1 00:00:05,080 --> 00:00:08,600 Speaker 1: Hello, Hello, Hello. This is Smart Talks with IBM, a 2 00:00:08,680 --> 00:00:13,840 Speaker 1: podcast from Pushkin Industries. I heart Media and IBM about 3 00:00:13,880 --> 00:00:16,759 Speaker 1: what it means to look at today's most challenging problems 4 00:00:17,079 --> 00:00:22,480 Speaker 1: in a new way. I'm Mountain Globo today I'm chatting 5 00:00:22,480 --> 00:00:26,360 Speaker 1: with IBM's new Senior Vice President and Chief Marketing Officer, 6 00:00:26,680 --> 00:00:31,200 Speaker 1: Carla Pinero Sublet. Though Carla is new to IBM, she's 7 00:00:31,200 --> 00:00:34,800 Speaker 1: a marketing industry veteran who has helped other tech companies 8 00:00:35,080 --> 00:00:40,559 Speaker 1: launch and transform their brands. I'll also be chatting with 9 00:00:40,720 --> 00:00:45,479 Speaker 1: Chimka Monk Buyer, co founder of Agrily. Chimka was recently 10 00:00:45,600 --> 00:00:48,599 Speaker 1: named one of IBM's Women Leaders in AI for her 11 00:00:48,640 --> 00:00:52,479 Speaker 1: work with Agrily, a digital platform that helps farmers make 12 00:00:52,560 --> 00:01:00,600 Speaker 1: smart decisions about their crops using IBM technology. Right now, 13 00:01:00,720 --> 00:01:05,040 Speaker 1: women make up an estimated of the AI workforce globally. 14 00:01:05,560 --> 00:01:09,720 Speaker 1: In their work, both Carla and Chimpka breakdown barriers in 15 00:01:09,760 --> 00:01:25,120 Speaker 1: the field and help make it more inclusive. Let's dive in. Well, Welcome, 16 00:01:25,400 --> 00:01:27,480 Speaker 1: It's a pleasure to meet you all, the two of you. 17 00:01:27,959 --> 00:01:31,240 Speaker 1: Carla your new to IBM. I'm told tell me where 18 00:01:31,240 --> 00:01:34,920 Speaker 1: you came from and what brought you to I be am. 19 00:01:34,959 --> 00:01:38,040 Speaker 1: I'm a twenty one you're veteran of the tech industry 20 00:01:38,720 --> 00:01:41,800 Speaker 1: that put myself through architecture school, running restaurants and bars. 21 00:01:42,120 --> 00:01:44,600 Speaker 1: And I say that because that's really informed who I 22 00:01:44,640 --> 00:01:47,119 Speaker 1: am and how I operate in the world. I came 23 00:01:47,160 --> 00:01:52,240 Speaker 1: to IBM for a very specific reason for starters. What 24 00:01:52,360 --> 00:01:55,680 Speaker 1: an iconic brand. It's not just any tech company, it's 25 00:01:55,720 --> 00:02:00,000 Speaker 1: the foundation of a whole industry. But secondly, the vale 26 00:02:00,160 --> 00:02:04,120 Speaker 1: use of the company really resonated with me, and the 27 00:02:04,360 --> 00:02:07,200 Speaker 1: heritage in the company as it stands with respect to 28 00:02:07,200 --> 00:02:10,440 Speaker 1: diversity inclusion was in particular very appealing to me. So 29 00:02:10,480 --> 00:02:14,840 Speaker 1: that's a big reason why I'm here. Yeah, Jim, what 30 00:02:14,960 --> 00:02:18,840 Speaker 1: brought you into this world of AI and associated things? 31 00:02:19,720 --> 00:02:22,440 Speaker 1: How I started it? I was a full right master's 32 00:02:22,520 --> 00:02:27,600 Speaker 1: student from two thowy and then in the final year 33 00:02:27,720 --> 00:02:30,800 Speaker 1: of my master's I teamed up with a bunch of 34 00:02:30,880 --> 00:02:35,360 Speaker 1: international students from Taiwan, India and Brazil, and then we 35 00:02:35,440 --> 00:02:38,040 Speaker 1: came up this idea. Because I was doing the research 36 00:02:38,080 --> 00:02:40,480 Speaker 1: and the rule of development and ongoing, I found that 37 00:02:40,680 --> 00:02:45,560 Speaker 1: interesting traditional business that is more about farming, and then 38 00:02:45,639 --> 00:02:48,919 Speaker 1: we just decided to work to build their mobile app 39 00:02:49,040 --> 00:02:51,880 Speaker 1: that could be connected to AI later it's a long 40 00:02:52,000 --> 00:02:56,560 Speaker 1: term plun and then just fight against climate change most 41 00:02:56,600 --> 00:03:00,760 Speaker 1: importantly and then also solved like other challenges based by 42 00:03:00,960 --> 00:03:07,040 Speaker 1: small holders. Tell me exactly how this works. So I'm 43 00:03:07,080 --> 00:03:10,520 Speaker 1: a farmer in Mongolia and I have a I have 44 00:03:10,560 --> 00:03:15,160 Speaker 1: a smartphone and I I download the ugly app. What 45 00:03:15,240 --> 00:03:18,440 Speaker 1: does it help me do? So, if you're a farmer 46 00:03:18,480 --> 00:03:22,840 Speaker 1: malcom Mongolia, it's three to download. You will download the 47 00:03:22,919 --> 00:03:25,720 Speaker 1: app and then depending on the country, you have a 48 00:03:25,880 --> 00:03:29,280 Speaker 1: like farmer and code because you don't really just like 49 00:03:29,320 --> 00:03:31,720 Speaker 1: start farming on your own, because you just get a 50 00:03:31,760 --> 00:03:35,200 Speaker 1: permission to use a certain land right and then you're 51 00:03:35,320 --> 00:03:37,760 Speaker 1: using the water resource, et cetera. So you have a 52 00:03:37,800 --> 00:03:41,600 Speaker 1: farmer's code, and then you start putting your farmer's code. 53 00:03:42,120 --> 00:03:44,480 Speaker 1: And then because every month, you know, you have to 54 00:03:44,560 --> 00:03:48,400 Speaker 1: report like how much yield you're getting, you're just like 55 00:03:48,920 --> 00:03:52,160 Speaker 1: answering some questions and then like at the end of 56 00:03:52,320 --> 00:03:57,880 Speaker 1: quarter your report will be ready to download for agronomous Yeah, yeah, 57 00:03:58,120 --> 00:04:01,840 Speaker 1: So how does tell me? Carla chime in on this. 58 00:04:02,360 --> 00:04:05,440 Speaker 1: I'm just curious about. So how does IBM act as 59 00:04:05,440 --> 00:04:09,480 Speaker 1: a partner here? What is IBM doing to help make 60 00:04:09,520 --> 00:04:15,480 Speaker 1: this app um real So um So Chimp is actually 61 00:04:15,560 --> 00:04:20,440 Speaker 1: leveraging many of our Watson products, including our weather channel 62 00:04:20,480 --> 00:04:23,680 Speaker 1: product um for agrily, and I'll let her talk about 63 00:04:23,720 --> 00:04:26,719 Speaker 1: how she's leveraging them to put them together. There is 64 00:04:26,760 --> 00:04:32,520 Speaker 1: an existing set of tools which IBM has available which 65 00:04:32,560 --> 00:04:35,680 Speaker 1: people like China can come and customize for their own purposes. 66 00:04:36,040 --> 00:04:39,560 Speaker 1: That's right, like depending on like geography, for example, in 67 00:04:39,600 --> 00:04:43,080 Speaker 1: the Eastern like if you're Eastern part Eastern province, or 68 00:04:43,120 --> 00:04:46,960 Speaker 1: if you're um like central province of one going the 69 00:04:47,000 --> 00:04:49,280 Speaker 1: weather is very different. You know, you have to know 70 00:04:49,360 --> 00:04:52,599 Speaker 1: about your short term weather, what is your long term weather? 71 00:04:53,200 --> 00:04:57,640 Speaker 1: Uh So our app provides you daily, weekly, monthly, and 72 00:04:57,720 --> 00:05:02,320 Speaker 1: even annual weather forecasts book it's very location specific. And 73 00:05:02,360 --> 00:05:05,120 Speaker 1: then also like if you have um let's say, if 74 00:05:05,160 --> 00:05:07,880 Speaker 1: you're in the Central problem one of the Central provinces 75 00:05:07,880 --> 00:05:10,960 Speaker 1: and you want to contact or interact with other farmers, 76 00:05:11,279 --> 00:05:14,960 Speaker 1: there's a forum discussion session that you can just register 77 00:05:15,160 --> 00:05:19,440 Speaker 1: and then start interacting with other farmers from your area. 78 00:05:19,760 --> 00:05:23,240 Speaker 1: Uh And then also there's a marketplace like you know, 79 00:05:23,360 --> 00:05:25,839 Speaker 1: that's the most difficult part that we're trying to implement 80 00:05:25,920 --> 00:05:29,920 Speaker 1: because there's no market market ecosystem and mongolia for example, Uh, 81 00:05:30,040 --> 00:05:32,520 Speaker 1: that you want to sell you want to sell your 82 00:05:32,560 --> 00:05:36,080 Speaker 1: produce like in the local area or in the to 83 00:05:36,160 --> 00:05:38,799 Speaker 1: the urban area, so that you can use the app, 84 00:05:39,320 --> 00:05:42,840 Speaker 1: because maybe some restaurants from the open market they can 85 00:05:42,880 --> 00:05:46,159 Speaker 1: contact you through the through the app saying that we 86 00:05:46,200 --> 00:05:51,320 Speaker 1: want I don't know, like tones of like potatoes or carrots. 87 00:05:51,400 --> 00:05:54,080 Speaker 1: And then just you know, uh you can see your 88 00:05:54,160 --> 00:05:58,799 Speaker 1: long term UM weather prediction and just and then see 89 00:05:58,880 --> 00:06:03,040 Speaker 1: the app generation. Listen, you can tell like if you're 90 00:06:03,040 --> 00:06:06,280 Speaker 1: really able to you know, produce that much by end 91 00:06:06,279 --> 00:06:09,240 Speaker 1: of next year or something like that. So the what 92 00:06:09,320 --> 00:06:14,000 Speaker 1: the AI is doing is is taking the information, the 93 00:06:14,080 --> 00:06:19,320 Speaker 1: specific data from individual farmers and combining that with things 94 00:06:19,360 --> 00:06:23,760 Speaker 1: like weather data, weather predictions. I'm assuming other information as 95 00:06:23,760 --> 00:06:28,120 Speaker 1: well and generating a set of recommendations for what would 96 00:06:28,120 --> 00:06:33,080 Speaker 1: be the most efficient farming choices. Exactly exactly, That's what 97 00:06:33,240 --> 00:06:37,200 Speaker 1: we are in our building. Yeah, Carla, we're here in 98 00:06:37,279 --> 00:06:40,080 Speaker 1: part to talk about this women Leaders in AI program 99 00:06:40,279 --> 00:06:42,880 Speaker 1: that b M is sponsoring. Tell us a little bit 100 00:06:42,880 --> 00:06:45,159 Speaker 1: about that. What's what are the origins of it, the 101 00:06:45,200 --> 00:06:49,800 Speaker 1: goals of it, um and how who gets chosen for it. 102 00:06:50,120 --> 00:06:53,400 Speaker 1: So sketch that out for us. Sure. So first of all, 103 00:06:53,440 --> 00:06:58,200 Speaker 1: congratulations to Chimka. She is one of our Women Leaders 104 00:06:58,240 --> 00:07:02,360 Speaker 1: in AI honorees. And this program was really created to 105 00:07:02,480 --> 00:07:06,200 Speaker 1: shine a light on women that are playing a significant 106 00:07:06,320 --> 00:07:10,400 Speaker 1: role in artificial intelligence and machine learning and and really 107 00:07:10,440 --> 00:07:13,120 Speaker 1: it boils down to the fact that in order to 108 00:07:13,160 --> 00:07:16,000 Speaker 1: be it, you have to see it and UH and 109 00:07:16,080 --> 00:07:20,040 Speaker 1: we want to really give visibility and elevate women like 110 00:07:20,160 --> 00:07:23,480 Speaker 1: chim Ket and what they're doing today the field, about 111 00:07:23,480 --> 00:07:25,880 Speaker 1: twenty two percent of the field is made up by women, 112 00:07:26,800 --> 00:07:29,520 Speaker 1: and in reality it needs to be more in line 113 00:07:29,560 --> 00:07:32,560 Speaker 1: with our global population. We'd love to see fIF of 114 00:07:32,640 --> 00:07:35,520 Speaker 1: the a I community made up of women. And the 115 00:07:35,560 --> 00:07:38,960 Speaker 1: reason for that is quite obvious. And that diversity of 116 00:07:39,000 --> 00:07:43,440 Speaker 1: background and and all the ways means diversity of solutions 117 00:07:44,040 --> 00:07:48,440 Speaker 1: UM and it also means that we build AI algorithms 118 00:07:48,440 --> 00:07:52,360 Speaker 1: that are free of bias and uh and and some 119 00:07:52,440 --> 00:07:55,680 Speaker 1: of the traps that can occur when you have too 120 00:07:55,680 --> 00:07:59,960 Speaker 1: many like minded people working on a solution. How long 121 00:08:00,120 --> 00:08:04,400 Speaker 1: have has this Women in AI program been running at IBM. 122 00:08:04,520 --> 00:08:07,200 Speaker 1: We've been running it for three years now. How does 123 00:08:07,240 --> 00:08:10,760 Speaker 1: it work? So you either's a kind of slate of 124 00:08:11,320 --> 00:08:14,360 Speaker 1: grantees every year on how do you get how do 125 00:08:14,360 --> 00:08:18,480 Speaker 1: you get chosen to be a member of this program? Yes, 126 00:08:18,560 --> 00:08:22,200 Speaker 1: so we look for remain areas to recognize these women. 127 00:08:22,240 --> 00:08:26,000 Speaker 1: So we're looking for obviously women that represent diversity in 128 00:08:26,040 --> 00:08:30,000 Speaker 1: AI who are also looking to highlight progressive examples of 129 00:08:30,000 --> 00:08:33,880 Speaker 1: how AI and IBM Watson are being applied to business. 130 00:08:34,240 --> 00:08:39,240 Speaker 1: And we're curating firsthand examples of people that are pioneers 131 00:08:39,559 --> 00:08:42,440 Speaker 1: leveraging AI in business and toil because a perfect example 132 00:08:42,559 --> 00:08:45,240 Speaker 1: that and the way we select them is we're fortunate 133 00:08:45,240 --> 00:08:47,959 Speaker 1: to have many clients around the world that are using 134 00:08:48,040 --> 00:08:52,520 Speaker 1: AI and Watson to improve customer experience and gain efficiencies. 135 00:08:52,960 --> 00:08:56,680 Speaker 1: And what they've done is helped nominate for us. And 136 00:08:57,120 --> 00:09:00,680 Speaker 1: in return, what we do is we actually honor the 137 00:09:00,720 --> 00:09:03,840 Speaker 1: folks that we feel are actually not just making gains 138 00:09:03,880 --> 00:09:07,320 Speaker 1: in the field, but are actually delivering powerful business results. 139 00:09:07,480 --> 00:09:10,720 Speaker 1: And uh and we build a cohort. Yea, Kima, do 140 00:09:10,760 --> 00:09:14,400 Speaker 1: you do you interact with the other people who are 141 00:09:15,280 --> 00:09:18,360 Speaker 1: winners of the or nominees for the Women Leaders in 142 00:09:18,400 --> 00:09:21,280 Speaker 1: AI program? Is it a network and learning opportunity in 143 00:09:21,320 --> 00:09:24,320 Speaker 1: addition to being an honor Yeah. Of course. In our 144 00:09:24,360 --> 00:09:27,120 Speaker 1: Aggurly team, three of us are women, and we all 145 00:09:27,160 --> 00:09:31,240 Speaker 1: got nominated for the honor, and for sure we interact, 146 00:09:31,760 --> 00:09:34,200 Speaker 1: but also like at the same time, we just we 147 00:09:34,200 --> 00:09:38,120 Speaker 1: were exposed too many networks, and then we started making 148 00:09:38,120 --> 00:09:42,840 Speaker 1: connections and started interacting with each other what they're doing 149 00:09:42,880 --> 00:09:45,599 Speaker 1: and what we are doing, and trying to going to 150 00:09:45,760 --> 00:09:49,200 Speaker 1: exchange some ideas, like it's usually like on the social 151 00:09:49,240 --> 00:09:51,600 Speaker 1: media that we are doing it. So but like, I'm 152 00:09:51,640 --> 00:09:55,880 Speaker 1: still shocked that I was nominated because like the other 153 00:09:55,960 --> 00:09:59,400 Speaker 1: nominees are like they're they're such a strong woman, and 154 00:10:00,640 --> 00:10:04,040 Speaker 1: that's that was just very shocking us. But I'm so 155 00:10:04,160 --> 00:10:08,160 Speaker 1: happy to be nominated. That actually makes me so happy 156 00:10:08,240 --> 00:10:10,400 Speaker 1: to hear it because it I didn't realize it was 157 00:10:10,440 --> 00:10:13,240 Speaker 1: so organic. We call it a cohort, and I thought 158 00:10:13,360 --> 00:10:15,400 Speaker 1: for sure that it was something formal, But the fact 159 00:10:15,440 --> 00:10:17,480 Speaker 1: that you all are reaching out to each other makes 160 00:10:17,480 --> 00:10:21,200 Speaker 1: me so happy. Okay, I'd like I would always a 161 00:10:21,200 --> 00:10:24,640 Speaker 1: really dumb question. I was like asking dumb questions. But 162 00:10:25,520 --> 00:10:27,760 Speaker 1: I'm curious. You know, you you started this caror by 163 00:10:27,800 --> 00:10:32,760 Speaker 1: saying people in this field are women, and you guys 164 00:10:32,760 --> 00:10:36,160 Speaker 1: would like it to be more representative of of uh, 165 00:10:36,240 --> 00:10:39,840 Speaker 1: the actual population. Why is that this is a dumb question. 166 00:10:40,000 --> 00:10:44,480 Speaker 1: Why is it only Well, I think there's a variety 167 00:10:44,480 --> 00:10:46,520 Speaker 1: of reasons that you probably know the answer to this 168 00:10:46,600 --> 00:10:48,240 Speaker 1: better than I do, based on some of the books 169 00:10:48,240 --> 00:10:51,840 Speaker 1: you've written, Malcolm. But I think that that we have 170 00:10:52,240 --> 00:10:56,520 Speaker 1: a lack of pipeline and STEM is an obvious reason. Um. 171 00:10:56,600 --> 00:10:59,120 Speaker 1: But then I also think there are things like COVID. 172 00:10:59,360 --> 00:11:02,280 Speaker 1: For example, we lost millions of women in the workforce 173 00:11:02,840 --> 00:11:05,600 Speaker 1: in the last eighteen months as a result of COVID 174 00:11:05,640 --> 00:11:09,360 Speaker 1: and the role that women play in many households. So 175 00:11:09,480 --> 00:11:13,080 Speaker 1: there's a variety of factors at play here beyond just 176 00:11:13,880 --> 00:11:17,600 Speaker 1: women women entering STEM fields. Um, we're struggling to keep 177 00:11:17,640 --> 00:11:21,080 Speaker 1: women in the workforce. Um. But I think what's encouraging 178 00:11:21,120 --> 00:11:24,280 Speaker 1: to me, and I'm an optimist at heart, is hearing 179 00:11:24,360 --> 00:11:27,560 Speaker 1: chimkas story. I mean, it didn't sound like chimad that 180 00:11:27,559 --> 00:11:30,280 Speaker 1: you actually set out to go into a STEM field. 181 00:11:30,280 --> 00:11:33,120 Speaker 1: It sounds like you're an entrepreneur first and you came 182 00:11:33,160 --> 00:11:37,160 Speaker 1: into the technology, which for me is super inspiring because 183 00:11:37,200 --> 00:11:41,360 Speaker 1: that means that the technology is becoming ubiquitous and that 184 00:11:41,440 --> 00:11:43,839 Speaker 1: you don't actually have to be a person that comes 185 00:11:43,880 --> 00:11:46,319 Speaker 1: from a science or engineering background to be able to 186 00:11:46,400 --> 00:11:51,640 Speaker 1: leverage these tools. What you said, they were two other 187 00:11:51,679 --> 00:11:55,480 Speaker 1: women in Agerly who are also nominees, So augually seems 188 00:11:55,520 --> 00:11:57,920 Speaker 1: to have a pretty strong cohort of women at the top. 189 00:11:58,679 --> 00:12:01,160 Speaker 1: What difference does that make? What do you have an 190 00:12:01,240 --> 00:12:04,319 Speaker 1: organization that has as many women as that in positions 191 00:12:04,360 --> 00:12:07,240 Speaker 1: of leadership? Do you do things differently than if you 192 00:12:07,280 --> 00:12:11,040 Speaker 1: were a company that had entirely man at the top. 193 00:12:12,400 --> 00:12:16,160 Speaker 1: I think when we think about arguraally, like our solutions 194 00:12:16,200 --> 00:12:19,560 Speaker 1: tend to like our plan and resolutions tend to be 195 00:12:19,600 --> 00:12:23,680 Speaker 1: like more long term and like pretty much detail oriented, 196 00:12:23,800 --> 00:12:27,240 Speaker 1: you know, like we just see the every risk that 197 00:12:27,360 --> 00:12:29,839 Speaker 1: could just arise in the long term, and then we 198 00:12:29,960 --> 00:12:32,839 Speaker 1: just start thinking about like how we can address one 199 00:12:32,920 --> 00:12:37,520 Speaker 1: by one because usually uh in the startup world, for example, 200 00:12:37,559 --> 00:12:39,440 Speaker 1: it's very hard to predict like what's going to happen 201 00:12:39,480 --> 00:12:41,839 Speaker 1: in the long term. But like for us, actually we 202 00:12:41,920 --> 00:12:44,560 Speaker 1: always think about like short what's gonna happen in short term, 203 00:12:44,600 --> 00:12:46,839 Speaker 1: and then we also talk about and then think about 204 00:12:46,920 --> 00:12:50,400 Speaker 1: more about the long term plan. I think that could 205 00:12:50,400 --> 00:12:55,200 Speaker 1: be the difference. M hm, Carla, you you are a 206 00:12:55,240 --> 00:12:58,840 Speaker 1: woman in a field that historically has been very male. 207 00:12:59,600 --> 00:13:03,880 Speaker 1: I'm just curious over the course of your career, have 208 00:13:04,040 --> 00:13:08,240 Speaker 1: you what's what kind of transformation in terms of of 209 00:13:08,240 --> 00:13:13,760 Speaker 1: of representation have you seen in the tech world. Wow, 210 00:13:13,800 --> 00:13:16,840 Speaker 1: I've seen a huge shift. In the beginning of my career, 211 00:13:16,960 --> 00:13:19,200 Speaker 1: I there were many times where I was the only 212 00:13:19,240 --> 00:13:22,720 Speaker 1: woman in the room, and fast forward to now, I 213 00:13:22,760 --> 00:13:26,080 Speaker 1: actually feel in most of the rooms that sometimes there 214 00:13:26,120 --> 00:13:28,160 Speaker 1: are more women than men. And that's something that I 215 00:13:28,200 --> 00:13:32,560 Speaker 1: hadn't seen in the and in my past. Um. I'll 216 00:13:32,679 --> 00:13:36,880 Speaker 1: also say, Malcolm, I was interesting this this, I realized 217 00:13:36,920 --> 00:13:40,280 Speaker 1: this actually today I was speaking to a group of people. 218 00:13:41,640 --> 00:13:44,880 Speaker 1: I feel like because of that, I personally have been 219 00:13:44,920 --> 00:13:49,000 Speaker 1: able to be more of myself um and and It's 220 00:13:49,000 --> 00:13:52,520 Speaker 1: been this journey to authenticity over the course of my career. 221 00:13:53,160 --> 00:13:57,360 Speaker 1: And the more I'm surrounded with people like me, the 222 00:13:57,440 --> 00:14:01,760 Speaker 1: more comfortable I become. Um and UH and it's it's 223 00:14:01,880 --> 00:14:04,480 Speaker 1: nice to be working with diverse teams. And again it's 224 00:14:04,520 --> 00:14:06,520 Speaker 1: a big reason of why I chose to come to 225 00:14:06,600 --> 00:14:10,319 Speaker 1: IBM because there's such a focus on diversity and inclusion. 226 00:14:11,480 --> 00:14:14,360 Speaker 1: We had an equal pay policy that predated the Civil 227 00:14:14,440 --> 00:14:17,559 Speaker 1: Rights Act, for example, and UH and so we've been 228 00:14:17,559 --> 00:14:21,640 Speaker 1: working on diversity and inclusion initiatives since nineteen eleven. Um, 229 00:14:21,680 --> 00:14:23,680 Speaker 1: it's kind of mind blowing to think about, and and 230 00:14:23,720 --> 00:14:26,400 Speaker 1: that's very much a part of who IBM is and 231 00:14:26,440 --> 00:14:29,920 Speaker 1: what we're about, both internally and externally. When I was 232 00:14:29,960 --> 00:14:33,040 Speaker 1: fascinated by something you said, which was that you it's 233 00:14:33,080 --> 00:14:38,120 Speaker 1: much easier to be yourself in environments where so compare 234 00:14:38,160 --> 00:14:45,000 Speaker 1: your self, Carla to your I don't want to put 235 00:14:45,040 --> 00:14:49,320 Speaker 1: a number date on when you started up. So what 236 00:14:49,400 --> 00:14:51,200 Speaker 1: was it like? I dig into that for a moment. 237 00:14:51,600 --> 00:14:53,960 Speaker 1: What is the difference between the self you can be 238 00:14:54,040 --> 00:14:56,640 Speaker 1: now and the self you are when you started out 239 00:14:56,640 --> 00:15:02,880 Speaker 1: in male dominated environments? I mean I had peers that 240 00:15:03,040 --> 00:15:06,280 Speaker 1: used to walk the aisles the sales hallways with baseball 241 00:15:06,320 --> 00:15:10,000 Speaker 1: bats and uh, you know, and swing the baseball bats, 242 00:15:10,360 --> 00:15:15,520 Speaker 1: um to try and intimidate their sales organizations. You know. 243 00:15:15,640 --> 00:15:18,960 Speaker 1: It's it's uh and uh you know, fast forward to 244 00:15:19,560 --> 00:15:22,360 Speaker 1: here we are during COVID and and people are holding 245 00:15:22,360 --> 00:15:27,760 Speaker 1: their children while on screen. It's it's just such a juxtaposition. Um. 246 00:15:27,880 --> 00:15:31,720 Speaker 1: I I grew up in a very formal environment where 247 00:15:31,760 --> 00:15:34,680 Speaker 1: there were actual dress codes and you can only wear 248 00:15:34,800 --> 00:15:38,640 Speaker 1: certain things and uh, and so it's it's been a 249 00:15:38,720 --> 00:15:41,200 Speaker 1: complete and total change that I've witnessed over the last 250 00:15:41,200 --> 00:15:45,000 Speaker 1: twenty one plus years of my career in tech. Chimka. 251 00:15:45,120 --> 00:15:47,640 Speaker 1: Let's let's talk a little bit more about your own 252 00:15:48,720 --> 00:15:52,000 Speaker 1: personal story and then how you got involved with agrily. 253 00:15:52,280 --> 00:15:55,960 Speaker 1: So did you grow up in Mongolia? Yeah, I was 254 00:15:56,000 --> 00:15:59,560 Speaker 1: born and growing up in the Eastern Moss Province in Mongolia. 255 00:16:00,120 --> 00:16:03,480 Speaker 1: My grandpa she had a small field and greenhouse that 256 00:16:03,680 --> 00:16:06,840 Speaker 1: we used to just grow up tomato and cumber. It 257 00:16:06,920 --> 00:16:09,960 Speaker 1: was like back in uh ninety nineties, you know, just 258 00:16:10,000 --> 00:16:13,240 Speaker 1: like you cannot really find tomato or cucumba very easy 259 00:16:13,280 --> 00:16:19,040 Speaker 1: in Mongolia. But m grandma she used to like try 260 00:16:19,120 --> 00:16:22,800 Speaker 1: to fight against these climate conditions because in Mongolia is 261 00:16:22,840 --> 00:16:26,080 Speaker 1: the climate is quite extreme. We have like very cold winter, 262 00:16:26,280 --> 00:16:30,000 Speaker 1: we have like very windy spring, quite chilly autumn, and 263 00:16:30,040 --> 00:16:33,400 Speaker 1: then also very dry um summer. It could be. I 264 00:16:33,480 --> 00:16:35,880 Speaker 1: was like always curious about like how people in the 265 00:16:35,960 --> 00:16:38,520 Speaker 1: rule they are still like going on with white because 266 00:16:39,680 --> 00:16:44,040 Speaker 1: most of them are dependent on farming. And then later 267 00:16:44,840 --> 00:16:48,080 Speaker 1: I think starting into twos and fifteen or sixteen, I 268 00:16:48,160 --> 00:16:52,560 Speaker 1: started working with this international NGO to fight against human trafficking. 269 00:16:52,880 --> 00:16:55,480 Speaker 1: I had to travel a lot to the bordering areas 270 00:16:55,600 --> 00:16:58,960 Speaker 1: and then start training women there who are afflicted with 271 00:16:59,040 --> 00:17:01,920 Speaker 1: the human trafficking. And it started like telling them what 272 00:17:02,040 --> 00:17:04,399 Speaker 1: kind of problems they can solve in their rural area. 273 00:17:05,080 --> 00:17:07,840 Speaker 1: So that's how I just got into like attracted to 274 00:17:07,920 --> 00:17:11,199 Speaker 1: maybe I should learn about like more about entrepreneurship, Like 275 00:17:11,240 --> 00:17:14,800 Speaker 1: I should just change the idea of like starting traditional 276 00:17:14,800 --> 00:17:18,040 Speaker 1: business like something new, something related to technology or whatever, 277 00:17:18,160 --> 00:17:20,520 Speaker 1: Like you have to start thinking in a different way, 278 00:17:21,000 --> 00:17:23,440 Speaker 1: And how did you think in a different way? What 279 00:17:23,520 --> 00:17:26,439 Speaker 1: was your approach? I found like very common pattern, Like 280 00:17:26,480 --> 00:17:29,639 Speaker 1: two things I found. The first problem in rural area 281 00:17:29,800 --> 00:17:34,400 Speaker 1: was in Mongolia was youth employment. And then second one 282 00:17:34,880 --> 00:17:36,679 Speaker 1: like a lot of young people struggled to get a 283 00:17:36,760 --> 00:17:40,000 Speaker 1: job there because there's no job. And second one was 284 00:17:40,040 --> 00:17:44,360 Speaker 1: like there's nobody in farming, especially young people. They easily 285 00:17:44,359 --> 00:17:47,879 Speaker 1: give up job in farming. Then I questioned myself why so, 286 00:17:47,960 --> 00:17:51,920 Speaker 1: like I started talking to the specific smallholder family farmers, 287 00:17:51,960 --> 00:17:54,480 Speaker 1: like what could be the problems and then what would 288 00:17:54,520 --> 00:17:57,280 Speaker 1: be the solutions? And then I thought like maybe those 289 00:17:57,320 --> 00:18:00,840 Speaker 1: problems can be solved with a smartphone because the users 290 00:18:00,920 --> 00:18:03,440 Speaker 1: the coverage of the smartphone was quite high. In one boy, 291 00:18:03,520 --> 00:18:07,720 Speaker 1: everybody has Facebook, everybody has smartphone, so what do you 292 00:18:07,760 --> 00:18:11,479 Speaker 1: need now? That's what I thought. That's how aggerally idea 293 00:18:11,600 --> 00:18:18,720 Speaker 1: came up along with my teammates Carla. How how typical 294 00:18:18,920 --> 00:18:23,800 Speaker 1: is Chimpka and Agrily? Are there a lot of companies 295 00:18:23,840 --> 00:18:27,520 Speaker 1: young companies that IBM is working with like that. There 296 00:18:27,520 --> 00:18:30,359 Speaker 1: are quite a few, and I'm discovering them more and 297 00:18:30,400 --> 00:18:33,840 Speaker 1: more each day, and it inspires me so much to 298 00:18:33,960 --> 00:18:37,040 Speaker 1: hear these stories. And I actually see that as one 299 00:18:37,040 --> 00:18:40,400 Speaker 1: of the primary functions of my role in my organization's 300 00:18:40,520 --> 00:18:44,600 Speaker 1: role is to to elevate the Chimkas and Agerle's of 301 00:18:44,640 --> 00:18:48,560 Speaker 1: the world as examples for everyone else to follow. Her 302 00:18:48,600 --> 00:18:52,679 Speaker 1: story is so inspiring and as actually Chincas you were talking, 303 00:18:52,720 --> 00:18:55,400 Speaker 1: one of the things I was wondering is what's happened 304 00:18:55,400 --> 00:18:59,199 Speaker 1: to the business since you won the award? We have 305 00:18:59,359 --> 00:19:03,960 Speaker 1: been just piloting the the testing app. The first time 306 00:19:04,000 --> 00:19:08,520 Speaker 1: we piloted in Mongolia, like in three eastern provinces. We 307 00:19:08,640 --> 00:19:11,199 Speaker 1: reached out to a lot of farmers who can who 308 00:19:11,240 --> 00:19:14,439 Speaker 1: are interested in testing this kind of app because this 309 00:19:14,680 --> 00:19:18,919 Speaker 1: kind of like like mobile app and agriculture sector is 310 00:19:18,960 --> 00:19:23,200 Speaker 1: not really common thing. And then everybody was quite impressed 311 00:19:23,280 --> 00:19:26,080 Speaker 1: because you know, just like there's a young woman just 312 00:19:26,160 --> 00:19:29,800 Speaker 1: reaching out to people and talking about technology, mobile app 313 00:19:29,840 --> 00:19:32,600 Speaker 1: and agriculture. I have no idea like agriculture, I have 314 00:19:32,640 --> 00:19:35,560 Speaker 1: no idea about technology, right, But the only thing is 315 00:19:35,600 --> 00:19:37,960 Speaker 1: that I knew that there was a real problems that 316 00:19:38,040 --> 00:19:40,840 Speaker 1: we can solve. Uh. And then we started piloting in 317 00:19:40,880 --> 00:19:44,480 Speaker 1: Brazil in September, and then in November we started piloting 318 00:19:44,480 --> 00:19:49,040 Speaker 1: it in India. So these three countries are like totally 319 00:19:49,080 --> 00:19:52,520 Speaker 1: different in terms of like how agriculture has advanced. So 320 00:19:52,640 --> 00:19:57,080 Speaker 1: we started developing like local apps, tailor to Mongolia, tailor 321 00:19:57,160 --> 00:20:00,560 Speaker 1: to India, tailor to Brazil, and then that's how we 322 00:20:00,600 --> 00:20:03,720 Speaker 1: started in January, and now we are nearing the launch 323 00:20:03,840 --> 00:20:06,919 Speaker 1: date in Mongolia and also Brazil and India. We are 324 00:20:07,000 --> 00:20:10,760 Speaker 1: launching quite soon in the absence of AI. Can you 325 00:20:10,840 --> 00:20:14,320 Speaker 1: do this without what without piggybacking on Watson and what 326 00:20:14,400 --> 00:20:16,800 Speaker 1: was that? What would it look like without IBM as 327 00:20:16,840 --> 00:20:20,280 Speaker 1: a partner? Impossible or just clunky and not as good. 328 00:20:20,600 --> 00:20:25,080 Speaker 1: We cannot do anything without those kind of technology. You 329 00:20:25,119 --> 00:20:28,400 Speaker 1: know that IBM has like the for the weather for example, 330 00:20:28,520 --> 00:20:31,119 Speaker 1: like we cannot do it by ourselves of course, so 331 00:20:31,200 --> 00:20:35,760 Speaker 1: like this daily uh, you know just like weekly and monthly, uh, 332 00:20:36,040 --> 00:20:39,760 Speaker 1: like whether predictions are all from the Weather Company by IBM, 333 00:20:40,160 --> 00:20:43,000 Speaker 1: and then using our studio we are generating it. We 334 00:20:43,040 --> 00:20:47,760 Speaker 1: are generating the entire the entire long term forecast for 335 00:20:47,800 --> 00:20:51,240 Speaker 1: each cities in different countries. And then also we are 336 00:20:51,520 --> 00:20:55,359 Speaker 1: using the IBM cloud storage to put everything on the 337 00:20:55,400 --> 00:20:58,960 Speaker 1: server and then that's how people just can get it 338 00:20:59,000 --> 00:21:02,760 Speaker 1: through the app. Yeah. Yeah, so hundreds of thousands of 339 00:21:02,760 --> 00:21:07,639 Speaker 1: developers can leverage these tools to build applications, you know. 340 00:21:07,680 --> 00:21:11,080 Speaker 1: And there's another topic which I feel like IBM is 341 00:21:11,119 --> 00:21:14,360 Speaker 1: starting to establish some thought leadership around, which is not 342 00:21:14,440 --> 00:21:17,480 Speaker 1: just the tools themselves, with the ethics around the tools 343 00:21:18,280 --> 00:21:21,320 Speaker 1: and UH and making sure that the algorithms that are 344 00:21:21,320 --> 00:21:25,760 Speaker 1: being built that then entrepreneurs like Chimka are leveraging the 345 00:21:25,800 --> 00:21:31,080 Speaker 1: tools for are actually explainable and fair and UH and 346 00:21:31,200 --> 00:21:34,960 Speaker 1: that that she can be confident in the decision making 347 00:21:35,320 --> 00:21:39,119 Speaker 1: of those tools and that they're they're unbiased. And and 348 00:21:39,160 --> 00:21:43,199 Speaker 1: that requires building algorithms that are that are built on 349 00:21:43,280 --> 00:21:48,560 Speaker 1: hard evidence like standardized tests and transparent reporting and UM. 350 00:21:48,600 --> 00:21:50,720 Speaker 1: And this is something that our research team has been 351 00:21:51,280 --> 00:21:55,160 Speaker 1: very very focused on so that people like Chimka can 352 00:21:55,160 --> 00:21:57,600 Speaker 1: focus on our business and not have to worry about 353 00:21:57,640 --> 00:22:02,200 Speaker 1: those components of our tools. And does does the data 354 00:22:02,240 --> 00:22:05,880 Speaker 1: that you generate Chimpka and we'll be generating over time, 355 00:22:06,200 --> 00:22:09,440 Speaker 1: does that get fed back into Watson. Does Watson learn 356 00:22:09,480 --> 00:22:13,000 Speaker 1: from agrily as well as I really learned from Watson. Yeah, exactly, 357 00:22:13,040 --> 00:22:16,359 Speaker 1: That's that's what we are doing. So actually, team, we 358 00:22:16,400 --> 00:22:20,000 Speaker 1: are now working with the IBM open source technology. We 359 00:22:20,040 --> 00:22:22,639 Speaker 1: are trying to like that, as Carlo said, like you know, 360 00:22:22,720 --> 00:22:26,960 Speaker 1: we have to have something for free for farmers and 361 00:22:27,000 --> 00:22:29,520 Speaker 1: then for public that they can use. So we are 362 00:22:29,520 --> 00:22:33,680 Speaker 1: working with the IBM to uh to have some open 363 00:22:33,720 --> 00:22:37,520 Speaker 1: source technology which is like basically do this weather and 364 00:22:37,640 --> 00:22:40,040 Speaker 1: forum and then also crop risks. We are trying to 365 00:22:40,080 --> 00:22:42,840 Speaker 1: make it more open source. Like without IBM, like we 366 00:22:42,880 --> 00:22:45,560 Speaker 1: cannot do it. We cannot just create this such a 367 00:22:45,600 --> 00:22:49,000 Speaker 1: big network of wordwide and then like you know, just 368 00:22:49,040 --> 00:22:51,720 Speaker 1: to get a support from the people who are in 369 00:22:51,800 --> 00:22:56,560 Speaker 1: the different sectors. So like IBM is basically making it possible. Yeah, 370 00:22:56,600 --> 00:22:58,960 Speaker 1: we are learning a lot from Watson, and Watson can 371 00:22:59,000 --> 00:23:01,959 Speaker 1: absolutely learn our data and train itself and then just 372 00:23:02,040 --> 00:23:06,600 Speaker 1: like you back something really supporting data to like each 373 00:23:06,600 --> 00:23:10,119 Speaker 1: country in the forming are people that IBM surprised at 374 00:23:10,119 --> 00:23:16,240 Speaker 1: all the inventive uses that Watson and are being put to. No, 375 00:23:16,480 --> 00:23:19,760 Speaker 1: not at all. And in fact, I visited our headquarters 376 00:23:19,840 --> 00:23:22,440 Speaker 1: for the first time a couple of weeks ago, and 377 00:23:23,600 --> 00:23:27,679 Speaker 1: it really hit me what IBM has represented in the 378 00:23:27,720 --> 00:23:32,280 Speaker 1: world in the last hundred years and what has actually 379 00:23:32,320 --> 00:23:35,200 Speaker 1: come out of this company and and what it is 380 00:23:35,280 --> 00:23:39,200 Speaker 1: it has enabled. So as an example, our our research 381 00:23:39,280 --> 00:23:44,639 Speaker 1: team has received five Nobel Prizes. We invented the first 382 00:23:44,720 --> 00:23:50,040 Speaker 1: personal computer uh. We invented Lasik, the barcode, the technology 383 00:23:50,080 --> 00:23:53,280 Speaker 1: behind the A t M, just to name a few 384 00:23:53,400 --> 00:23:55,840 Speaker 1: very small things that we've invented that have changed the 385 00:23:55,840 --> 00:23:59,359 Speaker 1: course of how we work and live. So when I 386 00:23:59,440 --> 00:24:01,760 Speaker 1: think about the future of IBM and the fact that 387 00:24:01,840 --> 00:24:05,440 Speaker 1: we are building the tools and functionality that will then 388 00:24:05,640 --> 00:24:09,119 Speaker 1: enable people like Chimpka to create the next set of 389 00:24:09,119 --> 00:24:12,600 Speaker 1: technologies that will change the way that we work and live, 390 00:24:12,680 --> 00:24:15,160 Speaker 1: it's not surprising to me because that's part of our heritage. 391 00:24:15,200 --> 00:24:17,600 Speaker 1: That's what we've represented and and that's what we're going 392 00:24:17,600 --> 00:24:19,800 Speaker 1: to represent and enable in the future. So let me 393 00:24:19,840 --> 00:24:24,479 Speaker 1: ask you, you're we're talking about AI. Have companies like 394 00:24:24,640 --> 00:24:28,359 Speaker 1: IBM done a good job and explain to the public 395 00:24:28,400 --> 00:24:33,000 Speaker 1: what AI is all about. Like listening to Chimpka, this 396 00:24:33,040 --> 00:24:36,440 Speaker 1: is using a technology to solve problems in the lives 397 00:24:36,600 --> 00:24:41,760 Speaker 1: of an extraordiny number of people who nobody was bringing 398 00:24:41,800 --> 00:24:45,520 Speaker 1: them that level of technological sophistication and help before. Right, 399 00:24:45,560 --> 00:24:51,119 Speaker 1: there's I mean, is that story? Yeah, if I'm being honest, Malcolm, No, 400 00:24:51,359 --> 00:24:53,600 Speaker 1: it's it's part of it's part of my remit and 401 00:24:53,680 --> 00:24:56,760 Speaker 1: my organization's role to bring these stories to life. It's 402 00:24:56,800 --> 00:24:58,879 Speaker 1: part of why we're here with you today, so that 403 00:24:58,920 --> 00:25:02,800 Speaker 1: people can learn and what's possible. Um And and and I 404 00:25:02,840 --> 00:25:07,080 Speaker 1: think that it is our responsibility to to tell these 405 00:25:07,119 --> 00:25:11,440 Speaker 1: stories so that we can inspire folks to to leverage 406 00:25:11,480 --> 00:25:16,240 Speaker 1: these technologies to improve our lives and to solve significant 407 00:25:16,240 --> 00:25:20,280 Speaker 1: problems um whether they're from a business standpoint or from 408 00:25:20,280 --> 00:25:22,560 Speaker 1: a societal standpoint. And in chim Goes take case, I 409 00:25:22,560 --> 00:25:27,040 Speaker 1: think she's doing both. Yeah. Why is it hard to 410 00:25:27,119 --> 00:25:32,919 Speaker 1: tell these kinds of stories? I think there are a 411 00:25:32,920 --> 00:25:35,520 Speaker 1: couple of things at play here. I think it's hard 412 00:25:35,520 --> 00:25:38,320 Speaker 1: to tell these stories because there's so many of them 413 00:25:38,359 --> 00:25:41,920 Speaker 1: and they're so diverse, and picking the stories that you're 414 00:25:42,000 --> 00:25:45,399 Speaker 1: going to tell can sometimes be difficult because there's so 415 00:25:45,440 --> 00:25:48,840 Speaker 1: many different applications. UM. I also think we have a 416 00:25:48,880 --> 00:25:52,720 Speaker 1: business to run and and there are times where they 417 00:25:53,000 --> 00:25:56,600 Speaker 1: that we don't actually take the time to explain our technology. 418 00:25:56,640 --> 00:25:59,719 Speaker 1: There's an assumption because so many people are using it, 419 00:26:00,040 --> 00:26:03,720 Speaker 1: but the world already knows what it's doing. But even 420 00:26:03,800 --> 00:26:07,840 Speaker 1: I myself joining the company, I'm now starting to appreciate 421 00:26:08,320 --> 00:26:12,800 Speaker 1: how much of the world's backbone, from a technology standpoint, 422 00:26:14,040 --> 00:26:17,480 Speaker 1: is made up of IBM and UH. And we need 423 00:26:17,520 --> 00:26:21,800 Speaker 1: to tell these stories to to shepherd this next era 424 00:26:21,920 --> 00:26:26,080 Speaker 1: for the company, but also quite frankly, to inspire the 425 00:26:26,119 --> 00:26:32,560 Speaker 1: next Chimka mm hm. I asked that question about the 426 00:26:32,600 --> 00:26:35,040 Speaker 1: importance of these kinds of stories because one of the 427 00:26:35,080 --> 00:26:39,399 Speaker 1: things that struck me with COVID and with you know, 428 00:26:39,480 --> 00:26:44,120 Speaker 1: this problem of people who are vaccine resistant is I think, 429 00:26:44,440 --> 00:26:48,040 Speaker 1: on balance, a lot of resistance to vaccines is people 430 00:26:48,080 --> 00:26:52,560 Speaker 1: can't wrap their mind around the notion that people who 431 00:26:52,640 --> 00:26:58,040 Speaker 1: do science and technacological innovation are trying to help them. 432 00:26:58,080 --> 00:27:01,640 Speaker 1: We've gotten so cynical about technology that people assume, oh, 433 00:27:01,680 --> 00:27:03,960 Speaker 1: they're doing it, they must have some nefarious motive. There 434 00:27:04,000 --> 00:27:06,960 Speaker 1: must have been some big bucks involved, There must be 435 00:27:07,119 --> 00:27:09,960 Speaker 1: and it's not that it's like they actually just want 436 00:27:10,000 --> 00:27:13,240 Speaker 1: to save your life. And same thing listening to Chimka. 437 00:27:13,640 --> 00:27:16,920 Speaker 1: You know, I hope you get very rich, chim But 438 00:27:17,760 --> 00:27:21,000 Speaker 1: the motivation is is you want to help the people 439 00:27:22,400 --> 00:27:25,440 Speaker 1: back home in Mongolia, right like you you talked about 440 00:27:25,480 --> 00:27:28,080 Speaker 1: you you started talking about your grandmother for goodness sake, 441 00:27:28,119 --> 00:27:32,080 Speaker 1: Like that's your motivation. And I feel like somehow along 442 00:27:32,119 --> 00:27:35,679 Speaker 1: the way, we we've neglected to inform the world that 443 00:27:36,600 --> 00:27:39,240 Speaker 1: people who do this, this kind of innovation have the 444 00:27:39,320 --> 00:27:45,080 Speaker 1: most human of motivations. Yeah, thank you. Purest of intent there, 445 00:27:45,560 --> 00:27:48,560 Speaker 1: the purest of intent not only in Mongolia, Like I'm 446 00:27:48,600 --> 00:27:51,680 Speaker 1: gonna apply it, like we're gonna apply it to the 447 00:27:51,720 --> 00:27:55,119 Speaker 1: whole world, like all emerging markets you will see, Like, 448 00:27:55,240 --> 00:28:00,960 Speaker 1: thank you. That's wonderful. Thank you. This has been so fun. 449 00:28:01,440 --> 00:28:04,560 Speaker 1: I really enjoyed chatting with you, and I, um my 450 00:28:04,640 --> 00:28:07,000 Speaker 1: hat is off to both of you for telling these 451 00:28:07,040 --> 00:28:11,600 Speaker 1: kinds of stories. Thank you, thank you so much. Really, 452 00:28:13,760 --> 00:28:17,840 Speaker 1: thank you, Carla, thank you Morcom. When we see the 453 00:28:17,880 --> 00:28:21,000 Speaker 1: positive impact made by women in the field, it's obvious 454 00:28:21,040 --> 00:28:25,800 Speaker 1: that tech companies must become more inclusive to stay innovative. 455 00:28:26,320 --> 00:28:29,920 Speaker 1: People like Chimka and Carla are driving that impact, using 456 00:28:30,000 --> 00:28:33,280 Speaker 1: tech solutions to solve problems that most people in the 457 00:28:33,280 --> 00:28:37,600 Speaker 1: industry haven't thought of. Thanks again to Carla Pinero, Sublett 458 00:28:37,840 --> 00:28:40,959 Speaker 1: and Chimka Monk Buyer for talking with me. It was 459 00:28:41,280 --> 00:28:44,640 Speaker 1: such a pleasure. Smart Talk with IBM is produced by 460 00:28:44,640 --> 00:28:50,320 Speaker 1: Emily Rosstak with Carl Migliori, edited by Karen Shakerji engineering 461 00:28:50,320 --> 00:28:55,040 Speaker 1: by Martin Gonzalez, mixed and mastered by Jason Gambrel, music 462 00:28:55,400 --> 00:29:02,880 Speaker 1: by Grandmasco. Special thanks to Molly Sosha, Andy Kelly, Mia LaBelle, Jacobisberg, Catafane, 463 00:29:03,120 --> 00:29:06,760 Speaker 1: Eric Sander, and Maggie Taylor, and the teams at eight 464 00:29:06,840 --> 00:29:10,600 Speaker 1: Bar and IBM. Smart Talks with IBM is a production 465 00:29:10,600 --> 00:29:13,840 Speaker 1: of Pushkin Industries and I Heart Media. You can find 466 00:29:13,880 --> 00:29:18,920 Speaker 1: more episodes at IBM dot com slash smart Talks, and 467 00:29:19,000 --> 00:29:21,680 Speaker 1: you can find more Pushkin podcasts on the I Heart 468 00:29:21,760 --> 00:29:25,400 Speaker 1: Radio app, Apple Podcasts, or wherever you like to listen. 469 00:29:26,080 --> 00:29:28,800 Speaker 1: I'm Malcolm Gladwell, See you next time.