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Reshaping the Future with Women in AI · The Steve Harvey Morning Show

0:05
Speaker 1: Hello, Hello, Hello. This is Smart Talks with IBM, a
0:08
Speaker 1: podcast from Pushkin Industries. iHeartMedia and IBM about what it
0:14
Speaker 1: means to look at today's most challenging problems in a
0:17
Speaker 1: new way. I'm Malcolm Glapwell. Today I'm chatting with IBM's
0:23
Speaker 1: new Senior Vice President and Chief Marketing Officer, Carla Pinero Sublet.
0:29
Speaker 1: Though Carla is new to IBM, she's a marketing industry
0:32
Speaker 1: veteran who has helped other tech companies launch and transform
0:36
Speaker 1: their brands. I'll also be chatting with Chimka Munkbeyer, co
0:42
Speaker 1: founder of Agrilly. Chimka was recently named one of IBM's
0:46
Speaker 1: Women Leaders in AI for her work with Agrilly, a
0:50
Speaker 1: digital platform that helps farmers make smart decisions about their
0:53
Speaker 1: crops using IBM technology. Right now, women make up an
1:01
Speaker 1: estimated twenty two percent of the AI workforce globally. In
1:05
Speaker 1: their work, both Carla and Chimka break down barriers in
1:09
Speaker 1: the field and help make it more inclusive. Let's dive in. Well, Welcome,
1:25
Speaker 1: It's a pleasure to meet you all, the two of you. Carla,
1:28
Speaker 1: you're new to IBM. I'm told tell me where you
1:31
Speaker 1: came from? And what brought you to IBM.
1:35
Speaker 2: I'm a twenty one year veteran of the tech industry
1:38
Speaker 2: that put myself through architecture school, running restaurants and bars,
1:42
Speaker 2: and I say that because that's really informed who I
1:44
Speaker 2: am and how I operate in the world. I came
1:47
Speaker 2: to IBM for a very specific reason for starters. What
1:52
Speaker 2: an iconic brand. It's not just any tech company, it's
1:55
Speaker 2: the foundation of a whole industry. But secondly, the use
2:00
Speaker 2: of the company really resonated with me, and the hair
2:04
Speaker 2: Digits company as it stands with respect to diversity inclusion
2:08
Speaker 2: was in particular very appealing to me. So that's a
2:11
Speaker 2: big reason why I'm here.
2:12
Speaker 1: Yeah, yeah, Chimka. What brought you into this world of
2:17
Speaker 1: AI and associated things?
2:19
Speaker 3: How I started it? I was a full fright master
2:22
Speaker 3: student from twenty eighteen to twenty twenty, and then in
2:26
Speaker 3: the final year of my masters, I teamed up with
2:30
Speaker 3: a bunch of international students from Taiwan, India and Brazil,
2:34
Speaker 3: and then we came up this idea because I was
2:37
Speaker 3: doing the research in rural development in Mongolian and I
2:40
Speaker 3: found that interesting traditional business that is more about farming,
2:45
Speaker 3: and then we just decided to work to build that
2:48
Speaker 3: mobile app that could be connected to AI later. It's
2:51
Speaker 3: a long term plan and then just fight against climate
2:55
Speaker 3: change most importantly and then also solve like other challenges
3:00
Speaker 3: based by small holders.
3:02
Speaker 1: Tell me exactly how this works. So I'm a farmer
3:07
Speaker 1: in Mongolia and I have a smartphone and I download
3:13
Speaker 1: the ugly app. What does it help me do?
3:17
Speaker 4: So, if you're.
3:17
Speaker 3: A farmer well Cala, it's free to download. You will
3:22
Speaker 3: download the app, and then depending on the country, you
3:25
Speaker 3: have a light farmer and code because you don't really
3:29
Speaker 3: just like start farming on your own, because you just
3:31
Speaker 3: get a permission to use a certain land, right, and
3:34
Speaker 3: then you're using the water resource, et cetera. So you
3:37
Speaker 3: have a farmer's code, and then you start putting your
3:40
Speaker 3: farmer's code. And then because every month, you know, you
3:44
Speaker 3: have to report like how much yield you're getting, you're
3:48
Speaker 3: just like answering some questions and then like at the
3:51
Speaker 3: end of quarter your report will be ready to download
3:54
Speaker 3: for adronomous.
3:56
Speaker 1: Yeah, yeah, So how does tell me? Carlub chime in
4:01
Speaker 1: on this. I'm just curious about. So how does IBM
4:05
Speaker 1: act as a partner here? What is IBM doing to
4:09
Speaker 1: help make this app real.
4:13
Speaker 2: So so Chimkin is actually leveraging many of our Watson products,
4:18
Speaker 2: including our weather Channel product for Agrile, and I'll let
4:23
Speaker 2: her talk about how she's leveraging them to put them together.
4:26
Speaker 1: There is an existing set of tools which IBM has
4:31
Speaker 1: available which people like Chika can come and customize for
4:34
Speaker 1: their own purposes.
4:36
Speaker 2: That's right.
4:36
Speaker 3: But depending on like geography, for example, in the Eastern
4:40
Speaker 3: like if you're eastern part of Eastern Province or if
4:43
Speaker 3: you're like Central province of one going, the weather is
4:47
Speaker 3: very different. You know, you have to know about your
4:50
Speaker 3: short term weather, what is your long term weather. So
4:54
Speaker 3: our app provides you daily, weekly, monthly, and even animal
4:58
Speaker 3: weather for casts. It's very location specific. And then also
5:02
Speaker 3: like if you have let's say, if you're in the
5:05
Speaker 3: Central problem one of the Central provinces and you want
5:08
Speaker 3: to contact or interact with the other farmers, there's a
5:11
Speaker 3: forum discussion session that you can just register and then
5:15
Speaker 3: start interacting with the other farmers from your area.
5:19
Speaker 4: Uh.
5:20
Speaker 3: And then also there's a marketplace like you know, that's
5:23
Speaker 3: the most difficult part that we are trying to implement
5:25
Speaker 3: because there's no market market ecosystem in Mongolia for example.
5:29
Speaker 4: Uh that you want to sell, you want to sell
5:32
Speaker 4: your produce.
5:33
Speaker 3: Like in the local area or in the to the
5:36
Speaker 3: urban area, so that you can use the app, because
5:39
Speaker 3: maybe some restaurants from the open market they can contact
5:43
Speaker 3: you through the through the app saying that we want
5:46
Speaker 3: I don't know, like tones of like potatoes or carrots.
5:51
Speaker 3: And then just you know, you can see your long
5:54
Speaker 3: term weather prediction and just and then see the app generation. Listen,
6:01
Speaker 3: you can tell if you're really able to produce.
6:05
Speaker 4: That much by end of next year or something like that.
6:08
Speaker 1: So what the AI is doing is is taking the information,
6:13
Speaker 1: the specific data from individual farmers and combining that with
6:19
Speaker 1: things like weather data, weather predictions, I'm assuming other information
6:23
Speaker 1: as well, and generating a set of recommendations for what
6:27
Speaker 1: would be the most efficient farming choices.
6:31
Speaker 4: Exactly exactly, that's what we are building.
6:35
Speaker 1: Yeah, Carla, we're here in part to talk about this
6:38
Speaker 1: women leaders in AI program that IBM is sponsoring. Tell
6:42
Speaker 1: us a little bit about that. What are the origins
6:44
Speaker 1: of it, the goals of it, and how who gets
6:49
Speaker 1: chosen for it. So sketch that out for us.
6:52
Speaker 2: Sure, So, first of all, congratulations to chimk she is
6:55
Speaker 2: one of our twenty twenty one Women Leaders in AI honorees.
7:00
Speaker 2: And this program was really created to shine a light
7:03
Speaker 2: on women that are playing a significant role in artificial
7:07
Speaker 2: intelligence and machine learning. And really it boils down to
7:11
Speaker 2: the fact that in order to be it, you have
7:14
Speaker 2: to see it, and we want to really give visibility
7:19
Speaker 2: and elevate women like chimcut and what they're doing today
7:22
Speaker 2: the field, about twenty two percent of the field is
7:25
Speaker 2: made up by women. In reality, it needs to be
7:28
Speaker 2: more in line with our global population. We'd love to
7:31
Speaker 2: see fifty percent of the AI community made up of women.
7:35
Speaker 2: And the reason for that is quite obvious in that
7:38
Speaker 2: diversity of background and all the ways means diversity of solutions,
7:44
Speaker 2: and it also means that we build AI algorithms that
7:48
Speaker 2: are free of bias and some of the traps that
7:54
Speaker 2: can occur when you have too many like minded people
7:56
Speaker 2: working on a solution.
7:59
Speaker 1: Long have has this Women in AI program been running
8:03
Speaker 1: at IBM?
8:04
Speaker 2: We've been running it for three years now.
8:06
Speaker 1: How does it work? So there's a kind of slate
8:10
Speaker 1: of grantees every year, and how do you get how
8:14
Speaker 1: do you get chosen to be a member of this program.
8:18
Speaker 2: Yeah, So we look for three main areas to recognize
8:21
Speaker 2: these women. So we're looking for obviously women that represent
8:25
Speaker 2: diversity in AI, who are also looking to highlight progressive
8:29
Speaker 2: examples of how AI and IBM Watson are being applied
8:33
Speaker 2: to business. And we're curating firsthand examples of people that
8:38
Speaker 2: are pioneers leveraging AI in business and because of a
8:41
Speaker 2: perfect example of that, and the way we select them
8:44
Speaker 2: is we're fortunate to have many clients around the world
8:47
Speaker 2: that are using AI and Watson to improve customer experience
8:50
Speaker 2: and gain efficiencies, and what they've done is help nominate
8:55
Speaker 2: for us, and in return, what we do is we
8:59
Speaker 2: actually we honor the folks that we feel are actually
9:02
Speaker 2: not just making gains in the field, but are actually
9:05
Speaker 2: delivering powerful business results, and we build a cohort.
9:09
Speaker 1: Yeah, Chimka, do you do you interact with the other
9:13
Speaker 1: people who are winners of the or nominees for the
9:17
Speaker 1: Women Leaders in AI program? Is it a network and
9:20
Speaker 1: learning opportunity in addition to being an honor.
9:23
Speaker 5: Yeah.
9:23
Speaker 1: Of course.
9:24
Speaker 3: In our Agurly team, three of us are women and
9:26
Speaker 3: we all got nominated for the honor, and for sure
9:30
Speaker 3: we interact, but also like at the same time, we
9:33
Speaker 3: just we were exposed too many networks, and then we
9:37
Speaker 3: started making connections and started interacting with each other what
9:42
Speaker 3: they're doing and what we are doing, and trying to
9:45
Speaker 3: exchange some ideas. Like it's usually like on the social
9:49
Speaker 3: media that we are doing it. But like, I'm still
9:52
Speaker 3: shocked that I was nominated because like the other nominees
9:56
Speaker 3: are like they're such a strong woman, and that was
10:01
Speaker 3: just very shocking use to me. But I'm so happy
10:04
Speaker 3: to be nominated.
10:06
Speaker 2: That actually makes me so happy to hear it because
10:09
Speaker 2: I didn't realize it was so organic. We call it
10:12
Speaker 2: a cohort, and I thought for sure that it was
10:14
Speaker 2: something formal, But the fact that you all are reaching
10:16
Speaker 2: out to each other makes me so happy.
10:19
Speaker 1: I'd like, I'm going to ask a really dumb question.
10:22
Speaker 1: I was like asking dumb questions, but I'm curious. You know,
10:26
Speaker 1: you started this, Karla by saying twenty two percent of
10:30
Speaker 1: people in this field are women, and you guys would
10:33
Speaker 1: like it to be more representative of the actual population.
10:37
Speaker 1: Why is that This is a dumb question. Why is
10:40
Speaker 1: it only twenty two percent?
10:42
Speaker 2: Well, I think there's a variety of reasons that you
10:45
Speaker 2: probably know the answer this better than I do, based
10:47
Speaker 2: on some of the books you've written, Malcolm. But I
10:49
Speaker 2: think that we have a lack of pipeline in STEM
10:54
Speaker 2: is an obvious reason. But then I also think there
10:57
Speaker 2: are things like COVID. For example, well, we lost millions
11:01
Speaker 2: of women in the workforce in the last eighteen months
11:04
Speaker 2: as a result of COVID in the role that women
11:07
Speaker 2: play in many households, so there's a variety of factors
11:11
Speaker 2: at play here beyond just women entering STEM fields. We're
11:16
Speaker 2: struggling to keep women in the workforce. But I think
11:20
Speaker 2: what's encouraging to me, and I'm an optimist at heart,
11:23
Speaker 2: is hearing Chimka's story. I mean, it didn't sound like
11:27
Speaker 2: Chimkid that you actually set out to go into a
11:29
Speaker 2: STEM field. It sounds like you're an entrepreneur first and
11:32
Speaker 2: you came into the technology, which for me is super
11:35
Speaker 2: inspiring because that means that the technology is becoming ubiquitous
11:41
Speaker 2: and that you don't actually have to be a person
11:43
Speaker 2: that comes from a science or engineering background to be
11:46
Speaker 2: able to leverage these tools.
11:50
Speaker 1: What you said, there were two other women at Agili
11:52
Speaker 1: who are also nominees. So aguly seems to have a
11:55
Speaker 1: pretty strong cohort of women at the top. What difference
11:59
Speaker 1: does that make When do you have an organization that
12:01
Speaker 1: has as many women as that in positions of leadership,
12:05
Speaker 1: do you do things differently than if you were a
12:08
Speaker 1: company that had entirely men at the top.
12:12
Speaker 3: I think when we think about Augury, like our solutions
12:16
Speaker 3: tend to, like our plan and resolutions tend to be
12:19
Speaker 3: like more long term and like pretty much detail oriented,
12:23
Speaker 3: you know, like we just see the every risk that
12:27
Speaker 3: could just arise in the long term, and then we
12:30
Speaker 3: just start thinking about like how we can address one
12:32
Speaker 3: by one because usually in the startup world, for example,
12:37
Speaker 3: it's very hard to predict like what's going to happen
12:39
Speaker 3: in the long term. But like for us, augurally we
12:41
Speaker 3: always think about like short what's going to happen in
12:44
Speaker 3: short term, and then we also talk about and then
12:46
Speaker 3: think about more about the long term plan.
12:48
Speaker 4: I think that could be the difference.
12:53
Speaker 1: Corlor you you are a woman in a field that
12:57
Speaker 1: historically has been very male. I'm just curious, over the
13:00
Speaker 1: course of your career, have you what what kind of
13:05
Speaker 1: transformation in terms of of of representation have you seen
13:11
Speaker 1: in the tech world?
13:13
Speaker 2: Wow, I've seen a huge shift. In the beginning of
13:16
Speaker 2: my career, there were many times where I was the
13:19
Speaker 2: only woman in the room, and fast forward to now,
13:22
Speaker 2: I actually feel in most of the rooms that sometimes
13:26
Speaker 2: there is more women than men, and that's something that
13:28
Speaker 2: I hadn't seen in the in in my past. I'll
13:32
Speaker 2: also say, Malcolm, I was it was interesting this this,
13:36
Speaker 2: I realized this actually today I was speaking to a
13:39
Speaker 2: group of people. I feel like because of that, I
13:44
Speaker 2: personally have been able to be more of myself and
13:48
Speaker 2: and it's been this journey to authenticity over the course
13:52
Speaker 2: of my career. And the more I'm surrounded with people
13:56
Speaker 2: like me, the more comfortable I become. And it's nice
14:02
Speaker 2: to be working with diverse teams. And again it's a
14:04
Speaker 2: big reason of why I chose to come to IBM,
14:07
Speaker 2: because there's such a focus on diversity and inclusion. We
14:11
Speaker 2: had an equal pay policy that predated the Civil Rights Act,
14:15
Speaker 2: for example, and so we've been working on diversity and
14:18
Speaker 2: inclusion initiatives since nineteen eleven. It's kind of mind blowing
14:22
Speaker 2: to think about, and that's very much a part of
14:25
Speaker 2: who IBM is and what we're about both internally and externally.
14:29
Speaker 1: I was fascinated by something you said, which was that
14:32
Speaker 1: it's much easier to be yourself in environments where so
14:37
Speaker 1: compare your twenty twenty one self, Carla to your I
14:43
Speaker 1: don't want to put an data on when you started out,
14:49
Speaker 1: So what was it like dig into that for a moment.
14:51
Speaker 1: What is the difference between the self you can be
14:54
Speaker 1: now and the self you were when you started out
14:56
Speaker 1: in male dominated environments.
14:59
Speaker 5: I mean, I had peers that used to walk the
15:04
Speaker 5: aisles the sales hallways with baseball bats and you know,
15:08
Speaker 5: and swing the baseball bats to try and intimidate their
15:12
Speaker 5: sales organizations.
15:15
Speaker 2: You know, it's and you know, fast forward to here
15:19
Speaker 2: we are during COVID and people are holding their children
15:23
Speaker 2: while on screen. It's just such a juxtaposition. I grew
15:28
Speaker 2: up in a very formal environment where there were actual
15:32
Speaker 2: dress codes and you can only wear certain things, and
15:36
Speaker 2: so it's been a complete and total change that I've
15:40
Speaker 2: witnessed over the last twenty one plus years of my
15:42
Speaker 2: career in tech.
15:44
Speaker 1: Chimpkin, let's talk a little bit more about your own
15:48
Speaker 1: personal story and then how you got involved with agurly
15:52
Speaker 1: So did you grow up in Mongolia?
15:54
Speaker 3: Yeah, I was born and growing up in the Eastern.
15:57
Speaker 4: Mosque province in Mongolia.
16:00
Speaker 3: My grandma she had a small field and greenhouse that
16:03
Speaker 3: we used to just grow up.
16:05
Speaker 4: Tomato in Koomber.
16:06
Speaker 3: It was like back in ninety nineties, you know, just
16:10
Speaker 3: like you cannot really find tomato or cucumber very easy
16:13
Speaker 3: in Mongolia.
16:14
Speaker 4: But Grandma she used.
16:17
Speaker 3: To like try to fight against these climate conditions because
16:22
Speaker 3: in Mongolia is the climate is quite extreme. We have
16:25
Speaker 3: like very cold winter, we have like weary windy spring,
16:28
Speaker 3: quite chilly autumn, and then also very dry summer. It
16:32
Speaker 3: could be.
16:33
Speaker 4: I was like always curious about like how people.
16:35
Speaker 3: In the rule are still like going on with white
16:38
Speaker 3: because most of them are dependent on farming. And then later,
16:44
Speaker 3: I think starting in twenty fifteen or sixteen, I started
16:48
Speaker 3: working with this international ANGEO to fight against human trafficking.
16:52
Speaker 3: I had to travel a lot to the bordering areas
16:55
Speaker 3: and then start training women there who are afflicted with
16:59
Speaker 3: the human trafficking. And it started like telling them what
17:02
Speaker 3: kind of problems they can solve in their rural area.
17:05
Speaker 3: So that's how I just got into like attracted to
17:07
Speaker 3: maybe I should learn about like more about entrepreneurship, Like
17:11
Speaker 3: I should just change the idea of like starting traditional
17:14
Speaker 3: business like something new, something related to technology or whatever,
17:18
Speaker 3: Like you have to start thinking in a different way.
17:21
Speaker 1: And how did you think it a different way? What
17:23
Speaker 1: was your approach?
17:24
Speaker 4: I found like very common pattern.
17:26
Speaker 3: Like two things I found. The first problem in rural
17:29
Speaker 3: area was in Mongolia was youth employment.
17:33
Speaker 4: And then second one like a lot of young people.
17:35
Speaker 3: Struggled to get a job there because there's no job.
17:38
Speaker 3: And second one was like there's nobody in farming. They
17:42
Speaker 3: especially young people, they easily give up job in farming.
17:45
Speaker 3: Then I questioned myself why so, like I started talking
17:49
Speaker 3: to the specific smallholder family farmers, like what could be
17:52
Speaker 3: the problems and then what could be the solutions? And
17:55
Speaker 3: then I thought like maybe those problems can be solved
17:58
Speaker 3: with a smartphone because the users, the coverage of the
18:01
Speaker 3: smartphone was quite high in mon Boy. Everybody has Facebook,
18:05
Speaker 3: everybody has smartphone, So what do you need now? That's
18:08
Speaker 3: what I thought. That's how Agurley idea came up along
18:12
Speaker 3: with my teammates.
18:16
Speaker 1: Carla. How how typical is Chimka and agrilely? Are there
18:22
Speaker 1: a lot of companies young companies that IBM's working with
18:26
Speaker 1: like that.
18:27
Speaker 2: There are quite a few, and I'm discovering them more
18:30
Speaker 2: and more each day, and it inspires me so much
18:33
Speaker 2: to hear these stories. And I actually see that as
18:36
Speaker 2: one of the primary functions of my role in my
18:39
Speaker 2: organization's role is to elevate the Chimkas and Agriles of
18:44
Speaker 2: the world as examples for everyone else to follow. Her
18:48
Speaker 2: story is so inspiring and as actually chimkas you were talking.
18:52
Speaker 2: One of the things I was wondering is what's happened
18:55
Speaker 2: to the business since you won the award.
18:58
Speaker 3: We have been just piloting the testing apps. The first
19:03
Speaker 3: time we piloted in Mongolia, like in three eastern provinces.
19:08
Speaker 4: We reached out to a lot of farmers who are interested.
19:11
Speaker 3: In testing this kind of app because this kind of
19:15
Speaker 3: like like mobile app in agriculture sector is not really
19:19
Speaker 3: common thing. And then everybody was quite impressed because you know,
19:24
Speaker 3: just like there's a young woman just reaching out to
19:27
Speaker 3: people and talking about technology, mobile app and agriculture. I
19:30
Speaker 3: have no idea about agriculture. I have no idea about technology, right,
19:34
Speaker 3: But the only thing is, like I knew that there
19:36
Speaker 3: was a real problems that we can solve. And then
19:39
Speaker 3: we started piloting in Brazil in September, and then in
19:42
Speaker 3: November we started piloting it in India. So these three
19:47
Speaker 3: countries are like totally different in terms of like how
19:51
Speaker 3: agriculture is advanced. So we started developing like local apps,
19:55
Speaker 3: tailor to Mongolia, tailor to India, tailor to Brazil, and
19:59
Speaker 3: then that's how we started in January, and now we
20:02
Speaker 3: are nearing the launch date in Mongolia and also Brazil
20:06
Speaker 3: and India.
20:06
Speaker 4: We are launching quite.
20:07
Speaker 1: Soon in the absence of AI. Can you do this
20:11
Speaker 1: without what without piggybacking on Watson? What was that? What
20:14
Speaker 1: would it look like without IBM as a partner? Possible
20:18
Speaker 1: or just clunky and not as good.
20:20
Speaker 3: We cannot do anything without those kinds of technology.
20:25
Speaker 4: You know that IBM has.
20:26
Speaker 3: Like for the weather for example, like we cannot do
20:29
Speaker 3: it by ourselves of course, so like this daily you know,
20:33
Speaker 3: just like weekly and monthly uh like weather predictions are
20:37
Speaker 3: all from the Weather Company by IBM, and then using
20:41
Speaker 3: our studio we are generating it. We are generating the entire,
20:46
Speaker 3: entire long term forecasts for each cities in different countries
20:50
Speaker 3: and then also we are using the IBM cloud storage
20:53
Speaker 3: to put everything on the server and then that's how
20:57
Speaker 3: people just can get it through the app. Yeah.
21:00
Speaker 2: Yeah, so hundreds of thousands of developers can leverage these tools,
21:05
Speaker 2: uh to build applications.
21:07
Speaker 4: You know.
21:07
Speaker 2: And there's another topic of which I feel like IBM
21:11
Speaker 2: is starting to establish some thought leadership around, which is
21:14
Speaker 2: not just the tools themselves, but the ethics around the
21:17
Speaker 2: tools and UH and making sure that the algorithms that
21:21
Speaker 2: are being built that then entrepreneurs like Chimka are leveraging
21:25
Speaker 2: the tools for are actually explainable and fair and UH,
21:31
Speaker 2: and that that she can be confident in the decision
21:34
Speaker 2: making of those tools and that they're they're unbiased and
21:39
Speaker 2: and that requires building algorithms that are that are built
21:43
Speaker 2: on hard evidence like standardized tests and transparent reporting and UH.
21:48
Speaker 2: And this is something that our research team has been
21:51
Speaker 2: very very focused on so that people like Chimka can
21:55
Speaker 2: focus on our business and not have to worry about
21:57
Speaker 2: those components of our tools.
21:59
Speaker 1: Yeah. And does does the data that you generate, Chimka
22:04
Speaker 1: and will be generating over time, does that get fed
22:07
Speaker 1: back into Watson? Does Watson learn from agrilely as well
22:10
Speaker 1: as agurarly learn from Watson.
22:12
Speaker 3: Yeah, exactly, that's that's what we are doing so accurally team.
22:16
Speaker 3: We are now working with the IBM open source technology.
22:19
Speaker 3: We are trying to like that, as Carla said, like
22:22
Speaker 3: you know, we have to have something for free for
22:26
Speaker 3: farmers and then for public that.
22:28
Speaker 4: They can use.
22:28
Speaker 3: So we are working with the IBM to to have
22:33
Speaker 3: some open source technology which is like basically the weather
22:37
Speaker 3: and Forum and then also crop Risk. We are trying
22:40
Speaker 3: to make it more open source. Like without IBM, like
22:42
Speaker 3: we cannot do it. We cannot just create this such
22:45
Speaker 3: a big network of worldwide and then like you know,
22:49
Speaker 3: just to get a support from the people who are
22:51
Speaker 3: in the different sectors.
22:53
Speaker 4: So like IBM is basically making it possible. Yeah, we
22:56
Speaker 4: are learning a lot from Watson, and Watson can absolutely
22:59
Speaker 4: learn or data and train itself and then just like
23:02
Speaker 4: you back something really supporting data to each country and
23:07
Speaker 4: the farming.
23:08
Speaker 1: Are people at IBM surprised at all the inventive uses
23:11
Speaker 1: that Watson and AI are being put to.
23:16
Speaker 2: No, not at all, And in fact I visited our
23:19
Speaker 2: headquarters for the first time a couple of weeks ago
23:22
Speaker 2: and it really hit me what IBM has represented in
23:27
Speaker 2: the world in the last hundred years and what has
23:31
Speaker 2: actually come out of this company and what it has
23:35
Speaker 2: it has enabled. So as an example, our research team
23:39
Speaker 2: has received five Nobel Prizes. We invented the first personal computer,
23:46
Speaker 2: We invented Lasik, the barcode, the technology behind the ATM,
23:51
Speaker 2: just to name a few very small things that we've
23:54
Speaker 2: invented that have changed the course of how we work
23:57
Speaker 2: and live. So when I think about the future of
24:00
Speaker 2: IBM and the fact that we are building the tools
24:03
Speaker 2: and functionality that will then enable people like Chimka to
24:08
Speaker 2: create the next set of technologies that will change the
24:11
Speaker 2: way that we work and live, it's not surprising to
24:13
Speaker 2: me because that's part of our heritage. That's what we've represented,
24:16
Speaker 2: and that's what we're going to represent and enable in
24:18
Speaker 2: the future.
24:19
Speaker 1: So let me ask you, Connor, you're we're talking about AI.
24:23
Speaker 1: Have companies like IBM done a good job and explaining
24:27
Speaker 1: to the public what AI is all about. Like listening
24:31
Speaker 1: to Chimka, this is using a technology to solve problems
24:35
Speaker 1: in the lives of an extraordiny number of people who
24:40
Speaker 1: nobody was bringing them that level of technological sophistication and
24:44
Speaker 1: help before right, I mean, is that story?
24:49
Speaker 2: Yeah, if I'm being honest, Malcolm, No, it's it's part
24:52
Speaker 2: of my remit in my organization's role to bring these
24:55
Speaker 2: stories to life. It's part of why we're here with
24:58
Speaker 2: you today, so that people can learn what's possible. And
25:02
Speaker 2: I think that it is our responsibility to tell these
25:07
Speaker 2: stories so that we can inspire folks to leverage these
25:11
Speaker 2: technologies to improve our lives and to solve significant problems,
25:17
Speaker 2: whether they're from a business standpoint or from a societal standpoint.
25:21
Speaker 2: In a Chimko's case, I think she's doing both.
25:24
Speaker 1: Yeah, Why is it hard to tell these kinds of stories?
25:32
Speaker 2: I think there are a couple of things at play here.
25:34
Speaker 2: I think it's hard to tell these stories because there's
25:37
Speaker 2: so many of them and they're so diverse, and picking
25:40
Speaker 2: the stories that you're going to tell can sometimes be
25:44
Speaker 2: difficult because there's so many different applications. I also think
25:48
Speaker 2: we have a business to run, and there are times
25:51
Speaker 2: where that we don't actually take the time to explain
25:55
Speaker 2: our technology. There's an assumption, because so many people are
25:59
Speaker 2: using it, that the world already knows what it's doing.
26:02
Speaker 2: But even I'm myself joining the company. I'm now starting
26:07
Speaker 2: to appreciate how much of the world's backbone, from a
26:11
Speaker 2: technology standpoint, is made up of IBM. And we need
26:17
Speaker 2: to tell these stories to shepherd this next era for
26:22
Speaker 2: the company, but also quite frankly, to inspire the next Chimka.
26:29
Speaker 1: I asked that question about the importance of these kinds
26:33
Speaker 1: of stories because one of the things that struck me
26:37
Speaker 1: with COVID and with this problem of people who are
26:41
Speaker 1: vaccine resistant is I think, on balance, a lot of
26:46
Speaker 1: resistance to vaccines is people can't wrap their mind around
26:49
Speaker 1: the notion that people who do science and technological innovation
26:55
Speaker 1: are trying to help them. We've gotten so cynical about
26:59
Speaker 1: technology that people assume, oh, they're doing it, they must
27:02
Speaker 1: have some nefarious motive. There must have been some big
27:05
Speaker 1: bucks involved. There must be and it's not that it's
27:08
Speaker 1: like they actually just want to save your life. And
27:11
Speaker 1: same thing listening to Chimka. You know, I hope you
27:14
Speaker 1: get very rich, Chimka, But your motivation is is you
27:20
Speaker 1: want to help the people back home in Mongolia, right
27:24
Speaker 1: like you you talked about you you started talking about
27:26
Speaker 1: your grandmother. For goodness sake, like, yeah, that's your motivation.
27:30
Speaker 1: And I feel like somehow along the way, we we've
27:33
Speaker 1: neglected to inform the world that people who do this
27:37
Speaker 1: kind of innovation have the most human of motivations.
27:42
Speaker 2: Yeah, there's the purest of intent there, the purest of intent.
27:46
Speaker 3: Not only in Mongolia, Like I'm going to apply it,
27:49
Speaker 3: Like we're going to apply it to the whole world,
27:52
Speaker 3: like all emerging markets you will see, Like, thank you.
27:57
Speaker 1: That's wonderful. Well, thank you, this has been so fun.
28:01
Speaker 1: I really enjoyed chatting with you, and I my hat
28:05
Speaker 1: is off to both of you for telling these kinds
28:07
Speaker 1: of stories.
28:09
Speaker 4: Thank you, Thank you so much.
28:11
Speaker 3: Really, thank you, Chimka, thank you, Carla, thank you Moncom.
28:17
Speaker 1: When we see the positive impact made by women in
28:19
Speaker 1: the field, it's obvious that tech companies must become more
28:23
Speaker 1: inclusive to stay innovative. People like Chimka and Carla are
28:28
Speaker 1: driving that impact, using tech solutions to solve problems that
28:32
Speaker 1: most people in the industry haven't thought of. Thanks again
28:36
Speaker 1: to Carla Pinero Sublet and Chimka Mounkbayer for talking with me.
28:40
Speaker 1: It was such a pleasure. Smart Talks with IBM is
28:44
Speaker 1: produced by Emily Rosstak with Carl Migliori. Edited by Karen Shakherji,
28:49
Speaker 1: engineering by Martin Gonzalez, mixed and mastered by Jason Gambrel.
28:54
Speaker 1: Music by Gramosco. Special thanks to Molly's Soosha, Andy, Kelly,
29:00
Speaker 1: Mia Label, Jacob Iisberg Headafane, Eric Sandler, and Maggie Taylor,
29:05
Speaker 1: and the teams at eight Bar and IBM. Smart Talks
29:09
Speaker 1: with IBM is a production of Pushkin Industries and iHeartMedia.
29:13
Speaker 1: You can find more episodes at IBM dot com slash
29:17
Speaker 1: smart Talks, and you can find more Pushkin podcasts on
29:21
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you like to listen.
29:26
Speaker 1: I'm Malcolm Gladwell, See you next time.