The untold story of women transforming AI

Great. I think we are, we're getting people trickling in. I can see the number of people joining and we're ready to kick off with this webinar. This time I am the one doing the opening conversations considering that we're a little bit later in the European Asia time. It'll be early on the Pacific and Eastern time. But let me kick it off. My name is Mark Esposito. I'm the Chief Economist at MicroOne, and I happen to be also part of the faculty at Harvard University. We've been doing this series for a few months now covering some of the major thought leaders in the space that range across domains of expertise in artificial intelligence and beyond. And the series that we've been running at MicroOne really are designed to fundamentally capture the essence of where we're going with the conversation on technology. So Today is a particularly special day for me. Buna and I have been chatting in the background for a while and trying to get this event off the ground. And as you know, we're recording this and it'll become a video that we can eventually showcase later on. Very nice to have you in this conversation that we're having on AI for Good. Let me give you a very, very simple introduction before you probably going to take it to where it deserves to be. So I know that Bolor is your first name, Bazangel is your last name, but you go by Yubuna, and I'm happy that you allow me to call it this way. You are renowned in your field to be the first Deputy Minister in Digital Transformation in Mongolia. I think it was the first time that anybody was having that job, and I think first time that a woman was having the job. And that, I think, defined, I guess, the level of opportunity, but also responsibility that you feel on yourself in carrying forward this conversation about technology, women, but also in a part of the world that people don't necessarily always talk about, right, in terms of technology. So this is what I'm excited to welcome you to this conversation. But look, it would be fair for me to ask you, just tell us your journey, what has really characterized where you are right now, and welcome to this conversation that we are hosting with MicroOne.

Thank you. Thank you so much for having me, and I'm really, really glad that we managed to do this after chatting for so long and arranging our schedules. And Mark is everywhere. Whenever I talk to him, he's either in Dubai or in the US or now in London. So it's amazing to be able to do that. And as you've mentioned, my first name is Pilar. People call me Apuna, which is more like a family kind of coupling name, which later on became my kind of political career name for some reason, for some funny reason. And I was born in a small village in Mongolia. And my birthday is on the 24th of October. So every year on my birthday, my mom says, oh, I gave birth in this frozen hospital. And every year it's the same story. And she tells me like how lucky you are to be healthy and, you know, doing all this. And my whole family is a herder nomadic family. I still have my cousins, my uncles living as nomads. I myself own a couple, I mean, a couple hundred sheep and goats and a few horses and cows. And my whole family is really much embedded into nomadic culture. And in the first, so I did skip a few years in my middle school and high school. And when I tell this, everybody gets shocked because I moved to the city from the countryside when I was about 11 years old. And by the time when I moved there to public school in the city, I hated this school because they were bullying me. I had like big red cheeks and, you know, like people, everyone was calling me country girl. But I think the countryside education really gave me good foundational education. So from there, I was prepared for competitions, math competitions and stuff like that. And eventually I skipped 3 years in middle school and high school and finished my undergrad, finished my high school at 14, undergrad at 18. And then first 5 years of my career, I worked for international organizations, World Bank, Asian Development Bank, went off to Oxford, went back to working for the World Bank. And, and we did some project with Bill and Melinda Gates Foundation and Oxford University, which was a digital readiness assessment. I'm sure you do that a lot. And that led me to my political career, creating eMongolia platform, which digitalized all government services. So when we were doing that, we just wanted to give access to herder community. And now 95% of the Mongolian population actively use this platform on daily basis. It saves hundreds of million dollars every year, and in addition to that, it saves about 20 million sheets of paper every year. So I'd like that number more than the, more than the money part. Um, and after some time, and I see in the audience there are a number of Mongolians who I'm sure are with the story, but after some time I decided to leave politics and joined SpaceX and then later on came back to Mongolia to start AI Academy Asia.

Wow, there are many things I want to go a little bit deeper because they're fascinating. The first one is technology in Mongolia, and I think many people incorrectly they start thinking, what is the link? But from the conversation we were having, this is a country that has adopted quite, quite systematically, right? So tell us about the state of technology in a country like Mongolia. And considering what you were sharing with me, what has been the trigger to move a country to rapidly adopt the e-services you were mentioning? They clearly talk about the fact that technology integrated in the life of citizens, right? There are many parts of the world, and you know this by both the work you're doing around the world, but also the fact that if you were at the Washington, D.C. institution, you know that digital public infrastructure is an issue in many parts of the world, right? So what has defined the kind of success that, that you think Mongolia has been able to manage and achieve differently from other countries?

Yeah. That's, that's such an amazing, amazing question. And every time especially when I'm on an international conference. When I talk about Mongolia, it's shocking that a lot of people don't seem to know where it is or, or know it's an independent country. So we are very much centered between Russia and China. We only have two strong, very powerful neighbors. And Mongolia is— we have very big landscape, but we have 3.5 million people. So in the countryside, as you can imagine, there's a lot of herder families, which is considered one of the last actual nomadic communities in the world. And when we were designing in Mongolia, what we wanted was that the guy in the middle of nowhere in the countryside of Mongolia on a horse with the smartphone actually starting and registering a company online. Or actually doing Airbnb in his yurt. So, that was the dream that we wanted to allow. So, when we talk about technology, a lot of people think, like, oh, when you're adopting technology, you have to move to the city, it has to be metropolitan, it has to have skyscrapers. But the whole idea of technology should be around how accessibility it's creating for this kind of communities, whether it's in Peru or Bangladesh or Mongolia or Kazakhstan, anywhere in the world. So that was the idea. But we did have very good infrastructure in terms of fiber optic network, and later on we activated Starlink in Mongolia. And Mongolians are always very good at adaptability. So I think one of the key success factors of eMongolia is that people would have to spend hours to get to the government facility. And now they can spend seconds just, you know, get your government service. So that was the incentive. And I think we tried to deliver this not as a government project, but as like a product. So the way we pitched, the way we kind of did the marketing around it was more like a creating a story, making them understand we're making their lives easier. So when people do government projects, it's mostly forcing them to use our bureaucracy. So we use the exact opposite approach on that.

Wow, that's amazing. Bueno, as you speak, there's so many questions that are coming to my mind. I'll try to go with maybe one that I want to follow up on this. So you find yourself entering politics. You enter politics and you're appointed as a deputy minister. It's already an important role to have. And you're the first woman doing that in the specific function. What were the challenges that you were finding as you start the job? I mean, and also I'd love for people to understand what were you thinking before starting and what did you find out when you actually started for real? Just for us to understand the difference between the perception of what we see when it's more of an institutional framework like the government, and then the fact that you now are wearing those shoes and you're expected, right, to do something about it. So help us to understand really your journey as you were going from a pre and post and what were the first things that you had to deal with?

So the reason I joined the government was just to do the Three Mongolian Project. So I didn't think much. I just thought that because I, you know, I want my family, I want my sisters and brothers and the whole country to have access to a government. And that was it. So I didn't really think I was going into politics. And it was obviously very naive of me, but I really had like a big dream to create this accessibility for everyone and create such an amazing story and give access to girls and children. I do have an NGO called Girls Code where we train girls from nomadic and disadvantaged communities. And yes, so I did join the government. Within 3 months, we launched in Mongolia soon after COVID happened, and that was— people started using in Mongolia during the series of lockdowns very intensively. And we run a campaign called Run Your Errands with Government while in, in in lockdown. Trust me, it sounds way better in Mongolian, um, than in English. But a lot of people did. So, um, and, and as, as eMongolia was getting bigger and more successful, I was getting more attention— media attention, political attention. And when I joined the government, I was appointed as a chairwoman of an information technology agency. So there was no ministry. It was one of the lowest budget agencies in the government. They had only 32 people. They didn't even have a technological team. So we created all this. And by the time eMongolia turned one year, there was a big discussion creating a ministry because around the world, every single country was creating a ministry on digital. And I did participate and support that because we needed a lot of policy support around implementing and really improving this ICT sector. And then they did appoint me as the state secretary and deputy minister afterwards. And we supported a lot of startups. We encouraged them. We procured their products. Um, yeah, so, you know, the first day I, I got into the Parliament House, I, I went to the security and told them, like, I'm here to be appointed as a chairwoman of an agency. And the security guards were laughing, like, so hard. And then they said, no, you must be the waitress, new waitress in the cafeteria. So that was really the really the presumption of, of young woman entering politics. And I didn't even think I was entering politics, to be honest. I, I thought I was just there to do this project and complete this project. Um, but then it becomes bigger, uh, the platform becomes more successful, and the attention— there's more, um, political issues, um, and there's a lot of media attack and, you know, the social media attack and organized bots attacking you or all this and that. So, and throughout that journey, what I really focused on was just delivering good work. And I didn't want to address all the gossips or all the fake news and just focusing on work. And for 3 years, I didn't have any, any weekend off, um, and I did not let people have their weekends off as well. Um, so it was very intense 3 years in the government. But if you measure what we've done in that 3 years, it's, it's incredibly much. And it's not just me. I've had very amazing group of people who came and supported this project and this sector and me as well.

That's amazing. Look, there is something you said about, of course, the prejudice that we have in many parts of the world about women and not only in political position and positions of power, but women in tech, right, in general. And look, since our conversation, in a couple of moments you mentioned technology and girls. Several times. And the reason why I'm particularly sensitive to that is in one of the companies that I co-founded years ago, one of the things that I used to pride ourselves is that our team in Vietnam was mostly female engineers, right? So I kind of grew very sensitive to the idea that this is not just a quota, but it's really a transformation that is required. But I like to flip it in a slightly different way. There are many girls that don't get close to technology. Like they feel this is not what they should do. So what do you think from your experience now that you are, you're in the private sector, but you're also in the kind of in the public sphere where you're using your personal gravitas to create conversations with people. And I understand that what matters to you is to provide access to people that usually wouldn't have access, right? So what is the kind of the precondition that is holding girls away from this? Is it just legacy? Is it pressure from men? Is it the fact that women don't project themselves into this? Is it the resistance or rejections? What do you think are the reasons that when you're talking to girls, they might not get close to the idea of technology?

So when I was in the middle school, I remember telling my teacher that I wanted to become like a computer scientist. And the teacher said, no, that's not for a woman. You should be an accountant. And later on when I became vice minister, I went to the countryside and visited a school in the village. And I realized the situation has not changed. It's still the girls are very much discouraged from pursuing any ICT career, let alone AI or any advanced tech career. That's why we start doing Girls Code program. So it's been run for 5 years. We do have 150 graduates and we're training 200 female teachers and girls at this very moment. But from— so when I started Girls Code, I just wanted to do one boot camp for one summer and that was it. But those girls from the first year, They went off to Harvard, they went off to MIT, and these girls specifically come from disadvantaged families. So we, some of them never been to the city, some of them never had smart devices, but it really inspired me and the team in a way that's like, these girls, when you give them accessibilities, they can, they can do anything. And once one or two girls did that, the rest of the girls became very competitive. So they were all of a sudden, they were applying for Ivy League schools. And some of the girls were doing as working as a part-time coder, paying off their family's debt or, or paying off their tuition. And so from there, we really got inspired. So now we have a girl, for example, who created an app for safety for children. We have girls who are studying at NYU this year. And we have this girl who's studying AI in Korea, but she went off to AI for Good conference in Geneva this year in June. In July and really nailed it, really nailed it. It's only 17 years old. So, you know, this— I think it's changing. One of the reasons is there, there was no female role model in tech. So from probably Sheryl Sandberg, who was very inspiring for many people, and there are now— there are more and more figures coming out anywhere in the world are encouraging more and more girls to But here at AI Academy, our tech team is, I think, 90, 80% women. So we have AI engineers, we have female data scientists. Maybe I am very biased and tend to hire women, but they're doing amazing, amazing work. So, and it's very nice to see them. And when you talk, and I'm sure you're familiar with this, like let's say Meta, for example, or Amazon. For example, a couple of years back when they say we have very good gender balance, it's mostly the lowest paying jobs, paid jobs are filled by women and highest paid engineering and, you know, more technical jobs are filled by men. So now I am hoping that the high paying jobs are being occupied by women more and more year by year.

Wow. I'd love to ask you the following question, and look, it's fascinating to hear it from you because I think we can feel the passion that was driving you, right, as you were navigating these choices. And a side comment to any of us currently connecting online, there's a Q&A space if you want to ask a question to Abouna. I'll be moderating those towards the end of our time together, so please use it as a great way for Bruno also to get to know some of the thinking happening in the audience. But back to our conversation. So now this is technology, right? And a lot of this has shifted towards basically AI. And I, you know, this series that I'm curating is called AI for Good. How much then did you see the transformation going from digital to AI? Because as you know, there's a lot of technology conversation, but not necessarily all of them, they lead to AI, either infrastructure, culture, culture and data. So how much would you find that in the context of Mongolia and the girls that you're training today, it is really about AI, or would you say still a spin-off or digital transformation?

Um, that's, that's very good question. I think it's, uh, we are in the midst of transitioning and, uh, from digital to AI. And you know, for AI you need to have very good infrastructure You need to have very good data. You need to, for companies, they need to create their data lakehouse. And there's a lot of preparation that needs to be done to be able to fully transition into AI. So, but I think the most important part is, and also the beauty of AI is, whether you're 60, 70 years old, whether you're in finance or agriculture or in entertainment, you can learn AI. But on the other hand, AI is transforming every single sector, everything we do, right? So there is a lot needs to be done. But I think the first thing we need to pay attention is upskilling and reskilling on AI. How do we prepare our people, our teams, our younger generation on AI? And, you know, Geoffrey Hinton became very skeptical about the AI and really that kind of scares me off and a lot of people off that human kind for the first time created a smarter thing than they can operate, right? So it's very scary in the long term, but how do we manage that? We need to manage by educating ourselves. So I think the And many of the parts of digitalization, like let's say within a company, they just give the digitalization part to IT team and that's it. But for AI, it's cross-functional, whether you're a CEO or a security guard, you need to know AI, you need to be able to operate AI or work AI. So I think we're in the midst of transition and Obviously, it's just the beginning. It's not even a beginning, probably in the bigger picture, but we are getting into something we cannot predict for long term. And the best thing is to know how, let's say, LLMs are working, how the algorithms are operating. And the good thing is that we're starting off from same place. So many developing countries like Mongolia, usually we were called like, oh, you're 20 years behind. No, you're 15 years behind. So now it's, I think it's a momentum, very important moment for every single developing country in the world to start with, with the developed countries. And even in the bigger developed countries, you see, the technological hubs are very advanced, but not the general population. Right. So I think we're in— we're off to a very chaotic but good start. Hopefully.

That's great. I am happy that a couple of questions came. So I'm going to go to the question in a few minutes. I just want to kind of finish on a couple and then we're going to open up for the Q&A. The first one is related to Mongolia and the geography in which you are. You rightly said you have two big and powerful countries as neighbors, right? China, Russia. And this country also have been decoupling a little bit from US-centric technologies. Everybody knows that China has their own technology that they resemble something that might look like Amazon or Google or Uber, but it's kind of different, right? It has— it's an entirely different contextualization. So the question is, around the world, people, they tend to position themselves either by being more US-centric in terms of the technology they use. If you are in a country like the GCC, you will find that by the nature of their geography, they kind of dance both with the US technology and the Chinese technologies. But now in your case, geographically, you're kind of, kind of locked into these two countries. Did you find that the country became a little bit closer to technology coming from the side of the world, or did you find that the country was able to strike a balance between US-centric technology and basically Chinese-centric technology? From the experience of maybe what people would never imagine about what would Mongolia do, would they go for ChatGPT or DeepSync? So how would you navigate that conversation in Mongolia?

So Mongolia is a very democratic country. And another interesting factor is that that 70% of the population is under the age of 38. So we are very, you know, we're very young population with very rich history from Genghis Khan, and everybody knows about Mongolian history, but it's very dynamic. So the young generation are very open-minded, very westernized in some senses. So for sure, majority of Mongolians are using ChatGPT or Copilot or Gemini rather than DeepSik. But because China is next to us, it's easier to travel. So a lot of people visit China to see, let's say, robots, how robots are operating. So in a way, I think it creates advantage for us that we can experience work on the US-centric technologies, but also go and see how China is doing faster and better than any other countries in the world. So I think the younger generation of Mongolia is very open-minded and very tech-savvy, especially on the AI. And we get hundreds of hundreds of people learning AI at the Academy Asia, and it's growing. And I'd like to I have to say that one of our graduates was 60 years old. He was, in my opinion, he's the best graduate from AI Academy Asia so far. And he learned incredibly, incredibly well. So I think the beauty of AI is it doesn't matter your age, doesn't matter where you are, you can still learn, right? But to answer your question, we leverage both of that. We try to leverage our geography, geographical location. In many cases, it blocks us from many things. But in tech case, we do leverage that.

That's wonderful. Look, average age, very, very young in Mongolia. And I'm happy you brought it up. It really shows you why, you know, Joe Strategy Parrot, that I know for many years, one of his books was about the future is Asian. We understand it, right? Because demographically, it's going to be where the larger concentration of people on a younger percentages, right, will basically dominate the decisions, right? They'll become the reflection of a world that will be much more Asian-centric rather than Western-centric. And so clearly, I understand why Mongolia feels almost like leading the momentum is a combination of demography, but also the geography in which they are. Look, I'm going to finish my side of the conversation with you. We have a few questions that are waiting for you already on something that you did recently. So you were in Vienna. On a TED Talk, and it's actually TED AI, which I think is a dedicated series of TED Talks on AI. I think the talk is not yet available, right? But give us a trailer. What did you talk about during your TED Talk in Vienna? I am personally curious. I look forward to the talk when it's going to be published. But yeah, I'd love for you to tell us what was basically the story you were sharing in your TED Talk, and then we're moving on to the Q&A.

So by the way, TED experience has been amazing. So it's, I was told that TED AI is the second biggest TED in the world. So a lot of people confuse with that TEDx and I myself did that, but the whole experience has been amazing and I got to meet researchers, scientists from OpenAI and VP from AWS and a lot of amazing, amazing people who are working in the sector. But you know, my, my kind of area of focus is AI inclusion. So the way we're working in AI Academy Asia in Mongolia is that it's— AI Academy is an international company. And what we're doing is that we're piloting in Mongolia. So let's say on AI education, Coursera or any of the AI education platforms we have are very much westernized. That doesn't seem to apply to developing communities. So my speech has been around AI inclusion and how AI is becoming a fundamental human right. So having access to AI has AI has become very kind of strong factor in human rights issue because it creates a lot of gap, equality gap. So when you have AI access, you can improve your education, you can work, you can learn, you can improve your productivity, efficiency, a lot of accessibilities. But if you don't have access to AI, It's creating further gap from the developed part. So in general, the world has never seen a bigger inequality gap in the history of humankind. So the concern I genuinely have is that it's getting bigger and bigger that we will not be able to narrow down And I think that's very, very scary. But on the other hand, you can use AI to leverage and give access and let's say AI tutor, filling the lack of teachers in African countries, for example. But having no AI access means a lot of existential problems like poverty, lack of infrastructure, lack of electricity. So it is really important, but now it's too extreme, too extreme. And when you see the robots working in China or how OpenAI is doing in Silicon Valley to the poorest part of India or Bangladesh or African countries, it's creating biggest, biggest gap. So that was the idea. I don't know. I don't remember. I was so nervous. So I don't seem to remember how I did, but hopefully it is fine.

I think we all look forward to seeing and watching it and to like it. So we'll get the full details when we watch it. But this was kind of a live trailer from the person that was on stage. So I really appreciate that, Luna. Look, you animate if your question come in. You know, we have an average of 40 people always online and Considering that it's a bit early in the US, they will catch up by watching our recording. But the first person who asked a question is Nicolette, right? And Nicolette was asking a question about basically pay scale. And I think she was referring to the US. She's talking about the fact that women who are working in the gig economy, they do provide training for AI, they do support, but somehow their hourly wages has been going down. And this is in the context of the US. So I think their question is about women who are involved, but they don't capture the financial benefits of the AI ecosystem. So when you're dealing with AI inclusion and imagine you have a girl who basically said, I'd like to do this, I'm just afraid I might not be able to have the same level of financial rewards that my fellow friends who are men have. What would you say? Like, what, how do you think we can break that vicious cycle where The job is there, but it's not fairly paid, right? Basically, the disparity in the pay gap.

Yeah, that's an amazing, amazing question because it is a problem anywhere in the world, unfortunately. And the way I see it is that we're doing a journey as girls, as women in tech, where we are in the journey. To create more gender-balanced tech sector, AI sector in the world. And this sector has been dominated by men for centuries and centuries. And now in the past, let's say 20 years and maybe especially last 10 years, more and more women coming in. So we have a long way to go and you cannot just solve it by one day or one year. And it's a long journey. Journey. But the way is that we need to have more women in leadership in tech companies. And on the other hand, I think there are a lot of girls and women proving themselves that they're as good as men or even better. And I'd like to think better. Obviously, I'm biased, but, but yeah, so it's, it's a journey. And we're maybe very early on in the journey, but we're working on it. It's getting improved anywhere in the world. So I'm sure we'll get there soon.

Great. Well, I'm muting during the Italian talking because I'm in a working space. So it's the lunch break here in the UK and the people are talking. So I'm going to follow up. I'm going to go first with the people that have their names so I can call them, right? So Rajiv asked you quite an interesting question. So always in the context of inclusion, AI inclusion, In the context of what you're currently doing, well, what do you use as a way to measure that actually there is inclusion? Is it just number of, for example, girls that are hired? Is it the opportunity to improve the productivity at the labor place? Is it digital equity? So it's more about the question on how fair the data is about representing, for example, underprivileged or underrepresented groups. So when you talk about the inclusion, and you want to say, I like to measure whether or not people are getting more or less included, what do you use as a conversation? It could be a tool, a framework, a conversation, a theory, an approach, a practice. And I think it goes back from what you, when you were in the government all the way now that you're running the academy. So how would you define the measurement of social inclusion when you're dealing with AI? What defines for you a success story?

Mm-hmm. So the way, whether it's a framework, whether it's a policy, whether it's monitoring and evaluation or impact evaluation, the place we need to start is that how many people have access. So obviously we need to, and that's very basic infrastructure kind of problem, but that's the first statistics we have to see. How many people do have connectivity? How many people are using AI? But to advance from that, the way we need this inclusion is that the design of LLMs, design of AI tools. So today, 90, 80% of the LLMs are being trained in English and there are bigger languages like Spanish or Russian or French or Arabic. But what about languages like Mongolian, right? What about languages like German, Portuguese, Italian? Smaller countries. So from the LLMs, the way it's being designed is, and also the way it's being trained on the data that are available on the internet, is very much English-centric or bigger country-centric. So from the connectivity to the LLMs and design of the technologies or GenAI, it can get very, very complicated. Complicated. But for, let's say, local, local practice, for example, AI Academy, we trained 500 AI teachers who come from the public schools of village. And now these teachers went back and they are training 20,000 kids. And that's a number. And there is a qualitative function of that as well. Like, let's say how by creating AI assistant, how many hours that the teachers are saving and how are they using their saved hour because they're supposed to use that to have more in-person one-on-one session with the kids rather than just going off and doing their own thing, right? So I think it's, it can start from, from the accessibility to very complicated level, but it's from the whether it's local level or international or regional or even global. So it really matters on that.

That's great. So now you kind of COVID a little bit on the last question that we got from Jasbir about, you know, some of the challenges that women face and how they can eventually overcome them to get to a point of leadership. So I appreciate that it kind of happened naturally in the conversation. Now there's a question from I'm worried that I will try to integrate also with the question that came from an anonymous attendee. And so, you know, many countries are kind of going in the direction of developing their own LLMs. And I think we heard Mistral in France being one of the very first that was trying to move away from the dependence of the traditional players. But everywhere you go, right, you're kind of looking at these small language models that are coming very, very indigenously, right, at the local level. And there was a question about whether, you know, and the question about the indigenous population, right, the UNDRIP, I think it's the United Nations for the Rights of Indigenous Peoples. So I think it's a question about the representation of language and culture in large language models. So what is currently happening in Mongolia? Is it mostly about increasing the training on existing platforms in the local language so that you're building a data, a kind of an archival data that is reflecting on the local basically thought processes? Or did you find that the conversation goes in the direction of building a country-owned large language model? So where do you see this, the basically the pendulum shifting in the context of the country?

Oh, on that question, I can just bring out one of my friends' work, which is called Igune. So what they're doing is that— by the way, first of all, I think data sovereignty is very important for many countries, data security. It's very important. And there is a company called Tumega and Igune. It's It's a same guy who's a good friend of mine working. So what they do is that they are developing their own LLMs, but also they're doing language conversion. So, and they can translate or help ChatGPT answer better in Mongolian language, or they can connect through APIs with ChatGPT but translate their English into Mongolian. Way, way better than ChatGPT would do in Mongolian. So that's how we're doing. And for a small country, small populated country, obviously when you see the amount of data available in Mongolian language is way lower than you would have in English or Chinese or Russian or French, right? So I think it's very important that the countries are developing LLMs, but we need to do it in a more efficient way. So you cannot block, let's say, ChatGPT and just use your own LLM, but you have to learn from ChatGPT, connect it with them, improve it, and make it more friendlier and accurate and more culturally nuanced. Yeah, that's great.

Well, the last question that I'm going to ask you is the one coming from Chuckie, which kind Almost nicely leads me to the very final question of our webinar together. So how do you personally navigate all of this? How do you learn on which large language model you're using? How do you learn and maybe build an agent for yourself? So what is your personal approach to the learning? I think none of us are born with the full-fledged skills because this is such a fast-moving technology. So we all pivot in many ways, right? But what does Obuna do in terms of catching up with the pace to the point that you can inspire both in your public advocacy but also in the AI Academy Asia, right? What is basically gonna be maybe the direction to invest? Because you mentioned before reskilling, upskilling, training, retraining. This is the critical part of making sure that people do not get left behind. But what is your personal kind of routine about AI? How do you learn?

I mean, at this point, I have to say I'm very privileged because I have access to people like you where I can learn and people who are in the hub. I have a couple of friends working in OpenAI. I have several Chinese friends who are developing robots, who are working on AI efficiency tools. I was in, I'm involved in quite a few projects in the UK on AI and AI public policy and AI efficiency for governments. And we were discussing Dubai, for example. You know, it's in a way world is small and I think AI community is small. So somehow I got to have access to this community where I they can get more insider information and learn from that. But also we're trying so many things at AI Academy. It's rather a small company, but we're doing a lot of POC pilot projects and learn as we do it and many projects fail. And by the way, for people who are listening, many projects fail on AI because you have less resources, because the data is not ready, because your consumers or clients are not ready, or it's just not worth introducing AI in some cases, right? So when we talk about AI, because of this hype, there is a lot of people assume it's like a magic, it's like a magic button, you do it and then all of a sudden every problem goes away, but it's a journey. So the way everybody has to look at AI is that it is a long journey and we're just in the beginning of it. So we will have to learn as we go. But the important thing is to kind of understand how the algorithms are working, to have very good foundational knowledge of AI. So whether you're using, let's say, Canva or Gemini or any AI tools, you need to understand how the algorithm is working. And another kind of aspect that we pay so much attention is ethical use of AI. And especially that part we need to teach kids because, you know, there are many cases where ChatGPT or this LLMs like led many teenagers to suicide or many bad problems. So, and one of my friends from Cambridge was joking that he asked like, how many stones can you eat as a human? And to ChatGPT, and ChatGPT answered, you have to eat one small stone every day and then back it up with like a page-long science studies. And then they were tracking that down and it was on the joke website, right? But for kids, they would not even understand that. And it would be disaster if they end up eating stone, right? So, so we really need to understand that as much as it's creating so, so much value and creating big market in the world, it is also creating a lot of problems and we need to be ready for that. And the way, the best way to do that is to have very good foundational AI knowledge, which you can learn by yourself using internet. Or I always suggest that the foundational part, you should get it from a scientist or a trained teacher. And that makes it way easier to do further studies on AI.

Well, look, we are at time. It's quite mind-blowing how quickly our conversation took almost an hour, right, from the time that we started this. And usually we try to target about 45 minutes. And I'm happy that we were able to extend a bit more because I think your story is so fascinating. I think it's the story of a person that was able to really navigate what is very uncharted territory. If you tell the story about a Mongolian female leader who has been able to rise on a global stage and talking about this and inspiring hundreds of thousands of girls to invest into technology, by itself is a story worth being told, right? This doesn't come from Silicon Valley or from New York, right? It's coming from a part of the world that many people don't even know about. And therefore, I think it's great to see that in a much more multilateral world, stories of success and excellence are really distributed around the world. And the question is whether we can capture them and we can use them. You said something that was very touching for me. You said in the technology world, we don't really have a lot of female role models. And maybe what we need is more of those. They, they do help a lot when maybe girls are hesitating. That they can have an idol. And I'm happy to know that there are many girls in Mongolia that look up to you, right, as their role model in what they can do, no matter whether they are coming from nomadic family or they're living in the urban part of the country. It doesn't matter. It's the question is being inspired. And this is an important part of why, you know, giving voice to people like you is particularly important because of the amplification chamber that comes with that. Look, it's been such a pleasant conversation with you. I'm really happy that you and I connected before, and I know this is the beginning of you and I continuing to connect even after this webinar. I look forward to supporting you as much as I can to make sure that you, you can keep on changing people's lives even more. Once this will be finished, it'll be a recording that we can use definitely to inspire more people to watch our conversation. What an incredible way for me to spend my lunch break over in London to learn from your story and to have you here with us. So on behalf of the team at MicroOne that allows me to curate this series of AI for Good, thank you so much for being part of this. For those of you that joined us live the entire time, thank you so much. It was great. Your questions were amazing. I want to thank you again. It's been a pleasure to meet you. I wish you all the best. I know you're a little bit towards the end of your day. So it's wrapping up for you this Tuesday, but I hope to see you in person sometime soon.

Thank you. Thank you so much. Likewise. Thank you so much for having me. And we've had such an interesting conversation, which I enjoyed. And thank you for all the participants watching live. It is really a commitment to stay online and watched attentively for an hour. So thank you so much. And I really hope that we can help deliver the message for an inclusive world. And I think the momentum, I think it's very important that we need to use AI for good. And thank you for giving me the stage to share my story and also share this message. Thank you. Thank you.

Thanks so much. All the best. Bye-bye, Ivana. Cheers.

Bye-bye.

Bye-bye.