The world needs an AI superhero

I feel like AI can help not only economically improve or develop Africa, the continent, but also improve the access to education. Today you talk about AI to people or to young African men and women. They'll feel like you are a God, like you can do anything. People usually come to me and be like, oh, can you create this model that does this and that, that powerful side of what AI can can do for Africa.

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One last time. That's changelog.com weekly. Welcome to Practical AI, a weekly podcast that makes artificial intelligence practical, productive and accessible to everyone. This is where conversations around AI, machine learning and data science happen. Join the community and slack with us around various topics of the show@changelog.com community and follow us on Twitter. We're PracticalAIFN.

Welcome back to another edition of Pro Practical AI. We are the podcast that tries to make AI practical, productive and accessible to everyone. I'm Chris Benson, one of your co hosts. Today, Daniel Whitenack is not with us, but we have one of our former guests, Natalie Pistunovich. If I got the last name right, Natalie. You did. I did.

Excellent.

Zero Learning Shock. Zero Shock Learning.

Awesome.

Natalie is here to co host and I would like to dive right into introducing our guest. His name is Bonaventure Dosu. Did I get that close to right there?

Yes.

How would you pronounce it just so our listeners can hear you. Do it better.

Bonaventure do Su. Actually, it's not even a French. It's not even a French or English name, so the pronunciation is okay.

Okay. So before we started the show, you mentioned something about your name and what it means, and I would just like to throw that out because I thought it was pretty cool. You want to. You want to tell the. Tell the audience.

So my first name, Bonaventure, is actually a French name that is right at, like Bonaventure. I hope we split it into two. Like Bon Aventur. It means actually good adventures.

Okay, so we're gonna have a good adventure on today's podcast, I guess.

Yes.

So now that I've totally put you on the spot in the beginning, we like to have fun. You wanna tell us a little bit about, you know, your background, kind of how you got to this point where you're at right now. And then we'd like to dive into some of the stuff that you've been doing after that.

Okay. How I got here, that's a very long story.

You don't have to go all the way back to birth. It's okay. We'd like to hear it.

Yeah. So I can say that I did not at all plan credits, like, where I am right now. I was meant to be a genecologist as someone who helps pregnant women to give birth. I was trained in high school to become in biology and to become. Yeah, a doctor. I sucked at math and of course I love computer science because I had my first computer when I was 6. I envisioned myself doing it, but not that much. You know, medicine was my prior stuff, so a lot of things happened, family and personal issues. And finally I jumped right into the field of mathematics. So I then got a scholarship to study my bachelor study for my bachelor in mathematics in Russia, where I spent five years. That is cool. Yeah. And the education was full in Russian, so there was no English. Such things.

How long did it take you to get up to speed in Russian?

Well, I can say seven or eight months at least to know the basics of the language and everything. But then throughout the years at university, like, you don't have any other choice. We did not have any other choice. Don't like to speak. So I could say we're also, frankly, we're also like, lucky enough because studying math is the kind of studying on universal language. Right. So whether you speak good or not the language, I mean, you can still go to the board and write and like, you can understand each other. But there are some classes, for instance like logic or things like that where you need actually to understand the language and the stand the setup of the problem problem. So it was not so easy but it was also interesting to try something new out.

So before you go on, I'm just curious. You made a jump in there from medicine to math.

Yeah.

And somewhere you had to make a serious decision on what that was. How did you make that leave what. What happened there?

So like I said, I sucked in.

Math and then he's majoring in it. I'm struggling to follow. This is a great story though.

My D, I mean he's actually retired but he's an architect so I was living around mathematics. My sister works in finance, she's an accountant. So math is also a thing. But I was the only one who was lacking behind. So what happened is I luckily like met a professor when I was back in high school who actually showed me that people don't come into this world, they don't have the gift of being super duper good at math. Like at birth. Right. It's something like that you get trained on it's something, it's a science so you learn it. Right. It's not like someone comes with it. So he transmitted me their passion and that will actually to keep learning, learning, learning. And you know the most interesting fact about him was I could completely fail a, let me say an exam but he will focus on where I succeeded and give it as example. I mean it was not only me but it was many others. So actually he was not pointing out the fact that the overall grade or score was bad, but he was pointing out your strength. And I knew then that I was sucking at geometry but then good in arithmetics. And that's where the whole story start because I love to do computational calculation things like that. And then I started a lot of training. I could do a bunch of exercises. And so high school is divided into two main components or maybe part the first part you first do a four year, that's more general and after you have you pass a national exam and then the second part is where now you go into a more specialized field. Like if you want to go to do mathematics advanced math, then there's a fit for it if you want to do biology. So I performed well. I done a SNL exam in biology but not in math. So I was now with directing biology but in SS1 I met him. So the transition from the last SS SG, SG4 to SS1 was huge because people who were like top math students and everything. They saw that transformation, they were like okay, so what happened? And I would say that's from where the whole passion about math and doing something that involves math like study. And then after I still wanted to do to become a doctor because that's ever been my childhood dream. But then there came a conflict because I discovered computer science. I mean I used to, but then I discovered the real computer science like where you know all the math and algebra behind it gave me more, more passion. And on the other side, I also have few friends who were studying medicine back in Benin and they used to cram books, like anatomy books and things like that. And that's not my thing. I'm not super duper crammer. Like I prefer when I have the freedom to design my own thinking way to go to the target. Not that there's already pre existing things you have to learn. I'm also someone who learns from mistakes a lot. So I was like, okay, if you become a doctor, the mistakes here won't be cool, it will be humans life. So yeah, that was when, when like the, the thing came. But the more the big changing factor was when I wanted to come here in Canada to study medicine. And they told us that they do not give that privilege to international students, that I have to be a doctor to have at least a good background before coming over there. And then I'm like, okay, my dream was dead. So then I switched to mathematics which actually found out turns out to be a good choice.

Yeah, you know, just looking through some of the stuff that you, that you have on your resume, it sounds like you've made the best of a bad situation.

I'd say yes. Even when I was in Russia I wanted to do mathematics and computer science because I've never started in the mindset of going or becoming a math teacher. Right. So I wanted to do something applied but then they put me, my government somehow put me in math and mechanics. So I was not doing any more applied math, but I was doing poor theoretical math where I had a choice like to change and everything. But I still said I was like, okay, let me take it as a challenge. And yeah, it ended up like good.

So you're in math, but I know that right now you're in Quebec.

Yes.

Working in one of the coolest places on the planet Earth doing deep learning. There's still a little bit of a leap to get from math to, to what you're doing. And I know I haven't revealed like who's advising you and things like that. I'll let you do that, but you know, Quebec. Hint, hint folks. Anyway, go ahead and tell us a little bit. How did you get into that?

So the core of ML of AI is actually math, like differential equation statistics, probabilities. And at first me and my friend Chris, who also knows Mr. Daniel, we were like, okay, we were one year from our graduation and we knew that we wanted to do something that actually was on trend, like something a lot of people had attention to and things like that. So we did not want to do like the classical software engineering or things like that. We wanted to be more free. And we were thinking people like, who like giving crazy ideas. So like at 9, at 3am Won't be sleeping, he would just jump up and then like, like bring up a crazy idea, things like that. So we found out that doing research will be a better choice, right? Because that's when you can try out a lot of ideas. I mean we did not want to do research in math. And then which feed of computer is actually was actually like, let me call it a hotspot, it was AI. Then we looked at AI and we're like okay, how can we connect AI with the math that we are currently doing? That's where everything started. We now started reading books, watching tutorials, watching classes. And then we now connected the dots between poor theoretical math and application of deep learning. For instance, when you want to use self driving car, you know, like sometimes you have to actually be able to measure the instant like shift or movement of the car or maybe on the camera, things like that. But how is it done is by using differential equation, things like that. So when we tried to connect the dots, it came more and more obvious that we needed math. And actually what we are doing was something that was helping us. And then we now jump to machine translation because we're like okay, currently what can we start from? We see a lot of things. He asked me how why do you always speak in French when your mom speaks in phone in the other language? I'm like, I don't really know phone because in Benin you are trained and brainwashed to speak French. Otherwise you are considered as someone who is from village or who is not civilized. So that is something that not so many people by then have explored because the whole trend was around English and high resource languages. So we were like okay, let us then get into a space where we actually do something that not everybody is doing. That's like to bring something of some sort of novelty, novelty. And that's where it Started from then we started reading, reading readings, started like taking classes and see, okay, how does for instance this convolutional neural network, how does it work? Machine translation, how is the, the soft mass activation, things like that. Those are all based on mathematic formulas, exponential function, logarithmic function, things like that. So we tried then to see, okay, like theoretically and machine learning, how is that connected? I mean, I won't say we've done it, but I'll say that has been the click, the thing that made the change. And we are still learning, but so far has been so very helpful. I hope I help bringing down the gap between mathematics and machine learning right now.

So your next step after Russia was in Germany?

Yes.

How did that happen?

Okay, so I wanted to come here in Canada because of the pandemic. It was something already impossible. And also coming here as an international student in the North Americas is something like that is very, very, very expensive. Right? So we were like, okay, given the fact we can, I can have visa for Canada, it would be super duper complicated. Also from Russia and the fact that education over there will be like very, very expensive. Where in the neighborhood is the best fit and is Germany. I've always have a love for Germany. Sorry. My dad used to work with German people and that's when I started loving Germany. All of them were like, oh, this guy or this kid, he acts like German people. Also because of my hate, because they say German people are very tall or things like that. But I said that when I went to Germany, I also found myself that I have a lot of common behavior or with the culture in general. So given the fact that I love Germany, that my dad had work with people from there and everything, I was like, okay. And I also love the German language which unfortunately, because I've been moving around a lot, I have not been able to practice. But it's a language that I love. So I was like, okay, let me go there and then try that out. And then the next step could be Canada. That's actually how it went.

That's what brought you to Canada.

I mean, Canada was not predicted this soon, but I predicted Canada potentially when I will be finishing my master. But it happened just few months after I got into my master in Germany.

The changelog is deep discussions in and around the world of software and it's been going for over a decade. We interview hackers like Chris Anderson from 3D Robotics.

At the time, drones were like predators.

And global hawks and military industrial and they were classified and super, you know, $10 billion things. And we had just built a drone.

With Lego pieces around the dining room table programmed by a nine year old.

And it's like, okay, that should not be possible.

You know, when a nine year old can do something that is classified, that literally export control is munition with Lego with toy pieces. Something important in this world has changed.

Leaders like Devin Zugel from GitHub in.

The 10 to 15 year range or 20 year range. What I would really like is for if you have three 12 year olds hanging out and one of them's like, I want to be a firefighter. Another one's like, I want to be a lawyer.

I want one of them to say.

I want to be an open source developer.

And innovators like Amel Hussein.

I've yet to kind of see applications.

At scale that don't use multiple languages, that don't have just arcane stories behind why this weirdo thing exists.

You know, like, all right, when you.

Open this file, you're gonna have to.

Turn around three times and tap your nose once.

Like, it's just the most hilarious stories, you know. But applications are living, breathing, they have craft bits, no normal.

So I want to normalize weirdness because.

That'S just how applications evolve over time.

Welcome to the Changelog. Please listen to an episode from our catalog that interests you and subscribe today. We'd love to have you with us.

And so Canada, tell us about the interesting things that you're working on and with whom.

The Canadian episode is very interesting. I came here and I'm working currently with Atmila like you said, which is one of the, if not the biggest research center in deep learning, has always been my dream. The dream of Chris too. We have the person who is currently our advisor, Yoshua Benjo, who was one of the top figures that we're looking up to. I mean, you know, when you are kind of new in the field, you tend to look at someone be like, okay, he's achieved this and this his way of doing a thinking, humanitarian side and things like that. Things that actually like say, okay, you, I have a role or a role model or a model on the field. So that was him and we really wanted to come here. Plus also Mila has done very cool thing, very cool publication, things like that. And yeah, it happened and I came here in Montreal. I'm currently at Miller working on a drug discovery project. Except that I've also joined Rush Rush Canada. And that one is more in my passion of working toward biotechnological challenges or pharma related challenges using deep learning. So I've been doing both at the same time. I also had many other opportunities. For instance, I attended a scientist resilience program which was first dedicated specially for PhD students. Because when I applied they were like, okay, cool, your resume look good, but we're only looking for PhD student. I was a postdocs, I was like, okay. Then one day they just came, okay, fine, like out of all the applications we see you probably is the best fit. And then we jumped in. And then that was also an amazing thing because it was more practical. It was shifted away from like academia and then it was like brought down into. Brought into an industry. And I could actually see how people in the industry do it there because it's not only about creating a model to beat this baseline or this or this. It also about the business, how can you bring it to the market, how can you monetize it, things like that. And finally in January 2022 I joined Google and we've been working on building NLP language technologies for more Sub Saharan languages, including mine of course.

I'm guessing that's how you met Daniel as well, working in that space. Because I know we've had some episodes on the show here about all of the work that's being done in Africa that so many people outside of Africa weren't aware of. It's really amazing work in the LLP space. But I want to hear about some of the work you're doing. But I just have to comment. You are living the dream, man. To have worked where you're working in both of those opportunities especially is pretty cool. You want to dive into some of the project work you're doing and talk about kind of how you got into it, how you're approaching the problem and love to hear some of the detail.

Sure. So here at Mila, I'm working as a drug discovery research intern. So it's also like putting together AI and also biology. I'm currently focused antimicrobial peptides. So the background is there currently AMR antimicrobial resistance. It's actually been a very big, let me say threat to the whole humanity. And like big companies like Roche, like the World Health Organization has given that red flag are alarming it like if we don't do something to come up with new drugs, then potentially we could be dying from like very simple injury or things like that because bacteria are becoming more and more drug resistant. So I'm currently working on a kind of active learning framework that will help like develop, generate Good candidates amp candidate emps I cannot dive deep in because I mean to explain why EMPs, but they are actually the top one candidate and they're all companies who are in the field are I really focused on it. So there are some biological explanation about that, but I don't know yet. So coming to the machine learning part and active learning part where actually we try to maximize the production of good candidates now we are doing discovery, right? So we are not like in the mindset of rediscovering or recreating, regenerating what already exists. We want to do new things or like create or rediscover new things. So that's where the part that we call diversity part comes in. So it's basically like taking like setting up a lot of things. I tried and brought like a lot of ideas of course with some other co workers, my supervisor and co supervisors and we tried many bunch of things. And then I actually introduced a graph based method that uses actually like combative components to enhance and like to produce more diverse sequences. Is basically the idea is that if I take a reference set that I know that I do DMPs from the real world because data set that we are using is from a publicly available source that have been developed by biologists. So those sequences, the properties are well known. So if I take that and I compare it to what I have, does it give me good performance according to our metrics 1, 2 is that statistically different from the initial set? So that's two things. We want it to be actually having the more or equal to the same properties but at the same time to be different. Because you can imagine a protein, right when you have like a sequence of the same, you won't do as better as multi different sequences. It's also like a human being when you are specializing in one only one thing or let me say you are only good at math. But in life they are not. There's not only math, there's also biology, sports, things like that. So you have to be like good like mixing. That was the main idea and that's what I have been working on and I have good results, successful result and I've written a manuscript for it. Like looking forward to potentially sending to subscription submission to a journal. And like I used to tell my partner this paper will save the world, but it's just jerk because I know all the talented people who are working on it in the group. And we had also another extension to that which is called the gflow net which is also a powerful tool which is One of the main attention of Joshua Benjamin right now and we will potentially develop it and then increase it to the same task to see how it performs. And brush is more pharma related. Like we have already the drugs and then the pipeline, the way the drugs are created and everything. Let me say then that we have feedback from users, they felt this or this instead of feeling this. So there are symptoms or side effects. So what could be the root cause? What could be the reasons why they are doing it? Is it because that there's been a slight mistake during the production or maybe the manufacturing process, things like that. So collecting all those information altogether and then like making sense of it to improve actually the whole setup. Yeah, that's it. So at Google now, I recently just started and I know that the main focus would be on building multilingual NLP models for African languages or Sub Saharan languages. And I'll be with amazing people with whom I've been like working on a volunteering basis on many projects like cross lingual question and answering projects, NER project, things like that. So yeah, that's briefly what I'm working on right now. I hope it makes sense.

It does. I have a question. I know that you're doing NLP in both the Google work and the work for the drug discovery. I know you talk about being generative models when you discuss it, can you tell us a little bit like if somebody out there is listening, A lot of folks are tuning in to learn to listen, to get new ideas. Why approach it that way? What is it that is that made you say this is the way I want to solve this problem.

So I guess like I'm then targeting the project here at Miller technically generative models because we want to create new things. I don't know if you see the idea like if we are pretending to do discovery is because we are creating new chemical compounds and new structures, new materials. So you can do that by using a predictive model, for instance, where you already have set of things and then you just predict some value on it. Of course we are using predictive models or classification of recreation types in the process. It doesn't really add anything new. The new thing that you bring in does not really come from applying or from building a prediction model. It comes from like a generative one because it learns from the data distribution of an existing data set and then try to generate things that are similar but not equal. That's from where I guess that the discovery comes from.

So you mentioned all those interesting things that you are about to do at Google with the Sub Saharan languages. And you're also working with on that at Masahane. Yes, Masakane. What do you see as the future of those developments?

Okay, so it is pretty straightforward giving access to language technology to everybody. I started, I mean one of the motivation that made me start this is like trying to improve the communication between my mom and I. And I can say that actually I did not envision it to be way more important because after we launched, there was also a keyboard that we launched of native language of language in Benin and in many other countries. And with the translator, a lot of people use it. So a lot of artists are using people who actually create music in fun in the native or local language are using it. So it helps create more content, it helps create more data, it helps create. It helps actually to improve. Increase the representation of the language on the Internet. Because there's nothing frustrating more than oh, you come to from a country or things like that. Or even let's even say that sometimes people ask me where I'm from. I see I'm from Benin, but they don't know. It's frustrating. And I had to locate it like with respect to Nigeria, I have to say, oh, it's left to Nigeria. Oh, okay. Because people know Nigeria. Why? Because it's big. Why? Because there are a lot of people coming from there. I mean a lot of things why Nigeria is not right. So same thing apply here for the language. We have people like me who are abroad who actually can't actually speak the language. But the parents back at home are only speaking those languages. So how to improve the communication between those people and the parents? Also like language resources like when you go on the Internet or Facebook, things like that. The translation of those languages are pretty often not good at all. And it's also mislead. You can see also sometimes a lot of like hate speech going around. So actually building or working on such kind of model will definitely benefit the marginalized and underrepresented group and will also actually increase the representation of the language. We could go on the Internet like in the next few years. I hope I'll be able to have a voice assistant, for instance, for my kids. I hope my children which will be able to speak in front with them. Because that's also part of the culture, right? That's also part of the identity. Yeah, that's also another interesting part. Like working on those language helps to the restoration of the cultural identity of things like that. Like things actually are intrinsic to the, to the nature and Things like that. So it's way more above like just trying to build models now. It has a very deep root which like in preservation of those languages. We've seen like a lot of groups of people who actually got instanted because actually they stopped like being in numbers. So we don't want it to happen with our other languages and everything. So I think I touched a lot of points. That's mainly how vast the problem is and how I think that working on those kind of languages could actually help solving those problems and bringing language technologies to everyone.

It strikes me that though you're using similar tool sets, you're using deep learning in both problems, they're somewhat different, one being drug discovery and one being under resourced languages. But I also notice that you seem to gravitate toward doing kind of improving the state of the world things that will help people and help do that. Is that an active part of your decision making as you're looking for projects to work on to try to make the world a better place? Because that's the common thread between two otherwise apparently unrelated projects that you're working on.

Yeah, I did not mention it because I, I mean I like to look like a superhero.

I love the honesty in that.

But I don't like to show off too much either. Like there should be a very good trade off between both. But yeah, you said it. I've always been in the mindset that of course we are all mortal, so we came and we are going to die one day. I want actually to leave things that I can call a legacy so that when I leave people will still pointing at those things and remembering about me. We have like good mathematicians nowadays who are still like the theories or the formulas are still like helping into solving the world's problem of biology scientists. Einstein is there like since a while, but his name is as if he's not there because they keep calling him like he's still useful actually to the world. So yeah, I always have that mindset of whatever I'm working on, make it like somehow to the best of my capacities, improve humanity, impact people. I know, like a lot of friends, a lot of, let me say young people back in Africa. If you ask them, oh, what are you actually interested? I mean, who is your role model? Who is the person you look up to? I mean people who are actually in computer science or who have interest, they will point out at Bill Gates, they will point out that people like that. For instance, even myself, I point out as Joshua, who is a Canadian, but I would like to Be a role model for other people back in Africa, like from the next generation. And for doing that, I would definitely have to bring something new. Right. Definitely had to help in some way. So. So, yeah, I always have it in my mind. And the other part of why I'm working also on health or I'm really willing to do so, is because of the access to healthcare, which is actually very, very, very big problem. Access to healthcare should be like a primary need. Right. But in Africa, currently is a luxury to have access whenever you want to healthcare. People die from me. I had a sister that I never knew because she actually could not come because of a problem. And she came before her term and because of the lack of care and things like that, she could not. Many people live the same situation or people actually who could not have access to surgery because they don't have money or because there is lack of electricity or things like that. So there's access to health care is still a very big problem and a very big field that we scientists need to work on. So I would also like to bring something new into that, maybe through my research in AI, doing drug discoveries or things like that actually contribute actually to the development, to the emancipation of the continent. And not just focusing on myself, on the fact that, oh, I. I'm working at good places. There are people, places that everyone would like to be at, maybe also maybe making good money of doing this or vice versa.

You've gone from medicine to. You realize that you have invented the notion of the AI superhero, which I actually love that idea. It's a beautiful idea. It's great.

Yeah. I would say it's actually your positive. The fact that I wanted to be not the superhero, but someone who helps, someone who brought to the world, to the continent, to his country when he was alive. That one brought me to AI because I saw it as a tool to do those kind of things.

My advice, my friend, is to own it. And the reason I say that is kids will aspire to it. You'll bring the next generation along that way. So what would you. What are you thinking about as you look toward the future? You've kind of hit these two spectacular areas as projects. Drug discovery, which will presumably have a high impact on medicine in Africa and elsewhere, where you can really impact people's lives that way. And you've also simultaneously addressed a whole different problem that you're addressing in terms of underserved language. Under resource language to help people communicate better. You've kind of tackled two big things. What are you thinking about in the future, are there certain aspirations that you would like to say? I would like a swing at that and see what I can do to make it. Is there, is there anything that you can envision yourself after you complete these projects that you might want to go do?

I would say education. That's also one of my dream, to be able to come up with a research institute like Mila and any other places in Africa. You know, most of the found things, most of the researchers even like during this pandemic, most of the vaccine things like that did not come from Africa. We just waited and wait. We waited for people to have solutions and actually have been even suffering to have done vaccines. And at that time we think people are going stick, but they are not. They worked on it. They have to care about the people first. I feel like AI can help not only economically improve or develop Africa, the continent, but also improve the access to education. Because when today you talk about AI to some, to people or to people, to young African men and women, when they don't know like what is it actually, they'll feel like you are a God, like you can do anything. People usually come to me and be like, oh, can you create, create this model that does this and that? But I'll ask them, can you actually do that by yourself? Because if you can't actually teach a computer in a very system way, if you actually can't do it by yourself, so how are you going to teach it to a computer? Things like that. So there's still that lack of education. There are not so many universities for institutes, programs that are actually training the people, the young generation people to come to actually acquire that knowledge, that powerful side of what AI can do for Africa. And for that, like I was saying, one of my dream is to create a research or to have a research institute in Africa like the Mila, where actually world renowned scientists could actually come, where actually a lot of research would be ongoing, whether it's on vaccine, whether it's like on promoting the languages, working on tackling healthcare problems of the continent. Because we have problems over there that for instance here they do not have in Canada or in the US or in France or in Germany. So every continent, every country has its own reality. So one institute that will actually attract investors, that will attract world scientists to Africa and that will actually make it a bigger hub of AI. I mean, not bigger, I mean, I don't know how big it's going to be, but a reference, at least in Africa. If I succeed in making at least a reference In Africa, that would have already been great. So I talked about education, I talked about healthcare, and like I said earlier, there's also access to language technologies. I hope I'll be still alive to witness that. Our languages, like, incorporated into cell phones, so, like the keyboard we develop, people are already using, but the model that we develop, people are not. They have to go to a website to be able to use it. But what if it was integrated into an app? That's something I hope really wish achieved by this year with my friend Chris. But there was an app integrated in their phone so that they could just easily use it, like they use Google Translate or things like that. And those language technologies will come from AI and from many other groups, many other sectors, activities like electronics, things like that, to be interdisciplinary collaboration. But still, AI has its own role into creating those technologies so that other field could use it. So those are the potential three fields field and like three ways. I feel like AI can impact people's life in Africa. And I've also shared one of my dream of making one of them, like, happening.

That is so inspiring. And that is so many things, and yet it feels like something that is on your short list. You know, that's kind of like, Bonaventure, what is your next five year plan? And it's like gonna do that and then, you know, in five years we'll have you here again. So what's next with all the wonderful things that you're. You have achieved and you will be.

Achieving, I hope, too.

Well, Bonaventure, thank you so much for coming on as a guest today, and it's very inspiring. I'm looking forward to releasing the episode. I really actually hope some of the parents out there will share it with their kids. And Natalie, thank you very, very much for coming on and co hosting with me today.

That was such an interesting conversation.

It really was. Was. It was fantastic. Thank you very much, both.

Thank you.

Thank you so much for having me.

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Sa.