I feel like I can help not only economically improve or develop Africa, the continent, but also improve the access to education. So they're talking about AI to people or to young African men or 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 there?
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You're at Practical AI. Welcome back to another edition of 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 Whiteneck is not with us, but we have one of our former guests, Natalie Pustunovitch. If I got the last name right, Natalie. You did. I did.
Excellent. Zero learning shot. Zero learning. Awesome.
Natalie is here to co-host and I would like to die right into introducing our guest. His name is Bonaventure Dotsu. 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 Dotsu. Actually, it's not even in French.
It's not even in French or English. I'm sorry. Different languages. 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 tell the audience?
So my first name, Bonaventure, is actually a French name that is right at like Bonaventure. I hope we split into like Bonaventure. It means actually with adventure. Okay.
So we're going to 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 want to 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. I got here. That's a very long story. You don't have to go all the way back to birth.
It's okay. I'd like to hear it. Yeah. So I can say that I did not, I thought plan credits like where I am right now.
I was meant to be a genealogist, someone who perhaps pregnant. We're meant to give birth. I was training in high school to become in biology and to become. Yeah, I don't talk.
I suck at math. And of course, I love computer science because I had my first computer when I was six. I visual myself, 24, not that much. You know, medicine was my first.
So a lot of things happened, family and stuff, there's no 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. We have spent five years.
And it's cool. Yeah. And the education was full in Russian. So there was no English of English of 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 another basics of the language and everything. And then throughout the years, at the university, like you don't have any other choice. We did not have any other choice as done like to speak.
So I could see, well, so frankly, it was like enough because study map is the kind of study on universal language. Right. So whether you speak good or not, the language, I mean, you can still go to the body and write and like you can understand each other. There are some classes from as a logic of things that go away.
You need actually to understand the language and the extent of the problem. So it was not so easy, but it was also interesting to try something new. 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. What? How did you make that leave? What happened there?
So like I said, I sucked in that. And then he's majoring in it. I'm struggling to follow. There's a great story though.
My dad, I mean, he's actually retired, but he's an architect. So I was living around my my sister or works in finance. She's an accountant. So my sister's also a thing where I was the only one who was like in behind.
So what happened is I luckily like met a professor when I was back in the 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 that you get trained on.
It's something that you decide to learn it, right? It's not like someone comes with it. So he trust me. It trust me to meet that passion and that we actually to keep learning, learning, learning.
And you know, the most interesting fact about him was I could completely film a, let me say, an exam, but he would focus on where I succeeded and give it as a example. I mean, it was not only me, but it was many others. So actually, he was not pointing out the overall, like, graders, co-host, but it was much another strength. And I knew then that I was sucking a geometry, but then good in other things.
And that's where the whole story started because I love to look at computational calculations and things like that. And then I studied like a lot of training. I could do a bunch of exercises and everything. So high school is dividing to two main components, or maybe part of the first part of the first two, a four year, that's more general.
And after you have a professional exam. And then the second part is when you go into a more specialized field, like, if you want to go to do mathematics, I'd mention that. Then there's a fit for a particular biology, the fit. So I performed well, I done as an exam in biology, but not in math.
So I was now with a written biology, but in SS1, I met him. So the transition from the last SS, S, G, S, G four 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 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 was ever been my childhood dream. But then they came in conflict because I discovered computer science. I mean, I used to, but then I just got to do real computer science, like where you know the old math and algebra behind. Give me more passion.
And on the other side, I also have a few friends who were studying medicine, vacuum painting, and they used to cram books. Like I not told me books and things like that. And that's not my thing. I'm not super deep for crap.
Like I prefer one, I had a freedom to design my own thinking way to go to the target. Not that there's already the pre-existing thing that 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.
If you do, you might say, so yeah, that was when, when, like, they're thinking, but the more, the big change in fact 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 or to have at least a good background before coming over there. And then I'm like, okay, my dream of days. So then I switch 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 apply, but then they put me, my government might somehow put me in math and mechanics.
So I was not doing any more applied math. There was one poor theoretical math where I had a choice like to change and everything. But I still say, okay, let me take it as a challenge. And yeah, and you're not like good.
So you're in math, but I know that right now you're in Quebec 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 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 my 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 my 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 in there. So we didn't want to do the classical software and generator or things like that. I wanted to be more free and we were thinking people like who like giving crazy ages.
So like at nine at three, one, we sleep in, we just jump up and then like, like bring up a crazy idea of things like that. So we found out that doing the 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 we treat off computer is actually was actually like, let me call it a hotspot. It was AI. Then we looked at AI and we were like, okay, how can we call it 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 political math and application of declining. For instance, when you want to use some driving car, you know, like sometimes you have to actually be able to measure the instance, like shift or movement of the car or maybe on the camera. I think that they are the high z-dom is by using differential equation.
So when we tried to connect the dirt, it came more and more obvious that we needed math and actually what we are doing was something that was happiness. 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 you're not speaking from?
And you're like, I don't really know from because you've been in your train and when you watch to speak French, otherwise you're considered as someone who is from the village or who's not to the right. So that is something that not so many bad people by then have is plural because the whole term was around English and high resource language. So we're like, okay, let us then get into a space where we actually do something that not everyone is doing. That's like to bring something of some sort of novelty and that's where it started from.
Then we started reading, reading, reading, studying, taking classes and COK. How does, for instance, this convolutional neural network, how does it work? How is the machine transition? How is the softness activation things like that?
Those are all based on mathematical formulas, exponential function, logarithmic function things like that. So we tried then to COK like theoretically and machine learning, how is that connected? I mean, I want to say we've done it, but I'll say that has been the thing that made the change and we're still learning. But so far has been so very helpful.
I hope I have like 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 possible. And also coming here as international student in America is something like that. It's very, very expensive, right?
So we were like, okay, even if 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 specific where in the neighborhood is the best fit and it's Germany. I've always had a law 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, this kid, he asked 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 had a lot of common behavior or we do culture in general. So given that fact that I love Germany, that had what 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 brought you to Canada.
I mean, Canada was not pretty. This soon, I predicted Canada potentially when I would be finishing my master, but it happened just few months after I got into my master of the German. The change log 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.
The time drones were like predators and global hawks and military industrial, 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. It's not, when a nine-year-old can do something that's classified, that literally export control is an ignition with Lego with toy pieces, it was something important in this world has changed. Leaders like that have been Zugel from GitHub.
In the like 10 to 15-year range or 20-year range, what I would really like is for you have like 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 to be an open source developer. And innovators like I'm out who's saying. I've yet to kind of see applications that scale that don't use multiple languages that don't you have just arcane stories behind why this weirdo thing exists, you know?
Like, all right, when you open this file, you're going to 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 that's normal. So I want to normalize weirdness because that's just how applications evolve over time.
Welcome to the Change Log. Please listen to an episode from our catalog that interests you and subscribe today. We'd love to have you with us. 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 Atnilla, like you said, which is one of the if not the biggest research center in deep learning. It has always been my dream, the dream of Chris too. We have the person who is a current advisor, your stranger, who was one of the top figures that we're looking up to.
I mean, you know, when you're kind of new in the field, you tend to look at someone who's like, okay, he's achieved these and these, because we are doing a thinking, you're managing the internal side and things like that. The things that actually like say, okay, you have a role or a role model or a model on the field. So that was him and we really wanted to come here. So, Mila has done very cool things, very cool applications, things like that.
Yeah, it happened. I came here in Montreal. I'm currently at Mila working on a drug discovery project. I said that I also joined Rush, Rush Canada, and that one is more in my passion of working towards our technological challenges or from our political 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's business program, which was first dedicated station for PhD students, because when I applied, they were like, okay, cool, you're looking for PhD students. I was like, okay, I didn't want to be there. They just came there.
Okay, fine. Like out of all the application 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 business line or this or this. It's also about the business. How can you bring it to the market?
How can you monetize it? And finally, in January 2022, I joined Google and we'll be working on computing NLP, language technologies for more sub-sar and 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 working in the LFP 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 work where you're working, and both of those opportunities, especially, it's 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 Manila, I'm working as a direct discovery research intern. So it's also putting together AI and also biology. I'm currently focused anti-microbiopeptide.
So the background is that currently I'm anti-microbiope resistance. It's actually been a very big let me say, trek to the whole community. And big companies like Rush, the World Health Organization, has given that Red Flag are allowing me, if we don't do something to come up with new drugs, then potentially it could be done from very simple injury or things like that, because batteries are becoming more and more drug resistant. So I'm currently working on active learning from work that will help develop, generate good candidates, AMP candidate, AMP.
I cannot dive deep in because I'm interested in Y AMP's, but they are actually the top one candidate, and they're all companies who are in the field, are really focused on it. So there are some biological, Spanish, and developer, I don't know yet. So coming to the Mashman's Park and active learning part, we actually would try to maximize the production of good candidates. Now we are doing discovery, right?
So we are not like in the mindset that we're discovering or we're creating, we're already existing, we want to do new things, like create or we discover new things. So that's where the part that we call diversity part comes in. So it's basically like taking, setting up a lot of things, after I tried and brought a lot of patches, of course, some other cool work was my supervisor and cost providers, and we tried many bunch of things and then actually introduced a rough base like metal that uses actually like competitive components to enhance and produce more diverse sequences. It's basically the idea is that if I take a reference set that I know that a good MP is from the real world because data set that we are using is from a perfectly available source that have been developed by biologists.
So those sequences and properties are well known. So if I take that and I compare it to what I have, does it give me how good performance they call it in to our metrics? One, two, is that statistically different from the initial set? So that's two things we wanted to be actually having more people to the same properties, but at the same time to be different, because you can imagine the protein right when you have 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, but let me say you are only good at math, but in life they are not, there's not only math, there's also biology, so you have to be like good mixing. That was the main idea and that's what I have been working on and I have good results, successful results and I've written the manuscript for you like looking forward to potentially send it to a subscription submission to a journal and like I used to tell my partner, this paper will save the word, but it's just a joke. 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 Gfroned Net, which is a soy power foot too, which is one of the main attentional features right now and we put a station in like developing and then increase it to the same task to see how it performs. And brush is more formulated like we have already the drugs and then like the pipeline, the way the drugs are created and everything.
Let me say that we have feedback from users that defend this instead of feeling this or their symptoms or side effects. So what could be the root cause, what could be the reasons why they're 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 all together and then 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 living in the NLP models for African languages or sub-Saharan languages and I've been with amazing people with whom I've been working on a volunteer basis or like on many projects, of course, or in an on-screen project, any other project, things like that. So yeah, that's like briefly what I'm working on right now. I hope it makes sense.
I have a question. I know that you're doing NLP in both the Google work and the work for the drug discovery and 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 or to get new ideas. Why approach it that way?
What is it that made you say this is the way I want to solve this problem? So I guess then targeting the project here at meaner, right? They can be generative models because we want to create new things. I don't know if you see the idea.
Like if we are like 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 like set of things and then like just predict some value on it. Of course, we are using predictive models where our classification of the creation set in the process where 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 with Google with sub-Saharan languages and you're also working on that at Masahana. Masahana, yes. Masahana, what do you see as the future of those developments?
Okay, so it is pretty straightforward giving us as to language technology to everybody. I started on one of the main motivations that made me study is like trying to improve the communication between my mom and I and I can say that actually I did not like envision it to be way more important because after we launched like there was also a keyboard that we launched like 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 want to create music in front in the native or local language are using so it helps create more content, it helps create more data, it helps create, it helps actually to improve and 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 that are even less even say there's some type of ones where I'm from as you are from Benin, you believe them know it's frustrating and I also look at 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 something I play here for the language we have people like me who are broad who actually can't actually speak the language but the parent back at home are only speaking those languages so hard 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 not good at all and it's also misleading. You can see also some time 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 their representation of the language. We could go on the internet like in the next few years I hope I'll be able like to have a voice assistant for instance for my kids, my children which we'll be able to speak in front of 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 types to the restoration of the cultural identity of things that like things actually are intersected to the nature of things that so it's way more about like just trying to do more there's no 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 incented because actually they stopped like being in numbers so we don't want it to happen with our other language and everything so I think I touched a lot of points that's mainly how fast the problem is and how I think that working on those kind of language could actually be solving those problems and bringing language technologies to everyone. It strikes me that though you're using similar tool sets or using deep learning in both problems they're somewhat different one being drug discovery and one being under-resourced languages but I also noticed 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 is 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 do not mention it because I mean I like to look like a superhero. I love the honesty on that but I don't like to show off too much either like they should be very like good trade-on between both but yeah you say that I've always been in the mindset that of course we are all monsters so we came and we are going to die one day. I want actually to leave things like I can call a legacy so that when I leave people will still point out those things and remember about me we have like good mathematicians now there's one thing like the theories of the formula that's still like helping into solving the world's problem of biology scientists. Einstein is there like since the world 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 interest I mean who's your role model who is the person you look up to I mean people who are actually being computer science or who have interest that will point out and be engaged they'll put that at people like that for instance even myself I point out as you are just like who is a kid again but I would like to be a promoter for other people back in Africa like from the next generation and for doing that I would definitely have to bring something new right I think I had to have in some way so yeah always having 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 health care which is actually very very big problem. Access to health care should be like a prominent needs right but in Africa currently is a luxury so how I say whenever you want to health care people die from it I had a sister that I never knew because she actually could not come because of a problem and she came before her time and because of the lack of care and feels like that she could not maybe believe the situation or people actually who could not have access to surgery because they don't have money or because there's lack of electricity or things like that so there's access to health care it's 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 of things like that actually contribute actually to the development to the motivation of the continent and not just focusing on myself on the front that oh I'm working out good places where people place that everyone would like to be at maybe also maybe making good money of doing this or this or so. 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 that's great. Yeah I would say yes it's actually your point the fact that I wanted to be not the superhero but someone who helps someone who brought so the world to the continent to this 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 here 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 on any other places in Africa.
You know most of the fun things most of the researchers even doing this pandemic most of the vaccine things did not come from Africa. We just waited and waited for people to have solutions and actually have been even suffering to have done by since. And at that time we think people like going stick but they are not they work on it they have to care about the people first. I feel like AI can help like not only economically improve or develop Africa, the continent but also improve access to education because when today you talk about AI to some people or to people or to young African men or women when they don't know like how it weighs it actually they'll feel like you are a god like you can do anything people usually come to me and be like oh you create this model that does this and they will ask them can you actually do that by yourself because if you can't actually teach a computer in a very system way or if you actually can't do it by yourself so high granted to a computer things like that.
So they still don't like 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 can can can 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. We actually world-renowned scientists could actually come. We 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 because we have problems over there that for us a few not having 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 what 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 was 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 there our languages like incorporated into cell phones. So like the keyboard we developed people are 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 a lab there's something I hopefully wish I'd shift my this year with my friend Chris but if there was a lab integrated in the phone so that they could just easily use it like they use the computer and those languages who come from AI and from many other groups many other sectors activities like electronic systems that should be interdisciplinary collaboration but still AI has hit some point role into creating those technologies so that other field could use it. So those are the potential trade fields and the two ways I feel like AI can grant people's life in Africa and I also share one of my dreamers from 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 shortlist 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?
Well the wonderful things that you're you have achieved and you will be achieving. 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 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 it was fantastic thank you very much both. Thank you. Thank you so much for having me. All right that's Practical AI for this week thanks for listening if this is your first time with us subscribe now at practicalai.fm or simply search for practical AI in your favorite podcast app we're in there and if you're a longtime listener do us a solid by recommending the show to a friend word of mouth is still the number one way people find new podcasts they love special thanks to our partners for supporting our work vastly launched darkly and linode we appreciate it and to the mysterious breakmaster cylinder for cranking out new beats for us all the time that's all for now we'll talk to you again next week.