1 00:00:04,880 --> 00:00:10,840 Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. 2 00:00:12,560 --> 00:00:14,200 Speaker 2: Today, we are witnessed. 3 00:00:13,720 --> 00:00:16,960 Speaker 1: To one of those rare moments in history, the rise 4 00:00:17,079 --> 00:00:20,880 Speaker 1: of an innovative technology with the potential to radically transform 5 00:00:20,920 --> 00:00:26,640 Speaker 1: business and society forever. That technology, of course, is artificial intelligence, 6 00:00:26,840 --> 00:00:29,600 Speaker 1: and it's the central focus for this new season of 7 00:00:29,640 --> 00:00:33,720 Speaker 1: Smart Talks with IBM. Join hosts from your favorite Pushkin 8 00:00:33,840 --> 00:00:37,360 Speaker 1: podcasts as a talk with industry experts and leaders to 9 00:00:37,440 --> 00:00:41,199 Speaker 1: explore how businesses can integrate AI into their workflows and 10 00:00:41,360 --> 00:00:44,479 Speaker 1: help drive real change in this new era of AI, 11 00:00:45,040 --> 00:00:47,680 Speaker 1: and of course, host Malcolm Gladwell will be there to 12 00:00:47,720 --> 00:00:50,320 Speaker 1: guide you through the season and throw in his two 13 00:00:50,360 --> 00:00:53,400 Speaker 1: cents as well. Look out for new episodes of Smart 14 00:00:53,400 --> 00:00:56,600 Speaker 1: Talks with IBM every other week on the iHeartRadio app, 15 00:00:56,800 --> 00:01:00,400 Speaker 1: Apple Podcasts, or wherever you get your podcasts, and learn 16 00:01:00,440 --> 00:01:13,640 Speaker 1: more at IBM dot com, slash smart Talks. 17 00:01:11,200 --> 00:01:20,720 Speaker 3: Pushkin Hello, Hello, Welcome to Smart Talks with IBM, a 18 00:01:20,800 --> 00:01:25,800 Speaker 3: podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. 19 00:01:26,520 --> 00:01:29,839 Speaker 3: This season, we're diving back into the world of artificial intelligence, 20 00:01:30,240 --> 00:01:33,080 Speaker 3: but with a focus on the powerful concept of open 21 00:01:33,480 --> 00:01:38,480 Speaker 3: its possibilities implications and misconceptions. We'll look at openness from 22 00:01:38,520 --> 00:01:41,440 Speaker 3: a variety of angles and explore how the concept is 23 00:01:41,480 --> 00:01:45,360 Speaker 3: already reshaping industries, ways of doing business, and our very 24 00:01:45,440 --> 00:01:50,680 Speaker 3: notion of what's possible. I'm particularly excited for today's guest, 25 00:01:51,000 --> 00:01:55,160 Speaker 3: Brian Ryerson. He's Senior director of Digital Strategy at the 26 00:01:55,280 --> 00:01:58,760 Speaker 3: US Tennis Association, helping to oversee one of the most 27 00:01:59,000 --> 00:02:02,200 Speaker 3: iconic events in the world of sports, the US Open. 28 00:02:03,200 --> 00:02:06,520 Speaker 3: Brian sat down with Pushkin's own Jacob Goldstein, host of 29 00:02:06,560 --> 00:02:11,000 Speaker 3: the podcast What's Your Problem. A veteran business journalist, Jacob 30 00:02:11,040 --> 00:02:13,920 Speaker 3: has reported for The Wall Street Journal, the Miami Herald, 31 00:02:14,160 --> 00:02:17,799 Speaker 3: and was a longtime host of the NPR program Planet Money. 32 00:02:18,960 --> 00:02:22,320 Speaker 3: IBM has been the official technology partner of the US 33 00:02:22,320 --> 00:02:26,519 Speaker 3: Tennis Association for more than thirty years, and the more 34 00:02:26,560 --> 00:02:31,000 Speaker 3: recent evolution into generative AI has enhanced the world class 35 00:02:31,160 --> 00:02:35,560 Speaker 3: digital experiences that help more than fifteen million fans from 36 00:02:35,600 --> 00:02:39,160 Speaker 3: all over the world enjoy the US Open Tennis Championships. 37 00:02:39,600 --> 00:02:43,560 Speaker 3: In this episode, we will explore how generative AI is 38 00:02:43,639 --> 00:02:48,280 Speaker 3: being used to generate match insights, spoken commentary for match highlights, 39 00:02:48,560 --> 00:02:52,399 Speaker 3: and postmatch summaries at scale for fans to enjoy through 40 00:02:52,400 --> 00:02:55,760 Speaker 3: the US Open app and website. We'll explore how these 41 00:02:55,800 --> 00:03:00,240 Speaker 3: AI solutions enable the editorial team to cover more of 42 00:03:00,280 --> 00:03:04,440 Speaker 3: the tournament than ever before, bringing fans even closer to 43 00:03:04,520 --> 00:03:06,880 Speaker 3: the game they love, and we'll learn more about one 44 00:03:06,880 --> 00:03:11,040 Speaker 3: of the engines behind this AI powered content creation, a 45 00:03:11,160 --> 00:03:15,000 Speaker 3: large language model from the IBM Granite family, which is 46 00:03:15,040 --> 00:03:20,320 Speaker 3: trained and maintained using the wantsonex AI and data platform. Okay, 47 00:03:21,040 --> 00:03:21,760 Speaker 3: let's dive in. 48 00:03:23,000 --> 00:03:24,080 Speaker 2: Brian, Welcome to the show. 49 00:03:24,360 --> 00:03:25,800 Speaker 4: Thanks for having me. I'm excited to be here. 50 00:03:26,200 --> 00:03:27,720 Speaker 2: Can you say your name and your job. 51 00:03:28,080 --> 00:03:30,960 Speaker 4: Yeah, I'm a Brian Ryerson. I'm senior director of Digital 52 00:03:30,960 --> 00:03:32,160 Speaker 4: Strategy at the USTA. 53 00:03:32,520 --> 00:03:34,840 Speaker 2: Dumb question, what's the USTA. 54 00:03:34,639 --> 00:03:36,839 Speaker 4: The US Tennis Association. 55 00:03:37,120 --> 00:03:39,720 Speaker 2: And tell me about the USTA, Like, what is it? 56 00:03:40,120 --> 00:03:42,720 Speaker 4: Yeah? So the USTA is the governing body of tennis 57 00:03:42,720 --> 00:03:46,040 Speaker 4: in the US. Or mission is to grow the sport 58 00:03:46,080 --> 00:03:49,440 Speaker 4: of tennis across the US at all levels. Really, I 59 00:03:49,440 --> 00:03:51,600 Speaker 4: would say we're more like a health and wellness company 60 00:03:51,600 --> 00:03:54,200 Speaker 4: where tennis is the means to health and wellness. And 61 00:03:54,240 --> 00:03:56,720 Speaker 4: then the US Open is kind of our tenth pole 62 00:03:56,800 --> 00:03:59,400 Speaker 4: event that happens iver Ear and Flushing Meadows and is 63 00:03:59,400 --> 00:04:01,480 Speaker 4: really our chance. It's the showcase the sport of tennis 64 00:04:01,480 --> 00:04:03,920 Speaker 4: at its highest level to fans all around the world. 65 00:04:04,040 --> 00:04:06,200 Speaker 2: Yeah, I mean the US Open. I assume most people 66 00:04:06,240 --> 00:04:08,400 Speaker 2: know this, but it's Grand Slam. It's one of the 67 00:04:08,440 --> 00:04:11,520 Speaker 2: what four biggest tennis tournaments in the world. 68 00:04:11,640 --> 00:04:14,840 Speaker 4: Yes, yeah, every year, we especially the past couple of years, 69 00:04:14,880 --> 00:04:18,160 Speaker 4: we've seen immense growth and you know, we are very 70 00:04:18,240 --> 00:04:20,280 Speaker 4: hopeful this year and our big goals. Have over a 71 00:04:20,320 --> 00:04:22,720 Speaker 4: million fans on site during the three week window this year, 72 00:04:22,760 --> 00:04:24,960 Speaker 4: so it's an amazing event. I always say it's a 73 00:04:24,960 --> 00:04:27,840 Speaker 4: food and wine festival where tennis is the main attraction, 74 00:04:28,240 --> 00:04:30,479 Speaker 4: and it's a really fun, unique atmosphere. 75 00:04:31,160 --> 00:04:33,000 Speaker 2: How did you get into the tennis business. 76 00:04:33,160 --> 00:04:35,839 Speaker 4: It's a great question. It's not where I thought i'd 77 00:04:35,920 --> 00:04:38,360 Speaker 4: end up for especially being there for fourteen years. So 78 00:04:38,520 --> 00:04:41,880 Speaker 4: I was a marketing and technology major in school, and 79 00:04:42,600 --> 00:04:45,200 Speaker 4: I also played college lacrosse and sports was always a 80 00:04:45,200 --> 00:04:46,880 Speaker 4: big part of my life and always wanted to be 81 00:04:46,880 --> 00:04:49,320 Speaker 4: in the sports and entertainment world. I'm here from the 82 00:04:49,320 --> 00:04:50,880 Speaker 4: New York area. This is where I grew up. So 83 00:04:50,920 --> 00:04:53,320 Speaker 4: I moved back home and had a few friends who 84 00:04:53,400 --> 00:04:56,000 Speaker 4: worked there, and I started out more on the number 85 00:04:56,120 --> 00:04:58,360 Speaker 4: side of things and really digital analytics. It was really 86 00:04:58,360 --> 00:05:01,440 Speaker 4: the start of We're in Facebook and Twitter is just 87 00:05:01,480 --> 00:05:04,800 Speaker 4: starting and digital marketing and all of that. And you know, 88 00:05:04,839 --> 00:05:07,400 Speaker 4: I went to my first US Open not really knowing 89 00:05:07,440 --> 00:05:10,600 Speaker 4: what to expect, and again, I think the atmosphere kind 90 00:05:10,600 --> 00:05:12,960 Speaker 4: of captivated me and hooked me in. And I've been 91 00:05:12,960 --> 00:05:14,080 Speaker 4: there now fourteen years. 92 00:05:14,520 --> 00:05:18,760 Speaker 2: And so your title is Digital Director. What does that mean? 93 00:05:18,800 --> 00:05:19,400 Speaker 2: What's your job? 94 00:05:19,640 --> 00:05:22,640 Speaker 4: Yeah, so it's an interesting one because it's tough to 95 00:05:22,720 --> 00:05:25,240 Speaker 4: explain to folks who are not in the weeds on 96 00:05:25,320 --> 00:05:27,719 Speaker 4: all things us Open or even in the sports world. 97 00:05:27,720 --> 00:05:31,440 Speaker 4: But really I oversee all of our consumer facing digital property. 98 00:05:31,560 --> 00:05:34,040 Speaker 4: So that's the us open dot org, our website built 99 00:05:34,040 --> 00:05:36,920 Speaker 4: by IBM, as well as our mobile app. I oversee 100 00:05:36,920 --> 00:05:42,000 Speaker 4: our content strategy, our sponsorship integrations. So really anything consumer 101 00:05:42,080 --> 00:05:45,120 Speaker 4: facing that happens on the web is under my purview. 102 00:05:45,400 --> 00:05:48,200 Speaker 4: Even some of our new platform extensions and gaming and 103 00:05:48,240 --> 00:05:51,440 Speaker 4: things like that. Anything that you can physically interact with 104 00:05:51,839 --> 00:05:53,240 Speaker 4: is kind of under my purview. 105 00:05:54,000 --> 00:05:57,840 Speaker 2: And so you've been there now for fourteen ISSU years, 106 00:05:58,000 --> 00:06:01,640 Speaker 2: which in the digital world is a long time. How 107 00:06:01,680 --> 00:06:06,040 Speaker 2: has that sort of digital experience of sports changed over 108 00:06:06,080 --> 00:06:06,560 Speaker 2: that time. 109 00:06:06,800 --> 00:06:10,360 Speaker 4: Yeah, it's obviously grown digital now, is what we say 110 00:06:10,360 --> 00:06:12,479 Speaker 4: and what my team says. It's the number one way 111 00:06:12,520 --> 00:06:15,159 Speaker 4: to engage with fans that can't make it to the event, 112 00:06:15,360 --> 00:06:16,880 Speaker 4: as well as those fans who are at the event, 113 00:06:16,920 --> 00:06:18,400 Speaker 4: and how do you enrich their stay. So it's really 114 00:06:18,520 --> 00:06:22,840 Speaker 4: kind of you're tackling multiple fan personas. It's the international 115 00:06:22,880 --> 00:06:25,640 Speaker 4: fan who's staying up late to watch in other countries, 116 00:06:25,680 --> 00:06:28,400 Speaker 4: to the fan here who's maybe watching on broadcasts, and 117 00:06:28,400 --> 00:06:30,640 Speaker 4: we go on in a company and enrich that broadcast of 118 00:06:30,760 --> 00:06:33,080 Speaker 4: new stats and insights to the on site fan who 119 00:06:33,080 --> 00:06:35,320 Speaker 4: bought a ticket and maybe doesn't know what match is 120 00:06:35,320 --> 00:06:38,120 Speaker 4: happening on what court. We do have twenty plus courts 121 00:06:38,120 --> 00:06:40,760 Speaker 4: happening at a time with all different matches, So really 122 00:06:40,760 --> 00:06:43,800 Speaker 4: try to help all fans navigate the US Open the 123 00:06:43,800 --> 00:06:44,640 Speaker 4: best way possible. 124 00:06:45,040 --> 00:06:47,159 Speaker 2: And so, like, what are some of the sort of 125 00:06:47,200 --> 00:06:49,159 Speaker 2: problems you're trying to solve. What are some of the 126 00:06:49,160 --> 00:06:50,480 Speaker 2: hard things about your job? 127 00:06:50,800 --> 00:06:54,920 Speaker 4: Yeah, obviously technology changes at a rapid pace, right, So 128 00:06:55,000 --> 00:06:56,880 Speaker 4: I think part of it is how do we stay 129 00:06:56,880 --> 00:06:59,040 Speaker 4: on the forefront of that, and how do we do 130 00:06:59,080 --> 00:07:01,200 Speaker 4: that in the best way and make the best fan 131 00:07:01,320 --> 00:07:04,800 Speaker 4: experiences possible and the best user experience as possible. That's 132 00:07:04,839 --> 00:07:08,159 Speaker 4: always kind of driving factor number one. Then number two, 133 00:07:08,200 --> 00:07:11,800 Speaker 4: it's understanding and listening to our fans and what kind 134 00:07:11,840 --> 00:07:14,040 Speaker 4: of content they want. You hear me talk a lot 135 00:07:14,040 --> 00:07:16,560 Speaker 4: about storytelling. I feel like there's a lot of storytelling 136 00:07:16,560 --> 00:07:18,480 Speaker 4: that happens around the years open that we really want 137 00:07:18,520 --> 00:07:21,520 Speaker 4: to bring to fans. And that can be as simple 138 00:07:21,600 --> 00:07:24,840 Speaker 4: as storytelling of what's happening today and what you should 139 00:07:24,920 --> 00:07:28,240 Speaker 4: be watching too. Maybe it's your favorite players and what's 140 00:07:28,280 --> 00:07:31,840 Speaker 4: going on behind the scenes with them, to even introducing 141 00:07:32,040 --> 00:07:34,840 Speaker 4: I want to say, the casual fans to who they 142 00:07:34,840 --> 00:07:37,280 Speaker 4: should be watching, why they should follow certain players, and 143 00:07:37,280 --> 00:07:39,320 Speaker 4: more bringing that player's story to life. 144 00:07:39,960 --> 00:07:42,360 Speaker 2: Yeah, I mean, I feel like almost the whole point 145 00:07:42,400 --> 00:07:45,760 Speaker 2: of sports is to create stories for us to follow, right, 146 00:07:45,840 --> 00:07:49,440 Speaker 2: Like they're engineered to be stories. It's exactly this thing 147 00:07:49,520 --> 00:07:51,640 Speaker 2: is happening in front of you and there are two 148 00:07:52,200 --> 00:07:55,160 Speaker 2: antagonists and the stakes are high, and you don't know 149 00:07:55,200 --> 00:07:57,720 Speaker 2: how it's gonna end. Like it's built to be a story. 150 00:07:57,960 --> 00:08:00,240 Speaker 4: Yeah, And that's the main challenge of the job is 151 00:08:00,280 --> 00:08:03,000 Speaker 4: you can plan, plan, plan, but once you get on 152 00:08:03,160 --> 00:08:05,080 Speaker 4: two players on court and you don't know what that 153 00:08:05,120 --> 00:08:07,240 Speaker 4: outcome is going to be. It's now sitting and waiting 154 00:08:07,240 --> 00:08:09,680 Speaker 4: and watching and you become a fan yourself. And then 155 00:08:09,720 --> 00:08:13,120 Speaker 4: it's how do you really captivate that story and how 156 00:08:13,120 --> 00:08:15,600 Speaker 4: do you narrate it? How do you like translate up 157 00:08:15,640 --> 00:08:16,400 Speaker 4: to fans. 158 00:08:16,280 --> 00:08:17,720 Speaker 2: And it's like you kind of have to do it 159 00:08:17,760 --> 00:08:19,760 Speaker 2: in real time, right, Like the whole point of sports 160 00:08:19,840 --> 00:08:20,680 Speaker 2: is you don't know what's going. 161 00:08:20,680 --> 00:08:23,280 Speaker 4: To happen exactly, and that's the excitement. And it's also 162 00:08:23,440 --> 00:08:25,520 Speaker 4: there's so many different types of fans. You know, there's 163 00:08:25,560 --> 00:08:28,080 Speaker 4: the fans who want a lot of enriched data and 164 00:08:28,120 --> 00:08:30,360 Speaker 4: their tennis nerds for lack of better of saying it, 165 00:08:30,400 --> 00:08:32,760 Speaker 4: and that they really want to dive deep into the 166 00:08:32,800 --> 00:08:35,800 Speaker 4: intricacies of the game, versus the casual fan who maybe 167 00:08:35,840 --> 00:08:37,760 Speaker 4: just wants more of this high level storyline of what 168 00:08:37,800 --> 00:08:41,120 Speaker 4: does this mean? Why is it important? So it's really 169 00:08:41,120 --> 00:08:43,680 Speaker 4: trying to figure out how to deliver that at scale 170 00:08:43,679 --> 00:08:46,480 Speaker 4: and really help fans get what they're looking for and 171 00:08:46,520 --> 00:08:47,880 Speaker 4: the type of content they're looking for. 172 00:08:48,200 --> 00:08:51,400 Speaker 2: So, are there specific examples of, you know, how fan 173 00:08:51,520 --> 00:08:56,040 Speaker 2: feedback has led to specific features digital features you build? 174 00:08:56,400 --> 00:08:59,400 Speaker 2: Are there like particularly popular features you've come up with, 175 00:08:59,440 --> 00:09:01,200 Speaker 2: Like what are some specifics. 176 00:09:00,720 --> 00:09:03,240 Speaker 4: Yeah, some low hanging fruit type things that came from 177 00:09:03,280 --> 00:09:06,640 Speaker 4: fan feedback. Is simple things sometimes like managing time zones 178 00:09:06,760 --> 00:09:07,959 Speaker 4: when matches start. 179 00:09:07,960 --> 00:09:11,560 Speaker 2: A persistent problem where those of us work across. 180 00:09:11,280 --> 00:09:15,120 Speaker 4: Times exactly, and we do have, like I mentioned, twenty 181 00:09:15,120 --> 00:09:17,320 Speaker 4: plus courts happening at a time, So it's a lot 182 00:09:17,360 --> 00:09:19,720 Speaker 4: to follow, and how do you translate that to a fan, 183 00:09:20,160 --> 00:09:22,719 Speaker 4: whether it's to their native language or to their time 184 00:09:22,800 --> 00:09:25,080 Speaker 4: zone or things like that. So that's one thing that 185 00:09:25,120 --> 00:09:27,960 Speaker 4: came through fan feedback, and another one a three to 186 00:09:28,040 --> 00:09:30,760 Speaker 4: five hour match, especially when you're having twenty plus of 187 00:09:30,800 --> 00:09:32,880 Speaker 4: them happening at a time, is there's too much for 188 00:09:32,960 --> 00:09:36,520 Speaker 4: one person to follow. So how do you start from 189 00:09:36,520 --> 00:09:40,000 Speaker 4: an editorial perspective really helping with that storytelling and guiding 190 00:09:40,000 --> 00:09:42,400 Speaker 4: a fan to like, all right, whether there's an upset 191 00:09:42,440 --> 00:09:46,040 Speaker 4: about to happen, or here's your matches to watch, or 192 00:09:46,400 --> 00:09:48,280 Speaker 4: even some of the predictions we're starting to put in 193 00:09:48,360 --> 00:09:50,480 Speaker 4: is we really want to guide the fan before match, 194 00:09:50,840 --> 00:09:53,360 Speaker 4: here's where you should tune in to even after a 195 00:09:53,400 --> 00:09:56,640 Speaker 4: match of here's what's happened, here's what's important, and we're 196 00:09:56,679 --> 00:09:58,240 Speaker 4: really excited with some of the features we've built in 197 00:09:58,280 --> 00:10:00,920 Speaker 4: the last few years that I would say really helps 198 00:10:00,960 --> 00:10:02,600 Speaker 4: us do that at more scale than what we were 199 00:10:02,640 --> 00:10:04,960 Speaker 4: able to do with just writers following a match and 200 00:10:05,000 --> 00:10:06,240 Speaker 4: covering every single match. 201 00:10:06,400 --> 00:10:08,679 Speaker 2: Uh huh. So I want to talk a little bit 202 00:10:08,760 --> 00:10:14,560 Speaker 2: about the partnership between IBM and the USTA, Like, just 203 00:10:14,640 --> 00:10:16,360 Speaker 2: tell me about the work you do together. 204 00:10:16,760 --> 00:10:20,000 Speaker 4: So, IBM is our official digital and technology partner and 205 00:10:20,080 --> 00:10:23,079 Speaker 4: innovation partner of the US Open They predate me. It's 206 00:10:23,080 --> 00:10:26,000 Speaker 4: a thirty year partnership and it truly is a partnership. 207 00:10:26,040 --> 00:10:29,040 Speaker 4: So I view the IBM consulting team as an extension 208 00:10:29,120 --> 00:10:32,600 Speaker 4: of my USTA team, So we work with them year round. 209 00:10:32,880 --> 00:10:37,199 Speaker 4: They design, develop and deliver the digital properties. They help 210 00:10:37,280 --> 00:10:40,000 Speaker 4: us provide the tools to create content, to do things 211 00:10:40,000 --> 00:10:42,440 Speaker 4: at scale. They help us from stats and information and 212 00:10:42,760 --> 00:10:45,400 Speaker 4: really help us push from an innovation standpoint to make 213 00:10:45,400 --> 00:10:47,720 Speaker 4: sure that we are staying on that cutting edge of technology. 214 00:10:47,760 --> 00:10:52,280 Speaker 4: So I would truly say it's much more than a sponsorship, 215 00:10:52,280 --> 00:10:55,000 Speaker 4: where it's truly a partnership to deliver that fan experience. 216 00:10:55,440 --> 00:10:58,079 Speaker 2: And So, what are some of the specific things that 217 00:10:58,360 --> 00:10:59,480 Speaker 2: you have done with IBM. 218 00:10:59,720 --> 00:11:03,960 Speaker 4: Yeah, so I mean, there's countless ones to talk through. Obviously, 219 00:11:04,000 --> 00:11:06,600 Speaker 4: they thirty years ago. They helped us build our first 220 00:11:06,600 --> 00:11:10,120 Speaker 4: website and it's kind of grown from there over the 221 00:11:10,240 --> 00:11:12,080 Speaker 4: past few years. I would say I think it was 222 00:11:12,120 --> 00:11:14,680 Speaker 4: twenty eighteen as we started AI Highlights, So that was 223 00:11:14,720 --> 00:11:18,400 Speaker 4: really when we were able to have all twenty matches 224 00:11:18,440 --> 00:11:20,800 Speaker 4: going at a single time. We were able to quickly 225 00:11:20,880 --> 00:11:24,520 Speaker 4: deliver succinct highlights to fans to our digital platform, so 226 00:11:24,520 --> 00:11:26,760 Speaker 4: they could see highlights for every single core. 227 00:11:27,440 --> 00:11:30,360 Speaker 2: Is that video highlights? Is that tech summaries? What does 228 00:11:30,400 --> 00:11:30,840 Speaker 2: that mean? 229 00:11:31,360 --> 00:11:33,840 Speaker 4: At the time, it was video highlights, Okay, so it 230 00:11:33,880 --> 00:11:36,760 Speaker 4: was really taking that three to five hour match, let's say, 231 00:11:36,760 --> 00:11:39,280 Speaker 4: and cut it down to a three minute highlight that 232 00:11:39,400 --> 00:11:41,800 Speaker 4: could show up within moments after a match, ending to 233 00:11:41,840 --> 00:11:44,320 Speaker 4: our website and our mobile app, so fans could see 234 00:11:44,320 --> 00:11:46,000 Speaker 4: that all around the world and really kind of get 235 00:11:46,000 --> 00:11:48,760 Speaker 4: that three minute overview what happened in a match? 236 00:11:48,960 --> 00:11:51,800 Speaker 2: And was that AI enabled? Was AI a piece of 237 00:11:52,240 --> 00:11:52,920 Speaker 2: how to do that? 238 00:11:53,040 --> 00:11:55,680 Speaker 4: It was? It was probably our first FORAY into AI. 239 00:11:56,280 --> 00:12:01,200 Speaker 2: Back twenty eighteen is relatively early, yeah, exactly, cly for 240 00:12:01,320 --> 00:12:03,079 Speaker 2: tennis exactly. 241 00:12:03,200 --> 00:12:06,160 Speaker 4: Yeah. It really I want to say opened up our 242 00:12:06,200 --> 00:12:10,160 Speaker 4: ability to one against storyteller but attract new fans too. 243 00:12:10,240 --> 00:12:12,480 Speaker 4: Is video has actually been our number one growth area 244 00:12:12,600 --> 00:12:14,720 Speaker 4: since twenty eighteen, and I think a lot of that 245 00:12:14,800 --> 00:12:16,480 Speaker 4: has to do with the scale of how we deliver 246 00:12:16,559 --> 00:12:17,440 Speaker 4: that content. 247 00:12:17,440 --> 00:12:20,360 Speaker 2: Using AI and being able to deliver the sort of 248 00:12:20,480 --> 00:12:23,679 Speaker 2: video highlight reels at scale. 249 00:12:23,440 --> 00:12:26,040 Speaker 4: Yeah, and do it quickly. Right. We've always had highlights, 250 00:12:26,040 --> 00:12:28,040 Speaker 4: but it was a manual process where you had a 251 00:12:28,559 --> 00:12:32,280 Speaker 4: video ad or cutting through a three hour match, selecting 252 00:12:32,320 --> 00:12:34,360 Speaker 4: the right scene, stitching together it would have to get 253 00:12:34,400 --> 00:12:37,600 Speaker 4: voiced over, et cetera. We really have used AI to 254 00:12:37,600 --> 00:12:39,320 Speaker 4: make it, i want to say, much more efficient and 255 00:12:39,360 --> 00:12:42,320 Speaker 4: speed up that process and deliver it more quickly to 256 00:12:42,360 --> 00:12:42,840 Speaker 4: our fans. 257 00:12:43,240 --> 00:12:45,160 Speaker 2: I mean, it would be a bummer to get scooped 258 00:12:45,280 --> 00:12:48,400 Speaker 2: by whatever NBC News or Yes Pen or whatever. I'm 259 00:12:48,400 --> 00:12:49,920 Speaker 2: sure there are all your partners and you love them. 260 00:12:49,960 --> 00:12:52,679 Speaker 2: Most likely obviously you want to have the video first, right, 261 00:12:52,760 --> 00:12:53,480 Speaker 2: it's your match. 262 00:12:53,679 --> 00:12:56,160 Speaker 4: Yeah, And I think it's also important to us as 263 00:12:56,280 --> 00:13:00,400 Speaker 4: being the USTA is ensuring that it's not just you know, 264 00:13:00,600 --> 00:13:04,160 Speaker 4: the main marquee players, that every player and all those 265 00:13:04,200 --> 00:13:07,680 Speaker 4: storylines and that whether it's you know, the main singles 266 00:13:07,760 --> 00:13:10,360 Speaker 4: draw to our mixed doubles, et cetera. They all need 267 00:13:10,440 --> 00:13:12,880 Speaker 4: highlights and they all have their own stories to tell, 268 00:13:12,880 --> 00:13:14,640 Speaker 4: and how do we do that at scale? It was 269 00:13:14,679 --> 00:13:17,120 Speaker 4: something that before we had that product was not something 270 00:13:17,120 --> 00:13:17,880 Speaker 4: you were able to do. 271 00:13:18,320 --> 00:13:21,760 Speaker 2: Great, So let's let's talk in some more detail about 272 00:13:21,760 --> 00:13:25,160 Speaker 2: what you're working on. Let's start with the app. Tell 273 00:13:25,200 --> 00:13:28,240 Speaker 2: me about the us Open app and the Companion website. 274 00:13:28,320 --> 00:13:30,560 Speaker 4: Yeah, so, so I'll start with the app, and I 275 00:13:30,600 --> 00:13:33,920 Speaker 4: feel like they serve similar needs, but they're a little 276 00:13:33,960 --> 00:13:37,240 Speaker 4: different in their own respective manners. Is the app. Everybody 277 00:13:37,280 --> 00:13:39,040 Speaker 4: has a phone in their hands at this point. The 278 00:13:39,080 --> 00:13:41,480 Speaker 4: app is kind of their guide to when I say 279 00:13:41,520 --> 00:13:44,120 Speaker 4: a million fans on site, we view the app as 280 00:13:44,200 --> 00:13:46,240 Speaker 4: we want that to be their on site guide and 281 00:13:46,280 --> 00:13:47,720 Speaker 4: Companion a million. 282 00:13:47,840 --> 00:13:50,560 Speaker 2: Let's just pause on a million fans on site, right, 283 00:13:50,600 --> 00:13:54,320 Speaker 2: because like a big professional whatever, an NFL game or 284 00:13:54,360 --> 00:13:57,840 Speaker 2: something that's like one hundred thousand, this is ten x that. 285 00:13:58,200 --> 00:14:01,679 Speaker 4: Yeah, and a three week window in a very succinct, tight, 286 00:14:01,760 --> 00:14:05,800 Speaker 4: action packed window. There's a lot of action logistics. 287 00:14:05,840 --> 00:14:07,560 Speaker 2: Okay, so keep going. 288 00:14:07,679 --> 00:14:10,480 Speaker 4: So the app, you know, whether it's finding the schedules, 289 00:14:10,559 --> 00:14:13,440 Speaker 4: the live scores, what's happening on court. That's really the 290 00:14:13,480 --> 00:14:16,560 Speaker 4: focus point of the app, and what we're really focused 291 00:14:16,600 --> 00:14:18,840 Speaker 4: on this year is how do we build in some 292 00:14:18,880 --> 00:14:21,560 Speaker 4: of those match summaries into the app, into our slam 293 00:14:21,600 --> 00:14:24,880 Speaker 4: Tracker experience. So again, before match, that kind of match 294 00:14:24,920 --> 00:14:27,200 Speaker 4: preview of here's maybe if you have a ticket, here's 295 00:14:27,200 --> 00:14:30,040 Speaker 4: what to expect, here's you know are likely to win, 296 00:14:30,120 --> 00:14:32,600 Speaker 4: who we are predicting, so you can kind of get 297 00:14:32,640 --> 00:14:36,000 Speaker 4: some information heading in, and then after the match it's 298 00:14:36,040 --> 00:14:39,240 Speaker 4: more of what just happened, what it means for the 299 00:14:39,480 --> 00:14:42,240 Speaker 4: rest of the draw, who they're playing next, is this 300 00:14:42,360 --> 00:14:44,520 Speaker 4: the first time this has happened, et cetera, and really 301 00:14:44,600 --> 00:14:47,680 Speaker 4: enriching that experience as well. So the app is one 302 00:14:47,720 --> 00:14:50,080 Speaker 4: your guide to what you should be watching, but also 303 00:14:50,120 --> 00:14:52,840 Speaker 4: then giving that insights and context to what's happening on 304 00:14:52,880 --> 00:14:53,720 Speaker 4: that court as you're. 305 00:14:53,560 --> 00:14:57,200 Speaker 2: Watching, like the commentator in your pocket exactly. So you 306 00:14:57,320 --> 00:14:59,880 Speaker 2: used a phrase in there as if I already knew, 307 00:15:01,000 --> 00:15:02,560 Speaker 2: and I love the phrase, but I want you to 308 00:15:02,600 --> 00:15:05,480 Speaker 2: talk more about it. That phrase is slam Tracker. 309 00:15:05,680 --> 00:15:11,400 Speaker 4: Yes, so slam Tracker is our long standing live scores. 310 00:15:11,440 --> 00:15:13,720 Speaker 4: I want to say match center. It is okay, where 311 00:15:13,960 --> 00:15:16,680 Speaker 4: every single data point for every single match lives and 312 00:15:16,720 --> 00:15:19,960 Speaker 4: it really it helps showcase what's happening to match. I say, 313 00:15:20,000 --> 00:15:22,800 Speaker 4: it's our broadcast companion, So if you're watching live, it's 314 00:15:22,800 --> 00:15:25,360 Speaker 4: our in stadium companion. And it's also the best thing 315 00:15:25,400 --> 00:15:27,440 Speaker 4: to have if you aren't able to watch. 316 00:15:27,560 --> 00:15:29,320 Speaker 2: And so like, I'm on the app and there's a 317 00:15:29,360 --> 00:15:32,240 Speaker 2: thing called slam Tracker, and I like, tap slam Tracker. 318 00:15:32,240 --> 00:15:34,000 Speaker 2: What do I see on my phone when I tap 319 00:15:34,080 --> 00:15:37,400 Speaker 2: slam Tracker? You know, midday when the tournament's happening. 320 00:15:37,480 --> 00:15:39,320 Speaker 4: So before match, that's where you get a lot of 321 00:15:39,360 --> 00:15:41,560 Speaker 4: pre match content. That's where those live kind of our 322 00:15:41,560 --> 00:15:44,920 Speaker 4: predictions are. Likelihood to win lives within that. So likelihood 323 00:15:44,960 --> 00:15:47,560 Speaker 4: to win essentially pulls in a bunch of data points. 324 00:15:47,560 --> 00:15:50,680 Speaker 4: So pass matches, how many times these players have played 325 00:15:50,680 --> 00:15:53,360 Speaker 4: each other against each other, even some punditry and other 326 00:15:53,400 --> 00:15:56,200 Speaker 4: written articles that maybe our editorial team put out and 327 00:15:56,240 --> 00:15:58,880 Speaker 4: really kind of puts a prediction out there. 328 00:15:58,760 --> 00:16:00,800 Speaker 2: And so it's just a percentage chance. 329 00:16:00,800 --> 00:16:03,880 Speaker 4: Yes exactly, but it uses millions of data points to 330 00:16:03,920 --> 00:16:06,200 Speaker 4: come up with that. Yes, so it really helps you 331 00:16:06,280 --> 00:16:09,200 Speaker 4: kind of understand what you're getting into for that match. 332 00:16:09,600 --> 00:16:12,320 Speaker 4: During a live match, it is every single point so 333 00:16:12,720 --> 00:16:15,880 Speaker 4: point by point scoring as well as in depth analysis 334 00:16:15,880 --> 00:16:18,800 Speaker 4: in point commentary where also this year have a live 335 00:16:18,880 --> 00:16:21,840 Speaker 4: visualization that accompanies that that will really help bring the 336 00:16:21,960 --> 00:16:24,360 Speaker 4: match together. And what I mean by that is it 337 00:16:24,480 --> 00:16:27,720 Speaker 4: uses our ball tracking technology to really showcase the match 338 00:16:28,040 --> 00:16:30,960 Speaker 4: in near real time, so within seconds delay of where 339 00:16:30,960 --> 00:16:33,480 Speaker 4: the ball is being hit, where the players are, and 340 00:16:33,520 --> 00:16:36,320 Speaker 4: really bring a visualization to life and layered stats and 341 00:16:36,400 --> 00:16:37,080 Speaker 4: data on top of it. 342 00:16:37,240 --> 00:16:39,120 Speaker 2: Uh. Is that sort of like when I'm watching a 343 00:16:39,200 --> 00:16:41,920 Speaker 2: match on TV and there's like a close call as 344 00:16:41,960 --> 00:16:43,600 Speaker 2: the ball in or out and they do that thing 345 00:16:43,640 --> 00:16:45,640 Speaker 2: where they kind of show a sort of video game 346 00:16:45,720 --> 00:16:47,840 Speaker 2: version of where the ball landed. Does it look like that? 347 00:16:48,120 --> 00:16:50,840 Speaker 4: It's like that before every single shot, So it's not 348 00:16:50,960 --> 00:16:53,440 Speaker 4: just those close ones. It's our first foray to bring 349 00:16:53,480 --> 00:16:54,440 Speaker 4: that match to life. 350 00:16:54,960 --> 00:16:56,840 Speaker 2: Huh. And so what do I see on that kind 351 00:16:56,840 --> 00:16:59,040 Speaker 2: of view that I don't see from whatever watching the video? 352 00:16:59,120 --> 00:17:02,440 Speaker 4: Yeah, be able just to see more of the ball 353 00:17:02,480 --> 00:17:05,239 Speaker 4: trajectory and where the ball is being hit, But then 354 00:17:05,280 --> 00:17:07,840 Speaker 4: you can also start layering things and stats and insights 355 00:17:07,880 --> 00:17:10,560 Speaker 4: on top of that. So how many times has player 356 00:17:10,600 --> 00:17:13,600 Speaker 4: A hit the ball on a certain baseline, how fast 357 00:17:13,600 --> 00:17:16,199 Speaker 4: are they hitting it, maybe their serve percentage and a 358 00:17:16,240 --> 00:17:18,120 Speaker 4: certain side of the court, et cetera. So you can 359 00:17:18,119 --> 00:17:20,600 Speaker 4: really start layering in for the ones that really want 360 00:17:20,640 --> 00:17:21,640 Speaker 4: to dive deep into the. 361 00:17:21,880 --> 00:17:26,000 Speaker 2: For the nerds, it's for the it's the information rich exactly. 362 00:17:26,040 --> 00:17:28,560 Speaker 4: It's the strategy of tennis. It really should be an 363 00:17:28,600 --> 00:17:30,639 Speaker 4: interesting way to slice and dice a match. 364 00:17:30,840 --> 00:17:31,800 Speaker 2: Huh. 365 00:17:31,880 --> 00:17:35,200 Speaker 3: It's remarkable how the USTA is leveraging AI to enhance 366 00:17:35,280 --> 00:17:40,560 Speaker 3: fan engagement and deliver immersive experiences both on site and online. 367 00:17:40,960 --> 00:17:46,480 Speaker 3: Brian's emphasis on storytelling really underscores the evolution of sports marketing. 368 00:17:47,119 --> 00:17:50,920 Speaker 3: The slam Choker feature particularly caught my attention. It's essentially 369 00:17:50,960 --> 00:17:54,280 Speaker 3: bringing the excitement of a tennis match to life in 370 00:17:54,320 --> 00:17:58,520 Speaker 3: your palm, moment by moment. As someone who appreciates the 371 00:17:58,600 --> 00:18:02,320 Speaker 3: narrative intricacies of spoil, I find it compelling how AI 372 00:18:02,440 --> 00:18:06,280 Speaker 3: helps predict and analyze matches in real time. 373 00:18:07,320 --> 00:18:09,719 Speaker 2: Tell me about the AI commentary feature. 374 00:18:09,880 --> 00:18:13,520 Speaker 4: Yeah, I know. I mentioned AI highlights back in twenty eighteen. 375 00:18:13,600 --> 00:18:16,600 Speaker 4: It's now progressed for us. And again, if we go 376 00:18:16,720 --> 00:18:19,679 Speaker 4: back to before we had AA highlights, to have a 377 00:18:19,720 --> 00:18:22,840 Speaker 4: highlight ready for the site was a video editor cutting 378 00:18:22,840 --> 00:18:26,639 Speaker 4: the highlight and getting voiced over and then being published aside, 379 00:18:26,680 --> 00:18:30,280 Speaker 4: and it took probably an hour plus for that highlight 380 00:18:30,320 --> 00:18:34,000 Speaker 4: to really be created. Now with AI commentary, not only 381 00:18:34,040 --> 00:18:37,119 Speaker 4: are we creating and cutting the highlights using our AI technology, 382 00:18:37,160 --> 00:18:39,399 Speaker 4: but it's now using all the data points that we 383 00:18:39,400 --> 00:18:41,680 Speaker 4: have around the match, whether it's our live scoring data, 384 00:18:42,000 --> 00:18:45,399 Speaker 4: our ball tro directory data, etc. And it's really creating 385 00:18:45,400 --> 00:18:48,919 Speaker 4: a script that helped storytell around that match. That's all 386 00:18:49,000 --> 00:18:52,879 Speaker 4: using Watson X technology and then using text to speech 387 00:18:52,920 --> 00:18:55,440 Speaker 4: we're able to actually then create the commentary on top 388 00:18:55,480 --> 00:18:58,719 Speaker 4: of that, which all happens now within minutes. So our 389 00:18:58,760 --> 00:19:01,640 Speaker 4: team's able to now create fully voiced highlights for every 390 00:19:01,680 --> 00:19:04,879 Speaker 4: men's and women's singles match to our site within minutes. 391 00:19:05,640 --> 00:19:08,159 Speaker 2: So I know there's a new feature you're working on 392 00:19:08,200 --> 00:19:12,639 Speaker 2: for this year called match reports. Yes, what are match reports? 393 00:19:12,800 --> 00:19:17,359 Speaker 4: It's our ability to succicktly tell the story of a match, 394 00:19:17,880 --> 00:19:21,359 Speaker 4: so everything happens in five hours within that match down 395 00:19:21,440 --> 00:19:25,320 Speaker 4: to a couple paragraphs. That really helps a user understand 396 00:19:25,440 --> 00:19:29,040 Speaker 4: or a fan understand what just happened. Again, some key 397 00:19:29,080 --> 00:19:32,639 Speaker 4: stats what's upcoming really help us with that storytelling. In 398 00:19:32,680 --> 00:19:35,280 Speaker 4: the past when we have twenty two courts happening at 399 00:19:35,320 --> 00:19:37,520 Speaker 4: a certain time, we would have to pick and choose 400 00:19:37,560 --> 00:19:40,000 Speaker 4: which stories we think or which matches we think are 401 00:19:40,040 --> 00:19:42,000 Speaker 4: going to have the best stories, and that's a really 402 00:19:42,080 --> 00:19:45,040 Speaker 4: hard thing to predict from an editorial perspective. With our 403 00:19:45,040 --> 00:19:47,400 Speaker 4: match reports, now we'll be able to have full coverage 404 00:19:47,440 --> 00:19:49,399 Speaker 4: of every single match during the main draw. 405 00:19:50,160 --> 00:19:52,760 Speaker 2: So of course I want to talk about jeneritive AI. 406 00:19:53,000 --> 00:19:55,879 Speaker 2: How could we not talk about genitive Of course? What 407 00:19:55,920 --> 00:19:57,240 Speaker 2: are you working on with jenitive AI? 408 00:19:57,640 --> 00:20:00,360 Speaker 4: So match reports is the prime example of it. Match 409 00:20:00,400 --> 00:20:04,159 Speaker 4: reports will be completely using Watson next genera of AI technology, 410 00:20:04,160 --> 00:20:08,200 Speaker 4: and really again to us, it's how can we do 411 00:20:08,240 --> 00:20:12,320 Speaker 4: that storytelling at scale? Tennis is such a data rich sport. 412 00:20:12,600 --> 00:20:14,879 Speaker 4: All sports have data, but tennis has a lot of 413 00:20:14,920 --> 00:20:18,360 Speaker 4: shots and different shot types and ball trajectory and live 414 00:20:18,400 --> 00:20:21,880 Speaker 4: scoring data and umpire chair data and crowd and all that. 415 00:20:22,280 --> 00:20:25,080 Speaker 4: Factoring in jenera of AI really helps us take some 416 00:20:25,119 --> 00:20:29,080 Speaker 4: of that structured and unstructured data really one organize it 417 00:20:29,119 --> 00:20:32,600 Speaker 4: in a way, but then help us quickly tell that 418 00:20:32,720 --> 00:20:35,240 Speaker 4: story at scale to all of our fans. And I 419 00:20:35,280 --> 00:20:37,960 Speaker 4: think we're really just starting to scratch at some of 420 00:20:37,960 --> 00:20:41,280 Speaker 4: the capabilities, and we're really excited about where we're being, 421 00:20:41,320 --> 00:20:43,560 Speaker 4: but we also see the opportunity of even how we 422 00:20:43,600 --> 00:20:45,920 Speaker 4: can grow to new fans and new fans around the 423 00:20:45,960 --> 00:20:48,000 Speaker 4: world using jeneral of AI in the future. 424 00:20:49,320 --> 00:20:53,080 Speaker 2: So I'm curious, and you alluded to this a moment ago, 425 00:20:53,119 --> 00:20:54,720 Speaker 2: but I'd like to talk a little bit more about 426 00:20:54,760 --> 00:20:58,480 Speaker 2: it because it seems interesting as a technical problem. Right, 427 00:20:58,640 --> 00:21:03,920 Speaker 2: is the nature of turning tennis matches into stories, which 428 00:21:04,000 --> 00:21:06,399 Speaker 2: is fundamentally what we're talking about here in different ways 429 00:21:06,400 --> 00:21:11,640 Speaker 2: in different media, is about taking both structured data, right 430 00:21:11,760 --> 00:21:15,679 Speaker 2: like the stats who you know, points stats matches, and 431 00:21:15,840 --> 00:21:19,560 Speaker 2: also unstructured data, right like commentary and articles and the 432 00:21:19,640 --> 00:21:24,159 Speaker 2: kind of fuzzier parts of storytelling. And so I'm curious 433 00:21:24,280 --> 00:21:27,520 Speaker 2: how AI kind of helps you manage both the structured 434 00:21:27,560 --> 00:21:28,680 Speaker 2: and the unstructured data. 435 00:21:29,160 --> 00:21:32,960 Speaker 4: Yeah, so I think the structured data is pretty self experimentatory, 436 00:21:33,280 --> 00:21:35,160 Speaker 4: but when you get into the unstructured data and from 437 00:21:35,200 --> 00:21:37,520 Speaker 4: the punditry, that's where you get more of the opinion 438 00:21:37,600 --> 00:21:40,840 Speaker 4: pieces into it. Like a specific player matchup, this player 439 00:21:40,880 --> 00:21:43,439 Speaker 4: always plays well against so and so, or is they 440 00:21:43,480 --> 00:21:45,440 Speaker 4: play always played well at night, or they're a fan 441 00:21:45,560 --> 00:21:49,320 Speaker 4: favorite and the crowd, you know, adrenaline and the crowd 442 00:21:49,359 --> 00:21:51,439 Speaker 4: being behind you can really motivate you to play a 443 00:21:51,440 --> 00:21:55,080 Speaker 4: lot better. So it pulls in all those unstructured pieces 444 00:21:55,119 --> 00:21:57,640 Speaker 4: and helps us really put some more rigor around it 445 00:21:57,800 --> 00:22:00,160 Speaker 4: and help add and enrich our storytelling with it. 446 00:22:00,560 --> 00:22:04,200 Speaker 2: And so I'm curious when you're starting to use generative 447 00:22:04,200 --> 00:22:07,719 Speaker 2: AI over the past few years, like, what were your 448 00:22:07,760 --> 00:22:08,919 Speaker 2: concerns going into that. 449 00:22:09,320 --> 00:22:13,800 Speaker 4: I think our biggest concern is ensuring that one factually 450 00:22:14,080 --> 00:22:15,680 Speaker 4: it is correct, because it's only as good as the 451 00:22:15,760 --> 00:22:17,800 Speaker 4: data you feed in. And how do you really ensure 452 00:22:17,800 --> 00:22:20,479 Speaker 4: that your model's working right and that the output and 453 00:22:20,520 --> 00:22:23,320 Speaker 4: the data you're feeding it matches the output, and how 454 00:22:23,320 --> 00:22:25,399 Speaker 4: do you do that at scale? So we do have 455 00:22:25,440 --> 00:22:28,640 Speaker 4: a lot of human intervention. That's where the IBM consulting team, 456 00:22:28,720 --> 00:22:31,000 Speaker 4: they're on site with us for those full three weeks 457 00:22:31,040 --> 00:22:34,399 Speaker 4: really helping us review everything and we're constantly learning, especially 458 00:22:34,440 --> 00:22:37,160 Speaker 4: early in the tournament. And I would say the other 459 00:22:37,440 --> 00:22:40,080 Speaker 4: big concern, again it goes around to the data, is 460 00:22:40,359 --> 00:22:43,080 Speaker 4: what data do we have available that is trustworthy? So 461 00:22:43,280 --> 00:22:45,280 Speaker 4: you know, we are feel very confident with the data 462 00:22:45,280 --> 00:22:46,920 Speaker 4: that comes off of court, but when we get into 463 00:22:46,920 --> 00:22:50,640 Speaker 4: that unstructured piece. What are the right data sources, how 464 00:22:50,680 --> 00:22:53,040 Speaker 4: do we validate those data sources, and how do we 465 00:22:53,480 --> 00:22:56,280 Speaker 4: ensure that they're accurate? Because if the data that has 466 00:22:56,320 --> 00:22:58,960 Speaker 4: to go in has to be accurate for the output. 467 00:22:58,920 --> 00:23:01,760 Speaker 2: So how do you do that? That's the concern? How 468 00:23:01,800 --> 00:23:02,439 Speaker 2: do you address it? 469 00:23:02,680 --> 00:23:05,280 Speaker 4: Yeah, so I think there's a number of tools that 470 00:23:05,320 --> 00:23:08,040 Speaker 4: we use, all within the Watson X umbrella. We do 471 00:23:08,119 --> 00:23:10,760 Speaker 4: a lot of training with the IBM team, so we 472 00:23:10,840 --> 00:23:14,520 Speaker 4: have to constantly train and retrain that model. I think 473 00:23:14,560 --> 00:23:17,400 Speaker 4: the other piece that we're doing is again as we're 474 00:23:17,440 --> 00:23:20,200 Speaker 4: creating that content, and we have the IBM consulting team 475 00:23:20,240 --> 00:23:22,760 Speaker 4: on site helping us with that, is as we see 476 00:23:22,760 --> 00:23:25,760 Speaker 4: things and we see outputs, it's refeeding that back into 477 00:23:25,760 --> 00:23:27,560 Speaker 4: the model to make it better for the next time. 478 00:23:27,680 --> 00:23:31,160 Speaker 4: So it's a constantly learning process that we're undergoing. 479 00:23:31,760 --> 00:23:35,520 Speaker 2: So I want to talk about scale. Yes, you have 480 00:23:35,680 --> 00:23:39,440 Speaker 2: like what twenty two different courts with matches going all 481 00:23:39,480 --> 00:23:43,119 Speaker 2: at the same time. You're trying to, you know, approximately 482 00:23:43,480 --> 00:23:46,879 Speaker 2: instantly generate summaries of all these matches in something like 483 00:23:46,960 --> 00:23:51,760 Speaker 2: real time. And I'm curious in particular how the IBM 484 00:23:51,840 --> 00:23:56,040 Speaker 2: models you're using the IBM Granite models are helping you scale. 485 00:23:56,520 --> 00:23:59,280 Speaker 4: Yeah. So I think one of the big learnings we 486 00:23:59,320 --> 00:24:02,720 Speaker 4: had with the IBM granted models too is that we're 487 00:24:02,760 --> 00:24:05,760 Speaker 4: able to run it, you know, against last year's tournaments 488 00:24:05,800 --> 00:24:08,159 Speaker 4: and see what the what the expected outputs could be 489 00:24:08,600 --> 00:24:10,879 Speaker 4: and really help train that model heading into the tournament. 490 00:24:10,880 --> 00:24:13,199 Speaker 4: Because as we talked about in the beginning, is we 491 00:24:13,240 --> 00:24:15,480 Speaker 4: can plan, play and plan, but once two players get 492 00:24:15,520 --> 00:24:18,040 Speaker 4: on court, the outcome is unknown. So how do we 493 00:24:18,119 --> 00:24:20,560 Speaker 4: really run it through its paces and really make sure 494 00:24:20,600 --> 00:24:23,320 Speaker 4: that whatever that outcome could be and whatever that scenario is, 495 00:24:23,320 --> 00:24:26,520 Speaker 4: whether it's a fifth set tie break that happens, or 496 00:24:27,000 --> 00:24:29,400 Speaker 4: maybe there's a you know, a fault in the match 497 00:24:29,640 --> 00:24:32,720 Speaker 4: or something that we're not anticipating, that we have that 498 00:24:32,760 --> 00:24:35,360 Speaker 4: accounted for and that the a won't throw off that output. 499 00:24:35,400 --> 00:24:38,840 Speaker 4: So we really try to think through every scenario, which 500 00:24:39,640 --> 00:24:42,800 Speaker 4: is sometimes difficult, right because again live sports is the 501 00:24:42,880 --> 00:24:44,840 Speaker 4: unknown is the unknown, and that's what makes it fun. 502 00:24:45,160 --> 00:24:47,640 Speaker 4: We do spend a lot of time thinking through potential 503 00:24:47,640 --> 00:24:50,320 Speaker 4: scenarios and ensuring that we have the right data sets 504 00:24:50,359 --> 00:24:52,600 Speaker 4: and the model to predict that. 505 00:24:53,359 --> 00:24:56,680 Speaker 2: Tell me about match reports and the generative AI model, 506 00:24:56,720 --> 00:24:57,399 Speaker 2: you're using for that. 507 00:24:58,080 --> 00:25:00,560 Speaker 4: Yeah, so match reports would be new for us this year, 508 00:25:00,640 --> 00:25:03,160 Speaker 4: So we're in testing right now, so we're really excited 509 00:25:03,160 --> 00:25:05,480 Speaker 4: around it. But the model that we'll be able to 510 00:25:05,560 --> 00:25:08,960 Speaker 4: use using Watson X will use a bunch of different 511 00:25:09,000 --> 00:25:12,000 Speaker 4: parts of the suite of tools A meaning that again 512 00:25:12,040 --> 00:25:14,720 Speaker 4: of taking some of that punditry and the unstructured data 513 00:25:14,720 --> 00:25:18,040 Speaker 4: and the editorial spen, take our structured data as well. 514 00:25:18,359 --> 00:25:21,480 Speaker 4: And really what we're working on right now is figuring 515 00:25:21,520 --> 00:25:24,760 Speaker 4: out the right prompts for the AI to really ensure 516 00:25:25,160 --> 00:25:29,800 Speaker 4: that it tells the right structured story, meaning what just happened. Right, 517 00:25:29,920 --> 00:25:32,840 Speaker 4: So our recap is pretty standard. Here's what the data 518 00:25:32,880 --> 00:25:35,159 Speaker 4: is telling us, who won, who lost? How many sets? 519 00:25:35,160 --> 00:25:35,800 Speaker 4: Here's the score? 520 00:25:35,800 --> 00:25:37,800 Speaker 2: The structured data part, that's the easy part. 521 00:25:38,040 --> 00:25:40,760 Speaker 4: Yeah, and then really where it gets exciting is then 522 00:25:41,040 --> 00:25:44,680 Speaker 4: what does this mean? Meaning what's upcoming? So there's all 523 00:25:44,680 --> 00:25:46,920 Speaker 4: these different scenarios when you get into you know, two 524 00:25:47,000 --> 00:25:49,679 Speaker 4: hundred and fifty four players and a large straw. This 525 00:25:49,760 --> 00:25:52,320 Speaker 4: allows us to distill that down and really tell kind 526 00:25:52,359 --> 00:25:55,280 Speaker 4: of what could happen upcoming. The AI helps us do 527 00:25:55,400 --> 00:25:56,280 Speaker 4: that at scale. 528 00:25:56,520 --> 00:25:58,840 Speaker 2: So I want to sort of generalize for a moment 529 00:25:58,880 --> 00:26:02,399 Speaker 2: to talk about kind of you know, broader challenges with 530 00:26:02,480 --> 00:26:05,400 Speaker 2: AI and how you've solved them. You know a lot 531 00:26:05,440 --> 00:26:10,360 Speaker 2: of generative AI pilots fail because the data quality isn't 532 00:26:10,400 --> 00:26:13,679 Speaker 2: high enough, because the risk controls aren't there, and so 533 00:26:13,760 --> 00:26:17,040 Speaker 2: I'm curious how you dealt with those problems and are 534 00:26:17,080 --> 00:26:17,960 Speaker 2: dealing with them. 535 00:26:18,000 --> 00:26:20,959 Speaker 4: Data quality, Again, we feel common with the data that 536 00:26:21,119 --> 00:26:24,800 Speaker 4: is supplied from the US open and from the USTA, right, 537 00:26:24,920 --> 00:26:27,679 Speaker 4: So we have again that's our structure, scoring data and 538 00:26:27,720 --> 00:26:30,639 Speaker 4: all that. I think what we're constantly looking at is 539 00:26:30,640 --> 00:26:33,120 Speaker 4: when we get outside of our known sources and out 540 00:26:33,119 --> 00:26:35,280 Speaker 4: to third parties, is that's where a lot of the 541 00:26:35,359 --> 00:26:38,760 Speaker 4: testing and model work happens. So we pull in different 542 00:26:38,840 --> 00:26:42,320 Speaker 4: data sources and really tried to work through how it 543 00:26:42,440 --> 00:26:44,600 Speaker 4: changes that output. Again, some of that comes down to 544 00:26:44,720 --> 00:26:47,000 Speaker 4: where it's an open model and the transparency that we 545 00:26:47,160 --> 00:26:49,960 Speaker 4: have and the learning that comes behind it. That's where 546 00:26:50,000 --> 00:26:52,199 Speaker 4: a lot of that confidence can come from, and it 547 00:26:52,200 --> 00:26:55,240 Speaker 4: comes from a lot of testing and feeding it more data. 548 00:26:56,080 --> 00:26:58,480 Speaker 4: Your second question was a little bit more around the 549 00:26:58,520 --> 00:26:59,679 Speaker 4: output I believe. 550 00:26:59,480 --> 00:27:02,679 Speaker 2: Right, yeah, and risks, right, So risk I think of 551 00:27:02,800 --> 00:27:05,560 Speaker 2: risk more in terms of output, right, But the obvious 552 00:27:05,560 --> 00:27:08,640 Speaker 2: sphere is like, what if it says something wrong? Yeah, 553 00:27:08,680 --> 00:27:12,320 Speaker 2: inflammatory or whatever like that seems scary. 554 00:27:12,600 --> 00:27:15,000 Speaker 4: Yeah, it definitely is, and it's definitely one of our 555 00:27:15,040 --> 00:27:17,760 Speaker 4: largest concerns when we first took this foray. I would 556 00:27:17,760 --> 00:27:19,840 Speaker 4: say a lot of that comes through our work with 557 00:27:19,960 --> 00:27:23,560 Speaker 4: IBM and IBM consulting team and really ensuring that again 558 00:27:23,600 --> 00:27:26,359 Speaker 4: they're an extension and the partnership there of our team. 559 00:27:26,800 --> 00:27:29,800 Speaker 4: So whenever we are creating let's say it's the Match Report, 560 00:27:29,880 --> 00:27:32,199 Speaker 4: and we're going to be creating these extinct articles for 561 00:27:32,320 --> 00:27:35,719 Speaker 4: every single men's and women's single match that happens, is 562 00:27:35,760 --> 00:27:38,840 Speaker 4: all of those will have manual review and people looking 563 00:27:38,840 --> 00:27:41,520 Speaker 4: through them for accuracy to ensure that the model then 564 00:27:41,560 --> 00:27:44,040 Speaker 4: hallucinat or make up a fact or fill in the 565 00:27:44,080 --> 00:27:46,600 Speaker 4: gaps and things like that. That's the first step. And 566 00:27:46,640 --> 00:27:49,240 Speaker 4: then also when our editorial team goes to publish those 567 00:27:49,280 --> 00:27:51,680 Speaker 4: of the website, they're going to be checking it as well. 568 00:27:51,720 --> 00:27:54,760 Speaker 4: So there are manual interventions throughout that to really check 569 00:27:54,800 --> 00:27:58,080 Speaker 4: that model. But we feel that the ability to do 570 00:27:58,119 --> 00:28:00,879 Speaker 4: it at scale and with us more to that is 571 00:28:00,920 --> 00:28:03,159 Speaker 4: the efficiency problem that we've been looking to solve. 572 00:28:03,960 --> 00:28:07,159 Speaker 2: So the USTA and IBM have been working together on 573 00:28:07,400 --> 00:28:10,119 Speaker 2: digital innovation for like thirty years from you know, the 574 00:28:10,160 --> 00:28:14,880 Speaker 2: first website, yes for the USTA until now. So that's 575 00:28:14,920 --> 00:28:18,600 Speaker 2: the past thirty years. If you look ahead, what's the next. 576 00:28:18,400 --> 00:28:21,680 Speaker 4: Thirty thirty years is a really long time? About A 577 00:28:21,680 --> 00:28:25,760 Speaker 4: three Yeah, I think you know where I get excited, 578 00:28:25,800 --> 00:28:27,840 Speaker 4: and I think I alluded to it in the beginning 579 00:28:27,880 --> 00:28:30,439 Speaker 4: about how I feel like we're just scratching at the surface, 580 00:28:30,560 --> 00:28:32,720 Speaker 4: especially with journat of Ai, and where I see it 581 00:28:32,760 --> 00:28:35,920 Speaker 4: going is there's a lot of different fans out there, 582 00:28:36,200 --> 00:28:38,080 Speaker 4: and we're also very kind in the US open that 583 00:28:38,080 --> 00:28:40,480 Speaker 4: we're a worldwide event and that there's a lot of 584 00:28:40,480 --> 00:28:44,720 Speaker 4: different fans that we're not necessaryly creating content for bespoke, 585 00:28:44,840 --> 00:28:48,120 Speaker 4: meaning in their native language or maybe it's in that 586 00:28:48,200 --> 00:28:51,160 Speaker 4: native players language and things like that. Is where I 587 00:28:51,200 --> 00:28:54,360 Speaker 4: get excited is we've seen immense growth with a Highlights 588 00:28:54,360 --> 00:28:56,400 Speaker 4: and the ability to now do highlights at scale. Is 589 00:28:56,480 --> 00:29:00,880 Speaker 4: the ability for us to start creating content diferent languages, 590 00:29:01,240 --> 00:29:03,560 Speaker 4: maybe covering different parts of the match. So maybe you 591 00:29:03,600 --> 00:29:06,280 Speaker 4: do have that stats junkie who really wants just it's 592 00:29:06,360 --> 00:29:09,360 Speaker 4: the fastest serve and here's the deep insights versus the 593 00:29:09,480 --> 00:29:12,400 Speaker 4: casual fan who's looking for more of the storytelling around 594 00:29:12,840 --> 00:29:15,120 Speaker 4: how a player trains and what leading up to it 595 00:29:15,280 --> 00:29:17,960 Speaker 4: was like and what it means for them afterwards and 596 00:29:18,040 --> 00:29:20,280 Speaker 4: things like that. A lot of that takes a lot 597 00:29:20,280 --> 00:29:23,280 Speaker 4: of time. Now we're able to solve that efficiency problem 598 00:29:23,360 --> 00:29:25,920 Speaker 4: and do it in multiple languages, we can really create 599 00:29:26,160 --> 00:29:29,440 Speaker 4: I want to say, personalized content to a lot more 600 00:29:29,480 --> 00:29:32,720 Speaker 4: fans all around the world, which again helps us grow 601 00:29:32,720 --> 00:29:34,760 Speaker 4: the sport of tennis great. 602 00:29:35,560 --> 00:29:38,720 Speaker 2: So I want to finish with a speed round. Okay, 603 00:29:38,880 --> 00:29:39,560 Speaker 2: are you ready? 604 00:29:39,680 --> 00:29:40,520 Speaker 4: I am ready. 605 00:29:40,600 --> 00:29:44,080 Speaker 2: Okay, first thing that comes to mind, complete this sentence. 606 00:29:44,720 --> 00:29:46,480 Speaker 2: In five years, AI. 607 00:29:46,280 --> 00:29:49,440 Speaker 4: Will transform many parts of the business. 608 00:29:50,040 --> 00:29:54,320 Speaker 2: What is the number one thing that people misunderstand about AI? 609 00:29:54,840 --> 00:29:59,000 Speaker 4: That it's supplemental, not replacing, meaning that it helps it 610 00:29:59,040 --> 00:30:03,080 Speaker 4: with efficiencies, but it doesn't necessarily replace the creativity. 611 00:30:03,800 --> 00:30:07,520 Speaker 2: Right now, what advice would you give yourself ten years 612 00:30:07,520 --> 00:30:10,400 Speaker 2: ago to better prepare you for today? 613 00:30:11,480 --> 00:30:15,000 Speaker 4: I think it would have been, especially now that we're 614 00:30:15,000 --> 00:30:17,480 Speaker 4: able to take so much of that unstructured data and 615 00:30:18,000 --> 00:30:21,120 Speaker 4: pass content that we were created to help tell stories. 616 00:30:21,720 --> 00:30:24,560 Speaker 4: Was to I want to say, archive more of that 617 00:30:24,640 --> 00:30:26,480 Speaker 4: in a way that we could be using that to 618 00:30:26,560 --> 00:30:30,040 Speaker 4: help pull from that now. So you know, we've seen 619 00:30:30,160 --> 00:30:32,200 Speaker 4: kind of a change in the guard from some of 620 00:30:32,200 --> 00:30:35,080 Speaker 4: our start players to now new and up and comers, 621 00:30:35,160 --> 00:30:37,160 Speaker 4: and it would be really fascinating to me if there 622 00:30:37,200 --> 00:30:39,960 Speaker 4: was a way to to cross sections some of that 623 00:30:40,040 --> 00:30:42,800 Speaker 4: and saying like what tra directories are certain up and 624 00:30:42,840 --> 00:30:46,560 Speaker 4: coming players maybe filing from others. So it's more I 625 00:30:46,600 --> 00:30:48,960 Speaker 4: wish we kept more of the content we created. 626 00:30:48,640 --> 00:30:53,560 Speaker 2: Back fave the data exactly. Well are you saving it 627 00:30:53,640 --> 00:30:54,080 Speaker 2: all now? 628 00:30:54,320 --> 00:30:57,360 Speaker 4: Oh? Yeah, one hundred percent learned our lesson? Yes, yes. 629 00:30:57,960 --> 00:31:00,360 Speaker 2: So on the business side of AI, what do you 630 00:31:00,360 --> 00:31:01,600 Speaker 2: think is the next big thing? 631 00:31:02,240 --> 00:31:05,520 Speaker 4: I alluded to it earlier. I think it's personalization and 632 00:31:05,520 --> 00:31:09,160 Speaker 4: getting content that's catered to you at scale, whether you 633 00:31:09,160 --> 00:31:12,320 Speaker 4: know that's across the sports sphere or any type of 634 00:31:12,360 --> 00:31:16,920 Speaker 4: written content or news content. I feel like the ability 635 00:31:16,960 --> 00:31:19,920 Speaker 4: to really get contentated to the type of fan you 636 00:31:19,960 --> 00:31:22,920 Speaker 4: are and the insights you have is where we're all headed. 637 00:31:23,960 --> 00:31:27,800 Speaker 2: And in terms of your non work life, how do 638 00:31:27,840 --> 00:31:29,480 Speaker 2: you use AI day to day? 639 00:31:29,680 --> 00:31:32,040 Speaker 4: It's funny, I was just having this conversation with a 640 00:31:32,040 --> 00:31:34,280 Speaker 4: friend the other day and we were talking about that 641 00:31:34,960 --> 00:31:38,160 Speaker 4: sometimes when you're starting something new, the hardest thing to 642 00:31:38,240 --> 00:31:40,400 Speaker 4: do is you have a blank piece of paper or 643 00:31:40,400 --> 00:31:43,200 Speaker 4: a thought, and how do you get started. Sometimes with 644 00:31:43,320 --> 00:31:46,600 Speaker 4: these generative models, the easiest thing and the best thing 645 00:31:46,600 --> 00:31:49,280 Speaker 4: you can do is it helps you get started. Meaning 646 00:31:49,320 --> 00:31:51,360 Speaker 4: it may not be one hundred percent with that first prompt, 647 00:31:51,360 --> 00:31:54,240 Speaker 4: but it's that efficiency of whether it's an outline for 648 00:31:54,280 --> 00:31:56,640 Speaker 4: a new idea, or it's a marketing brief you have 649 00:31:56,720 --> 00:31:59,240 Speaker 4: to write, or sometimes even if it's an email you 650 00:31:59,280 --> 00:32:01,720 Speaker 4: have to write for personal something and you're not sure 651 00:32:01,800 --> 00:32:03,840 Speaker 4: how to word it the right way. It allows you 652 00:32:03,920 --> 00:32:06,200 Speaker 4: to have a start and then you can edit from there. 653 00:32:06,240 --> 00:32:09,360 Speaker 4: So again going back to my efficiency point, it helps 654 00:32:09,360 --> 00:32:10,080 Speaker 4: you become more. 655 00:32:09,920 --> 00:32:12,320 Speaker 2: Efficient, solves the blank page problem. 656 00:32:12,480 --> 00:32:13,000 Speaker 4: It does. 657 00:32:14,240 --> 00:32:15,960 Speaker 2: Brian, it was great to talk with you. Thank you 658 00:32:16,000 --> 00:32:16,800 Speaker 2: so much for your time. 659 00:32:16,920 --> 00:32:18,440 Speaker 4: Yeah, this was fun. Thanks for having me. 660 00:32:20,560 --> 00:32:22,800 Speaker 3: A huge thanks to Jacob and Brian for the deep 661 00:32:22,880 --> 00:32:26,959 Speaker 3: dive into the cutting edge innovations transforming the game of tennis. 662 00:32:27,560 --> 00:32:30,480 Speaker 3: Brian shed light on how the US opens partnership with 663 00:32:30,560 --> 00:32:35,800 Speaker 3: IBM is harnessing data driven insights to reshape storytelling in sports, 664 00:32:36,240 --> 00:32:41,760 Speaker 3: from AI generated commentary to match reports. As we look ahead, 665 00:32:41,840 --> 00:32:46,600 Speaker 3: I'm excited about the possibilities for personalizing content and reaching 666 00:32:46,640 --> 00:32:50,680 Speaker 3: fans in new ways. The future of AI promises more 667 00:32:50,760 --> 00:32:59,080 Speaker 3: than just efficiency. It's about enhancing fan experiences worldwide. Smart 668 00:32:59,080 --> 00:33:03,040 Speaker 3: Talks with ib produced by Matt Romano, Joey Fishground and 669 00:33:03,160 --> 00:33:07,640 Speaker 3: Jacob Goldstein. We're edited by Lydia gene Kott. Our engineers 670 00:33:07,680 --> 00:33:11,720 Speaker 3: are Sarah Bruguer and Ben Tolliday. Theme song by Gramscow. 671 00:33:12,680 --> 00:33:15,560 Speaker 3: Special thanks to the eight Bar and IBM teams, as 672 00:33:15,600 --> 00:33:19,080 Speaker 3: well as the Pushkin marketing team. Smart Talks with IBM 673 00:33:19,320 --> 00:33:23,680 Speaker 3: is a production of Pushkin Industries and Ruby Studio at iHeartMedia. 674 00:33:24,200 --> 00:33:28,360 Speaker 3: To find more Pushkin podcasts, listen on the iHeartRadio app, 675 00:33:28,680 --> 00:33:34,400 Speaker 3: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell. 676 00:33:34,720 --> 00:33:38,440 Speaker 3: This is a paid advertisement from IBM. The conversations on 677 00:33:38,480 --> 00:33:54,960 Speaker 3: this podcast don't necessarily represent IBM's positions, strategies or opinions.