1 00:00:00,160 --> 00:00:02,960 Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got 2 00:00:02,960 --> 00:00:05,240 Speaker 1: something a little bit different to share with you. It's 3 00:00:05,280 --> 00:00:09,400 Speaker 1: a new season of the Smart Talks with IBM podcast series. 4 00:00:09,720 --> 00:00:13,520 Speaker 2: This season on Smart Talks with IBM, Malcolm Gladwell is back, 5 00:00:13,520 --> 00:00:15,760 Speaker 2: and this time he's taking the show on the road. 6 00:00:15,880 --> 00:00:19,160 Speaker 2: Malcolm is stepping outside the studio to explore how IBM 7 00:00:19,239 --> 00:00:22,880 Speaker 2: clients are using artificial intelligence to solve real world challenges 8 00:00:23,079 --> 00:00:25,520 Speaker 2: and transform the way they do business. 9 00:00:25,480 --> 00:00:29,560 Speaker 1: From accelerating scientific breakthroughs to reimagining education. It's a fresh 10 00:00:29,600 --> 00:00:33,040 Speaker 1: look at innovation in action, where big ideas meet cutting 11 00:00:33,120 --> 00:00:33,960 Speaker 1: edge solutions. 12 00:00:34,320 --> 00:00:37,160 Speaker 2: You'll hear from industry leaders, creative thinkers, and of course 13 00:00:37,280 --> 00:00:40,920 Speaker 2: Malcolm Gladwell himself as he guides you through each story. 14 00:00:41,440 --> 00:00:44,680 Speaker 1: New episodes of Smart Talks with IBM drop every month 15 00:00:44,680 --> 00:00:48,279 Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you get 16 00:00:48,320 --> 00:00:52,240 Speaker 1: your podcasts. Learn more at IBM dot com slash smart Talks. 17 00:00:52,880 --> 00:00:56,240 Speaker 1: This is a paid advertisement from IBM. 18 00:00:56,520 --> 00:01:00,560 Speaker 3: To understand why the cosmetics supergiant Lorel Group is teaming 19 00:01:00,640 --> 00:01:03,640 Speaker 3: up with IBM, you must first take a closer look 20 00:01:03,680 --> 00:01:07,600 Speaker 3: at its products. Take lipstick, for example, it's one of 21 00:01:07,600 --> 00:01:10,800 Speaker 3: those things that seems straightforward. A waxy cylinder that you 22 00:01:10,880 --> 00:01:13,880 Speaker 3: rub on your lips to turn them a different color. Easy, right, 23 00:01:14,440 --> 00:01:18,200 Speaker 3: Well maybe not, as my colleague Lucy Sullivan found out 24 00:01:18,400 --> 00:01:21,480 Speaker 3: when I sent her an assignment to Lorel's North America 25 00:01:21,560 --> 00:01:23,279 Speaker 3: Research and Innovation Center. 26 00:01:24,360 --> 00:01:30,319 Speaker 4: All right, I'm reporting live from the Loreal visitor's parking lot. 27 00:01:31,240 --> 00:01:35,000 Speaker 4: Malcolm told me that he would be sending me to Paris, 28 00:01:35,120 --> 00:01:40,560 Speaker 4: France for this Looreal excursion, but instead I am in Clark, 29 00:01:40,760 --> 00:01:43,680 Speaker 4: New Jersey. Pass a lot of strip malls on the 30 00:01:43,720 --> 00:01:47,119 Speaker 4: way here. But to be fair to Clark, New Jersey 31 00:01:47,200 --> 00:01:50,440 Speaker 4: and Lorel, this is a beautiful compound. It kind of 32 00:01:50,440 --> 00:01:52,720 Speaker 4: looks like a spa. 33 00:01:53,440 --> 00:01:57,000 Speaker 3: Lucy went into the center and was blown away. The 34 00:01:57,040 --> 00:02:02,280 Speaker 3: facility houses about six hundred scientists and experts across skincare, makeup, fragrance, 35 00:02:02,360 --> 00:02:06,440 Speaker 3: hair care, innovative packaging, and tech. It is one of 36 00:02:06,480 --> 00:02:09,760 Speaker 3: the largest formulation lab spaces in the industry. It's the 37 00:02:09,800 --> 00:02:14,240 Speaker 3: size of six basketball courts. The reason Loreel's facility is 38 00:02:14,280 --> 00:02:17,240 Speaker 3: so big and has so many people is that everything 39 00:02:17,280 --> 00:02:20,160 Speaker 3: Loriel does to bring a product to market happens here, 40 00:02:20,760 --> 00:02:25,360 Speaker 3: from molecule discovery and product development to consumer testing. The 41 00:02:25,400 --> 00:02:28,960 Speaker 3: center even has its own mini factory. My conception of 42 00:02:29,000 --> 00:02:32,320 Speaker 3: lipstick that it's just a waxy stick was plain wrong. 43 00:02:33,120 --> 00:02:36,960 Speaker 3: Lipstick is a high performance product born from years of research, 44 00:02:37,320 --> 00:02:43,200 Speaker 3: consumer insights, and precision science. Lipstick isn't simple. It's incredibly complex, 45 00:02:43,800 --> 00:02:46,280 Speaker 3: and one of the main reasons it's so complex is 46 00:02:46,320 --> 00:02:49,160 Speaker 3: just a nature of fashion trends. The kind of lipstick 47 00:02:49,200 --> 00:02:51,639 Speaker 3: consumers want is constantly changing. 48 00:02:52,200 --> 00:02:54,480 Speaker 5: A lot of our consumer insights with Lorial is like, 49 00:02:54,680 --> 00:02:56,520 Speaker 5: where are consumers going in the future. 50 00:02:56,919 --> 00:03:00,440 Speaker 3: This is Nadine Gomez. She is vice president for Low's 51 00:03:00,480 --> 00:03:02,680 Speaker 3: research and innovation development team. 52 00:03:03,120 --> 00:03:05,440 Speaker 5: Our chemists are working on five six years down the line. 53 00:03:05,680 --> 00:03:09,280 Speaker 5: We predicted that consumers wanted more of a softer look 54 00:03:09,280 --> 00:03:10,200 Speaker 5: on their lips as well. 55 00:03:10,320 --> 00:03:11,880 Speaker 4: So how do you predict something like that. 56 00:03:12,400 --> 00:03:16,720 Speaker 5: We see slow signals from fashion houses and social media 57 00:03:16,800 --> 00:03:18,680 Speaker 5: and things like that. We kind of see that trend 58 00:03:18,760 --> 00:03:21,840 Speaker 5: evolving a little bit, and then we know at five 59 00:03:21,919 --> 00:03:23,240 Speaker 5: six years it's going to become big. 60 00:03:24,280 --> 00:03:27,000 Speaker 3: Lucy talked with her about the origins of one of 61 00:03:27,040 --> 00:03:30,600 Speaker 3: their products, Mabe Lene matt Ink liquid lipstick. 62 00:03:31,200 --> 00:03:33,520 Speaker 5: Our competitors have two steps. The first step is a 63 00:03:33,520 --> 00:03:36,160 Speaker 5: base code. It's super opique. You get the color and 64 00:03:36,200 --> 00:03:39,720 Speaker 5: you get the madity, but it's very very drying your lips. 65 00:03:39,800 --> 00:03:41,800 Speaker 5: You cannot wear that, honestly more than ten minutes. It 66 00:03:41,800 --> 00:03:44,560 Speaker 5: feels like your lips are like aching at one point. 67 00:03:44,800 --> 00:03:46,400 Speaker 5: So we had to develop a top cot and you'll 68 00:03:46,400 --> 00:03:48,160 Speaker 5: see many of our competitors did the same thing. It's 69 00:03:48,160 --> 00:03:50,200 Speaker 5: like a bomb. You put it on top, it's super comfortable. 70 00:03:50,840 --> 00:03:54,120 Speaker 5: But we also noticed that consumers kind of get tired 71 00:03:54,320 --> 00:03:56,720 Speaker 5: of reapplying a bomb, So we're like, what can we 72 00:03:56,760 --> 00:03:59,120 Speaker 5: do to create this two step into one step? 73 00:04:00,000 --> 00:04:02,720 Speaker 3: Well, Loriel had a challenge, how do you make a 74 00:04:02,800 --> 00:04:07,360 Speaker 3: comfortable liquid Matt lipstick that doesn't require consumers to reapply 75 00:04:07,520 --> 00:04:11,000 Speaker 3: a top layer of bomb. Solving this type of problem 76 00:04:11,200 --> 00:04:14,040 Speaker 3: takes a lot of resources and a lot of expertise, 77 00:04:14,400 --> 00:04:18,599 Speaker 3: and crucially, it takes time. Remember Nadine said that working 78 00:04:18,640 --> 00:04:21,600 Speaker 3: on a breakthrough product such as Matt inc can take 79 00:04:21,839 --> 00:04:26,520 Speaker 3: years before it comes out. But can this process be accelerated, 80 00:04:26,960 --> 00:04:30,720 Speaker 3: taken further, be even more sustainable. That's what IBM and 81 00:04:30,800 --> 00:04:36,080 Speaker 3: Lareel are hoping to find out. My name is Malcolm Gladwell. 82 00:04:36,200 --> 00:04:39,360 Speaker 3: You're listening to the latest episode of Smart Talks with IBM, 83 00:04:39,680 --> 00:04:42,159 Speaker 3: where we offer our listeners a glimpse behind the curtain 84 00:04:42,200 --> 00:04:46,159 Speaker 3: of the world of technology. In our last episode, we 85 00:04:46,200 --> 00:04:49,599 Speaker 3: talked about how an AI assistant created with IBM, Watson 86 00:04:49,800 --> 00:04:54,560 Speaker 3: X helps future teachers practice responsive teaching by simulating classroom 87 00:04:54,640 --> 00:04:59,320 Speaker 3: interactions with students. In this episode, we take you on 88 00:04:59,360 --> 00:05:03,920 Speaker 3: an even more or unexpected journey into the world of cosmetics, 89 00:05:04,200 --> 00:05:08,679 Speaker 3: hair care, skincare, fragrance, makeup and how a custom AI 90 00:05:08,800 --> 00:05:12,880 Speaker 3: model could help Loreel's researchers shape the future of what 91 00:05:12,920 --> 00:05:22,800 Speaker 3: we put on our faces every morning. I want to 92 00:05:22,800 --> 00:05:25,640 Speaker 3: say on lipstick a moment longer to help illustrate what 93 00:05:25,720 --> 00:05:29,679 Speaker 3: goes into Loriel's product development, and let's focus on matt 94 00:05:29,760 --> 00:05:34,000 Speaker 3: Inc lipstick. Loreel wanted to create something that was comfortable 95 00:05:34,440 --> 00:05:36,039 Speaker 3: and could be applied in one step. 96 00:05:36,680 --> 00:05:38,719 Speaker 6: So to go from two step to one step, we 97 00:05:38,760 --> 00:05:41,160 Speaker 6: had to look cross functionally and try to figure out 98 00:05:41,200 --> 00:05:43,600 Speaker 6: what can we bring into the product to make it 99 00:05:43,640 --> 00:05:46,839 Speaker 6: more comfortable and luckily we have many different types of 100 00:05:46,839 --> 00:05:47,760 Speaker 6: products at Lorel. 101 00:05:48,160 --> 00:05:51,400 Speaker 3: That's Alex good, a senior chemist who leads the lip 102 00:05:51,400 --> 00:05:54,600 Speaker 3: products team in North America. She says the trick to 103 00:05:54,680 --> 00:05:59,000 Speaker 3: making matt incwork was finding an elastomer, a substance they 104 00:05:59,000 --> 00:06:01,000 Speaker 3: were already using in foundation. 105 00:06:01,920 --> 00:06:04,760 Speaker 6: We have this elastomer that can give you like more 106 00:06:04,800 --> 00:06:07,480 Speaker 6: comfortable and make it feel like there's like something on 107 00:06:07,480 --> 00:06:08,440 Speaker 6: your lips, like a cushion. 108 00:06:09,240 --> 00:06:12,760 Speaker 3: She handed Lucy two jars. The first jar contained the 109 00:06:12,800 --> 00:06:15,279 Speaker 3: former version of the product that was used in super 110 00:06:15,320 --> 00:06:18,720 Speaker 3: State twenty four. By the way, this is exactly why 111 00:06:18,720 --> 00:06:22,719 Speaker 3: I sent Lucy to the lab in my place the samples. 112 00:06:22,680 --> 00:06:24,440 Speaker 6: And I actually have something for you to try here, 113 00:06:24,960 --> 00:06:29,039 Speaker 6: so you can try this is what was the initial product. 114 00:06:29,360 --> 00:06:32,920 Speaker 4: Okay, so this is like it sort of looks like okay, 115 00:06:32,920 --> 00:06:35,800 Speaker 4: it is. It looks like vacline that has like more 116 00:06:35,839 --> 00:06:38,679 Speaker 4: of a color. It's kind of a beige, looks like 117 00:06:38,720 --> 00:06:39,239 Speaker 4: some skin. 118 00:06:39,400 --> 00:06:39,799 Speaker 6: Okay. 119 00:06:40,040 --> 00:06:42,400 Speaker 4: So this was from the two steps this would go 120 00:06:42,400 --> 00:06:44,800 Speaker 4: on after Oh okay, right. 121 00:06:46,640 --> 00:06:49,560 Speaker 7: Islet Okay, So. 122 00:06:49,480 --> 00:06:52,720 Speaker 6: It feels like very wet. As you can see, it's 123 00:06:52,760 --> 00:06:55,680 Speaker 6: kind of it's gonna absorb into your skin and leave 124 00:06:56,200 --> 00:06:57,720 Speaker 6: and then you're gonna feel the dryness of. 125 00:06:57,680 --> 00:06:58,680 Speaker 7: The product once it's long. 126 00:06:58,839 --> 00:06:59,080 Speaker 8: Okay. 127 00:06:59,080 --> 00:07:01,479 Speaker 6: So we're gonna move from the clay product that you 128 00:07:01,520 --> 00:07:04,440 Speaker 6: have on your hand now to the elastimmer. 129 00:07:04,000 --> 00:07:07,320 Speaker 3: Or you try half hour this jar held the elastomer 130 00:07:07,520 --> 00:07:10,200 Speaker 3: that Lareel had spent years developing in the lab. 131 00:07:11,240 --> 00:07:15,400 Speaker 4: This one is it clear, looks like Aqua four, a 132 00:07:15,520 --> 00:07:17,320 Speaker 4: much player and. 133 00:07:17,320 --> 00:07:20,520 Speaker 6: You can see yeah, physical layer that you're putting on 134 00:07:20,560 --> 00:07:21,000 Speaker 6: your aids. 135 00:07:21,120 --> 00:07:25,360 Speaker 4: Yeah, so that's much thicker. It kind of like clumps together. Yeah, 136 00:07:26,320 --> 00:07:28,160 Speaker 4: it was more of a cloudy it's less shimmery though 137 00:07:29,400 --> 00:07:30,080 Speaker 4: that's intended. 138 00:07:30,320 --> 00:07:35,200 Speaker 6: Yes, So this is a like a powder this dispersed 139 00:07:35,320 --> 00:07:39,600 Speaker 6: in dimethicone and it creates like a comfort on your 140 00:07:39,600 --> 00:07:41,280 Speaker 6: lips a fields, like there's something. 141 00:07:41,040 --> 00:07:43,360 Speaker 9: There for a barrier to keep the. 142 00:07:42,920 --> 00:07:46,160 Speaker 6: Film form on. And that's like the key ingredient that 143 00:07:46,240 --> 00:07:49,160 Speaker 6: came from Foundation that we transferred into lipstick to give 144 00:07:49,240 --> 00:07:53,000 Speaker 6: us this innovative product ahead of the market. Yeah, this 145 00:07:53,120 --> 00:07:56,040 Speaker 6: is what gives it comfort. So the difference between super 146 00:07:56,040 --> 00:07:58,679 Speaker 6: State twenty four and matt Inc is really the comfort. 147 00:07:58,720 --> 00:08:02,040 Speaker 6: They both last a long time, but this matt Inc 148 00:08:02,080 --> 00:08:04,720 Speaker 6: you don't have to apply the bomb over and over again, 149 00:08:04,840 --> 00:08:07,120 Speaker 6: so you can apply matting once for the day and you're. 150 00:08:07,000 --> 00:08:07,720 Speaker 8: Good for it. 151 00:08:08,400 --> 00:08:11,800 Speaker 3: Alex Good is under selling it here, once for the 152 00:08:11,880 --> 00:08:16,120 Speaker 3: day and you're good. That's a liquid lipstick revolution. Literally, 153 00:08:16,320 --> 00:08:19,720 Speaker 3: millions of loreal consumers around the world have worn matt ink. 154 00:08:19,920 --> 00:08:24,920 Speaker 3: It's a blockbuster. It's also a marvel of science. The 155 00:08:24,960 --> 00:08:28,760 Speaker 3: world's first liquid lipstick was developed in the nineteen thirties, 156 00:08:28,800 --> 00:08:31,160 Speaker 3: and it was actually just a stain for your lips, 157 00:08:31,760 --> 00:08:35,320 Speaker 3: barely counts as lipstick. Then came another wave of liquid 158 00:08:35,360 --> 00:08:38,280 Speaker 3: lipstick when they were able to make it matt That 159 00:08:38,400 --> 00:08:41,400 Speaker 3: was a two step version. It felt heavy on your lips. 160 00:08:41,800 --> 00:08:45,520 Speaker 3: You had to keep reapplying the top coat. It was inconvenient. 161 00:08:46,640 --> 00:08:49,640 Speaker 3: Lorel tackled that challenge in the lab, with chemists like 162 00:08:49,760 --> 00:08:54,200 Speaker 3: Alex and the Dean leaving the charge their breakthrough matt Inc. 163 00:08:55,040 --> 00:08:58,560 Speaker 3: But creating matt Inc took a long time, trial and error, 164 00:08:58,760 --> 00:09:03,160 Speaker 3: the hard work of science experimentation. As Nadine told Lucy, 165 00:09:03,480 --> 00:09:06,199 Speaker 3: the lipstick team had to put the new product to 166 00:09:06,520 --> 00:09:07,560 Speaker 3: extensive tests. 167 00:09:08,040 --> 00:09:10,960 Speaker 5: We do a very robustability system here. You know, we 168 00:09:11,000 --> 00:09:14,360 Speaker 5: have color odor appearance. We monitor this in extreme conditions. 169 00:09:14,720 --> 00:09:17,320 Speaker 5: We simulate a forty five degrees celsius and that can 170 00:09:17,360 --> 00:09:19,520 Speaker 5: be something like a three year shelf life. I'm saying 171 00:09:20,120 --> 00:09:23,040 Speaker 5: we simulate your real life product, like if you leave 172 00:09:23,080 --> 00:09:25,680 Speaker 5: your lip gloss in the car in Arizona's one hundred 173 00:09:25,679 --> 00:09:27,480 Speaker 5: and twelve degrees worth three days. Is it still going 174 00:09:27,520 --> 00:09:30,440 Speaker 5: to perform? Is it gonna smell? Is it gonna look granted? 175 00:09:30,559 --> 00:09:31,600 Speaker 5: Is it gonna change colors? 176 00:09:31,840 --> 00:09:32,600 Speaker 10: We do all that? 177 00:09:33,240 --> 00:09:37,080 Speaker 3: See what I mean? Lipstick is complex. Most people would 178 00:09:37,120 --> 00:09:40,040 Speaker 3: never consider it a piece of technology, but one lip 179 00:09:40,040 --> 00:09:43,520 Speaker 3: product has millions of data points. 180 00:09:43,280 --> 00:09:45,040 Speaker 5: So much science behind. And you can see here how 181 00:09:45,040 --> 00:09:47,360 Speaker 5: many scientists we have. You know, some of them have PhDs, 182 00:09:47,360 --> 00:09:50,559 Speaker 5: some of them have master's degrees chemistry, biology, psychology. 183 00:09:50,600 --> 00:09:54,319 Speaker 3: Also, when I first heard about this collaboration between LORI 184 00:09:54,520 --> 00:09:58,120 Speaker 3: L and IBM, I was surprised. I thought, these are 185 00:09:58,160 --> 00:10:01,520 Speaker 3: two very different companies. What do they really have in common? 186 00:10:02,040 --> 00:10:03,520 Speaker 3: You guys? 187 00:10:03,800 --> 00:10:04,040 Speaker 4: Yeah. 188 00:10:04,200 --> 00:10:06,400 Speaker 3: To find out, I went to the IBM research Center 189 00:10:06,440 --> 00:10:08,880 Speaker 3: outside New York City, which I have to say is 190 00:10:08,920 --> 00:10:11,280 Speaker 3: one of the coolest buildings I've ever been in. A 191 00:10:11,320 --> 00:10:15,920 Speaker 3: semi circular modernist masterpiece with a long curving wall of windows, 192 00:10:16,120 --> 00:10:19,160 Speaker 3: looks like something out of a Stanley Kubrick movie. I 193 00:10:19,240 --> 00:10:21,560 Speaker 3: was there to talk with two experts from research and 194 00:10:21,600 --> 00:10:27,520 Speaker 3: innovation at LOREL Metheu Cassier and Gabriel Bertoli metthew IS 195 00:10:27,640 --> 00:10:31,880 Speaker 3: VP for Digital and Transformation, Gabrielle is a Chief Digital 196 00:10:31,920 --> 00:10:36,440 Speaker 3: Transformation Officer for Formulation. These are the people whose jobs 197 00:10:36,440 --> 00:10:40,120 Speaker 3: are to oversee big changes within the company. And Methu 198 00:10:40,200 --> 00:10:42,000 Speaker 3: told me to try on some lipstick. 199 00:10:42,440 --> 00:10:44,280 Speaker 7: I'm gonna make you try this one. 200 00:10:44,679 --> 00:10:48,760 Speaker 3: Okay, this is super stay vinin Final Inc. 201 00:10:49,000 --> 00:10:50,080 Speaker 7: Yeah, so that's a glosse. 202 00:10:50,160 --> 00:10:51,679 Speaker 3: Never in my life put on lipsey. I've no idea 203 00:10:51,720 --> 00:10:52,120 Speaker 3: what I'm doing. 204 00:10:52,120 --> 00:10:52,800 Speaker 8: You don't have to put it. 205 00:10:52,840 --> 00:10:53,800 Speaker 7: You can try it virtually. 206 00:10:54,679 --> 00:10:58,079 Speaker 3: Oh this may not be news to people who buy makeup, 207 00:10:58,320 --> 00:11:00,760 Speaker 3: but it was news to me. You can try on 208 00:11:00,840 --> 00:11:06,040 Speaker 3: loreal products virtually. They call it augmented beauty. Oh my goodness. 209 00:11:06,720 --> 00:11:09,040 Speaker 3: That is the strangest thing I've ever said. I look 210 00:11:09,120 --> 00:11:10,920 Speaker 3: quite fetching, that's the way. 211 00:11:10,920 --> 00:11:14,680 Speaker 8: It amazing. And I can just hit you can choose 212 00:11:14,679 --> 00:11:15,560 Speaker 8: your color absolutely. 213 00:11:16,280 --> 00:11:18,640 Speaker 3: So I'm on a little app it's looking at me 214 00:11:18,960 --> 00:11:21,280 Speaker 3: and it's just showing me exactly how I would look 215 00:11:21,280 --> 00:11:23,800 Speaker 3: with different shades of lipstick. So the odd idea of 216 00:11:23,840 --> 00:11:25,760 Speaker 3: going into a store and trying on each one, you 217 00:11:25,840 --> 00:11:27,400 Speaker 3: cannot do that from home, if you're not even at 218 00:11:27,440 --> 00:11:27,800 Speaker 3: the store. 219 00:11:28,280 --> 00:11:31,280 Speaker 7: Yeah, absolutely, that's all purpose. If you want to manage 220 00:11:31,320 --> 00:11:33,400 Speaker 7: a trend that would go for something more like pitch. 221 00:11:34,440 --> 00:11:37,520 Speaker 3: You think I'm a peach person. I don't know that 222 00:11:37,600 --> 00:11:40,000 Speaker 3: looks I have to say that looks kind of natural. 223 00:11:40,000 --> 00:11:43,160 Speaker 3: It just is enhanced. It's given me a boys share 224 00:11:43,200 --> 00:11:48,320 Speaker 3: I would not otherwise have. This is why Loreel says 225 00:11:48,360 --> 00:11:52,880 Speaker 3: it creates beauty products and beauty experiences. Loriel is a 226 00:11:52,920 --> 00:11:57,360 Speaker 3: beauty tech company. Over the last decade, Loriel has seized 227 00:11:57,360 --> 00:12:01,760 Speaker 3: the power of AI and more recently, generative AI technology 228 00:12:01,760 --> 00:12:06,040 Speaker 3: has become a driving force alongside science and creativity. And 229 00:12:06,080 --> 00:12:09,959 Speaker 3: while some of this digital technology is relatively new, Matthew 230 00:12:10,200 --> 00:12:13,920 Speaker 3: helped me see that IBM and Lorel have always had 231 00:12:13,920 --> 00:12:14,680 Speaker 3: a lot in common. 232 00:12:15,400 --> 00:12:18,800 Speaker 7: So the original creator of Loyal, Jentulier, was a chemist 233 00:12:18,800 --> 00:12:21,839 Speaker 7: in nineteen or nine, so one hundred and sixteen years ago, 234 00:12:22,480 --> 00:12:25,640 Speaker 7: and he created this new air color type for the 235 00:12:25,720 --> 00:12:29,080 Speaker 7: market in France, and then little by little, it has 236 00:12:29,120 --> 00:12:31,360 Speaker 7: been always a very scientific company. So if you look 237 00:12:31,360 --> 00:12:34,440 Speaker 7: a little bit at key facts, we invented sun filters 238 00:12:34,679 --> 00:12:37,920 Speaker 7: in the nineteen thirties, there was a very very big 239 00:12:37,960 --> 00:12:40,440 Speaker 7: milestone where we also invented not only product, but a 240 00:12:40,480 --> 00:12:45,040 Speaker 7: reconstructed skin. So if you look at nineteen seventeen nine, 241 00:12:45,640 --> 00:12:48,400 Speaker 7: we've been the created this reconstructed skin that helped us 242 00:12:48,440 --> 00:12:50,560 Speaker 7: to go out of animal testing very fast and by 243 00:12:50,600 --> 00:12:53,560 Speaker 7: the way, before the law even asked it to cosmetic companies. 244 00:12:54,160 --> 00:12:57,640 Speaker 7: And then more recently, because it's a history of innovation, 245 00:12:57,760 --> 00:13:00,480 Speaker 7: we launch on new molecules like one you can find 246 00:13:00,480 --> 00:13:03,959 Speaker 7: in naoche pose Milibi three, which is really helping people 247 00:13:04,000 --> 00:13:07,360 Speaker 7: to find against some you know, spots they could have 248 00:13:07,400 --> 00:13:09,920 Speaker 7: on their skin. It's all about like big mountation, how 249 00:13:09,960 --> 00:13:10,599 Speaker 7: to regulate it. 250 00:13:11,320 --> 00:13:14,840 Speaker 3: Loreel and IBM were both started in the early twentieth century, 251 00:13:15,240 --> 00:13:18,840 Speaker 3: Loreel in nineteen oh nine and IBM in nineteen eleven. 252 00:13:19,320 --> 00:13:23,199 Speaker 3: Both companies have long standing histories of innovation, of using 253 00:13:23,240 --> 00:13:26,400 Speaker 3: trial and error to improve everything they do. The two 254 00:13:26,400 --> 00:13:29,199 Speaker 3: companies have been doing that in parallel for more than 255 00:13:29,200 --> 00:13:33,520 Speaker 3: a century until recently. When does that start when doll 256 00:13:33,559 --> 00:13:35,160 Speaker 3: Lorel and IBM start working together. 257 00:13:36,000 --> 00:13:38,880 Speaker 8: So we started in twenty twenty three at the end 258 00:13:38,920 --> 00:13:39,320 Speaker 8: of the year. 259 00:13:39,360 --> 00:13:43,520 Speaker 11: But you know, really the discussion is really recent, absolutely absolutely, 260 00:13:43,880 --> 00:13:46,320 Speaker 11: it's really recent in reality. You know, I would say 261 00:13:47,000 --> 00:13:49,880 Speaker 11: the first really interaction happened at the beginning of twenty 262 00:13:49,920 --> 00:13:50,480 Speaker 11: twenty four. 263 00:13:51,280 --> 00:13:54,720 Speaker 3: This is Gabriel Bertoli, who I spoke to alongside Matthew. 264 00:13:55,440 --> 00:13:58,600 Speaker 11: What really played a key role here is we wanted 265 00:13:58,640 --> 00:14:02,840 Speaker 11: to bring from a logic perspective to R and D together, 266 00:14:04,080 --> 00:14:07,520 Speaker 11: which normally, you know companies like us, you just go 267 00:14:07,640 --> 00:14:10,439 Speaker 11: to a provider. You know, it's a customer and the 268 00:14:10,520 --> 00:14:12,440 Speaker 11: supplier and new work they delivered to you. 269 00:14:12,760 --> 00:14:14,480 Speaker 8: Here the concept was totally different. 270 00:14:15,360 --> 00:14:18,839 Speaker 3: Mid two said that the collaboration began with simple conversations. 271 00:14:19,120 --> 00:14:22,000 Speaker 7: So if you look at the way IBM entered into 272 00:14:22,600 --> 00:14:26,840 Speaker 7: Loreal Labs, it's started by interviewing people what would help 273 00:14:26,880 --> 00:14:27,680 Speaker 7: you to do your job? 274 00:14:27,960 --> 00:14:29,120 Speaker 8: What is your business need? 275 00:14:29,640 --> 00:14:32,120 Speaker 7: So it was, by the way, two months ago, a 276 00:14:32,320 --> 00:14:36,120 Speaker 7: long series of interviews and from all the people around 277 00:14:36,160 --> 00:14:39,160 Speaker 7: the world we have in research in Brazil, in India, 278 00:14:39,440 --> 00:14:44,120 Speaker 7: in China, Japan, US, France of course, So we really 279 00:14:44,160 --> 00:14:45,760 Speaker 7: want to make sure that at the end of the day, 280 00:14:46,120 --> 00:14:48,200 Speaker 7: this new model, this new tool that we will give 281 00:14:48,240 --> 00:14:50,960 Speaker 7: to people is really people centrick in the way that 282 00:14:51,040 --> 00:14:52,480 Speaker 7: it selves their daily need. 283 00:14:53,120 --> 00:14:57,120 Speaker 3: More the point, Lourel has leveraged technology for decades and 284 00:14:57,200 --> 00:15:02,680 Speaker 3: accumulated amounted of scientific knowledge, everything from consumer aspirations and 285 00:15:02,720 --> 00:15:06,040 Speaker 3: market trends to the results of all the experiments conducted 286 00:15:06,120 --> 00:15:10,200 Speaker 3: during product development. To which formulations melt in a hot car. 287 00:15:10,960 --> 00:15:14,000 Speaker 3: It's hard to get your head around. Loreal isn't just 288 00:15:14,040 --> 00:15:18,080 Speaker 3: a cosmetics company. It's a beauty data powerhouse. 289 00:15:19,000 --> 00:15:23,840 Speaker 11: If we have sixteen thousand terabat of data coming from 290 00:15:24,160 --> 00:15:31,760 Speaker 11: consumer insights, coming from market research coming from sales, well 291 00:15:31,880 --> 00:15:36,720 Speaker 11: with the new technology, maybe by aligning those two and 292 00:15:36,840 --> 00:15:39,840 Speaker 11: using best in class technology you can solve that problem. 293 00:15:39,920 --> 00:15:42,640 Speaker 3: So you say you have sixteen terabytes of data, put 294 00:15:42,640 --> 00:15:44,560 Speaker 3: that in perspective. How much data is that? 295 00:15:45,160 --> 00:15:45,480 Speaker 8: Give me? 296 00:15:46,800 --> 00:15:50,760 Speaker 11: This is one hundred year of Looreal data based on 297 00:15:50,960 --> 00:15:55,440 Speaker 11: the last forty years of data in the systems. So 298 00:15:55,720 --> 00:15:57,560 Speaker 11: this is really I mean, we're talking about one hundred 299 00:15:57,640 --> 00:15:59,280 Speaker 11: year of data that only Lorial have. 300 00:16:00,160 --> 00:16:01,760 Speaker 8: Take the example of the ellipsis. 301 00:16:01,840 --> 00:16:04,920 Speaker 11: I mean, you know, if ellipsex can be between twenty 302 00:16:04,960 --> 00:16:08,480 Speaker 11: and thirty arrow material, each raw material will have I 303 00:16:08,480 --> 00:16:14,320 Speaker 11: would say ten or fifteen way of doing things. 304 00:16:15,760 --> 00:16:18,240 Speaker 3: Gabrielle is talking about how things used to be done. 305 00:16:18,560 --> 00:16:22,440 Speaker 3: Researchers at Lorel needed roughly twenty five ingredients for a 306 00:16:22,480 --> 00:16:25,600 Speaker 3: new lipstick formulation, but they have to choose from a 307 00:16:25,600 --> 00:16:29,920 Speaker 3: pool of hundreds, if not thousands, of raw materials, and 308 00:16:30,000 --> 00:16:32,240 Speaker 3: even after they settle on the ones they want. They 309 00:16:32,240 --> 00:16:34,640 Speaker 3: have to figure out how much of each ingredient they 310 00:16:34,680 --> 00:16:39,040 Speaker 3: need and in what form, what molecular weight, what combination. 311 00:16:39,800 --> 00:16:42,760 Speaker 3: It's not just a math problem. It's a problem that 312 00:16:42,840 --> 00:16:50,360 Speaker 3: requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability 313 00:16:50,440 --> 00:16:54,240 Speaker 3: and more. It can take years. But what if you 314 00:16:54,240 --> 00:16:58,120 Speaker 3: could simulate hundreds of cars parked in a sweltering heat. 315 00:16:58,360 --> 00:17:00,920 Speaker 3: What if you could do all those files and errors 316 00:17:01,240 --> 00:17:05,240 Speaker 3: virtually over and over and over again. What if instead 317 00:17:05,280 --> 00:17:09,080 Speaker 3: of mixing materials together by hand, you could ask AI 318 00:17:09,200 --> 00:17:13,120 Speaker 3: to predict what combinations might work best and then try 319 00:17:13,200 --> 00:17:14,119 Speaker 3: those out first. 320 00:17:14,720 --> 00:17:17,720 Speaker 8: This is ten on the power of twenty five. 321 00:17:18,680 --> 00:17:22,359 Speaker 11: This is one hundred billion of years for a human 322 00:17:23,359 --> 00:17:27,280 Speaker 11: to do a change in the formula or the possibility 323 00:17:27,359 --> 00:17:31,640 Speaker 11: they have. You can only do this by using technology, 324 00:17:32,800 --> 00:17:35,040 Speaker 11: power of technology and data that you have. 325 00:17:35,720 --> 00:17:38,960 Speaker 3: This, Matthew says, is where IBM can come in to 326 00:17:39,080 --> 00:17:43,760 Speaker 3: help take things further. Using artificial intelligence. IBM can help 327 00:17:43,800 --> 00:17:47,480 Speaker 3: Lorio create a custom AI model that helps to crunch 328 00:17:47,560 --> 00:17:51,480 Speaker 3: those numbers, to be a companion to the researchers, to 329 00:17:51,520 --> 00:17:52,600 Speaker 3: give them superpowers. 330 00:17:52,840 --> 00:17:54,840 Speaker 7: We don't want to replace the intuition of The sentis 331 00:17:54,960 --> 00:17:57,919 Speaker 7: we just want to make sure that this intuition is 332 00:17:57,960 --> 00:18:02,280 Speaker 7: really augmented by some inclination poor that, as Gabrielle said, 333 00:18:02,280 --> 00:18:04,840 Speaker 7: then does those ten at the poor of twenty five 334 00:18:05,040 --> 00:18:08,320 Speaker 7: solution and seem probably try this one, this one, this one, 335 00:18:08,520 --> 00:18:11,480 Speaker 7: it looks like a better solution and Thentimately that's really 336 00:18:11,520 --> 00:18:13,639 Speaker 7: the decision of the chemist to make it happen. 337 00:18:16,320 --> 00:18:19,400 Speaker 3: Well. To make a predictive AI model that can give 338 00:18:19,480 --> 00:18:23,480 Speaker 3: Lorel researchers those superpowers, you'd need that mountain of data, 339 00:18:24,040 --> 00:18:28,240 Speaker 3: years worth of laboratory testing and all Loreal's data digitized 340 00:18:28,359 --> 00:18:32,400 Speaker 3: and AI ready. You'd need to train artificial intelligence on 341 00:18:32,600 --> 00:18:35,399 Speaker 3: everything the company has already done in order for it 342 00:18:35,560 --> 00:18:37,080 Speaker 3: to predict what it could do. 343 00:18:37,600 --> 00:18:41,880 Speaker 10: Loriial has one hundred years of worse of data, fifty 344 00:18:42,000 --> 00:18:43,680 Speaker 10: years of digitized EGGDA. 345 00:18:44,400 --> 00:18:48,040 Speaker 3: This is Mariam Ashuri, Senior director of Product Management for 346 00:18:48,160 --> 00:18:51,600 Speaker 3: IBM Watson X. Loreal has the data and part of 347 00:18:51,640 --> 00:18:54,480 Speaker 3: IBM's job is to help put that data to work, 348 00:18:54,960 --> 00:18:59,240 Speaker 3: which involves ensuring data quality. Mariam talked about the concept 349 00:18:59,480 --> 00:19:00,919 Speaker 3: of a ready data. 350 00:19:01,600 --> 00:19:04,439 Speaker 10: The sole purpose of this data engineering pipeline is to 351 00:19:04,560 --> 00:19:09,040 Speaker 10: clean the data, and we call them AI ready data 352 00:19:09,240 --> 00:19:13,040 Speaker 10: makes them ready to be consumed by AI, so basically 353 00:19:13,080 --> 00:19:16,800 Speaker 10: looking into biases in the data to fix the distribution, 354 00:19:17,040 --> 00:19:20,480 Speaker 10: looking into guard brains that you're putting into place in 355 00:19:20,560 --> 00:19:23,000 Speaker 10: terms of removing personal information. 356 00:19:23,840 --> 00:19:26,920 Speaker 3: Mariam that explained that a custom model like the one 357 00:19:27,000 --> 00:19:30,199 Speaker 3: IBM is creating with Lorel can be more efficient and 358 00:19:30,359 --> 00:19:33,640 Speaker 3: targeted than the larger general purpose AI models. 359 00:19:33,840 --> 00:19:37,200 Speaker 10: You've heard about large language models. The reason that they 360 00:19:37,240 --> 00:19:40,800 Speaker 10: call them large language model is they are exposed into 361 00:19:42,200 --> 00:19:46,240 Speaker 10: really large amount of data. So the larger the model, 362 00:19:46,280 --> 00:19:50,240 Speaker 10: the more capable the models are, but also the larger 363 00:19:50,280 --> 00:19:55,040 Speaker 10: computed requires that translatestand increase carbon footprint and energy consumption 364 00:19:55,200 --> 00:19:59,840 Speaker 10: that translates, stand increase latency that's your response time, that translates, 365 00:20:00,040 --> 00:20:05,000 Speaker 10: and increase costs. So we started seeing that enterprises started 366 00:20:05,760 --> 00:20:09,840 Speaker 10: grabbing a much smaller model customize it on their proprietary 367 00:20:09,920 --> 00:20:13,399 Speaker 10: data that's the data, their DOMAINO specific data, or the 368 00:20:13,480 --> 00:20:17,919 Speaker 10: data about their users to create something differentiated that is 369 00:20:18,000 --> 00:20:22,119 Speaker 10: applicable to a real world use case but also delivers 370 00:20:22,200 --> 00:20:25,280 Speaker 10: the performance that they needed for a fraction of the costs. 371 00:20:25,680 --> 00:20:28,600 Speaker 10: And that's why there's been a lot of push around 372 00:20:28,800 --> 00:20:32,800 Speaker 10: using custom models versus very large general purpose models. 373 00:20:33,560 --> 00:20:37,000 Speaker 3: So how is a custom model created? Miriam says, you 374 00:20:37,040 --> 00:20:40,159 Speaker 3: start with the base model. Imagine you're buying a car. 375 00:20:40,640 --> 00:20:42,800 Speaker 3: You could get a minivan, or a sedan or a 376 00:20:42,800 --> 00:20:45,679 Speaker 3: sports car, and then you get to customize it. You 377 00:20:45,680 --> 00:20:48,960 Speaker 3: could add a sunroof, leather seats, or a rearview camera. 378 00:20:49,440 --> 00:20:51,520 Speaker 3: Turns out you could do the same thing with your 379 00:20:51,560 --> 00:20:55,080 Speaker 3: AI model. You pick a base and then you customize it. 380 00:20:55,480 --> 00:20:58,520 Speaker 3: You tune it on the data unique to your organization. 381 00:20:58,960 --> 00:21:02,800 Speaker 10: We do believe that one model doesn't fit all use cases. 382 00:21:03,480 --> 00:21:07,439 Speaker 10: You want to truly have access to any model anywhere, 383 00:21:07,520 --> 00:21:11,920 Speaker 10: and by any model anywhere. I really mean any model anywhere, 384 00:21:12,000 --> 00:21:16,879 Speaker 10: open source, proprietary, low call out your machine. Wherever the 385 00:21:16,920 --> 00:21:20,360 Speaker 10: model is. You want to host it yourself, because then 386 00:21:20,560 --> 00:21:23,399 Speaker 10: you would be able to take advantage of the best 387 00:21:23,440 --> 00:21:26,280 Speaker 10: of the technology at any point and pick the right 388 00:21:26,359 --> 00:21:27,840 Speaker 10: model for the target use case. 389 00:21:28,200 --> 00:21:31,600 Speaker 3: So a custom model tuned on Lorel's data would be 390 00:21:31,680 --> 00:21:35,840 Speaker 3: more targeted and efficient than a general purpose model. It 391 00:21:35,840 --> 00:21:41,040 Speaker 3: would understand the researchers world and provide transparency into its workings. 392 00:21:41,400 --> 00:21:44,240 Speaker 3: That's part of the magic. And what could a custom 393 00:21:44,359 --> 00:21:48,320 Speaker 3: AI foundation model do for a company like Lorel. 394 00:21:48,840 --> 00:21:54,280 Speaker 12: Accord with that moder is contain the complexity of the formulation. 395 00:21:54,960 --> 00:21:59,000 Speaker 3: That's game La Moline, an IBM distinguished engineer and one 396 00:21:59,080 --> 00:22:01,240 Speaker 3: of the people working on the AI model. 397 00:22:01,480 --> 00:22:07,119 Speaker 12: And to hype as the formulator to go not only faster, 398 00:22:07,560 --> 00:22:11,960 Speaker 12: but also I would say, be able to include more 399 00:22:12,000 --> 00:22:17,040 Speaker 12: complexity or so in their formulation, more personalization, more certain ability, 400 00:22:17,640 --> 00:22:21,560 Speaker 12: better selected ingredient. So it's really a tool to help 401 00:22:21,680 --> 00:22:26,679 Speaker 12: them and to also help them to unniche the creativity. 402 00:22:29,080 --> 00:22:33,000 Speaker 3: THEAOMI saying that with its custom AI model, Lorel could 403 00:22:33,040 --> 00:22:36,600 Speaker 3: improve every step of its product development pipeline, make the 404 00:22:36,640 --> 00:22:40,560 Speaker 3: process faster and more sustainable. But he's also saying that 405 00:22:40,600 --> 00:22:43,520 Speaker 3: the model could help Lorel create something that's never been 406 00:22:43,560 --> 00:22:50,840 Speaker 3: done before. What could that product be? So I'm mourning 407 00:22:50,840 --> 00:22:52,400 Speaker 3: you with that. All my questions are going to be 408 00:22:52,520 --> 00:22:53,200 Speaker 3: really dumb. 409 00:22:54,000 --> 00:22:56,000 Speaker 9: Okay, now, please by all means. 410 00:22:57,440 --> 00:22:59,600 Speaker 3: To find out what people at Lorel are dreaming of. 411 00:23:00,119 --> 00:23:03,960 Speaker 3: I spoke with Trisha Iyagari, global general manager at Loriel's 412 00:23:03,960 --> 00:23:07,560 Speaker 3: Mabeline brand, and they asked her about her own dreams 413 00:23:07,680 --> 00:23:10,720 Speaker 3: and how technology and science could help bring those dreams 414 00:23:11,040 --> 00:23:14,000 Speaker 3: into the world. Do you have a secret wish list 415 00:23:14,200 --> 00:23:17,439 Speaker 3: of things you think that this partnership could produce, Like, 416 00:23:17,560 --> 00:23:19,560 Speaker 3: is there a product out there that's been technically too 417 00:23:19,640 --> 00:23:23,360 Speaker 3: difficult that you think could be a worthy target? 418 00:23:23,680 --> 00:23:25,720 Speaker 9: There is one that I think could be really amazing. 419 00:23:25,920 --> 00:23:26,240 Speaker 3: What's that? 420 00:23:27,040 --> 00:23:27,159 Speaker 8: So? 421 00:23:27,240 --> 00:23:30,200 Speaker 9: Shine products in general are harder to create, and we're 422 00:23:30,359 --> 00:23:37,280 Speaker 9: unable to create a shiny, long wearing eyeshadow. So basically 423 00:23:37,320 --> 00:23:39,480 Speaker 9: like a shadow that could stay on your eyelids, that 424 00:23:39,560 --> 00:23:42,280 Speaker 9: won't settle into creases, that won't move all over your face, 425 00:23:43,359 --> 00:23:45,440 Speaker 9: that has a glossy effect. It's like the holy Grail. 426 00:23:45,560 --> 00:23:48,400 Speaker 3: That's the holy grail. H Yeah, you may have seen 427 00:23:48,400 --> 00:23:53,159 Speaker 3: that look in fashion shows, but that look isn't real, 428 00:23:53,760 --> 00:23:55,080 Speaker 3: not for people like me and Lucy. 429 00:23:55,119 --> 00:23:57,680 Speaker 9: Anyway, if you're walking down a runway, you see a 430 00:23:57,720 --> 00:23:59,800 Speaker 9: lot of makeup artists doing techniques where they put some 431 00:24:00,000 --> 00:24:03,320 Speaker 9: shadow on, they layer vasaline over it on, like slather 432 00:24:03,440 --> 00:24:06,560 Speaker 9: vassaline on somebody's eyes to create this very like glossy look. 433 00:24:06,840 --> 00:24:08,920 Speaker 9: But you know, within five minutes after they walk down 434 00:24:08,920 --> 00:24:10,720 Speaker 9: the runway, I'm sure it's all over their face or 435 00:24:10,760 --> 00:24:17,680 Speaker 9: being washed off. So the look is kind of more 436 00:24:17,720 --> 00:24:20,160 Speaker 9: of like a fashion look that we've been unable to create. 437 00:24:20,200 --> 00:24:23,160 Speaker 9: And real, real consumers can't wear it because it would 438 00:24:23,160 --> 00:24:24,040 Speaker 9: get it everywhere. 439 00:24:24,440 --> 00:24:26,760 Speaker 3: Tricia had another thing on her wish list too. 440 00:24:27,119 --> 00:24:30,800 Speaker 9: The other that we would really like is semi prominence makeup. 441 00:24:31,720 --> 00:24:37,040 Speaker 9: So we've talked a lot about really really comfortable thin 442 00:24:37,200 --> 00:24:39,760 Speaker 9: film makeup that you could wear all over your face 443 00:24:39,880 --> 00:24:41,800 Speaker 9: and that you can sleep in, and that it will 444 00:24:41,880 --> 00:24:45,200 Speaker 9: last a couple of days basically, so whether it be 445 00:24:45,280 --> 00:24:47,960 Speaker 9: on your face, on your lashes, on your brows. So 446 00:24:48,040 --> 00:24:50,919 Speaker 9: anything that's like more of a semi permanent meaning lasting 447 00:24:51,000 --> 00:24:53,120 Speaker 9: for three days or more, would be amazing. 448 00:24:53,560 --> 00:24:53,960 Speaker 2: Yeah. 449 00:24:54,119 --> 00:24:55,920 Speaker 3: Yeah, And you say those two things have been the 450 00:24:55,960 --> 00:24:58,840 Speaker 3: whole How long have they been on the wish list 451 00:24:58,880 --> 00:24:59,400 Speaker 3: of Loreel? 452 00:25:00,000 --> 00:25:00,119 Speaker 2: Oh? 453 00:25:00,119 --> 00:25:02,560 Speaker 9: My gosh. I have been trying to develop this shiny 454 00:25:02,560 --> 00:25:06,520 Speaker 9: eyeshadow since I started. What year did I start, Like 455 00:25:06,800 --> 00:25:10,040 Speaker 9: twenty ten? And I'm sure many people had asked before me, 456 00:25:10,119 --> 00:25:14,239 Speaker 9: and we tried so many iterations of it. Nobody's been 457 00:25:14,240 --> 00:25:18,400 Speaker 9: able to achieve it. 458 00:25:18,400 --> 00:25:22,800 Speaker 3: It's clear that Loreel's experts Electricia have a lot of ideas. 459 00:25:26,160 --> 00:25:28,200 Speaker 3: I once said what I called a magic wand project, 460 00:25:28,480 --> 00:25:31,160 Speaker 3: where I called up scientists and technologists in as many 461 00:25:31,160 --> 00:25:34,399 Speaker 3: different fields as possible and asked them what they could 462 00:25:34,440 --> 00:25:37,399 Speaker 3: create if they could just wave a magic wand and 463 00:25:37,440 --> 00:25:41,000 Speaker 3: make it real, and everyone had something they'd want to 464 00:25:41,000 --> 00:25:44,439 Speaker 3: create everyone. That's not the issue. The issue is that 465 00:25:44,480 --> 00:25:47,880 Speaker 3: there are a million different impediments to make the ideas 466 00:25:47,880 --> 00:25:51,280 Speaker 3: on the wish list reel. Lack of resources, lack of time, 467 00:25:51,480 --> 00:25:54,200 Speaker 3: some crucial bit of know how is lacking. There's a 468 00:25:54,240 --> 00:25:57,280 Speaker 3: gap between what we want and what we can actually have. 469 00:25:58,280 --> 00:26:00,040 Speaker 3: And one of the simplest ways to think of the 470 00:26:00,080 --> 00:26:03,040 Speaker 3: promise of AI is that it can narrow that gap, 471 00:26:04,040 --> 00:26:07,160 Speaker 3: not close it, of course, but do enough that people 472 00:26:07,200 --> 00:26:10,439 Speaker 3: with dreams realize there are more things within their grasp 473 00:26:11,080 --> 00:26:29,800 Speaker 3: than they could ever have imagined. Smart Talks with IBM 474 00:26:29,920 --> 00:26:34,040 Speaker 3: is produced by Matt Romano, Amy Gaines, McQuaid, Lucy Sullivan, 475 00:26:34,520 --> 00:26:38,720 Speaker 3: and Jake Harper. Were edited by Lacy Roberts, Engineering by 476 00:26:38,840 --> 00:26:43,439 Speaker 3: Nina Bird Lawrence, mastering by Sarah Bruguerer, music by Gramoscope 477 00:26:43,880 --> 00:26:47,920 Speaker 3: Special thanks to Tatiana Lieberman and Cassidy Meyer. Smart Talks 478 00:26:47,920 --> 00:26:50,920 Speaker 3: with IBM is a production of Pushkin Industries and Ruby 479 00:26:51,000 --> 00:26:55,680 Speaker 3: Studio at iHeartMedia. To find more Pushkin podcasts, listen on 480 00:26:55,720 --> 00:26:59,960 Speaker 3: the iHeartRadio app, Apple Podcasts or wherever you get your podcasts. 481 00:27:00,640 --> 00:27:04,800 Speaker 3: I'm Malcolm Glaubo. This is a paid advertisement from I 482 00:27:04,880 --> 00:27:08,800 Speaker 3: b M. The conversations on this podcast don't necessarily represent 483 00:27:09,080 --> 00:27:24,240 Speaker 3: I b m's positions, strategies, or opinions.