WEBVTT - Smart Talks with IBM and Malcolm Gladwell: Reshaping the Future with Women in AI 0:00:05.080 --> 0:00:08.600 Hello, Hello, Hello. This is Smart Talks with IBM, a 0:00:08.680 --> 0:00:13.840 podcast from Pushkin Industries. I heart Media and IBM about 0:00:13.880 --> 0:00:16.759 what it means to look at today's most challenging problems 0:00:17.079 --> 0:00:22.480 in a new way. I'm Mountain Globo today I'm chatting 0:00:22.480 --> 0:00:26.360 with IBM's new Senior Vice President and Chief Marketing Officer, 0:00:26.680 --> 0:00:31.200 Carla Pinero Sublet. Though Carla is new to IBM, she's 0:00:31.200 --> 0:00:34.800 a marketing industry veteran who has helped other tech companies 0:00:35.080 --> 0:00:40.559 launch and transform their brands. I'll also be chatting with 0:00:40.720 --> 0:00:45.479 Chimka Monk Buyer, co founder of Agrily. Chimka was recently 0:00:45.600 --> 0:00:48.599 named one of IBM's Women Leaders in AI for her 0:00:48.640 --> 0:00:52.479 work with Agrily, a digital platform that helps farmers make 0:00:52.560 --> 0:01:00.600 smart decisions about their crops using IBM technology. Right now, 0:01:00.720 --> 0:01:05.040 women make up an estimated of the AI workforce globally. 0:01:05.560 --> 0:01:09.720 In their work, both Carla and Chimpka breakdown barriers in 0:01:09.760 --> 0:01:25.120 the field and help make it more inclusive. Let's dive in. Well, Welcome, 0:01:25.400 --> 0:01:27.480 It's a pleasure to meet you all, the two of you. 0:01:27.959 --> 0:01:31.240 Carla your new to IBM. I'm told tell me where 0:01:31.240 --> 0:01:34.920 you came from and what brought you to I be am. 0:01:34.959 --> 0:01:38.040 I'm a twenty one you're veteran of the tech industry 0:01:38.720 --> 0:01:41.800 that put myself through architecture school, running restaurants and bars. 0:01:42.120 --> 0:01:44.600 And I say that because that's really informed who I 0:01:44.640 --> 0:01:47.119 am and how I operate in the world. I came 0:01:47.160 --> 0:01:52.240 to IBM for a very specific reason for starters. What 0:01:52.360 --> 0:01:55.680 an iconic brand. It's not just any tech company, it's 0:01:55.720 --> 0:02:00.000 the foundation of a whole industry. But secondly, the vale 0:02:00.160 --> 0:02:04.120 use of the company really resonated with me, and the 0:02:04.360 --> 0:02:07.200 heritage in the company as it stands with respect to 0:02:07.200 --> 0:02:10.440 diversity inclusion was in particular very appealing to me. So 0:02:10.480 --> 0:02:14.840 that's a big reason why I'm here. Yeah, Jim, what 0:02:14.960 --> 0:02:18.840 brought you into this world of AI and associated things? 0:02:19.720 --> 0:02:22.440 How I started it? I was a full right master's 0:02:22.520 --> 0:02:27.600 student from two thowy and then in the final year 0:02:27.720 --> 0:02:30.800 of my master's I teamed up with a bunch of 0:02:30.880 --> 0:02:35.360 international students from Taiwan, India and Brazil, and then we 0:02:35.440 --> 0:02:38.040 came up this idea. Because I was doing the research 0:02:38.080 --> 0:02:40.480 and the rule of development and ongoing, I found that 0:02:40.680 --> 0:02:45.560 interesting traditional business that is more about farming, and then 0:02:45.639 --> 0:02:48.919 we just decided to work to build their mobile app 0:02:49.040 --> 0:02:51.880 that could be connected to AI later it's a long 0:02:52.000 --> 0:02:56.560 term plun and then just fight against climate change most 0:02:56.600 --> 0:03:00.760 importantly and then also solved like other challenges based by 0:03:00.960 --> 0:03:07.040 small holders. Tell me exactly how this works. So I'm 0:03:07.080 --> 0:03:10.520 a farmer in Mongolia and I have a I have 0:03:10.560 --> 0:03:15.160 a smartphone and I I download the ugly app. What 0:03:15.240 --> 0:03:18.440 does it help me do? So, if you're a farmer 0:03:18.480 --> 0:03:22.840 malcom Mongolia, it's three to download. You will download the 0:03:22.919 --> 0:03:25.720 app and then depending on the country, you have a 0:03:25.880 --> 0:03:29.280 like farmer and code because you don't really just like 0:03:29.320 --> 0:03:31.720 start farming on your own, because you just get a 0:03:31.760 --> 0:03:35.200 permission to use a certain land right and then you're 0:03:35.320 --> 0:03:37.760 using the water resource, et cetera. So you have a 0:03:37.800 --> 0:03:41.600 farmer's code, and then you start putting your farmer's code. 0:03:42.120 --> 0:03:44.480 And then because every month, you know, you have to 0:03:44.560 --> 0:03:48.400 report like how much yield you're getting, you're just like 0:03:48.920 --> 0:03:52.160 answering some questions and then like at the end of 0:03:52.320 --> 0:03:57.880 quarter your report will be ready to download for agronomous Yeah, yeah, 0:03:58.120 --> 0:04:01.840 So how does tell me? Carla chime in on this. 0:04:02.360 --> 0:04:05.440 I'm just curious about. So how does IBM act as 0:04:05.440 --> 0:04:09.480 a partner here? What is IBM doing to help make 0:04:09.520 --> 0:04:15.480 this app um real So um So Chimp is actually 0:04:15.560 --> 0:04:20.440 leveraging many of our Watson products, including our weather channel 0:04:20.480 --> 0:04:23.680 product um for agrily, and I'll let her talk about 0:04:23.720 --> 0:04:26.719 how she's leveraging them to put them together. There is 0:04:26.760 --> 0:04:32.520 an existing set of tools which IBM has available which 0:04:32.560 --> 0:04:35.680 people like China can come and customize for their own purposes. 0:04:36.040 --> 0:04:39.560 That's right, like depending on like geography, for example, in 0:04:39.600 --> 0:04:43.080 the Eastern like if you're Eastern part Eastern province, or 0:04:43.120 --> 0:04:46.960 if you're um like central province of one going the 0:04:47.000 --> 0:04:49.280 weather is very different. You know, you have to know 0:04:49.360 --> 0:04:52.599 about your short term weather, what is your long term weather? 0:04:53.200 --> 0:04:57.640 Uh So our app provides you daily, weekly, monthly, and 0:04:57.720 --> 0:05:02.320 even annual weather forecasts book it's very location specific. And 0:05:02.360 --> 0:05:05.120 then also like if you have um let's say, if 0:05:05.160 --> 0:05:07.880 you're in the Central problem one of the Central provinces 0:05:07.880 --> 0:05:10.960 and you want to contact or interact with other farmers, 0:05:11.279 --> 0:05:14.960 there's a forum discussion session that you can just register 0:05:15.160 --> 0:05:19.440 and then start interacting with other farmers from your area. 0:05:19.760 --> 0:05:23.240 Uh And then also there's a marketplace like you know, 0:05:23.360 --> 0:05:25.839 that's the most difficult part that we're trying to implement 0:05:25.920 --> 0:05:29.920 because there's no market market ecosystem and mongolia for example, Uh, 0:05:30.040 --> 0:05:32.520 that you want to sell you want to sell your 0:05:32.560 --> 0:05:36.080 produce like in the local area or in the to 0:05:36.160 --> 0:05:38.799 the urban area, so that you can use the app, 0:05:39.320 --> 0:05:42.840 because maybe some restaurants from the open market they can 0:05:42.880 --> 0:05:46.159 contact you through the through the app saying that we 0:05:46.200 --> 0:05:51.320 want I don't know, like tones of like potatoes or carrots. 0:05:51.400 --> 0:05:54.080 And then just you know, uh you can see your 0:05:54.160 --> 0:05:58.799 long term UM weather prediction and just and then see 0:05:58.880 --> 0:06:03.040 the app generation. Listen, you can tell like if you're 0:06:03.040 --> 0:06:06.280 really able to you know, produce that much by end 0:06:06.279 --> 0:06:09.240 of next year or something like that. So the what 0:06:09.320 --> 0:06:14.000 the AI is doing is is taking the information, the 0:06:14.080 --> 0:06:19.320 specific data from individual farmers and combining that with things 0:06:19.360 --> 0:06:23.760 like weather data, weather predictions. I'm assuming other information as 0:06:23.760 --> 0:06:28.120 well and generating a set of recommendations for what would 0:06:28.120 --> 0:06:33.080 be the most efficient farming choices. Exactly exactly, That's what 0:06:33.240 --> 0:06:37.200 we are in our building. Yeah, Carla, we're here in 0:06:37.279 --> 0:06:40.080 part to talk about this women Leaders in AI program 0:06:40.279 --> 0:06:42.880 that b M is sponsoring. Tell us a little bit 0:06:42.880 --> 0:06:45.159 about that. What's what are the origins of it, the 0:06:45.200 --> 0:06:49.800 goals of it, um and how who gets chosen for it. 0:06:50.120 --> 0:06:53.400 So sketch that out for us. Sure. So first of all, 0:06:53.440 --> 0:06:58.200 congratulations to Chimka. She is one of our Women Leaders 0:06:58.240 --> 0:07:02.360 in AI honorees. And this program was really created to 0:07:02.480 --> 0:07:06.200 shine a light on women that are playing a significant 0:07:06.320 --> 0:07:10.400 role in artificial intelligence and machine learning and and really 0:07:10.440 --> 0:07:13.120 it boils down to the fact that in order to 0:07:13.160 --> 0:07:16.000 be it, you have to see it and UH and 0:07:16.080 --> 0:07:20.040 we want to really give visibility and elevate women like 0:07:20.160 --> 0:07:23.480 chim Ket and what they're doing today the field, about 0:07:23.480 --> 0:07:25.880 twenty two percent of the field is made up by women, 0:07:26.800 --> 0:07:29.520 and in reality it needs to be more in line 0:07:29.560 --> 0:07:32.560 with our global population. We'd love to see fIF of 0:07:32.640 --> 0:07:35.520 the a I community made up of women. And the 0:07:35.560 --> 0:07:38.960 reason for that is quite obvious. And that diversity of 0:07:39.000 --> 0:07:43.440 background and and all the ways means diversity of solutions 0:07:44.040 --> 0:07:48.440 UM and it also means that we build AI algorithms 0:07:48.440 --> 0:07:52.360 that are free of bias and uh and and some 0:07:52.440 --> 0:07:55.680 of the traps that can occur when you have too 0:07:55.680 --> 0:07:59.960 many like minded people working on a solution. How long 0:08:00.120 --> 0:08:04.400 have has this Women in AI program been running at IBM. 0:08:04.520 --> 0:08:07.200 We've been running it for three years now. How does 0:08:07.240 --> 0:08:10.760 it work? So you either's a kind of slate of 0:08:11.320 --> 0:08:14.360 grantees every year on how do you get how do 0:08:14.360 --> 0:08:18.480 you get chosen to be a member of this program? Yes, 0:08:18.560 --> 0:08:22.200 so we look for remain areas to recognize these women. 0:08:22.240 --> 0:08:26.000 So we're looking for obviously women that represent diversity in 0:08:26.040 --> 0:08:30.000 AI who are also looking to highlight progressive examples of 0:08:30.000 --> 0:08:33.880 how AI and IBM Watson are being applied to business. 0:08:34.240 --> 0:08:39.240 And we're curating firsthand examples of people that are pioneers 0:08:39.559 --> 0:08:42.440 leveraging AI in business and toil because a perfect example 0:08:42.559 --> 0:08:45.240 that and the way we select them is we're fortunate 0:08:45.240 --> 0:08:47.959 to have many clients around the world that are using 0:08:48.040 --> 0:08:52.520 AI and Watson to improve customer experience and gain efficiencies. 0:08:52.960 --> 0:08:56.680 And what they've done is helped nominate for us. And 0:08:57.120 --> 0:09:00.680 in return, what we do is we actually honor the 0:09:00.720 --> 0:09:03.840 folks that we feel are actually not just making gains 0:09:03.880 --> 0:09:07.320 in the field, but are actually delivering powerful business results. 0:09:07.480 --> 0:09:10.720 And uh and we build a cohort. Yea, Kima, do 0:09:10.760 --> 0:09:14.400 you do you interact with the other people who are 0:09:15.280 --> 0:09:18.360 winners of the or nominees for the Women Leaders in 0:09:18.400 --> 0:09:21.280 AI program? Is it a network and learning opportunity in 0:09:21.320 --> 0:09:24.320 addition to being an honor Yeah. Of course. In our 0:09:24.360 --> 0:09:27.120 Aggurly team, three of us are women, and we all 0:09:27.160 --> 0:09:31.240 got nominated for the honor, and for sure we interact, 0:09:31.760 --> 0:09:34.200 but also like at the same time, we just we 0:09:34.200 --> 0:09:38.120 were exposed too many networks, and then we started making 0:09:38.120 --> 0:09:42.840 connections and started interacting with each other what they're doing 0:09:42.880 --> 0:09:45.599 and what we are doing, and trying to going to 0:09:45.760 --> 0:09:49.200 exchange some ideas, like it's usually like on the social 0:09:49.240 --> 0:09:51.600 media that we are doing it. So but like, I'm 0:09:51.640 --> 0:09:55.880 still shocked that I was nominated because like the other 0:09:55.960 --> 0:09:59.400 nominees are like they're they're such a strong woman, and 0:10:00.640 --> 0:10:04.040 that's that was just very shocking us. But I'm so 0:10:04.160 --> 0:10:08.160 happy to be nominated. That actually makes me so happy 0:10:08.240 --> 0:10:10.400 to hear it because it I didn't realize it was 0:10:10.440 --> 0:10:13.240 so organic. We call it a cohort, and I thought 0:10:13.360 --> 0:10:15.400 for sure that it was something formal, But the fact 0:10:15.440 --> 0:10:17.480 that you all are reaching out to each other makes 0:10:17.480 --> 0:10:21.200 me so happy. Okay, I'd like I would always a 0:10:21.200 --> 0:10:24.640 really dumb question. I was like asking dumb questions. But 0:10:25.520 --> 0:10:27.760 I'm curious. You know, you you started this caror by 0:10:27.800 --> 0:10:32.760 saying people in this field are women, and you guys 0:10:32.760 --> 0:10:36.160 would like it to be more representative of of uh, 0:10:36.240 --> 0:10:39.840 the actual population. Why is that this is a dumb question. 0:10:40.000 --> 0:10:44.480 Why is it only Well, I think there's a variety 0:10:44.480 --> 0:10:46.520 of reasons that you probably know the answer to this 0:10:46.600 --> 0:10:48.240 better than I do, based on some of the books 0:10:48.240 --> 0:10:51.840 you've written, Malcolm. But I think that that we have 0:10:52.240 --> 0:10:56.520 a lack of pipeline and STEM is an obvious reason. Um. 0:10:56.600 --> 0:10:59.120 But then I also think there are things like COVID. 0:10:59.360 --> 0:11:02.280 For example, we lost millions of women in the workforce 0:11:02.840 --> 0:11:05.600 in the last eighteen months as a result of COVID 0:11:05.640 --> 0:11:09.360 and the role that women play in many households. So 0:11:09.480 --> 0:11:13.080 there's a variety of factors at play here beyond just 0:11:13.880 --> 0:11:17.600 women women entering STEM fields. Um, we're struggling to keep 0:11:17.640 --> 0:11:21.080 women in the workforce. Um. But I think what's encouraging 0:11:21.120 --> 0:11:24.280 to me, and I'm an optimist at heart, is hearing 0:11:24.360 --> 0:11:27.560 chimkas story. I mean, it didn't sound like chimad that 0:11:27.559 --> 0:11:30.280 you actually set out to go into a STEM field. 0:11:30.280 --> 0:11:33.120 It sounds like you're an entrepreneur first and you came 0:11:33.160 --> 0:11:37.160 into the technology, which for me is super inspiring because 0:11:37.200 --> 0:11:41.360 that means that the technology is becoming ubiquitous and that 0:11:41.440 --> 0:11:43.839 you don't actually have to be a person that comes 0:11:43.880 --> 0:11:46.319 from a science or engineering background to be able to 0:11:46.400 --> 0:11:51.640 leverage these tools. What you said, they were two other 0:11:51.679 --> 0:11:55.480 women in Agerly who are also nominees, So augually seems 0:11:55.520 --> 0:11:57.920 to have a pretty strong cohort of women at the top. 0:11:58.679 --> 0:12:01.160 What difference does that make? What do you have an 0:12:01.240 --> 0:12:04.319 organization that has as many women as that in positions 0:12:04.360 --> 0:12:07.240 of leadership? Do you do things differently than if you 0:12:07.280 --> 0:12:11.040 were a company that had entirely man at the top. 0:12:12.400 --> 0:12:16.160 I think when we think about arguraally, like our solutions 0:12:16.200 --> 0:12:19.560 tend to like our plan and resolutions tend to be 0:12:19.600 --> 0:12:23.680 like more long term and like pretty much detail oriented, 0:12:23.800 --> 0:12:27.240 you know, like we just see the every risk that 0:12:27.360 --> 0:12:29.839 could just arise in the long term, and then we 0:12:29.960 --> 0:12:32.839 just start thinking about like how we can address one 0:12:32.920 --> 0:12:37.520 by one because usually uh in the startup world, for example, 0:12:37.559 --> 0:12:39.440 it's very hard to predict like what's going to happen 0:12:39.480 --> 0:12:41.839 in the long term. But like for us, actually we 0:12:41.920 --> 0:12:44.560 always think about like short what's gonna happen in short term, 0:12:44.600 --> 0:12:46.839 and then we also talk about and then think about 0:12:46.920 --> 0:12:50.400 more about the long term plan. I think that could 0:12:50.400 --> 0:12:55.200 be the difference. M hm, Carla, you you are a 0:12:55.240 --> 0:12:58.840 woman in a field that historically has been very male. 0:12:59.600 --> 0:13:03.880 I'm just curious over the course of your career, have 0:13:04.040 --> 0:13:08.240 you what's what kind of transformation in terms of of 0:13:08.240 --> 0:13:13.760 of representation have you seen in the tech world. Wow, 0:13:13.800 --> 0:13:16.840 I've seen a huge shift. In the beginning of my career, 0:13:16.960 --> 0:13:19.200 I there were many times where I was the only 0:13:19.240 --> 0:13:22.720 woman in the room, and fast forward to now, I 0:13:22.760 --> 0:13:26.080 actually feel in most of the rooms that sometimes there 0:13:26.120 --> 0:13:28.160 are more women than men. And that's something that I 0:13:28.200 --> 0:13:32.560 hadn't seen in the and in my past. Um. I'll 0:13:32.679 --> 0:13:36.880 also say, Malcolm, I was interesting this this, I realized 0:13:36.920 --> 0:13:40.280 this actually today I was speaking to a group of people. 0:13:41.640 --> 0:13:44.880 I feel like because of that, I personally have been 0:13:44.920 --> 0:13:49.000 able to be more of myself um and and It's 0:13:49.000 --> 0:13:52.520 been this journey to authenticity over the course of my career. 0:13:53.160 --> 0:13:57.360 And the more I'm surrounded with people like me, the 0:13:57.440 --> 0:14:01.760 more comfortable I become. Um and UH and it's it's 0:14:01.880 --> 0:14:04.480 nice to be working with diverse teams. And again it's 0:14:04.520 --> 0:14:06.520 a big reason of why I chose to come to 0:14:06.600 --> 0:14:10.319 IBM because there's such a focus on diversity and inclusion. 0:14:11.480 --> 0:14:14.360 We had an equal pay policy that predated the Civil 0:14:14.440 --> 0:14:17.559 Rights Act, for example, and UH and so we've been 0:14:17.559 --> 0:14:21.640 working on diversity and inclusion initiatives since nineteen eleven. Um, 0:14:21.680 --> 0:14:23.680 it's kind of mind blowing to think about, and and 0:14:23.720 --> 0:14:26.400 that's very much a part of who IBM is and 0:14:26.440 --> 0:14:29.920 what we're about, both internally and externally. When I was 0:14:29.960 --> 0:14:33.040 fascinated by something you said, which was that you it's 0:14:33.080 --> 0:14:38.120 much easier to be yourself in environments where so compare 0:14:38.160 --> 0:14:45.000 your self, Carla to your I don't want to put 0:14:45.040 --> 0:14:49.320 a number date on when you started up. So what 0:14:49.400 --> 0:14:51.200 was it like? I dig into that for a moment. 0:14:51.600 --> 0:14:53.960 What is the difference between the self you can be 0:14:54.040 --> 0:14:56.640 now and the self you are when you started out 0:14:56.640 --> 0:15:02.880 in male dominated environments? I mean I had peers that 0:15:03.040 --> 0:15:06.280 used to walk the aisles the sales hallways with baseball 0:15:06.320 --> 0:15:10.000 bats and uh, you know, and swing the baseball bats, 0:15:10.360 --> 0:15:15.520 um to try and intimidate their sales organizations. You know. 0:15:15.640 --> 0:15:18.960 It's it's uh and uh you know, fast forward to 0:15:19.560 --> 0:15:22.360 here we are during COVID and and people are holding 0:15:22.360 --> 0:15:27.760 their children while on screen. It's it's just such a juxtaposition. Um. 0:15:27.880 --> 0:15:31.720 I I grew up in a very formal environment where 0:15:31.760 --> 0:15:34.680 there were actual dress codes and you can only wear 0:15:34.800 --> 0:15:38.640 certain things and uh, and so it's it's been a 0:15:38.720 --> 0:15:41.200 complete and total change that I've witnessed over the last 0:15:41.200 --> 0:15:45.000 twenty one plus years of my career in tech. Chimka. 0:15:45.120 --> 0:15:47.640 Let's let's talk a little bit more about your own 0:15:48.720 --> 0:15:52.000 personal story and then how you got involved with agrily. 0:15:52.280 --> 0:15:55.960 So did you grow up in Mongolia? Yeah, I was 0:15:56.000 --> 0:15:59.560 born and growing up in the Eastern Moss Province in Mongolia. 0:16:00.120 --> 0:16:03.480 My grandpa she had a small field and greenhouse that 0:16:03.680 --> 0:16:06.840 we used to just grow up tomato and cumber. It 0:16:06.920 --> 0:16:09.960 was like back in uh ninety nineties, you know, just 0:16:10.000 --> 0:16:13.240 like you cannot really find tomato or cucumba very easy 0:16:13.280 --> 0:16:19.040 in Mongolia. But m grandma she used to like try 0:16:19.120 --> 0:16:22.800 to fight against these climate conditions because in Mongolia is 0:16:22.840 --> 0:16:26.080 the climate is quite extreme. We have like very cold winter, 0:16:26.280 --> 0:16:30.000 we have like very windy spring, quite chilly autumn, and 0:16:30.040 --> 0:16:33.400 then also very dry um summer. It could be. I 0:16:33.480 --> 0:16:35.880 was like always curious about like how people in the 0:16:35.960 --> 0:16:38.520 rule they are still like going on with white because 0:16:39.680 --> 0:16:44.040 most of them are dependent on farming. And then later 0:16:44.840 --> 0:16:48.080 I think starting into twos and fifteen or sixteen, I 0:16:48.160 --> 0:16:52.560 started working with this international NGO to fight against human trafficking. 0:16:52.880 --> 0:16:55.480 I had to travel a lot to the bordering areas 0:16:55.600 --> 0:16:58.960 and then start training women there who are afflicted with 0:16:59.040 --> 0:17:01.920 the human trafficking. And it started like telling them what 0:17:02.040 --> 0:17:04.399 kind of problems they can solve in their rural area. 0:17:05.080 --> 0:17:07.840 So that's how I just got into like attracted to 0:17:07.920 --> 0:17:11.199 maybe I should learn about like more about entrepreneurship, Like 0:17:11.240 --> 0:17:14.800 I should just change the idea of like starting traditional 0:17:14.800 --> 0:17:18.040 business like something new, something related to technology or whatever, 0:17:18.160 --> 0:17:20.520 Like you have to start thinking in a different way, 0:17:21.000 --> 0:17:23.440 And how did you think in a different way? What 0:17:23.520 --> 0:17:26.439 was your approach? I found like very common pattern, Like 0:17:26.480 --> 0:17:29.639 two things I found. The first problem in rural area 0:17:29.800 --> 0:17:34.400 was in Mongolia was youth employment. And then second one 0:17:34.880 --> 0:17:36.679 like a lot of young people struggled to get a 0:17:36.760 --> 0:17:40.000 job there because there's no job. And second one was 0:17:40.040 --> 0:17:44.360 like there's nobody in farming, especially young people. They easily 0:17:44.359 --> 0:17:47.879 give up job in farming. Then I questioned myself why so, 0:17:47.960 --> 0:17:51.920 like I started talking to the specific smallholder family farmers, 0:17:51.960 --> 0:17:54.480 like what could be the problems and then what would 0:17:54.520 --> 0:17:57.280 be the solutions? And then I thought like maybe those 0:17:57.320 --> 0:18:00.840 problems can be solved with a smartphone because the users 0:18:00.920 --> 0:18:03.440 the coverage of the smartphone was quite high. In one boy, 0:18:03.520 --> 0:18:07.720 everybody has Facebook, everybody has smartphone, so what do you 0:18:07.760 --> 0:18:11.479 need now? That's what I thought. That's how aggerally idea 0:18:11.600 --> 0:18:18.720 came up along with my teammates Carla. How how typical 0:18:18.920 --> 0:18:23.800 is Chimpka and Agrily? Are there a lot of companies 0:18:23.840 --> 0:18:27.520 young companies that IBM is working with like that. There 0:18:27.520 --> 0:18:30.359 are quite a few, and I'm discovering them more and 0:18:30.400 --> 0:18:33.840 more each day, and it inspires me so much to 0:18:33.960 --> 0:18:37.040 hear these stories. And I actually see that as one 0:18:37.040 --> 0:18:40.400 of the primary functions of my role in my organization's 0:18:40.520 --> 0:18:44.600 role is to to elevate the Chimkas and Agerle's of 0:18:44.640 --> 0:18:48.560 the world as examples for everyone else to follow. Her 0:18:48.600 --> 0:18:52.679 story is so inspiring and as actually Chincas you were talking, 0:18:52.720 --> 0:18:55.400 one of the things I was wondering is what's happened 0:18:55.400 --> 0:18:59.199 to the business since you won the award? We have 0:18:59.359 --> 0:19:03.960 been just piloting the the testing app. The first time 0:19:04.000 --> 0:19:08.520 we piloted in Mongolia, like in three eastern provinces. We 0:19:08.640 --> 0:19:11.199 reached out to a lot of farmers who can who 0:19:11.240 --> 0:19:14.439 are interested in testing this kind of app because this 0:19:14.680 --> 0:19:18.919 kind of like like mobile app and agriculture sector is 0:19:18.960 --> 0:19:23.200 not really common thing. And then everybody was quite impressed 0:19:23.280 --> 0:19:26.080 because you know, just like there's a young woman just 0:19:26.160 --> 0:19:29.800 reaching out to people and talking about technology, mobile app 0:19:29.840 --> 0:19:32.600 and agriculture. I have no idea like agriculture, I have 0:19:32.640 --> 0:19:35.560 no idea about technology, right, But the only thing is 0:19:35.600 --> 0:19:37.960 that I knew that there was a real problems that 0:19:38.040 --> 0:19:40.840 we can solve. Uh. And then we started piloting in 0:19:40.880 --> 0:19:44.480 Brazil in September, and then in November we started piloting 0:19:44.480 --> 0:19:49.040 it in India. So these three countries are like totally 0:19:49.080 --> 0:19:52.520 different in terms of like how agriculture has advanced. So 0:19:52.640 --> 0:19:57.080 we started developing like local apps, tailor to Mongolia, tailor 0:19:57.160 --> 0:20:00.560 to India, tailor to Brazil, and then that's how we 0:20:00.600 --> 0:20:03.720 started in January, and now we are nearing the launch 0:20:03.840 --> 0:20:06.919 date in Mongolia and also Brazil and India. We are 0:20:07.000 --> 0:20:10.760 launching quite soon in the absence of AI. Can you 0:20:10.840 --> 0:20:14.320 do this without what without piggybacking on Watson and what 0:20:14.400 --> 0:20:16.800 was that? What would it look like without IBM as 0:20:16.840 --> 0:20:20.280 a partner? Impossible or just clunky and not as good. 0:20:20.600 --> 0:20:25.080 We cannot do anything without those kind of technology. You 0:20:25.119 --> 0:20:28.400 know that IBM has like the for the weather for example, 0:20:28.520 --> 0:20:31.119 like we cannot do it by ourselves of course, so 0:20:31.200 --> 0:20:35.760 like this daily uh, you know just like weekly and monthly, uh, 0:20:36.040 --> 0:20:39.760 like whether predictions are all from the Weather Company by IBM, 0:20:40.160 --> 0:20:43.000 and then using our studio we are generating it. We 0:20:43.040 --> 0:20:47.760 are generating the entire the entire long term forecast for 0:20:47.800 --> 0:20:51.240 each cities in different countries. And then also we are 0:20:51.520 --> 0:20:55.359 using the IBM cloud storage to put everything on the 0:20:55.400 --> 0:20:58.960 server and then that's how people just can get it 0:20:59.000 --> 0:21:02.760 through the app. Yeah. Yeah, so hundreds of thousands of 0:21:02.760 --> 0:21:07.639 developers can leverage these tools to build applications, you know. 0:21:07.680 --> 0:21:11.080 And there's another topic which I feel like IBM is 0:21:11.119 --> 0:21:14.360 starting to establish some thought leadership around, which is not 0:21:14.440 --> 0:21:17.480 just the tools themselves, with the ethics around the tools 0:21:18.280 --> 0:21:21.320 and UH and making sure that the algorithms that are 0:21:21.320 --> 0:21:25.760 being built that then entrepreneurs like Chimka are leveraging the 0:21:25.800 --> 0:21:31.080 tools for are actually explainable and fair and UH and 0:21:31.200 --> 0:21:34.960 that that she can be confident in the decision making 0:21:35.320 --> 0:21:39.119 of those tools and that they're they're unbiased. And and 0:21:39.160 --> 0:21:43.199 that requires building algorithms that are that are built on 0:21:43.280 --> 0:21:48.560 hard evidence like standardized tests and transparent reporting and UM. 0:21:48.600 --> 0:21:50.720 And this is something that our research team has been 0:21:51.280 --> 0:21:55.160 very very focused on so that people like Chimka can 0:21:55.160 --> 0:21:57.600 focus on our business and not have to worry about 0:21:57.640 --> 0:22:02.200 those components of our tools. And does does the data 0:22:02.240 --> 0:22:05.880 that you generate Chimpka and we'll be generating over time, 0:22:06.200 --> 0:22:09.440 does that get fed back into Watson. Does Watson learn 0:22:09.480 --> 0:22:13.000 from agrily as well as I really learned from Watson. Yeah, exactly, 0:22:13.040 --> 0:22:16.359 That's that's what we are doing. So actually, team, we 0:22:16.400 --> 0:22:20.000 are now working with the IBM open source technology. We 0:22:20.040 --> 0:22:22.639 are trying to like that, as Carlo said, like you know, 0:22:22.720 --> 0:22:26.960 we have to have something for free for farmers and 0:22:27.000 --> 0:22:29.520 then for public that they can use. So we are 0:22:29.520 --> 0:22:33.680 working with the IBM to uh to have some open 0:22:33.720 --> 0:22:37.520 source technology which is like basically do this weather and 0:22:37.640 --> 0:22:40.040 forum and then also crop risks. We are trying to 0:22:40.080 --> 0:22:42.840 make it more open source. Like without IBM, like we 0:22:42.880 --> 0:22:45.560 cannot do it. We cannot just create this such a 0:22:45.600 --> 0:22:49.000 big network of wordwide and then like you know, just 0:22:49.040 --> 0:22:51.720 to get a support from the people who are in 0:22:51.800 --> 0:22:56.560 the different sectors. So like IBM is basically making it possible. Yeah, 0:22:56.600 --> 0:22:58.960 we are learning a lot from Watson, and Watson can 0:22:59.000 --> 0:23:01.959 absolutely learn our data and train itself and then just 0:23:02.040 --> 0:23:06.600 like you back something really supporting data to like each 0:23:06.600 --> 0:23:10.119 country in the forming are people that IBM surprised at 0:23:10.119 --> 0:23:16.240 all the inventive uses that Watson and are being put to. No, 0:23:16.480 --> 0:23:19.760 not at all. And in fact, I visited our headquarters 0:23:19.840 --> 0:23:22.440 for the first time a couple of weeks ago, and 0:23:23.600 --> 0:23:27.679 it really hit me what IBM has represented in the 0:23:27.720 --> 0:23:32.280 world in the last hundred years and what has actually 0:23:32.320 --> 0:23:35.200 come out of this company and and what it is 0:23:35.280 --> 0:23:39.200 it has enabled. So as an example, our our research 0:23:39.280 --> 0:23:44.639 team has received five Nobel Prizes. We invented the first 0:23:44.720 --> 0:23:50.040 personal computer uh. We invented Lasik, the barcode, the technology 0:23:50.080 --> 0:23:53.280 behind the A t M, just to name a few 0:23:53.400 --> 0:23:55.840 very small things that we've invented that have changed the 0:23:55.840 --> 0:23:59.359 course of how we work and live. So when I 0:23:59.440 --> 0:24:01.760 think about the future of IBM and the fact that 0:24:01.840 --> 0:24:05.440 we are building the tools and functionality that will then 0:24:05.640 --> 0:24:09.119 enable people like Chimpka to create the next set of 0:24:09.119 --> 0:24:12.600 technologies that will change the way that we work and live, 0:24:12.680 --> 0:24:15.160 it's not surprising to me because that's part of our heritage. 0:24:15.200 --> 0:24:17.600 That's what we've represented and and that's what we're going 0:24:17.600 --> 0:24:19.800 to represent and enable in the future. So let me 0:24:19.840 --> 0:24:24.479 ask you, you're we're talking about AI. Have companies like 0:24:24.640 --> 0:24:28.359 IBM done a good job and explain to the public 0:24:28.400 --> 0:24:33.000 what AI is all about. Like listening to Chimpka, this 0:24:33.040 --> 0:24:36.440 is using a technology to solve problems in the lives 0:24:36.600 --> 0:24:41.760 of an extraordiny number of people who nobody was bringing 0:24:41.800 --> 0:24:45.520 them that level of technological sophistication and help before. Right, 0:24:45.560 --> 0:24:51.119 there's I mean, is that story? Yeah, if I'm being honest, Malcolm, No, 0:24:51.359 --> 0:24:53.600 it's it's part of it's part of my remit and 0:24:53.680 --> 0:24:56.760