Speaker 1: Hello, Hello, Hello. This is Smart Talks with IBM, a
Speaker 1: podcast from Pushkin Industries. I heart Media and IBM about
Speaker 1: what it means to look at today's most challenging problems
Speaker 1: in a new way. I'm Mountain Globo today I'm chatting
Speaker 1: with IBM's new Senior Vice President and Chief Marketing Officer,
Speaker 1: Carla Pinero Sublet. Though Carla is new to IBM, she's
Speaker 1: a marketing industry veteran who has helped other tech companies
Speaker 1: launch and transform their brands. I'll also be chatting with
Speaker 1: Chimka Monk Buyer, co founder of Agrily. Chimka was recently
Speaker 1: named one of IBM's Women Leaders in AI for her
Speaker 1: work with Agrily, a digital platform that helps farmers make
Speaker 1: smart decisions about their crops using IBM technology. Right now,
Speaker 1: women make up an estimated of the AI workforce globally.
Speaker 1: In their work, both Carla and Chimpka breakdown barriers in
Speaker 1: the field and help make it more inclusive. Let's dive in. Well, Welcome,
Speaker 1: It's a pleasure to meet you all, the two of you.
Speaker 1: Carla your new to IBM. I'm told tell me where
Speaker 1: you came from and what brought you to I be am.
Speaker 1: I'm a twenty one you're veteran of the tech industry
Speaker 1: that put myself through architecture school, running restaurants and bars.
Speaker 1: And I say that because that's really informed who I
Speaker 1: am and how I operate in the world. I came
Speaker 1: to IBM for a very specific reason for starters. What
Speaker 1: an iconic brand. It's not just any tech company, it's
Speaker 1: the foundation of a whole industry. But secondly, the vale
Speaker 1: use of the company really resonated with me, and the
Speaker 1: heritage in the company as it stands with respect to
Speaker 1: diversity inclusion was in particular very appealing to me. So
Speaker 1: that's a big reason why I'm here. Yeah, Jim, what
Speaker 1: brought you into this world of AI and associated things?
Speaker 1: How I started it? I was a full right master's
Speaker 1: student from two thowy and then in the final year
Speaker 1: of my master's I teamed up with a bunch of
Speaker 1: international students from Taiwan, India and Brazil, and then we
Speaker 1: came up this idea. Because I was doing the research
Speaker 1: and the rule of development and ongoing, I found that
Speaker 1: interesting traditional business that is more about farming, and then
Speaker 1: we just decided to work to build their mobile app
Speaker 1: that could be connected to AI later it's a long
Speaker 1: term plun and then just fight against climate change most
Speaker 1: importantly and then also solved like other challenges based by
Speaker 1: small holders. Tell me exactly how this works. So I'm
Speaker 1: a farmer in Mongolia and I have a I have
Speaker 1: a smartphone and I I download the ugly app. What
Speaker 1: does it help me do? So, if you're a farmer
Speaker 1: malcom Mongolia, it's three to download. You will download the
Speaker 1: app and then depending on the country, you have a
Speaker 1: like farmer and code because you don't really just like
Speaker 1: start farming on your own, because you just get a
Speaker 1: permission to use a certain land right and then you're
Speaker 1: using the water resource, et cetera. So you have a
Speaker 1: farmer's code, and then you start putting your farmer's code.
Speaker 1: And then because every month, you know, you have to
Speaker 1: report like how much yield you're getting, you're just like
Speaker 1: answering some questions and then like at the end of
Speaker 1: quarter your report will be ready to download for agronomous Yeah, yeah,
Speaker 1: So how does tell me? Carla chime in on this.
Speaker 1: I'm just curious about. So how does IBM act as
Speaker 1: a partner here? What is IBM doing to help make
Speaker 1: this app um real So um So Chimp is actually
Speaker 1: leveraging many of our Watson products, including our weather channel
Speaker 1: product um for agrily, and I'll let her talk about
Speaker 1: how she's leveraging them to put them together. There is
Speaker 1: an existing set of tools which IBM has available which
Speaker 1: people like China can come and customize for their own purposes.
Speaker 1: That's right, like depending on like geography, for example, in
Speaker 1: the Eastern like if you're Eastern part Eastern province, or
Speaker 1: if you're um like central province of one going the
Speaker 1: weather is very different. You know, you have to know
Speaker 1: about your short term weather, what is your long term weather?
Speaker 1: Uh So our app provides you daily, weekly, monthly, and
Speaker 1: even annual weather forecasts book it's very location specific. And
Speaker 1: then also like if you have um let's say, if
Speaker 1: you're in the Central problem one of the Central provinces
Speaker 1: and you want to contact or interact with other farmers,
Speaker 1: there's a forum discussion session that you can just register
Speaker 1: and then start interacting with other farmers from your area.
Speaker 1: Uh And then also there's a marketplace like you know,
Speaker 1: that's the most difficult part that we're trying to implement
Speaker 1: because there's no market market ecosystem and mongolia for example, Uh,
Speaker 1: that you want to sell you want to sell your
Speaker 1: produce like in the local area or in the to
Speaker 1: the urban area, so that you can use the app,
Speaker 1: because maybe some restaurants from the open market they can
Speaker 1: contact you through the through the app saying that we
Speaker 1: want I don't know, like tones of like potatoes or carrots.
Speaker 1: And then just you know, uh you can see your
Speaker 1: long term UM weather prediction and just and then see
Speaker 1: the app generation. Listen, you can tell like if you're
Speaker 1: really able to you know, produce that much by end
Speaker 1: of next year or something like that. So the what
Speaker 1: the AI is doing is is taking the information, the
Speaker 1: specific data from individual farmers and combining that with things
Speaker 1: like weather data, weather predictions. I'm assuming other information as
Speaker 1: well and generating a set of recommendations for what would
Speaker 1: be the most efficient farming choices. Exactly exactly, That's what
Speaker 1: we are in our building. Yeah, Carla, we're here in
Speaker 1: part to talk about this women Leaders in AI program
Speaker 1: that b M is sponsoring. Tell us a little bit
Speaker 1: about that. What's what are the origins of it, the
Speaker 1: goals of it, um and how who gets chosen for it.
Speaker 1: So sketch that out for us. Sure. So first of all,
Speaker 1: congratulations to Chimka. She is one of our Women Leaders
Speaker 1: in AI honorees. And this program was really created to
Speaker 1: shine a light on women that are playing a significant
Speaker 1: role in artificial intelligence and machine learning and and really
Speaker 1: it boils down to the fact that in order to
Speaker 1: be it, you have to see it and UH and
Speaker 1: we want to really give visibility and elevate women like
Speaker 1: chim Ket and what they're doing today the field, about
Speaker 1: twenty two percent of the field is made up by women,
Speaker 1: and in reality it needs to be more in line
Speaker 1: with our global population. We'd love to see fIF of
Speaker 1: the a I community made up of women. And the
Speaker 1: reason for that is quite obvious. And that diversity of
Speaker 1: background and and all the ways means diversity of solutions
Speaker 1: UM and it also means that we build AI algorithms
Speaker 1: that are free of bias and uh and and some
Speaker 1: of the traps that can occur when you have too
Speaker 1: many like minded people working on a solution. How long
Speaker 1: have has this Women in AI program been running at IBM.
Speaker 1: We've been running it for three years now. How does
Speaker 1: it work? So you either's a kind of slate of
Speaker 1: grantees every year on how do you get how do
Speaker 1: you get chosen to be a member of this program? Yes,
Speaker 1: so we look for remain areas to recognize these women.
Speaker 1: So we're looking for obviously women that represent diversity in
Speaker 1: AI who are also looking to highlight progressive examples of
Speaker 1: how AI and IBM Watson are being applied to business.
Speaker 1: And we're curating firsthand examples of people that are pioneers
Speaker 1: leveraging AI in business and toil because a perfect example
Speaker 1: that and the way we select them is we're fortunate
Speaker 1: to have many clients around the world that are using
Speaker 1: AI and Watson to improve customer experience and gain efficiencies.
Speaker 1: And what they've done is helped nominate for us. And
Speaker 1: in return, what we do is we actually honor the
Speaker 1: folks that we feel are actually not just making gains
Speaker 1: in the field, but are actually delivering powerful business results.
Speaker 1: And uh and we build a cohort. Yea, Kima, do
Speaker 1: you do you interact with the other people who are
Speaker 1: winners of the or nominees for the Women Leaders in
Speaker 1: AI program? Is it a network and learning opportunity in
Speaker 1: addition to being an honor Yeah. Of course. In our
Speaker 1: Aggurly team, three of us are women, and we all
Speaker 1: got nominated for the honor, and for sure we interact,
Speaker 1: but also like at the same time, we just we
Speaker 1: were exposed too many networks, and then we started making
Speaker 1: connections and started interacting with each other what they're doing
Speaker 1: and what we are doing, and trying to going to
Speaker 1: exchange some ideas, like it's usually like on the social
Speaker 1: media that we are doing it. So but like, I'm
Speaker 1: still shocked that I was nominated because like the other
Speaker 1: nominees are like they're they're such a strong woman, and
Speaker 1: that's that was just very shocking us. But I'm so
Speaker 1: happy to be nominated. That actually makes me so happy
Speaker 1: to hear it because it I didn't realize it was
Speaker 1: so organic. We call it a cohort, and I thought
Speaker 1: for sure that it was something formal, But the fact
Speaker 1: that you all are reaching out to each other makes
Speaker 1: me so happy. Okay, I'd like I would always a
Speaker 1: really dumb question. I was like asking dumb questions. But
Speaker 1: I'm curious. You know, you you started this caror by
Speaker 1: saying people in this field are women, and you guys
Speaker 1: would like it to be more representative of of uh,
Speaker 1: the actual population. Why is that this is a dumb question.
Speaker 1: Why is it only Well, I think there's a variety
Speaker 1: of reasons that you probably know the answer to this
Speaker 1: better than I do, based on some of the books
Speaker 1: you've written, Malcolm. But I think that that we have
Speaker 1: a lack of pipeline and STEM is an obvious reason. Um.
Speaker 1: But then I also think there are things like COVID.
Speaker 1: For example, we lost millions of women in the workforce
Speaker 1: in the last eighteen months as a result of COVID
Speaker 1: and the role that women play in many households. So
Speaker 1: there's a variety of factors at play here beyond just
Speaker 1: women women entering STEM fields. Um, we're struggling to keep
Speaker 1: women in the workforce. Um. But I think what's encouraging
Speaker 1: to me, and I'm an optimist at heart, is hearing
Speaker 1: chimkas story. I mean, it didn't sound like chimad that
Speaker 1: you actually set out to go into a STEM field.
Speaker 1: It sounds like you're an entrepreneur first and you came
Speaker 1: into the technology, which for me is super inspiring because
Speaker 1: that means that the technology is becoming ubiquitous and that
Speaker 1: you don't actually have to be a person that comes
Speaker 1: from a science or engineering background to be able to
Speaker 1: leverage these tools. What you said, they were two other
Speaker 1: women in Agerly who are also nominees, So augually seems
Speaker 1: to have a pretty strong cohort of women at the top.
Speaker 1: What difference does that make? What do you have an
Speaker 1: organization that has as many women as that in positions
Speaker 1: of leadership? Do you do things differently than if you
Speaker 1: were a company that had entirely man at the top.
Speaker 1: I think when we think about arguraally, like our solutions
Speaker 1: tend to like our plan and resolutions tend to be
Speaker 1: like more long term and like pretty much detail oriented,
Speaker 1: you know, like we just see the every risk that
Speaker 1: could just arise in the long term, and then we
Speaker 1: just start thinking about like how we can address one
Speaker 1: by one because usually uh in the startup world, for example,
Speaker 1: it's very hard to predict like what's going to happen
Speaker 1: in the long term. But like for us, actually we
Speaker 1: always think about like short what's gonna happen in short term,
Speaker 1: and then we also talk about and then think about
Speaker 1: more about the long term plan. I think that could
Speaker 1: be the difference. M hm, Carla, you you are a
Speaker 1: woman in a field that historically has been very male.
Speaker 1: I'm just curious over the course of your career, have
Speaker 1: you what's what kind of transformation in terms of of
Speaker 1: of representation have you seen in the tech world. Wow,
Speaker 1: I've seen a huge shift. In the beginning of my career,
Speaker 1: I there were many times where I was the only
Speaker 1: woman in the room, and fast forward to now, I
Speaker 1: actually feel in most of the rooms that sometimes there
Speaker 1: are more women than men. And that's something that I
Speaker 1: hadn't seen in the and in my past. Um. I'll
Speaker 1: also say, Malcolm, I was interesting this this, I realized
Speaker 1: this actually today I was speaking to a group of people.
Speaker 1: I feel like because of that, I personally have been
Speaker 1: able to be more of myself um and and It's
Speaker 1: been this journey to authenticity over the course of my career.
Speaker 1: And the more I'm surrounded with people like me, the
Speaker 1: more comfortable I become. Um and UH and it's it's
Speaker 1: nice to be working with diverse teams. And again it's
Speaker 1: a big reason of why I chose to come to
Speaker 1: IBM because there's such a focus on diversity and inclusion.
Speaker 1: We had an equal pay policy that predated the Civil
Speaker 1: Rights Act, for example, and UH and so we've been
Speaker 1: working on diversity and inclusion initiatives since nineteen eleven. Um,
Speaker 1: it's kind of mind blowing to think about, and and
Speaker 1: that's very much a part of who IBM is and
Speaker 1: what we're about, both internally and externally. When I was
Speaker 1: fascinated by something you said, which was that you it's
Speaker 1: much easier to be yourself in environments where so compare
Speaker 1: your self, Carla to your I don't want to put
Speaker 1: a number date on when you started up. So what
Speaker 1: was it like? I dig into that for a moment.
Speaker 1: What is the difference between the self you can be
Speaker 1: now and the self you are when you started out
Speaker 1: in male dominated environments? I mean I had peers that
Speaker 1: used to walk the aisles the sales hallways with baseball
Speaker 1: bats and uh, you know, and swing the baseball bats,
Speaker 1: um to try and intimidate their sales organizations. You know.
Speaker 1: It's it's uh and uh you know, fast forward to
Speaker 1: here we are during COVID and and people are holding
Speaker 1: their children while on screen. It's it's just such a juxtaposition. Um.
Speaker 1: I I grew up in a very formal environment where
Speaker 1: there were actual dress codes and you can only wear
Speaker 1: certain things and uh, and so it's it's been a
Speaker 1: complete and total change that I've witnessed over the last
Speaker 1: twenty one plus years of my career in tech. Chimka.
Speaker 1: Let's let's talk a little bit more about your own
Speaker 1: personal story and then how you got involved with agrily.
Speaker 1: So did you grow up in Mongolia? Yeah, I was
Speaker 1: born and growing up in the Eastern Moss Province in Mongolia.
Speaker 1: My grandpa she had a small field and greenhouse that
Speaker 1: we used to just grow up tomato and cumber. It
Speaker 1: was like back in uh ninety nineties, you know, just
Speaker 1: like you cannot really find tomato or cucumba very easy
Speaker 1: in Mongolia. But m grandma she used to like try
Speaker 1: to fight against these climate conditions because in Mongolia is
Speaker 1: the climate is quite extreme. We have like very cold winter,
Speaker 1: we have like very windy spring, quite chilly autumn, and
Speaker 1: then also very dry um summer. It could be. I
Speaker 1: was like always curious about like how people in the
Speaker 1: rule they are still like going on with white because
Speaker 1: most of them are dependent on farming. And then later
Speaker 1: I think starting into twos and fifteen or sixteen, I
Speaker 1: started working with this international NGO to fight against human trafficking.
Speaker 1: I had to travel a lot to the bordering areas
Speaker 1: and then start training women there who are afflicted with
Speaker 1: the human trafficking. And it started like telling them what
Speaker 1: kind of problems they can solve in their rural area.
Speaker 1: So that's how I just got into like attracted to
Speaker 1: maybe I should learn about like more about entrepreneurship, Like
Speaker 1: I should just change the idea of like starting traditional
Speaker 1: business like something new, something related to technology or whatever,
Speaker 1: Like you have to start thinking in a different way,
Speaker 1: And how did you think in a different way? What
Speaker 1: was your approach? I found like very common pattern, Like
Speaker 1: two things I found. The first problem in rural area
Speaker 1: was in Mongolia was youth employment. And then second one
Speaker 1: like a lot of young people struggled to get a
Speaker 1: job there because there's no job. And second one was
Speaker 1: like there's nobody in farming, especially young people. They easily
Speaker 1: give up job in farming. Then I questioned myself why so,
Speaker 1: like I started talking to the specific smallholder family farmers,
Speaker 1: like what could be the problems and then what would
Speaker 1: be the solutions? And then I thought like maybe those
Speaker 1: problems can be solved with a smartphone because the users
Speaker 1: the coverage of the smartphone was quite high. In one boy,
Speaker 1: everybody has Facebook, everybody has smartphone, so what do you
Speaker 1: need now? That's what I thought. That's how aggerally idea
Speaker 1: came up along with my teammates Carla. How how typical
Speaker 1: is Chimpka and Agrily? Are there a lot of companies
Speaker 1: young companies that IBM is working with like that. There
Speaker 1: are quite a few, and I'm discovering them more and
Speaker 1: more each day, and it inspires me so much to
Speaker 1: hear these stories. And I actually see that as one
Speaker 1: of the primary functions of my role in my organization's
Speaker 1: role is to to elevate the Chimkas and Agerle's of
Speaker 1: the world as examples for everyone else to follow. Her
Speaker 1: story is so inspiring and as actually Chincas you were talking,
Speaker 1: one of the things I was wondering is what's happened
Speaker 1: to the business since you won the award? We have
Speaker 1: been just piloting the the testing app. The first time
Speaker 1: we piloted in Mongolia, like in three eastern provinces. We
Speaker 1: reached out to a lot of farmers who can who
Speaker 1: are interested in testing this kind of app because this
Speaker 1: kind of like like mobile app and agriculture sector is
Speaker 1: not really common thing. And then everybody was quite impressed
Speaker 1: because you know, just like there's a young woman just
Speaker 1: reaching out to people and talking about technology, mobile app
Speaker 1: and agriculture. I have no idea like agriculture, I have
Speaker 1: no idea about technology, right, But the only thing is
Speaker 1: that I knew that there was a real problems that
Speaker 1: we can solve. Uh. And then we started piloting in
Speaker 1: Brazil in September, and then in November we started piloting
Speaker 1: it in India. So these three countries are like totally
Speaker 1: different in terms of like how agriculture has advanced. So
Speaker 1: we started developing like local apps, tailor to Mongolia, tailor
Speaker 1: to India, tailor to Brazil, and then that's how we
Speaker 1: started in January, and now we are nearing the launch
Speaker 1: date in Mongolia and also Brazil and India. We are
Speaker 1: launching quite soon in the absence of AI. Can you
Speaker 1: do this without what without piggybacking on Watson and what
Speaker 1: was that? What would it look like without IBM as
Speaker 1: a partner? Impossible or just clunky and not as good.
Speaker 1: We cannot do anything without those kind of technology. You
Speaker 1: know that IBM has like the for the weather for example,
Speaker 1: like we cannot do it by ourselves of course, so
Speaker 1: like this daily uh, you know just like weekly and monthly, uh,
Speaker 1: like whether predictions are all from the Weather Company by IBM,
Speaker 1: and then using our studio we are generating it. We
Speaker 1: are generating the entire the entire long term forecast for
Speaker 1: each cities in different countries. And then also we are
Speaker 1: using the IBM cloud storage to put everything on the
Speaker 1: server and then that's how people just can get it
Speaker 1: through the app. Yeah. Yeah, so hundreds of thousands of
Speaker 1: developers can leverage these tools to build applications, you know.
Speaker 1: And there's another topic which I feel like IBM is
Speaker 1: starting to establish some thought leadership around, which is not
Speaker 1: just the tools themselves, with the ethics around the tools
Speaker 1: and UH and making sure that the algorithms that are
Speaker 1: being built that then entrepreneurs like Chimka are leveraging the
Speaker 1: tools for are actually explainable and fair and UH and
Speaker 1: that that she can be confident in the decision making
Speaker 1: of those tools and that they're they're unbiased. And and
Speaker 1: that requires building algorithms that are that are built on
Speaker 1: hard evidence like standardized tests and transparent reporting and UM.
Speaker 1: And this is something that our research team has been
Speaker 1: very very focused on so that people like Chimka can
Speaker 1: focus on our business and not have to worry about
Speaker 1: those components of our tools. And does does the data
Speaker 1: that you generate Chimpka and we'll be generating over time,
Speaker 1: does that get fed back into Watson. Does Watson learn
Speaker 1: from agrily as well as I really learned from Watson. Yeah, exactly,
Speaker 1: That's that's what we are doing. So actually, team, we
Speaker 1: are now working with the IBM open source technology. We
Speaker 1: are trying to like that, as Carlo said, like you know,
Speaker 1: we have to have something for free for farmers and
Speaker 1: then for public that they can use. So we are
Speaker 1: working with the IBM to uh to have some open
Speaker 1: source technology which is like basically do this weather and
Speaker 1: forum and then also crop risks. We are trying to
Speaker 1: make it more open source. Like without IBM, like we
Speaker 1: cannot do it. We cannot just create this such a
Speaker 1: big network of wordwide and then like you know, just
Speaker 1: to get a support from the people who are in
Speaker 1: the different sectors. So like IBM is basically making it possible. Yeah,
Speaker 1: we are learning a lot from Watson, and Watson can
Speaker 1: absolutely learn our data and train itself and then just
Speaker 1: like you back something really supporting data to like each
Speaker 1: country in the forming are people that IBM surprised at
Speaker 1: all the inventive uses that Watson and are being put to. No,
Speaker 1: not at all. And in fact, I visited our headquarters
Speaker 1: for the first time a couple of weeks ago, and
Speaker 1: it really hit me what IBM has represented in the
Speaker 1: world in the last hundred years and what has actually
Speaker 1: come out of this company and and what it is
Speaker 1: it has enabled. So as an example, our our research
Speaker 1: team has received five Nobel Prizes. We invented the first
Speaker 1: personal computer uh. We invented Lasik, the barcode, the technology
Speaker 1: behind the A t M, just to name a few
Speaker 1: very small things that we've invented that have changed the
Speaker 1: course of how we work and live. So when I
Speaker 1: think about the future of IBM and the fact that
Speaker 1: we are building the tools and functionality that will then
Speaker 1: enable people like Chimpka to create the next set of
Speaker 1: technologies that will change the way that we work and live,
Speaker 1: it's not surprising to me because that's part of our heritage.
Speaker 1: That's what we've represented and and that's what we're going
Speaker 1: to represent and enable in the future. So let me
Speaker 1: ask you, you're we're talking about AI. Have companies like
Speaker 1: IBM done a good job and explain to the public
Speaker 1: what AI is all about. Like listening to Chimpka, this
Speaker 1: is using a technology to solve problems in the lives
Speaker 1: of an extraordiny number of people who nobody was bringing
Speaker 1: them that level of technological sophistication and help before. Right,
Speaker 1: there's I mean, is that story? Yeah, if I'm being honest, Malcolm, No,
Speaker 1: it's it's part of it's part of my remit and
Speaker 1: my organization's role to bring these stories to life. It's
Speaker 1: part of why we're here with you today, so that
Speaker 1: people can learn and what's possible. Um And and and I
Speaker 1: think that it is our responsibility to to tell these
Speaker 1: stories so that we can inspire folks to to leverage
Speaker 1: these technologies to improve our lives and to solve significant
Speaker 1: problems um whether they're from a business standpoint or from
Speaker 1: a societal standpoint. And in chim Goes take case, I
Speaker 1: think she's doing both. Yeah. Why is it hard to
Speaker 1: tell these kinds of stories? I think there are a
Speaker 1: couple of things at play here. I think it's hard
Speaker 1: to tell these stories because there's so many of them
Speaker 1: and they're so diverse, and picking the stories that you're
Speaker 1: going to tell can sometimes be difficult because there's so
Speaker 1: many different applications. UM. I also think we have a
Speaker 1: business to run and and there are times where they
Speaker 1: that we don't actually take the time to explain our technology.
Speaker 1: There's an assumption because so many people are using it,
Speaker 1: but the world already knows what it's doing. But even
Speaker 1: I myself joining the company, I'm now starting to appreciate
Speaker 1: how much of the world's backbone, from a technology standpoint,
Speaker 1: is made up of IBM and UH. And we need
Speaker 1: to tell these stories to to shepherd this next era
Speaker 1: for the company, but also quite frankly, to inspire the
Speaker 1: next Chimka mm hm. I asked that question about the
Speaker 1: importance of these kinds of stories because one of the
Speaker 1: things that struck me with COVID and with you know,
Speaker 1: this problem of people who are vaccine resistant is I think,
Speaker 1: on balance, a lot of resistance to vaccines is people
Speaker 1: can't wrap their mind around the notion that people who
Speaker 1: do science and technacological innovation are trying to help them.
Speaker 1: We've gotten so cynical about technology that people assume, oh,
Speaker 1: they're doing it, they must have some nefarious motive. There
Speaker 1: must have been some big bucks involved, There must be
Speaker 1: and it's not that it's like they actually just want
Speaker 1: to save your life. And same thing listening to Chimka.
Speaker 1: You know, I hope you get very rich, chim But
Speaker 1: the motivation is is you want to help the people
Speaker 1: back home in Mongolia, right like you you talked about
Speaker 1: you you started talking about your grandmother for goodness sake,
Speaker 1: Like that's your motivation. And I feel like somehow along
Speaker 1: the way, we we've neglected to inform the world that
Speaker 1: people who do this, this kind of innovation have the
Speaker 1: most human of motivations. Yeah, thank you. Purest of intent there,
Speaker 1: the purest of intent not only in Mongolia, Like I'm
Speaker 1: gonna apply it, like we're gonna apply it to the
Speaker 1: whole world, like all emerging markets you will see, Like,
Speaker 1: thank you. That's wonderful. Thank you. This has been so fun.
Speaker 1: I really enjoyed chatting with you, and I, um my
Speaker 1: hat is off to both of you for telling these
Speaker 1: kinds of stories. Thank you, thank you so much. Really,
Speaker 1: thank you, Carla, thank you Morcom. When we see the
Speaker 1: positive impact made by women in the field, it's obvious
Speaker 1: that tech companies must become more inclusive to stay innovative.
Speaker 1: People like Chimka and Carla are driving that impact, using
Speaker 1: tech solutions to solve problems that most people in the
Speaker 1: industry haven't thought of. Thanks again to Carla Pinero, Sublett
Speaker 1: and Chimka Monk Buyer for talking with me. It was
Speaker 1: such a pleasure. Smart Talk with IBM is produced by
Speaker 1: Emily Rosstak with Carl Migliori, edited by Karen Shakerji engineering
Speaker 1: by Martin Gonzalez, mixed and mastered by Jason Gambrel, music
Speaker 1: by Grandmasco. Special thanks to Molly Sosha, Andy Kelly, Mia LaBelle, Jacobisberg, Catafane,
Speaker 1: Eric Sander, and Maggie Taylor, and the teams at eight
Speaker 1: Bar and IBM. Smart Talks with IBM is a production
Speaker 1: of Pushkin Industries and I Heart Media. You can find
Speaker 1: more episodes at IBM dot com slash smart Talks, and
Speaker 1: you can find more Pushkin podcasts on the I Heart
Speaker 1: Radio app, Apple Podcasts, or wherever you like to listen.
Speaker 1: I'm Malcolm Gladwell, See you next time.