UC online. Um you can study UCT has partnered with get smarter. Ah yes. Yes. So you got that opportunity to do what what that opportunity offered you to do business technology. Data analytics. So I studied Yeah. through the study bary I studied in analytics and then they awarded me a second bary again this year and that's when I branched out into business analytics. Okay. Okay. What do you do

32:45 now working for yourself? Because that's what Dell is encouraging when you go into this business. I mean it's a it's a brilliant time to go into AI as a career because there aren't too many people and some people are busy wasting time critiquing and disagreeing with it and whatever. Once people are doing that, you guys I like the fact that the little women, women, black women in Bhut are getting on with the program. What do you do in your self-employment? So, I am working as a freelance data analyst. But then apart from that, I'm also working as a virtual assistant. So I I help so I basically help businesses um automate their workflows and their operations.

33:30 Um I assist different individuals in terms of um what working remotely entails um where to apply for jobs. So you find that a lot of people don't even know that um places like Fiverr exist for you to actually apply for jobs. not necessarily in South Africa but across the world. So there's Fiverr, Indeed, LinkedIn and a lot of people don't even know that those platforms exist. So I basically help you automate your workflow, your operations. Um, and how do you make the money but virtually? How do you make the money? Do you get paid by the companies that end up recruiting them or do you charge the people you are helping to get the jobs? So the people I'm helping is just to I'm

34:17 showing them the opportunities that are available. Yeah. Can apply for platforms. So I'm only charging for that part but once they get the job the money go straight to them. Okay. Also you charge them just for that. So it's not like you are not an employment agency. You are a virtual assistant. So you help the individuals get the job. So if someone came to you and say can can I can I just ask? So I I I assist individuals in terms of understanding what a VA is, which is a virtual assistant, but I also acquire clients because a virtual assistant is basically you're a personal assistant but virtually. So I can get a client in Cape Town or Johannesburg um or Dubai or Egypt, wherever the case

35:04 is. And I'm based in South Africa. So explain the acquired client. So you get a company that meets clients to do VA job for them. So I can get either a self an individual client Mhm. um who could need maybe um assistance with calendar scheduling their calendar, their meetings and so forth or a company that needs maybe assistance with typing um or I'm also a self-taught web designer so I can help with um designing websites. Um so it depends on on the work that they need and the scope of the job. All right. That that's that's fantastic. And I'm hoping that people are taking notes.

35:50 Do you know personal assistant but virtually that's that's all it is to personal assistant but virtually. I have seen we on on on on LinkedIn jobs being advertised South Africa virtual executive assistant and I was like I wonder what this is but this is what you're talking about really. So you're basically assisting with um the calendar, scheduling of meetings, typing, um doing presentations. So it's basically a personal assistant but virtually. Yeah, he's a personal assistant but virtually. So they could be sitting in Cambodia and I'm here. Definitely. Okay. And that's one job that graduates can do actually. Yeah, definitely. Yeah. Yeah. I I hope all my

36:37 mentees who tell who've been telling me that they don't have jobs and blah blah blah, they're listening because I said to one last week who said to me um they have a degree in business management, they're looking for a job. And I said, "What kind of a job?" And and they said office to be an office manager. And so they've never worked. They're very young in their early 20ies. Um, and so I said to her, you know, do you know that your competition, you're competing with AI AI machines because there are AI tools that can do that, you know, I'm sure I'm sure there are but then they also competing with many other people all over the world from what we was telling who might have the

37:23 degree or not. It's really neither here nor there. It's about whether you can do the job. True. Okay. So D talk to me about this because now what we hearing from Vipo Villo I'm going to whilst I'm talking to I want you to think about whether you are willing to tell us whether you are making money. You don't have to give me an amount but but I want to know what's evidence that you are making money. Okay but think about it. I don't want to catch you off guard and and embarrass you. So think about it just so because what we're trying to do is to say to young people of our country h AI is not the end of you AI comes with an opportunity and the opportunity for you unemployed graduates and and people

38:08 who what this is an opportunity people who are there are people who are very unhappy in their careers and they want to pivot to AI because that's what they like we are trying to say to them this is what you can do so DO Tell us about some of the AI tools that sort of can do some of the jobs that people like we just talked about the office manager. Is there other things that you can think of off the top of your head that you say well the AI you know there's this skill then that skill that skill that AI can do. So actually if you at university because you spoke like h professor Mariva last week uh who actually challenged us to think he care heavily about why we learn

38:53 and you said h we always have to think about why are we doing what we are doing. So you said there's a lot of data, lots of data out there. H there's lots of things that you can do if you're curious basically always thinking about why are you doing what you're doing and what's the problem that exists here and so on. sort of give us some idea of some of some of those skills that AI can do that that young students who are adversity now people who are sitting at home know that they shouldn't actually think that that's the skill that's going to get them a job but they need to know a little bit more. Yeah. Cool. So like I think when you actually asking this question I am I was thinking about for example for people now I think there's

39:41 conversations about um the people who are in the art space for example saying that yeah so saying that like nowadays this AI can actually generate canate portraits can do all of this but in the background of that like as an artist one can even use that because at the end of the day the idea comes comes from you. You are inputting into the AI to create what you want. So the IP sort of stills with you. So for example, we can have the people in the art space for example using AI to create help them create some of their artwork, craft all of that. Um maybe still in the AI I mean in the art space um one can even start looking into like you know how they can

40:27 create some of their their poetries to sell out there as well. So I think that's like you know a good faith. But one of the cool things also about like the current AI that we have is that say I am just sitting um in a village and I'm seeing some of the problems right there that can I can help like the government like the municipalities instead of like operationalizing the processes that we facing. You can actually also use AI in the same space to craft your business plans for you to speak to the government to say maybe water systems for example like I think that's why I say this space does can solve problems in all the spaces we have we have water problems in um some of the

41:13 locations around us. So one thing is try to find out if we do have data that governs the water pipe the water system how can we best operationalize that can do we know when people mostly use their water can we optimize that to that there's a lot of things that people can think of even in their local spaces where they are um and it can even help them even with the initial processes like ideate your your business and then start crafting some of the business plans for you. So, you know, back in the day, you'd always have to find I need to go find a company that can help me with a business plan. I need to do that. I need to do that. Most of the time now, you can literally have a visual assistant, a a helper, you know, a

41:58 mentor that can guide you through with the business, that business plan so that you can start making moves, you know. Yeah. Even with your CV, making sure that like now your C CV looks proper. What do you need in there as well? if you're going to go root the the the corporate type of route as well. So there's like I think you vast amount of things that that we can do and think of. It's just maybe thinking about the problem that is affecting you in the current moment and once we have that solved how can we then manif that that's a good point because you're saying look at the problems that are in your community. Okay. The the solution is not just to go into but is to think is there a possibility that I can

42:45 utilize the AI tools that are around to solve the problem and then you go to the municipality and say hey I can solve the problem this is how but you you solve so it's a problem solving actually problem solving approach that you can have right we someone has asked whether you're doing was suggesting that you do virtual office and you said you're already doing it so what's a virtual office is the um it's basically just having an online presence. So people would just so like your typical office physical office um so you would have a platform where people could register sign up and mention the type of um problems they with. So asmemes are you struggling with

43:31 for example creating a business plan or your operations or HR or whatever the case may be. So I do have that it's just not formally set up online. So I don't have a website where you can go and do the things that I just mentioned. Okay. Okay. But but basically if someone is starting a business like D is saying well you can look at the problem that your municipality is facing. um get a some II tools to help you whatever you make a proposal you can help one municipality but then you can have an office that actually handles that kind of problem and other municipalities can actually find you there good it's that kind of thing but but whilst you are there you can say well I'm also a virtual assistant so I can

44:17 actually help you so you can offer the services virtual services you put them there on your website people can find them and and and you are just managing them you know from basically your home. True. Right. So are you making money with pillow? How do we know? Because because we are asking people we are we are we are we are telling people that they don't have to sit at home wait for a grant. I mean I just want our young people to have a little bit of pride to say I went to university, I studied. I don't want free money. I want to work for it. I want to feel worthwhile. and and so um I'm not going to queue for 350 or for 750 or for 2,000 because I can make more. What's

45:04 the evidence that you're making money? Um I am making money but like any other startup we all need to start somewhere. So it's not the money that I'd like to make. It's not coming in on a consistent basis. Um the company is fairly young. Yes. But right now, my focus is on building relationships. Yes. So that I can have long-term clients instead of just having a once off client. So I'm in the process of building those relationships. Yes. Yes. I mean the issue is that you are not begging anyone for food, for rent, for transport. You are not begging. You are not sitting and begging some guy to No. No. But that's not in my view.

45:52 That's that's enough evidence because we've got people begging for 150 rand on on on social media and you are not one of them because you are running this thing. So anyone who's sitting out there are opportunities if you don't want a career where you get employed in corporate to do AI as Dell said you can be an entrepreneur and we pillow is showing us one way of being an entrepreneur. It's not the only way. It's just one way and it's make it's she's making it happen and it's growing for her. D I want us as we go towards the end um to to sort of listen. So let's say I've listened to this and and then I'm thinking you my my

46:38 politics degree is not helping me. We can't all be political analysts. You know what I mean? Um, so what do I do? I want to go in to to this. Oh, let's before I go, someone is asking for the name of your company. What is it? It is called Immaculate Group. The what? Um, Immaculate Group. Immaculate. Immaculate. Yeah. But you can find me on all social media platforms. Um, LinkedIn, Facebook, Instagram. Yeah. with my loop. So, where do I start with my degree majoring in politics and history and I'm struggling to get a job or I have an internship that I don't like what I'm

47:26 doing in this internship. I don't want to see myself doing that for life and I want to get into AI. I have a laptop. I have access to internet. How do I start searching for the right place to go to to start building this career? Building that. Yeah. So, I think that's a that's a very good question um mak. So, I think one of the things that people can start following is the online platform that will provide you the fundamentals, right? So, there's um there's online there's Udacity. Um so, if you're going to go Yeah. If you're going to go the online route, there's Udacity. Um there's Udemy so the classes are also quite affordable. Um so like where if someone can afford um to do it like some

48:13 of them are 250 250 rands I think um even if someone can quick list ones at 250 you know or for you to finish your course um so one can start doing those courses and um there's also platforms and communities online where people actually show how they solve certain problems like spaces like um towards data science what Say that again. Towards data science towards data science. Yeah. Um spaces um such as um medium. So they're all there and kegle. Kegle is a is a is is a good one because they also provide the problem that they're solving as well. Which one is that? Say that again the name. Kegle. Kegle. Yeah. So then the cool thing about it is

48:59 that it also has um problems as well as data set. So people would have um solved some of the problems. So you can also start learning how to solve the problems from other people's perspective. And in South Africa there's Zindi. So that's the cool thing. Zindi um Z I N D Y. Yes. D I D I Okay. Z I N D I. Yeah. Yes. So um that one is like a similar platform such as Kegle but an an African context solving African problems. So they put up problems. Um and one thing there as well is that some of the things that they do they they put up the problems where one if you went through your course and you're like I'm now comfortable to start actually doing this. So they put out

49:44 problems that you can start solving, right? So the problem is a problem statement. It has a data set. So you can start doing those um problems online, competitions, pushing those and if you win, you get money for that. See like for now and once you so basically like the whole thing of building a profile for yourself. So you have that now you start start to build online presence. You might want to have like spaces like GitHub so that people can see the problems that you solve. if you solve a problem from Cindy, this is how you went about solving it. So you can start, you know, sharing some of the work out there. So I think that's like um a good place to start because it it doesn't only provide um just the foundational or fundamental skills or theoretical skills. There's also the application

50:31 side of it and you just thinking out of the box in terms of how you going to solve the problem itself. So um yeah, that's great. So Dell in your view um I mean usually you and I encourage young people to take mathematics and we are not stopping they we still want them to do mathematics because we still want the people with the hardcore quantitative skills to get into data science and and and and AI and machine learning but in your view there's no excuse for anyone from from someone who studied biblical studies theology to general ism to sociology and politics and art they can all come into this space. Yes. I think

51:20 anyone who is curious like I said because I think one of the things that we um we sort of kind of discuss even from a getting people in into the space thing is that most of the time you we do have the people with like all the degrees and the fundamentals and we will always be needed because AI is built by those people right um for for everyone else to use those things in the back end um the the systems need to be built by the core fundamentals people. But even if you don't want to go into the fundamentals, if you just have the great mentality. Um, so that's also one of the things that we look at when we get people into joining us as well. It's like you're a problem solver in terms of the tools. You can always learn the

52:06 tools. The tools, it's easy to learn the tools. It's easy to learn. Well, I can't say it's easy to learn programming, but you can now get tools that can help you. It's unlike back in the day where I had to always write code on paper first for me to know if it works or not and what not. Now I can write my code and say okay please check for me you know AI just check this for me if it does what I want it to do. So you see so it's like if you just have that I I would like to solve this problem and um and I want to grow and I'm willing to take to do whatever it takes. I'm willing to stay up at night learning how to program learning how to solve this. That's what it is. The tools will figure it out um as we go. Yeah. Okay. D I'm going to speak with and I'm going to close it

52:52 with with I want us to close later. But I want to in the meantime And who I know very well, student leader from University of the Free State is asking a question. He's studying education. So I want you to look for the question from Andel Madu and then we can just answer it because it's a it's about a teaching thing. We build on your advice. you you've walked the journey. Um and and there are people who are stuck like NSF is telling them N plus what what they have overused their time now they can't study they are but they're not stupid you know they're not stupid often times these young people are not stupid sometimes sometime even the ones who are academically excluded by the way they are usually not stupid it's either they made a bad decision or they made the

53:38 wrong choice or whatever there's always a reason they are not stupid nobody who gets admitted to university or whatever who finish metric is But they can do something. True. If you were to advise them to to start, I mean D has given us sort of a list of things where to start for you. What where would you say they should start? Um firstly just have a curious mindset. Yeah. And I think that's where we all need to start. Be curious. Yeah. Um that's the only way we're going to learn. Like Dinoa mentioned that there's so many platforms and there's so many tools out there. It does get overwhelming. M um but just be curious and sit down and have a conversation with yourself in terms of which direction do you actually want to take

54:24 don't get into data analytics because I'm doing it or don't get into data science because D is doing it. Maybe you're supposed to be a digital marketer. Maybe you're supposed to go into cyber security. So find your niche and from there you will then get to establish that learning mindset. you would you will you will then have that can do spirit where you actually want to sit down learn and work in that space. So I think just having a curious mindset number one um and the less tools you know the better. Oh that is something that has worked for me. these you know the better because today it's T GPT tomorrow it's D there's just it's

55:12 evolving so much and so quickly literally overnight okay that you cannot know it all so basically a tool you should choose a tool and stay loyal to it so that you grow with it yes until you understand how it works how it's beneficial for you and how you can build your company or your entrepreneurship journey and once you know and understand that then you learn another one. Aha. Okay. That's excellent advice. That's excellent advice. Even for us who are not planning to have careers in AI, we belong because it's easy to go deepseek and then you know whoever Alexa and then ch and whoever comes and we we

56:00 go and then of course during dinners we boast about how much how many I know and which one is better and so on. But you all know a little bit of it. So you are saying know this one, go deep, find out the way you can use it um for your own business or for your own work because in that way then you can maximize it rather than just ticking that you know the tool. True. Yeah. and and and I think it answers the question that global global health biotech is asking because he's saying which AI tool is better to write a business plan. So it depends or deepseek

56:46 or deepseek okay global health you have I just want to add that with all these platforms that are available these AI platforms that are available it's all in the prompt it's all in the prompt that's another thing we will get into that because I will come back the less the less tools you use the better because now you using PT and deep seek and and and and but then you're asking I'm not detail I'm going to have to call you guys back one day because you are right that it's all in the what do you ask this two to do so basically you have to know how to ask questions exactly you have to know

57:32 what you want you need to know what you want what you looking for what is the outcome what are the the final What's the fine print? What's that? How do you want this to look like? Yes. So, if you're just saying, "What's my hair color?" It's very basic. But as you as you can imagine, I mean, that's why I was saying last week that teachers have to think carefully about how they use AI and inter inter integrate it into their teaching because even that skill is a skill that we can teach at school. True. rather than prohibit students from using it. University students that can be part of the task to see whether how they how their prompts are working because it it helps you get better at using these

58:18 tools that are not going anywhere that they are here with us. True. You know my it's actually a whole career now. It's called prompt engineering. It's like you know you have to use both in in a in a corporate setting. Most of the time you need to have um business understanding like what exactly the business like the the business knowledge the yeah so you need to fully understand that so from there then you are able to create like a prompt that can get what you want from from an AI tool but without that it's a tool that we just use like you know and what you put into it um it's like the old saying about garbage in garbage out garbage in

59:03 garbage out Okay. So you can have a stupid AI too because you are not asking it good questions. You're not asking. Yes. Yes. So it's a whole it's a whole thing. It's a whole process. You don't right. I like I like the prompt engineering but we must pause here. Do you know can you answer the question that they asked about teaching and so on? Okay. Yeah. So I think just for the viewers so the question is may I ask which AI tool can help me get um data that can help one solve problems faced by learners when you are a teacher right that's actually a very great question um so to answer that what I'm going to say is before we even get for example to the AI tool the

59:51 the problem that I'm seeing here the problem that um Angela would like to solve is problems faced by learners right first thing is that we need to understand the problems that are faced by learners. So that can be done by collecting data. Um so so basically like uh attendance so that can be the some some of the data points you can use to collect um learn and I know it's already been collected but what you would like to want to do is probably collect it at a larger scale so that you have more learners in there you have more data like this learner has has not been coming to school for you know every every month I see the learner not coming to school on this particular days like that can already start telling something then as a teacher you can also use those

1:00:37 insights to start like finding out from the learner what is the problem. So and you find that like I think there's actually research that's out that that speaks about absentism from girls in particular you see that they don't come to school usually um when you when they start doing the work to understand is they don't come to school because they're on their periods and they don't have for example um you know stuff like that. So those are things that can be uncovered from data. So um before we even get to the tool the thing will be like the data component of it understanding from you know what what is causing the problems and how can we do that we can do that from now start gathering the data then from there um we can start putting some of the some of

1:01:24 the data that we have into analytics and I think there's um a process that is currently being used in schools I'm not sure if it's in all schools sams um that helps with like data collections. Um it's like a Yeah. So that's that tool and um so with that with all the data then what what one can do now is start to start to start understanding the insights. So it doesn't always also have to be AI at the end of the day like it can just be normal descriptive stats. let me just understand what is happening on the ground as long as you have the data um to just make sure that you can understand the problems. So yeah um I don't know if I've answered that for but

1:02:09 I can't say this is the tool that you can go with at the moment. Yeah that's fine. Thank you very much D. Thank you so much. All right. Can I quickly just add on what you know just mentioned a little bit because I know just I love the fact that she mentioned the importance of collecting data because most of the time we don't know how to collect data and even if we do we don't know how to use it. So what she just explained now it's literally something that is taken for granted by so many SMMES individuals. Um I mean even on social media platforms the data is there for us to use but we don't know how to use it. And I love the fact that she did not even provide an AI

1:02:57 tool because sometimes it's not about the AI tool. Yeah. So a platform like Excel, Google Workspace, you can already um collect data from just using a Google form and it automates it into an Excel spreadsheet and you already have your data. Yeah, I saw I saw Andy say he's already saying thank you very much 100%. So thank you. Thank you. Okay. So, let me close. I mean, but saying thank you very much D and B. Um, as I said, I I would like to bring you back at some stage, then we can talk a little bit more. But to but just to say to people if you missed everything and you just joined us, I was talking to Dell Doro and we Malibillo just joined us on this

1:03:46 platform and she's been brilliant. But now here's the thing. Dell studied mathematics, mathematical sciences, computational and applied mathematics. She's a mathematician who didn't know that she she can get into data science and and AI and machine learning. Weo on the other hand is a student who dropped out at university and went into a program offered by EBL which is um which is um was in partnership which is in partnership with MTN and partnering with the University of the Free State and through that institution that offers digital jobs in the Free State or developing people to get digital jobs in the Free State, Northern Cape, Eastern Cape, Western Cape and KZM.

1:04:31 She has managed to develop a career for herself. Dell is in corporate. Dell works for a telco company, a big telco company doing data science, machine learning, AI and so on. Weo is an entrepreneur. Weo works for herself and she runs a company the Immaculate Group. You can look for it, right? She runs the Immaculate Group. So and I'm telling you these things so that you can see the contrast that we Bill didn't study science or mathematics or comput computer science we bill and den so whether you studied them or not there is an opportunity for you it's different kinds of opportunities obviously but you

1:05:17 can build your career in AI irrespective of which discipline you come from they've given us pointers They said you can go online just look for Udacity. Udacity the programs that are offered by Udacity or UDI EU DNY or you look for a community just Google towards data science h and check that community join these communities get to know what they're about. But they also talked about Kettle and Cindy. Zindi Zindi Z I N D I um focuses on solving African problems. And in fact there's competition there. There are problems. They give you data. If you engage and get it right, you even stand a chance of winning some money. So so if you're

1:06:04 really really interested just get into these platforms. You'll get more. There will be more that you will that you will find out because some institutions South African uh based institutions that are Sitta accredited accredited can actually help you to get this education or to get into this area without having to pay money. EBL is now SIA accredited. So check them out. H and as you heard uh you you can check um we journey you'll see that opportunities are are plenty. So you have a choice you can either sit there get angry at AI the presence of AI criticize or make yourself the police to see who's used and punished people and

1:06:51 get them excluded at university or you can actually grab the opportunity make use of it to build a new career for yourself. So if you are sitting at home with a degree in biblical studies, theology, sociology, politics, mathematics, or physics, and you don't have a job, stop crying about unemployment as a graduate. Explore careers in AI. There's a possibility for each one of us. You don't have to suffer. There's a possibility for each one of us. Thank you so much to D and Weo. I really appreciate it, guys. Join me again next week. I apologize for starting late to today h but I'm glad that you were here and this video will be saved on my YouTube channel as well as on on Instagram and you can come back

1:07:36 and look at it again in full. Thank you so much. Have a good evening. Have a good evening we'll be in touch. Thank you ma. Thank you. Bye bye. Thank you D. Thank you so much. All right. Thank you so much to all of you on YouTube. I really appreciate your patience. We'll see you again next week.