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 --> 00:00:25,440 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 --> 00:01:02,160 Speaker 1: This is a paid advertisement from IBM pushkin. 18 00:01:07,440 --> 00:01:09,400 Speaker 3: I never went to a Formula one race as a 19 00:01:09,480 --> 00:01:11,959 Speaker 3: kid because we lived in southern Ontario and there was 20 00:01:12,040 --> 00:01:14,800 Speaker 3: just one F one race in Canada. That race was 21 00:01:14,840 --> 00:01:18,640 Speaker 3: in Montreal. A good seven hour drive away. I never 22 00:01:18,680 --> 00:01:22,320 Speaker 3: watched F one race on television either, because we didn't. 23 00:01:22,040 --> 00:01:22,880 Speaker 4: Have a television. 24 00:01:23,840 --> 00:01:26,360 Speaker 3: But what I did have was a subscription to the 25 00:01:26,360 --> 00:01:29,480 Speaker 3: car magazine Road and Track, and Roadent Track took F 26 00:01:29,520 --> 00:01:33,199 Speaker 3: one very seriously. Every month my new issue would arrive, 27 00:01:33,560 --> 00:01:36,600 Speaker 3: I would turn immediately to the long, detailed account of 28 00:01:36,600 --> 00:01:43,080 Speaker 3: that month's race, and I fell in love. It's been 29 00:01:43,160 --> 00:01:45,800 Speaker 3: fifty years, but I can still rattle off the names 30 00:01:45,800 --> 00:01:48,040 Speaker 3: of all the top drivers of that era from memory. 31 00:01:48,440 --> 00:01:52,360 Speaker 3: James Hunt, Mario Andretti, Carlos Pace, Jacques Lafitte, and of 32 00:01:52,400 --> 00:01:56,360 Speaker 3: course the greatest of them all, my adolescent idol, Niki Lauda, 33 00:01:56,960 --> 00:01:59,920 Speaker 3: who won two world championships in the mid nineteen seventies 34 00:02:00,440 --> 00:02:02,040 Speaker 3: with Scuderia Ferrari. 35 00:02:02,880 --> 00:02:05,919 Speaker 4: He was world championship material from the moment he joined 36 00:02:05,960 --> 00:02:09,120 Speaker 4: the Ferrari team. 37 00:02:09,639 --> 00:02:12,640 Speaker 2: There's no question that in seventy five seventy six I 38 00:02:12,760 --> 00:02:16,680 Speaker 2: was really dominating the whole thing without any mistake. So 39 00:02:16,800 --> 00:02:17,639 Speaker 2: I did nothing wrong. 40 00:02:17,800 --> 00:02:19,680 Speaker 5: I mean, this was perfect driving. 41 00:02:20,360 --> 00:02:22,800 Speaker 3: If you had met skinny pre adolescent Malcolm in the 42 00:02:22,800 --> 00:02:25,960 Speaker 3: mid nineteen seventies in rural Ontario, there's a good chance 43 00:02:25,960 --> 00:02:28,440 Speaker 3: you would have seen me in my prize Ferrari T shirt. 44 00:02:28,880 --> 00:02:32,040 Speaker 3: I was a fan, one of what the Italians called tifosi, 45 00:02:32,440 --> 00:02:35,239 Speaker 3: a Ferrari devote, And that's what it meant to be 46 00:02:35,280 --> 00:02:39,440 Speaker 3: a fan fifty years ago, t shirts, magazine stories, and 47 00:02:39,480 --> 00:02:42,480 Speaker 3: a big Nikki Lauta poster on your wall. But what 48 00:02:42,520 --> 00:02:45,560 Speaker 3: does it mean to be a fan today? Today we 49 00:02:45,600 --> 00:02:48,160 Speaker 3: have the Internet and streaming and big data and AI 50 00:02:48,240 --> 00:02:50,960 Speaker 3: and all the other accouterment of the digital age. Is 51 00:02:51,000 --> 00:03:00,120 Speaker 3: there a chance to reinvent the meaning of fandom? My 52 00:03:00,160 --> 00:03:02,840 Speaker 3: name is Malcolm Gladwell. You're listening to the latest episode 53 00:03:02,880 --> 00:03:05,840 Speaker 3: of Smart Talks with IBM, where we offer our listeners 54 00:03:05,880 --> 00:03:08,440 Speaker 3: a glimpse behind the curtain of the world of technology. 55 00:03:09,200 --> 00:03:11,960 Speaker 3: In our first episode, we talked about how an AI 56 00:03:12,040 --> 00:03:17,720 Speaker 3: assistant created with IBM watsonex helps future teachers practice responsive teaching. 57 00:03:18,160 --> 00:03:21,440 Speaker 3: Our second episode was how a custom AI model could 58 00:03:21,520 --> 00:03:24,600 Speaker 3: help Loreel's researchers shape the future of what we put 59 00:03:24,680 --> 00:03:29,560 Speaker 3: on our faces every morning. In this episode, how IBM, 60 00:03:29,800 --> 00:03:33,239 Speaker 3: one of the world's pre eminent technology companies, is joining 61 00:03:33,280 --> 00:03:35,840 Speaker 3: up with one of the world's pre eminent racing brands 62 00:03:36,200 --> 00:03:44,280 Speaker 3: to fundamentally change how fans interact with their favorite team. 63 00:03:44,600 --> 00:03:48,640 Speaker 3: The size of the Scuderia Ferrari HP fan base is staggering. 64 00:03:49,200 --> 00:03:52,440 Speaker 3: Three hundred and ninety six million people around the world 65 00:03:52,480 --> 00:03:56,720 Speaker 3: identify as Ferrari fans. Three hundred and ninety six million. 66 00:03:57,240 --> 00:03:59,560 Speaker 3: The only other fan base is that big belong to 67 00:03:59,600 --> 00:04:03,480 Speaker 3: the icon Premier League Football teams like Manchester United or 68 00:04:03,560 --> 00:04:07,640 Speaker 3: Chelsea FC. I don't believe there is any other Formula 69 00:04:07,640 --> 00:04:12,000 Speaker 3: one team that inspires that kind of devotion. Ferrari's job, then, 70 00:04:12,120 --> 00:04:15,600 Speaker 3: isn't necessarily grow its fan base. Three hundred and ninety 71 00:04:15,640 --> 00:04:18,680 Speaker 3: six million is more than enough fans. Their job is 72 00:04:18,720 --> 00:04:21,840 Speaker 3: to deepen the connection people feel with the scudery A 73 00:04:21,880 --> 00:04:25,279 Speaker 3: Ferrari team. But if I'm Ferrari, how do I find 74 00:04:25,279 --> 00:04:28,000 Speaker 3: out more about who my fans are, what they care about, 75 00:04:28,320 --> 00:04:30,520 Speaker 3: what they want? How do I use my archives and 76 00:04:30,600 --> 00:04:34,000 Speaker 3: data to create experiences that matter to them? How do 77 00:04:34,080 --> 00:04:36,600 Speaker 3: I say to the guy who spent his childhood eagerly 78 00:04:36,640 --> 00:04:39,880 Speaker 3: reading roadent track every month? Here are other ways you 79 00:04:39,920 --> 00:04:43,160 Speaker 3: can get involved with your favorite F one team Today. 80 00:04:44,120 --> 00:04:47,440 Speaker 3: The task of deepening an emotional connection in the digital 81 00:04:47,480 --> 00:04:52,400 Speaker 3: age begins as an information problem, which is where IBM 82 00:04:52,480 --> 00:04:55,360 Speaker 3: comes in. How would you describe what you do. 83 00:04:56,279 --> 00:04:58,320 Speaker 6: I describe it as the probably the best job, but 84 00:04:58,400 --> 00:04:58,760 Speaker 6: I bem. 85 00:04:59,000 --> 00:05:00,599 Speaker 3: Yeah, I was going to say, I was going to 86 00:05:00,600 --> 00:05:02,640 Speaker 3: ask you, do you have the best job at IBM? 87 00:05:03,600 --> 00:05:04,159 Speaker 6: I think so. 88 00:05:04,800 --> 00:05:07,719 Speaker 3: I'm talking to Fred Baker, who leads sports and Entertainment 89 00:05:07,760 --> 00:05:11,120 Speaker 3: for IBM consulting in Europe, the Middle East and Africa. 90 00:05:11,920 --> 00:05:14,880 Speaker 3: You can probably guess from the accent he's from New Zealand. 91 00:05:15,240 --> 00:05:17,560 Speaker 6: We've had a really interesting range of experience over the 92 00:05:17,640 --> 00:05:20,159 Speaker 6: past sort of five six years. We've worked with Premier 93 00:05:20,240 --> 00:05:23,960 Speaker 6: League clubs like Liverpool Football. We've worked with England Rugby 94 00:05:24,360 --> 00:05:28,120 Speaker 6: St Andrews Links. We also globally, we've got a global team, 95 00:05:28,200 --> 00:05:31,679 Speaker 6: so we work with the Masters, the US Open, ESPN, 96 00:05:31,760 --> 00:05:33,880 Speaker 6: Fantasy Football, the Grammys. 97 00:05:34,279 --> 00:05:36,719 Speaker 4: You do the tennis stuff. Is it all under Europe? 98 00:05:36,800 --> 00:05:38,880 Speaker 6: Yeah, so we do Wimbledon as well. Yep, that's under 99 00:05:38,880 --> 00:05:39,279 Speaker 6: my rima. 100 00:05:39,680 --> 00:05:42,400 Speaker 3: If you've ever watched Wimbledon on television, I'm sure you've 101 00:05:42,400 --> 00:05:45,240 Speaker 3: seen at various moments a little IBM logo on the 102 00:05:45,240 --> 00:05:49,159 Speaker 3: bottom of the screen. That's because IBM has been Wimbledon's 103 00:05:49,200 --> 00:05:54,480 Speaker 3: official information technology partner since nineteen ninety, when the idea 104 00:05:54,520 --> 00:05:57,599 Speaker 3: of a collaboration between Ferrari and IBM was first broached. 105 00:05:58,040 --> 00:06:00,680 Speaker 3: Baker actually took people from Ferrari on a tour of 106 00:06:00,800 --> 00:06:04,000 Speaker 3: IBM's Wimbledon operation, just so they could see what a 107 00:06:04,000 --> 00:06:07,440 Speaker 3: tech company like IBM could do for a sports franchise. 108 00:06:08,240 --> 00:06:08,960 Speaker 4: Which Wimbledon. 109 00:06:09,040 --> 00:06:12,400 Speaker 6: Did you take them to last year's champs? 110 00:06:12,560 --> 00:06:14,520 Speaker 3: Tell me what you showed them. 111 00:06:15,000 --> 00:06:17,560 Speaker 6: We take them into what we call the bunker, so 112 00:06:17,800 --> 00:06:21,360 Speaker 6: it's literally underground at the Champs, and showed them how 113 00:06:21,400 --> 00:06:25,240 Speaker 6: we bring everything to life from the data capture off 114 00:06:25,240 --> 00:06:29,920 Speaker 6: the courts, how we real time categorize, serve all those 115 00:06:29,920 --> 00:06:32,359 Speaker 6: points to broadcasters and serve them into the app the 116 00:06:32,400 --> 00:06:34,760 Speaker 6: website for millions of fans around the world. They were 117 00:06:34,760 --> 00:06:35,680 Speaker 6: really impressed by that. 118 00:06:36,320 --> 00:06:39,560 Speaker 3: I'm also impressed by that IBM trained it on the 119 00:06:39,640 --> 00:06:42,400 Speaker 3: language of tennis, and not only the language of tennis, 120 00:06:42,720 --> 00:06:46,159 Speaker 3: but specifically the language of tennis at Wimbledon. 121 00:06:46,920 --> 00:06:51,359 Speaker 6: So it can then decipher what an unforced era or 122 00:06:51,400 --> 00:06:54,839 Speaker 6: a winner or a lob or you know, idiosyncrasies in 123 00:06:54,839 --> 00:06:56,680 Speaker 6: the language. It can decipher all of that, and then 124 00:06:56,680 --> 00:06:58,800 Speaker 6: it can also tell what is a broadcast like to 125 00:06:58,800 --> 00:07:01,719 Speaker 6: talk about that is in to a fan. You know, 126 00:07:01,839 --> 00:07:04,360 Speaker 6: we've trained it so it can not only analyze everything 127 00:07:04,360 --> 00:07:07,279 Speaker 6: going on in the match. It can analyze past performances 128 00:07:07,320 --> 00:07:12,240 Speaker 6: and rationalize results based on conditions or form and then 129 00:07:12,280 --> 00:07:14,520 Speaker 6: make predictions that fans can learn from. But it can 130 00:07:14,560 --> 00:07:19,480 Speaker 6: also pull out on the spot really interesting milestones, moments, 131 00:07:19,560 --> 00:07:21,960 Speaker 6: data points that then come out of the mouth of 132 00:07:22,000 --> 00:07:22,600 Speaker 6: a broadcaster. 133 00:07:23,480 --> 00:07:26,080 Speaker 3: IBM is running an AI model that has been trained 134 00:07:26,160 --> 00:07:29,320 Speaker 3: on huge amounts of tennis data in order to give 135 00:07:29,400 --> 00:07:33,040 Speaker 3: human broadcasters ideas on what they can talk about. And 136 00:07:33,080 --> 00:07:36,800 Speaker 3: it all takes place underground, right near the courts. 137 00:07:37,280 --> 00:07:41,160 Speaker 6: It's literally like it's the underground floor of the broadcast 138 00:07:41,200 --> 00:07:44,200 Speaker 6: center at Wimbledon. It's literally almost under the courts. 139 00:07:44,440 --> 00:07:46,000 Speaker 4: Is IBM got the entire bunker? 140 00:07:46,720 --> 00:07:47,160 Speaker 6: Yeah? 141 00:07:47,240 --> 00:07:48,640 Speaker 4: How big is the room? 142 00:07:49,960 --> 00:07:52,000 Speaker 6: I'm sure our team would like it to be bigger, 143 00:07:52,040 --> 00:07:56,040 Speaker 6: but it's big enough. There's probably thirty forty IBM is 144 00:07:56,080 --> 00:08:00,760 Speaker 6: down there. Man. Seeing it live is just impressive when 145 00:08:00,800 --> 00:08:04,440 Speaker 6: you see how much work and intelligence goes on to 146 00:08:04,520 --> 00:08:06,960 Speaker 6: then make an end experience for a fan that is 147 00:08:07,680 --> 00:08:11,520 Speaker 6: really beautiful and representative of their brand and tradition. 148 00:08:12,520 --> 00:08:16,120 Speaker 3: IBM's goal in taking Ferrari to the Wimbledon bunker was 149 00:08:16,160 --> 00:08:18,600 Speaker 3: to show them what it looks like to harness the 150 00:08:18,600 --> 00:08:21,600 Speaker 3: power of data and how this could help shape Scuderia 151 00:08:21,640 --> 00:08:26,320 Speaker 3: Ferrari's fan and digital experiences. Could AI learn the language 152 00:08:26,560 --> 00:08:30,040 Speaker 3: of Scuderia Ferrari. What was the original app like before 153 00:08:30,080 --> 00:08:33,880 Speaker 3: IBM got involved. I'm speaking with Stefano Pollard, who runs 154 00:08:33,920 --> 00:08:36,200 Speaker 3: fan development for Ferrari's f one team. 155 00:08:36,360 --> 00:08:39,760 Speaker 7: It was quite a good app, a very good digital product, 156 00:08:39,760 --> 00:08:43,320 Speaker 7: but just an editorial product. So we were providing fans 157 00:08:43,600 --> 00:08:47,320 Speaker 7: news and videos, articles and it was mainly about that. 158 00:08:48,040 --> 00:08:51,080 Speaker 7: The strategy and the idea was trying to use the 159 00:08:51,120 --> 00:08:54,839 Speaker 7: app to have a deeper connection and interaction with our fans, 160 00:08:54,880 --> 00:08:59,520 Speaker 7: make it more interactive. So turning it from an editorial product, 161 00:08:59,760 --> 00:09:02,760 Speaker 7: which was a very good editorial product, to a more 162 00:09:03,120 --> 00:09:05,320 Speaker 7: interactive product, digital product. 163 00:09:05,160 --> 00:09:08,560 Speaker 3: With such a massive undertaking. I asked Stephen how it 164 00:09:08,600 --> 00:09:10,960 Speaker 3: all started once IBM got involved. 165 00:09:11,840 --> 00:09:14,720 Speaker 7: We started really with a very long couple of months 166 00:09:14,720 --> 00:09:18,480 Speaker 7: of discovery phase. So looking at the current app, looking 167 00:09:18,520 --> 00:09:21,640 Speaker 7: at fans, looking at what fans wanted from an app. 168 00:09:21,920 --> 00:09:23,800 Speaker 3: Tell me a little bit more about that phrase something 169 00:09:23,840 --> 00:09:26,680 Speaker 3: a fan wanted, What is it that the super fan 170 00:09:27,840 --> 00:09:31,560 Speaker 3: wasn't getting before? That was something that would tie them 171 00:09:31,559 --> 00:09:32,960 Speaker 3: even closer to Ferrari. 172 00:09:33,240 --> 00:09:38,040 Speaker 7: Having run some focused group, having having read that market research, 173 00:09:38,080 --> 00:09:40,920 Speaker 7: having spoken to fans, and being a fan. The strongest 174 00:09:41,040 --> 00:09:44,880 Speaker 7: inside is feridy. Fans and super fans want to be 175 00:09:45,040 --> 00:09:48,200 Speaker 7: part of something, want to belong to something, so they 176 00:09:48,240 --> 00:09:50,679 Speaker 7: want to be part of a community, and ultimately they 177 00:09:50,679 --> 00:09:53,880 Speaker 7: want to be part of a winning team, so they 178 00:09:53,920 --> 00:09:56,679 Speaker 7: want to feel closer and get access. 179 00:10:01,320 --> 00:10:05,000 Speaker 3: The way Stefano side. The opportunity wasn't with race days 180 00:10:05,520 --> 00:10:07,960 Speaker 3: when the cars are on the track, the tafosi are 181 00:10:08,000 --> 00:10:11,280 Speaker 3: already locked in, but there was an opportunity to engage 182 00:10:11,280 --> 00:10:14,360 Speaker 3: Ferrari fans on the other days of the week or 183 00:10:14,440 --> 00:10:15,400 Speaker 3: during the off season. 184 00:10:15,760 --> 00:10:19,200 Speaker 6: Formula One is so much more than just the race. 185 00:10:19,520 --> 00:10:20,760 Speaker 4: This is Fred Baker again. 186 00:10:21,040 --> 00:10:24,800 Speaker 6: What we can do is relive the race and bring 187 00:10:24,840 --> 00:10:28,040 Speaker 6: it to life after the fact. We can help them prepare, 188 00:10:28,080 --> 00:10:30,000 Speaker 6: we can help them relive the past, and we can 189 00:10:30,040 --> 00:10:33,200 Speaker 6: also bring the experience around race weekend to life as well. 190 00:10:33,760 --> 00:10:36,720 Speaker 3: That of course, maybe wonder how do you engage fans 191 00:10:36,760 --> 00:10:40,640 Speaker 3: when there's not a race happening. Baker says it all 192 00:10:40,640 --> 00:10:44,480 Speaker 3: comes back to data and information. Talk a little bit 193 00:10:44,440 --> 00:10:47,240 Speaker 3: about data collection, because you're talking about a brand with 194 00:10:47,880 --> 00:10:50,400 Speaker 3: tentacles everywhere, and you're trying to bring a lot of 195 00:10:50,440 --> 00:10:51,680 Speaker 3: that stuff together in the app. 196 00:10:52,200 --> 00:10:55,440 Speaker 6: This is an organization that has for decades used data 197 00:10:55,800 --> 00:10:59,960 Speaker 6: for racing the performance, it's not historically used that data 198 00:11:00,360 --> 00:11:04,080 Speaker 6: for everyone in the world to see. What we're trying 199 00:11:04,120 --> 00:11:05,560 Speaker 6: to do is expose as much of it as we 200 00:11:05,600 --> 00:11:08,360 Speaker 6: can to fans. So part of collecting the data, the 201 00:11:08,440 --> 00:11:10,880 Speaker 6: challenge with around how you go across all the disparate 202 00:11:11,040 --> 00:11:14,920 Speaker 6: different groups that collect data for different purposes. The team 203 00:11:14,960 --> 00:11:17,480 Speaker 6: that collects data on tires, the team that has data 204 00:11:17,559 --> 00:11:20,920 Speaker 6: on drivers, on whether or on competitors, and so on. 205 00:11:21,600 --> 00:11:23,760 Speaker 6: So you're trying to bring all that together and source 206 00:11:23,800 --> 00:11:26,720 Speaker 6: it and make sense of it and train our AI 207 00:11:26,800 --> 00:11:30,440 Speaker 6: to understand what it means, what things on team radio mean, 208 00:11:30,640 --> 00:11:35,160 Speaker 6: what nicknames mean, what abbreviations and slang and idiosyncrasies on 209 00:11:35,640 --> 00:11:38,960 Speaker 6: car specifics and track specifics and so on mean. And 210 00:11:39,000 --> 00:11:42,679 Speaker 6: you're also trying to design for something that is going 211 00:11:42,720 --> 00:11:47,120 Speaker 6: to be fan engaging but also appropriate to all the 212 00:11:47,160 --> 00:11:51,720 Speaker 6: sensitivities of the privacy that's necessary. So you want it 213 00:11:51,760 --> 00:11:53,480 Speaker 6: to be able to do all of that, collect all 214 00:11:53,480 --> 00:11:56,160 Speaker 6: the data, produce something for fans in an automated way. 215 00:11:57,120 --> 00:12:00,800 Speaker 3: But in order to design something to expertly engage the tafosi, 216 00:12:01,200 --> 00:12:04,600 Speaker 3: it's necessary to understand more about the passion and the 217 00:12:04,640 --> 00:12:08,959 Speaker 3: type of national identity behind the fan base. You need 218 00:12:09,160 --> 00:12:12,800 Speaker 3: to get inside the mind of the super fan. If 219 00:12:12,840 --> 00:12:15,040 Speaker 3: you wanted to meet some modern day tafosi in the 220 00:12:15,120 --> 00:12:17,720 Speaker 3: United States, you could head to a bar in Midtown 221 00:12:17,760 --> 00:12:22,200 Speaker 3: Manhattan called Fela. Every race day, Formula One fans gathered 222 00:12:22,240 --> 00:12:25,600 Speaker 3: Feala to cheer on their favorite drivers, their favorite teams, 223 00:12:25,640 --> 00:12:31,079 Speaker 3: and I mean really cheer. I sent our producer Jake 224 00:12:31,120 --> 00:12:33,760 Speaker 3: Harper to Feala on the day of the Canadian Grand 225 00:12:33,800 --> 00:12:36,880 Speaker 3: Prix so you could see the fandom up close. The 226 00:12:36,920 --> 00:12:39,840 Speaker 3: bar gets loud and so crowded it's hard to move. 227 00:12:40,440 --> 00:12:43,960 Speaker 3: Today the room is packed with Scuderia Ferrari HP fans. 228 00:12:44,280 --> 00:12:45,760 Speaker 6: Even your glasses are Ferrari. 229 00:12:45,840 --> 00:12:48,040 Speaker 3: I just noticed that Jake talked to a Ferrari fan 230 00:12:48,120 --> 00:12:51,600 Speaker 3: named Gino who was dressed head to toe in Ferrari's 231 00:12:51,640 --> 00:12:53,400 Speaker 3: signature red and black. 232 00:12:53,320 --> 00:12:54,960 Speaker 8: And my shoes are Ferraria. 233 00:12:55,480 --> 00:12:56,280 Speaker 6: Fully checked out. 234 00:12:56,360 --> 00:12:58,120 Speaker 5: They were making fun of me last time I was here. 235 00:12:58,160 --> 00:13:01,680 Speaker 5: They're like, is your underwear Ferrari? I texted my girlfriend, like, Babe, 236 00:13:01,720 --> 00:13:03,080 Speaker 5: I need Ferrari underwear. 237 00:13:03,960 --> 00:13:04,600 Speaker 6: Did you get it? 238 00:13:04,679 --> 00:13:06,680 Speaker 8: Not yet? Not yet, I'll work on it. 239 00:13:07,160 --> 00:13:09,800 Speaker 3: Gino's fandom started with Ferrari as a brand. 240 00:13:10,720 --> 00:13:11,600 Speaker 8: I love the cars. 241 00:13:11,720 --> 00:13:13,800 Speaker 5: I think the four fifty eight Scoodaria is like the 242 00:13:13,840 --> 00:13:15,680 Speaker 5: pinnacle of automotive engineering. 243 00:13:15,760 --> 00:13:18,200 Speaker 8: That's my dream car. The four point thirty with the 244 00:13:18,240 --> 00:13:21,160 Speaker 8: glass house for the engine. I mean, that's They're all gorgeous. 245 00:13:21,480 --> 00:13:24,160 Speaker 5: It's always been an aspiration of mine to own one, 246 00:13:25,040 --> 00:13:27,040 Speaker 5: so that naturally. 247 00:13:26,640 --> 00:13:28,360 Speaker 8: Made me gravitate towards Ferrari. 248 00:13:28,760 --> 00:13:32,120 Speaker 5: Even when the company I worked for a sponsored AMG Petronis, 249 00:13:32,600 --> 00:13:38,679 Speaker 5: I was secretly like hiding my taphosi at the races, like. 250 00:13:38,800 --> 00:13:41,880 Speaker 6: Clark Kenton Superman. You're just hiding the uniform underneath. 251 00:13:41,960 --> 00:13:43,520 Speaker 8: I love that. I love that. Yeah, I was wearing 252 00:13:43,520 --> 00:13:45,280 Speaker 8: a Ferrari shirt underneath my suit. 253 00:13:45,960 --> 00:13:48,400 Speaker 3: In one sense, Gino is typical of what Ferrari has 254 00:13:48,480 --> 00:13:51,160 Speaker 3: learned about its followers. A lot of F one fans, 255 00:13:51,440 --> 00:13:55,439 Speaker 3: especially newer fans, are fans of drivers, but the tafosi 256 00:13:55,960 --> 00:13:59,200 Speaker 3: love Ferrari. It's the oldest brand in Formula One, the 257 00:13:59,240 --> 00:14:01,800 Speaker 3: only team that is stuck around since the series was 258 00:14:01,840 --> 00:14:05,920 Speaker 3: founded in nineteen fifty. But in another sense, Gino is 259 00:14:06,000 --> 00:14:08,840 Speaker 3: not typical. He lives in New York. He can go 260 00:14:08,920 --> 00:14:11,400 Speaker 3: to Fala to celebrate F one with other tefosi. 261 00:14:11,600 --> 00:14:14,600 Speaker 5: I'm a big racing fan, and coming to this bar, 262 00:14:15,400 --> 00:14:17,080 Speaker 5: I found a bunch of people that were in a 263 00:14:17,360 --> 00:14:17,680 Speaker 5: F one. 264 00:14:17,720 --> 00:14:18,360 Speaker 8: Now I'm at this. 265 00:14:18,360 --> 00:14:22,160 Speaker 5: Bar every weekend just about with four or five friends 266 00:14:22,160 --> 00:14:23,600 Speaker 5: that I made just through racings. 267 00:14:25,120 --> 00:14:27,800 Speaker 3: But lots and lots of Scuderia Ferraris three hundred and 268 00:14:27,880 --> 00:14:30,680 Speaker 3: ninety six million fans don't live in a big city 269 00:14:30,680 --> 00:14:33,440 Speaker 3: with a Ferrari bar, and lots of those three hundred 270 00:14:33,440 --> 00:14:36,240 Speaker 3: and ninety six million fans aren't the kind of hardcore 271 00:14:36,320 --> 00:14:38,880 Speaker 3: fan who dressed head to toe in Ferrari's signature red 272 00:14:38,920 --> 00:14:43,840 Speaker 3: and black. A group that large is diverse, necessarily, and 273 00:14:43,880 --> 00:14:46,360 Speaker 3: one of the first tasks that IBM and Ferrari set 274 00:14:46,360 --> 00:14:49,400 Speaker 3: out to do was to understand the full range of 275 00:14:49,440 --> 00:14:54,560 Speaker 3: the tafosi phenomenon. People like Gino hardcore fans. They were easy, 276 00:14:54,800 --> 00:14:57,920 Speaker 3: They would follow Scuderia Ferrari HB anywhere it wanted to go. 277 00:14:58,480 --> 00:15:02,000 Speaker 3: But who else was out there? The most interesting addition 278 00:15:02,280 --> 00:15:04,600 Speaker 3: to the F one fan base were those who watched 279 00:15:04,600 --> 00:15:09,840 Speaker 3: the phenomenally successful Netflix documentary Drive to Survive. These tended 280 00:15:09,880 --> 00:15:13,800 Speaker 3: to be newcomers to the sport, more Americans than Europeans. 281 00:15:14,360 --> 00:15:18,560 Speaker 3: What was their emotional perspective? What did they want? Here's 282 00:15:18,600 --> 00:15:21,360 Speaker 3: Fred Baker again, the guy with the coolest job at IBM. 283 00:15:21,600 --> 00:15:23,320 Speaker 6: If I'm a passionate fan, I want to read a 284 00:15:23,360 --> 00:15:26,440 Speaker 6: totally different thing on the app to a casual fan 285 00:15:26,480 --> 00:15:30,000 Speaker 6: who is of the Netflix drive to Survive generation versus 286 00:15:30,480 --> 00:15:32,600 Speaker 6: you know, some really niche personas that we found that 287 00:15:32,880 --> 00:15:36,120 Speaker 6: are super interested but don't find it accessible yet until 288 00:15:36,160 --> 00:15:39,080 Speaker 6: we start to deliver to quite different needs that they have. 289 00:15:39,320 --> 00:15:42,440 Speaker 3: Working with IBM WATSONEX, Baker and his team began to 290 00:15:42,480 --> 00:15:47,560 Speaker 3: develop personas archetypes of all the possible kinds of Ferrari fans, 291 00:15:48,120 --> 00:15:50,360 Speaker 3: because if Ferrari wanted to get better at talking to 292 00:15:50,400 --> 00:15:53,600 Speaker 3: their fans, they had to understand who the fans were, 293 00:15:54,240 --> 00:15:57,960 Speaker 3: and the personas are helping Ferrari and IBM create an 294 00:15:58,000 --> 00:16:01,800 Speaker 3: app that caters to the tafosi in all their iterations. 295 00:16:02,960 --> 00:16:05,280 Speaker 4: How many personas did you come up with? 296 00:16:05,960 --> 00:16:08,240 Speaker 6: I think we had over ten in the end, maybe 297 00:16:08,280 --> 00:16:10,840 Speaker 6: a dozen. And this is different, yeah, archetypes of people. 298 00:16:10,840 --> 00:16:13,560 Speaker 6: Even that process is helped by AI, so we train 299 00:16:13,640 --> 00:16:16,480 Speaker 6: AI to help us develop out a persona. We can 300 00:16:16,520 --> 00:16:20,840 Speaker 6: get really detailed as to what each archetype is and 301 00:16:20,880 --> 00:16:24,160 Speaker 6: their hobbies and backgrounds and so on. So our own 302 00:16:24,160 --> 00:16:28,120 Speaker 6: watsonex helped us in developing those personas, like our research 303 00:16:28,440 --> 00:16:31,800 Speaker 6: helped us uncover a segment of middle aged women in 304 00:16:31,880 --> 00:16:35,920 Speaker 6: China who Ferrari is a real status symbol and they're 305 00:16:35,960 --> 00:16:38,480 Speaker 6: really interested in the Scuderia Ferrari brand and now they 306 00:16:38,520 --> 00:16:42,680 Speaker 6: can engage more with it, but it wasn't yet accessible 307 00:16:42,760 --> 00:16:44,880 Speaker 6: or inclusive enough for them to feel comfortable doing so. 308 00:16:45,120 --> 00:16:48,720 Speaker 6: Real spectrums of fans across those dozen personas that we 309 00:16:48,760 --> 00:16:49,720 Speaker 6: had to design for. 310 00:16:50,360 --> 00:16:54,440 Speaker 3: Give me some more examples of personas. Can you give 311 00:16:54,440 --> 00:16:55,840 Speaker 3: you a couple more just so get a flavor? 312 00:16:56,000 --> 00:16:58,080 Speaker 6: Yeah? Sure. So. The other obvious one is the Drive 313 00:16:58,120 --> 00:17:01,360 Speaker 6: to Survive fan and that they're probably not a diehard 314 00:17:01,600 --> 00:17:03,760 Speaker 6: all their lives Scuterier Ferrari fan, but they've really got 315 00:17:03,760 --> 00:17:06,200 Speaker 6: into the more social side of formula one that's been 316 00:17:06,680 --> 00:17:09,520 Speaker 6: born out of the really popular series Drive to Survive 317 00:17:09,600 --> 00:17:12,680 Speaker 6: on Netflix. You then have gamer personas who are into 318 00:17:13,400 --> 00:17:17,159 Speaker 6: esports is growing massively in motorsport, and they're probably not 319 00:17:17,200 --> 00:17:19,600 Speaker 6: necessarily into the real life racing quite so much, but 320 00:17:19,600 --> 00:17:21,520 Speaker 6: they're certainly into gaming, So how do you appeal to 321 00:17:21,560 --> 00:17:24,040 Speaker 6: them then casual fans who are sort of into the 322 00:17:24,119 --> 00:17:27,359 Speaker 6: luxury of scudo Ferrari but not the sport necessarily do 323 00:17:27,480 --> 00:17:30,760 Speaker 6: the personas have names? Yeah, I mean we give them 324 00:17:30,840 --> 00:17:34,360 Speaker 6: human names. So we had a Max, we had a Alfonso. 325 00:17:34,600 --> 00:17:35,960 Speaker 6: I think we had a Pedro. 326 00:17:36,760 --> 00:17:38,359 Speaker 4: Two women in China. 327 00:17:38,560 --> 00:17:40,760 Speaker 3: Is she watching F one or is she interested more 328 00:17:40,800 --> 00:17:42,359 Speaker 3: in the brand and what it signifies? 329 00:17:42,520 --> 00:17:45,119 Speaker 6: Yeah, more in the brand and being part of a community. 330 00:17:45,440 --> 00:17:48,080 Speaker 6: If I'm that persona in China, then I probably don't 331 00:17:48,080 --> 00:17:52,280 Speaker 6: feel like I belong to it truly yet, but I'd 332 00:17:52,280 --> 00:17:54,120 Speaker 6: love to feel like I do, so I could start 333 00:17:54,160 --> 00:17:56,600 Speaker 6: to become a part of a digital community, learn more 334 00:17:56,640 --> 00:18:01,119 Speaker 6: about the brand, probably get access to exclusive merchandise, or 335 00:18:01,880 --> 00:18:04,800 Speaker 6: you know, if I can't necessarily own a Ferrari car, 336 00:18:04,800 --> 00:18:07,119 Speaker 6: which let's face it, not many people can. And if 337 00:18:07,119 --> 00:18:09,000 Speaker 6: we're relyinged only on the people who can own a car, 338 00:18:09,040 --> 00:18:11,320 Speaker 6: then we're probably not going to get much engagement. So 339 00:18:11,359 --> 00:18:13,760 Speaker 6: how do we make others feel that they're still a 340 00:18:13,800 --> 00:18:15,359 Speaker 6: part of that community. 341 00:18:16,119 --> 00:18:17,800 Speaker 3: This is what I mean when I say the task 342 00:18:17,880 --> 00:18:20,520 Speaker 3: of relating to the Ferrari fan base is a data 343 00:18:20,640 --> 00:18:25,360 Speaker 3: and information problem. It's about collecting, organizing, and analyzing the 344 00:18:25,359 --> 00:18:29,000 Speaker 3: needs and wants of an enormous pool of people and 345 00:18:29,080 --> 00:18:32,520 Speaker 3: speaking to each of them in their own emotional language. 346 00:18:34,040 --> 00:18:36,960 Speaker 3: Giving all the work Fred put into understanding Ferrari's fan base, 347 00:18:37,400 --> 00:18:40,480 Speaker 3: I was curious to know how his framework would categorize me. 348 00:18:41,600 --> 00:18:44,439 Speaker 3: I want to figure out which persona I am. So 349 00:18:44,680 --> 00:18:47,399 Speaker 3: I'll describe to you my relationship to Ferrari and you 350 00:18:47,480 --> 00:18:52,920 Speaker 3: tell me so what I am is a huge car, 351 00:18:53,119 --> 00:18:59,199 Speaker 3: not so like all cars. Yeah, obsessively collect on a 352 00:18:59,280 --> 00:19:05,280 Speaker 3: very limited stage vintage cars, read serious car magazines, spend 353 00:19:05,280 --> 00:19:08,040 Speaker 3: a lot of time my car websites. Have a historical 354 00:19:08,080 --> 00:19:10,480 Speaker 3: relationship to have one. Because I grew up with Nikki 355 00:19:10,560 --> 00:19:15,480 Speaker 3: Lauda battling James Hunt in loudest for our years. I 356 00:19:15,520 --> 00:19:19,480 Speaker 3: have a grit nostalgic connection. Went to Italy with my 357 00:19:19,720 --> 00:19:23,199 Speaker 3: nephew and went to the Ferrari factory and rented one 358 00:19:23,240 --> 00:19:26,760 Speaker 3: of those to drive around, you know, and. 359 00:19:26,760 --> 00:19:27,960 Speaker 4: I follow that F one. But I wouldn't. 360 00:19:28,000 --> 00:19:30,320 Speaker 3: I would don't think I would ever go to an 361 00:19:30,480 --> 00:19:33,040 Speaker 3: I wouldn't fly to Miami for F one. Miami, I 362 00:19:33,040 --> 00:19:36,399 Speaker 3: wouldn't go that far, and I don't have time to 363 00:19:36,440 --> 00:19:38,720 Speaker 3: watch a F one on TV on a regular basis, 364 00:19:39,160 --> 00:19:43,119 Speaker 3: but I'm interested. And I have a red Frari T 365 00:19:43,240 --> 00:19:46,000 Speaker 3: shirt which I've been known to wear, and if you, 366 00:19:46,320 --> 00:19:48,320 Speaker 3: if I ever got really rich, would I buy it 367 00:19:48,359 --> 00:19:48,560 Speaker 3: for Aur? 368 00:19:48,720 --> 00:19:48,880 Speaker 6: Yes? 369 00:19:48,920 --> 00:19:49,240 Speaker 9: I would. 370 00:19:49,880 --> 00:19:53,440 Speaker 4: Okay, So where am I? Where am I in your breakdown? 371 00:19:53,720 --> 00:19:57,200 Speaker 6: Yeah? I think you're probably a combination of the I 372 00:19:57,200 --> 00:20:00,760 Speaker 6: think it's casual loyalist. It's not going to not going 373 00:20:00,840 --> 00:20:03,840 Speaker 6: to overtly go out of their way to sort of 374 00:20:03,880 --> 00:20:05,879 Speaker 6: spend money on the racing, but they are loyal to 375 00:20:05,920 --> 00:20:08,600 Speaker 6: the Ferrari brand and they have nostalgia with it or 376 00:20:08,640 --> 00:20:11,240 Speaker 6: whatever it might be. And then the luxury enthusiasts as well, 377 00:20:11,400 --> 00:20:14,840 Speaker 6: so and that type of fan. You're right, We're probably 378 00:20:14,840 --> 00:20:16,680 Speaker 6: not going to engage you by doing a ton more 379 00:20:16,720 --> 00:20:19,320 Speaker 6: on race weekend, but we can engage you by bringing 380 00:20:20,119 --> 00:20:25,600 Speaker 6: this hugely rich amount of archive material, footage, feelings and 381 00:20:26,200 --> 00:20:31,160 Speaker 6: past drivers of yesteryear, by bringing them to life. 382 00:20:36,680 --> 00:20:41,399 Speaker 9: In zero an app that you saw another brand doing 383 00:20:41,800 --> 00:20:43,600 Speaker 9: that served as a kind of model. I don't mean 384 00:20:43,640 --> 00:20:47,159 Speaker 9: within F one, I'm talking about it from any other film. 385 00:20:47,400 --> 00:20:48,040 Speaker 4: On top of. 386 00:20:47,960 --> 00:20:53,760 Speaker 7: Being a very sport passionate. I'm, let's say, a marketing passionate, 387 00:20:53,840 --> 00:20:57,520 Speaker 7: a digital passionate guy. So I have a lot of apps, 388 00:20:57,600 --> 00:20:59,680 Speaker 7: and he also for my job. I tried to look 389 00:20:59,680 --> 00:21:01,600 Speaker 7: at the and different apps. 390 00:21:01,760 --> 00:21:04,800 Speaker 3: As we were talking, I was thinking about Strava. I'm 391 00:21:04,840 --> 00:21:07,680 Speaker 3: a huge Stravahead. It's my favorite app. If you don't 392 00:21:07,720 --> 00:21:10,760 Speaker 3: already know, Strava is used by millions of active people 393 00:21:10,800 --> 00:21:13,320 Speaker 3: around the world. I'm a runner, and the app shows 394 00:21:13,359 --> 00:21:16,160 Speaker 3: me a map of where I went, how fast I ran, 395 00:21:16,600 --> 00:21:18,679 Speaker 3: what my heart ray was, what the weather was, on 396 00:21:18,760 --> 00:21:19,120 Speaker 3: and on. 397 00:21:19,400 --> 00:21:21,240 Speaker 4: Are you're a cyclostore runner, I'm a runner. 398 00:21:21,240 --> 00:21:21,800 Speaker 6: I'm a runner. 399 00:21:22,040 --> 00:21:26,120 Speaker 7: I run marathons and ultra marathons. I did let's see 400 00:21:26,160 --> 00:21:27,280 Speaker 7: one hundred kilometers. 401 00:21:27,400 --> 00:21:31,200 Speaker 3: As it turns out, Stefano is a Stravahead too. Right 402 00:21:31,240 --> 00:21:33,679 Speaker 3: after I spoke to him, I followed him on Strava. 403 00:21:33,760 --> 00:21:35,800 Speaker 3: He and I run roughly the same distance every day 404 00:21:35,840 --> 00:21:38,040 Speaker 3: at the same pace, and if I'm ever in Milan, 405 00:21:38,280 --> 00:21:40,320 Speaker 3: I'm almost certainly going to look him up to see 406 00:21:40,320 --> 00:21:41,960 Speaker 3: if you'll take me out on one of his favorite 407 00:21:42,000 --> 00:21:45,000 Speaker 3: routes through the city. This is what I love about Strava. 408 00:21:45,320 --> 00:21:47,959 Speaker 3: You can find people to run with and interact with 409 00:21:48,400 --> 00:21:51,600 Speaker 3: Strava is a community of like minded people and for 410 00:21:51,640 --> 00:21:54,880 Speaker 3: those like me, the Strava app becomes a regular part 411 00:21:54,960 --> 00:21:58,119 Speaker 3: of my daily routine, and that's what Stefano wanted for 412 00:21:58,160 --> 00:22:01,480 Speaker 3: the Ferrari app. Are you interested allowing creating sort of 413 00:22:01,560 --> 00:22:05,760 Speaker 3: robust forums for our evans to communicate with each other? 414 00:22:07,080 --> 00:22:10,520 Speaker 7: I think you have to work in three directions. So 415 00:22:10,640 --> 00:22:14,840 Speaker 7: direction number one is a Ferrari to fans, so providing 416 00:22:14,880 --> 00:22:18,280 Speaker 7: them something which is compelling, which has value, and this 417 00:22:18,520 --> 00:22:20,320 Speaker 7: I think we're already doing and we're working on it. 418 00:22:20,760 --> 00:22:25,679 Speaker 7: Second way is fans to Ferrari, so help like allowing 419 00:22:25,720 --> 00:22:28,679 Speaker 7: fans to better interact with us, which was something we 420 00:22:28,720 --> 00:22:31,640 Speaker 7: were not doing with the previous app. For example, we've 421 00:22:31,800 --> 00:22:35,040 Speaker 7: just introduced two features which are polls, so basic ones, 422 00:22:35,080 --> 00:22:38,639 Speaker 7: but polls and the possibility like the submit your message feature. 423 00:22:38,880 --> 00:22:42,040 Speaker 7: So really to work on the way fans to Ferrari. 424 00:22:42,200 --> 00:22:45,240 Speaker 7: And then the third important way to build a community 425 00:22:45,280 --> 00:22:48,640 Speaker 7: and nature community is like fans to fans. So if 426 00:22:48,680 --> 00:22:51,560 Speaker 7: you were able to work on those trae dimensions of 427 00:22:51,640 --> 00:22:54,359 Speaker 7: Ferrari to fans, fans to Ferrari and fans to fans, 428 00:22:55,000 --> 00:22:58,399 Speaker 7: that's how you could really create a strong community and 429 00:22:58,480 --> 00:23:01,119 Speaker 7: start really monetizing and create in value. I think we 430 00:23:01,119 --> 00:23:04,320 Speaker 7: are very strong in the first dimension right now, we're 431 00:23:04,359 --> 00:23:07,400 Speaker 7: building the second one, so fans to Ferrari and then 432 00:23:07,440 --> 00:23:10,600 Speaker 7: definitely the third one has to be there in order 433 00:23:10,680 --> 00:23:13,040 Speaker 7: to have a complete community engagement. 434 00:23:13,600 --> 00:23:15,080 Speaker 4: So let's talk about results. 435 00:23:15,520 --> 00:23:18,720 Speaker 3: Scuddery of Ferrari HP launched the new app at the 436 00:23:18,760 --> 00:23:23,000 Speaker 3: Miami Grand Prix in twenty twenty five, incorporating AI elements 437 00:23:23,080 --> 00:23:28,840 Speaker 3: and tailoring it to those archetypes. Was talking about, are 438 00:23:28,880 --> 00:23:32,120 Speaker 3: more people using the app? Are users spending more time 439 00:23:32,160 --> 00:23:34,159 Speaker 3: on it than they did on the older version of 440 00:23:34,200 --> 00:23:34,560 Speaker 3: the app? 441 00:23:35,160 --> 00:23:38,920 Speaker 7: Yes, we doubled these months the daily active users we 442 00:23:38,920 --> 00:23:41,600 Speaker 7: were having last season, so compared to the average of 443 00:23:41,680 --> 00:23:44,600 Speaker 7: twenty twenty four season, we have more than double of 444 00:23:44,840 --> 00:23:50,040 Speaker 7: daily active users. Also, we're doubling normal months down downloads, 445 00:23:50,080 --> 00:23:53,440 Speaker 7: so we did in these months more than two times 446 00:23:53,480 --> 00:23:56,040 Speaker 7: the download we are doing in a normal months. We 447 00:23:56,320 --> 00:24:00,639 Speaker 7: are increasing by thirty five percent the average times. So 448 00:24:00,720 --> 00:24:01,600 Speaker 7: keep your eyes out good. 449 00:24:02,880 --> 00:24:10,200 Speaker 3: If you build the right app, they will come for generations. 450 00:24:10,359 --> 00:24:14,080 Speaker 3: Fans of all varieties have met in public places, the 451 00:24:14,119 --> 00:24:17,199 Speaker 3: stands of stadiums, in bars to watch races, and matches 452 00:24:17,200 --> 00:24:20,560 Speaker 3: on television. But there's a chance now for fandom to 453 00:24:20,600 --> 00:24:24,240 Speaker 3: exist on a higher and broader level, for a community 454 00:24:24,400 --> 00:24:27,479 Speaker 3: to be created over the Internet, even when the fans 455 00:24:27,520 --> 00:24:34,480 Speaker 3: are vastly different people who live vast distances apart. I 456 00:24:34,480 --> 00:24:37,760 Speaker 3: can imagine Gino in his Ferrari Red and Black, using 457 00:24:37,800 --> 00:24:40,399 Speaker 3: the scoodery of Ferrari app relating to me as I 458 00:24:40,440 --> 00:24:43,240 Speaker 3: relive my memories of Niki Lauder from the nineteen seventies. 459 00:24:44,000 --> 00:24:45,920 Speaker 3: Maybe I could use the app to learn something from 460 00:24:45,920 --> 00:24:49,520 Speaker 3: the woman in China, the Tafosie newcomer, or some seventeen 461 00:24:49,560 --> 00:24:52,320 Speaker 3: year old who got sucked in first by drive to survive. 462 00:24:53,240 --> 00:24:57,320 Speaker 3: I can imagine myself as part of that vision, taking 463 00:24:57,400 --> 00:25:14,000 Speaker 3: my lifelong obsession to the next level. Smart Talks with 464 00:25:14,040 --> 00:25:18,440 Speaker 3: IBM is produced by Matt Ramano, Amy Gains, McQuaid, Trina Menino, 465 00:25:18,960 --> 00:25:23,320 Speaker 3: and Jake Harper were edited by Lacy Roberts. Engineering by 466 00:25:23,440 --> 00:25:27,680 Speaker 3: Nina Bird Lawrence, mastering by Sarah Bruguier, music by Gramoscope, 467 00:25:27,920 --> 00:25:32,639 Speaker 3: Strategy by Tatiana Lieberman and Cassidy Meyer. Special thanks to 468 00:25:32,680 --> 00:25:36,760 Speaker 3: Scuderia Ferrari HP and the bar and restaurant Feala in 469 00:25:36,840 --> 00:25:39,800 Speaker 3: New York City Smart Talks with IBM is a production 470 00:25:39,880 --> 00:25:44,119 Speaker 3: of Pushkin Industries and Ruby Studio at iHeartMedia. To find 471 00:25:44,160 --> 00:25:48,520 Speaker 3: more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, 472 00:25:48,640 --> 00:25:53,240 Speaker 3: or wherever you listen to podcasts. I'm Malcolm Glappo. This 473 00:25:53,400 --> 00:25:56,919 Speaker 3: is a paid advertisement from IBM. The conversations on this 474 00:25:57,000 --> 00:26:12,720 Speaker 3: podcast don't necessarily represent IBM's position, strategies or opinions. I 475 00:26:12,760 --> 00:26:16,040 Speaker 3: asked Fred Baker to come up with a hypothetical something 476 00:26:16,119 --> 00:26:20,160 Speaker 3: that could fulfill my childhood dreams, something that this type 477 00:26:20,160 --> 00:26:23,560 Speaker 3: of technology could theoretically do that might appeal to a 478 00:26:23,600 --> 00:26:26,680 Speaker 3: fan like me, someone who's interested in the sport went 479 00:26:26,760 --> 00:26:31,480 Speaker 3: back fifty years. He said, what about using AI to 480 00:26:31,520 --> 00:26:33,000 Speaker 3: bring historical cars to. 481 00:26:33,000 --> 00:26:36,680 Speaker 6: Life, bringing to live cars of the past and allows 482 00:26:36,720 --> 00:26:42,760 Speaker 6: fans to simulate a nineteen fifty Ferrari race versus nineteen 483 00:26:42,840 --> 00:26:45,000 Speaker 6: seventy one to see who which car would be faster. 484 00:26:45,280 --> 00:26:46,600 Speaker 6: So it's those sorts of trade offs. 485 00:26:47,280 --> 00:26:49,000 Speaker 4: Wait, you could do it. Wait you could do that? 486 00:26:49,040 --> 00:26:51,720 Speaker 3: Tell me about last thing you said you can run 487 00:26:51,760 --> 00:26:54,520 Speaker 3: simulations race simulations out of the app. 488 00:26:54,960 --> 00:26:57,080 Speaker 6: You can't out of the app at this point, so 489 00:26:58,359 --> 00:27:01,760 Speaker 6: I know, potentially, potentially, Yeah, it can you know, simulates 490 00:27:02,400 --> 00:27:04,360 Speaker 6: based on a whole range of factors that we can 491 00:27:04,359 --> 00:27:05,320 Speaker 6: feed and train it on. 492 00:27:06,200 --> 00:27:09,520 Speaker 3: Wait, so I could hypothetically you could allow me to 493 00:27:09,600 --> 00:27:15,560 Speaker 3: compare Niki Lauder, for example, to a contemporary driver, and 494 00:27:15,600 --> 00:27:17,520 Speaker 3: I could say if I put Nicki Lauder in a 495 00:27:17,520 --> 00:27:21,080 Speaker 3: contemporary car, what you're saying is that there is a 496 00:27:21,119 --> 00:27:26,840 Speaker 3: scenario where I could recreate that era in modern cars 497 00:27:27,240 --> 00:27:30,480 Speaker 3: and get a sense of how my childhood heroes were performing, 498 00:27:31,240 --> 00:27:32,520 Speaker 3: would have performed a break day? 499 00:27:32,920 --> 00:27:35,119 Speaker 6: Yeah? Yeah, So you can analyze and understand how you 500 00:27:35,160 --> 00:27:37,879 Speaker 6: would rank all drivers of all time based on the 501 00:27:37,920 --> 00:27:39,800 Speaker 6: different traits of a driver, right, So you can say 502 00:27:39,800 --> 00:27:42,240 Speaker 6: who's the best late breaking, Who's who was typically the 503 00:27:42,280 --> 00:27:44,720 Speaker 6: best on a tight track with limited overtaking opportunities, who 504 00:27:44,760 --> 00:27:46,560 Speaker 6: was the best overtaker, who was the best of all 505 00:27:46,560 --> 00:27:49,320 Speaker 6: these traits. You then apply those traits and rankings to 506 00:27:49,680 --> 00:27:52,879 Speaker 6: different tracks and different cars where you know different Some 507 00:27:52,960 --> 00:27:55,200 Speaker 6: different cars are better for a late breaker, some different 508 00:27:55,240 --> 00:27:57,159 Speaker 6: cars are better for a you know, on straits and 509 00:27:57,160 --> 00:28:00,240 Speaker 6: so on. So you can simulate, You could hypothetically our 510 00:28:00,280 --> 00:28:03,359 Speaker 6: fans to simulate any scenario. You could say, who's going 511 00:28:03,400 --> 00:28:07,840 Speaker 6: to win in Monaco on a nineteen eighty model car. 512 00:28:08,560 --> 00:28:10,760 Speaker 6: You can put a current driver in a nineteen eighty 513 00:28:10,800 --> 00:28:12,720 Speaker 6: car equally, so you can do all sorts of fun 514 00:28:12,840 --> 00:28:13,600 Speaker 6: and simulations. 515 00:28:15,080 --> 00:28:16,240 Speaker 4: And that's just the beginning.