1 00:00:03,360 --> 00:00:06,880 Malcolm Gladwell: Hello, Hello, Welcome to Smart Talks with IBM, a podcast 2 00:00:06,880 --> 00:00:12,600 Malcolm Gladwell: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, 3 00:00:12,680 --> 00:00:15,840 Malcolm Gladwell: we're diving back into the world of artificial intelligence, but 4 00:00:15,960 --> 00:00:21,040 Malcolm Gladwell: with a focus on the powerful concept of open its possibilities, implications, 5 00:00:21,079 --> 00:00:24,639 Malcolm Gladwell: and misconceptions. We'll look at openness from a variety of 6 00:00:24,640 --> 00:00:28,600 Malcolm Gladwell: angles and explore how the concept is already reshaping industries, 7 00:00:29,000 --> 00:00:32,440 Malcolm Gladwell: ways of doing business and our very notion of what's possible. 8 00:00:34,200 --> 00:00:38,479 Malcolm Gladwell: I'm particularly excited for today's guest, Brian Ryerson. He's Senior 9 00:00:38,520 --> 00:00:43,080 Malcolm Gladwell: Director of Digital Strategy at the US Tennis Association, helping 10 00:00:43,120 --> 00:00:45,600 Malcolm Gladwell: to oversee one of the most iconic events in the 11 00:00:45,600 --> 00:00:49,600 Malcolm Gladwell: world of sports, the US Open. Brian sat down with 12 00:00:49,600 --> 00:00:54,320 Malcolm Gladwell: Pushkin's own Jacob Goldstein, host of the podcast What's Your Problem. 13 00:00:54,360 --> 00:00:57,600 Malcolm Gladwell: A veteran business journalist, Jacob has reported for The Wall 14 00:00:57,600 --> 00:01:00,960 Malcolm Gladwell: Street Journal, the Miami Herald, and was a longtime host 15 00:01:01,040 --> 00:01:05,360 Malcolm Gladwell: of the NPR program Planet Money. IBM has been the 16 00:01:05,400 --> 00:01:09,560 Malcolm Gladwell: official technology partner of the US Tennis Association for more 17 00:01:09,640 --> 00:01:13,839 Malcolm Gladwell: than thirty years, and the more recent evolution into generative 18 00:01:13,880 --> 00:01:18,680 Malcolm Gladwell: AI has enhanced the world class digital experiences that help 19 00:01:18,760 --> 00:01:21,959 Malcolm Gladwell: more than fifteen million fans from all over the world 20 00:01:22,319 --> 00:01:26,319 Malcolm Gladwell: enjoy the US Open Tennis Championships. In this episode, we 21 00:01:26,400 --> 00:01:30,399 Malcolm Gladwell: will explore how generative AI is being used to generate 22 00:01:30,520 --> 00:01:35,560 Malcolm Gladwell: match insights, spoken commentary for match highlights, and postmatch summaries 23 00:01:35,640 --> 00:01:38,640 Malcolm Gladwell: at scale for fans to enjoy through the US Open 24 00:01:38,640 --> 00:01:43,080 Malcolm Gladwell: app and website. We'll explore how these AI solutions enable 25 00:01:43,160 --> 00:01:46,600 Malcolm Gladwell: the editorial team to cover more of the tournament than 26 00:01:46,680 --> 00:01:50,800 Malcolm Gladwell: ever before, bringing fans even closer to the game they love, 27 00:01:51,200 --> 00:01:53,680 Malcolm Gladwell: and we'll learn more about one of the engines behind 28 00:01:53,760 --> 00:01:58,440 Malcolm Gladwell: this AI powered content creation, a large language model from 29 00:01:58,480 --> 00:02:02,480 Malcolm Gladwell: the IBM Granite family, which is trained and maintained using 30 00:02:02,520 --> 00:02:07,280 Malcolm Gladwell: the wantonex AI and data platform. Okay, let's dive in. 31 00:02:08,560 --> 00:02:09,640 Jacob Goldstein: Brian, welcome to the show. 32 00:02:09,880 --> 00:02:11,320 Brian Ryerson: Thanks for having me. I'm excited to be here. 33 00:02:11,720 --> 00:02:13,240 Jacob Goldstein: Can you say your name and your job. 34 00:02:13,600 --> 00:02:16,960 Brian Ryerson: Yeah, I'm Brian Ryerson. I'm senior director of Digital Strategy 35 00:02:16,960 --> 00:02:17,679 Brian Ryerson: at the USTA. 36 00:02:18,040 --> 00:02:20,400 Jacob Goldstein: Some question, what's the USTA. 37 00:02:20,200 --> 00:02:22,359 Brian Ryerson: The US Tennis Association. 38 00:02:22,680 --> 00:02:24,639 Jacob Goldstein: And tell me about the USTA, Like. 39 00:02:24,720 --> 00:02:27,480 Brian Ryerson: What is it? Yeah? So, the USTA is the governing 40 00:02:27,560 --> 00:02:30,960 Brian Ryerson: body of tennis in the US. Or mission is to 41 00:02:31,000 --> 00:02:34,720 Brian Ryerson: grow the sport of tennis across the US at all levels. Really, 42 00:02:34,919 --> 00:02:36,760 Brian Ryerson: I would say we're more like a health and wellness 43 00:02:36,800 --> 00:02:39,160 Brian Ryerson: company where tennis is the means to health and wellness. 44 00:02:39,600 --> 00:02:42,000 Brian Ryerson: And then the US Open is kind of our tenth 45 00:02:42,040 --> 00:02:44,919 Brian Ryerson: pole event that happens everyear in Flushing Meadows and is 46 00:02:44,960 --> 00:02:47,160 Brian Ryerson: really our chance to showcase the support of tennis at 47 00:02:47,160 --> 00:02:49,399 Brian Ryerson: its highest level to fans all around the world. 48 00:02:49,560 --> 00:02:51,720 Jacob Goldstein: Yeah, I mean the US Open. I assume most people 49 00:02:51,800 --> 00:02:53,920 Jacob Goldstein: know this, but it's Grand Slam. It's one of the 50 00:02:54,000 --> 00:02:57,040 Jacob Goldstein: what four biggest tennis tournaments in the world. 51 00:02:57,160 --> 00:03:00,400 Brian Ryerson: Yes, yeah, every year, we especially the past couple of years, 52 00:03:00,440 --> 00:03:03,680 Brian Ryerson: we've seen immense growth and you know, we are very 53 00:03:03,800 --> 00:03:05,840 Brian Ryerson: hopeful this year and our big goals have over a 54 00:03:05,840 --> 00:03:08,240 Brian Ryerson: million fans on site during the three week window this year, 55 00:03:08,280 --> 00:03:10,480 Brian Ryerson: so it's an amazing event. I always say it's a 56 00:03:10,480 --> 00:03:13,359 Brian Ryerson: food and wine festival where tennis is the main attraction 57 00:03:13,760 --> 00:03:16,000 Brian Ryerson: and it's a really fun, unique atmosphere. 58 00:03:16,680 --> 00:03:19,000 Jacob Goldstein: How did you get into the tennis business. 59 00:03:18,680 --> 00:03:21,400 Brian Ryerson: It's a great question. It's not where I thought i'd 60 00:03:21,440 --> 00:03:23,880 Brian Ryerson: end up for especially being there for fourteen years. So 61 00:03:24,040 --> 00:03:27,440 Brian Ryerson: I was a marketing and technology major in school, and 62 00:03:28,120 --> 00:03:30,720 Brian Ryerson: I also played college lacrosse and sports was always a 63 00:03:30,720 --> 00:03:32,359 Brian Ryerson: big part of my life and always wanted to be 64 00:03:32,440 --> 00:03:34,840 Brian Ryerson: in the sports and entertainment world. I'm here from the 65 00:03:34,880 --> 00:03:36,400 Brian Ryerson: New York area. This is where I grew up. So 66 00:03:36,440 --> 00:03:38,920 Brian Ryerson: I moved back home and had a few friends who 67 00:03:38,920 --> 00:03:41,560 Brian Ryerson: worked there, and I started out more on the number 68 00:03:41,640 --> 00:03:43,880 Brian Ryerson: side of things and really digital analytics. It was really 69 00:03:43,880 --> 00:03:47,440 Brian Ryerson: the start of when Facebook and Twitter is just starting 70 00:03:47,480 --> 00:03:50,320 Brian Ryerson: and digital marketing and all of that. And you know, 71 00:03:50,360 --> 00:03:52,520 Brian Ryerson: I went to my first year so Open not really 72 00:03:52,600 --> 00:03:55,920 Brian Ryerson: knowing what to expect, and again, I think the atmosphere 73 00:03:56,000 --> 00:03:58,320 Brian Ryerson: kind of captivated me and hooked me in. And I've 74 00:03:58,360 --> 00:03:59,600 Brian Ryerson: been there now fourteen years. 75 00:04:00,080 --> 00:04:04,280 Jacob Goldstein: And so your title is Digital Director. What does that mean? 76 00:04:04,320 --> 00:04:04,920 Jacob Goldstein: What's your job? 77 00:04:05,160 --> 00:04:08,160 Brian Ryerson: Yeah, so it's an interesting one because it's tough to 78 00:04:08,240 --> 00:04:10,760 Brian Ryerson: explain to folks who are not in the weeds on 79 00:04:10,840 --> 00:04:13,240 Brian Ryerson: all things US open or even in the sports world. 80 00:04:13,240 --> 00:04:16,960 Brian Ryerson: But really I oversee all of our consumer facing digital property. 81 00:04:17,080 --> 00:04:19,599 Brian Ryerson: So that's the us open dot org, our website built 82 00:04:19,600 --> 00:04:22,440 Brian Ryerson: by IBM, as well as our mobile app. I oversee 83 00:04:22,480 --> 00:04:27,520 Brian Ryerson: our content strategy, our sponsorship integrations. So really anything consumer 84 00:04:27,600 --> 00:04:30,640 Brian Ryerson: facing that happens on the web is under my purview. 85 00:04:30,920 --> 00:04:33,719 Brian Ryerson: Even some of our new platform extensions and gaming and 86 00:04:33,760 --> 00:04:37,000 Brian Ryerson: things like that. Anything that you can physically interact with 87 00:04:37,360 --> 00:04:38,800 Brian Ryerson: is kind of under my purview. 88 00:04:39,520 --> 00:04:43,360 Jacob Goldstein: And so you've been there now for fourteen ISSU years, 89 00:04:43,560 --> 00:04:47,160 Jacob Goldstein: which in the digital world is a long time. How 90 00:04:47,200 --> 00:04:51,560 Jacob Goldstein: has that sort of digital experience of sports changed over 91 00:04:51,600 --> 00:04:52,080 Jacob Goldstein: that time. 92 00:04:52,320 --> 00:04:55,880 Brian Ryerson: Yeah, it's obviously grown digital now, is what we say 93 00:04:55,880 --> 00:04:58,000 Brian Ryerson: and what my team says. It's the number one way 94 00:04:58,040 --> 00:05:00,320 Brian Ryerson: to engage with fans that can't make make it to 95 00:05:00,360 --> 00:05:02,160 Brian Ryerson: the event, as well as those fans who are at 96 00:05:02,160 --> 00:05:03,760 Brian Ryerson: the event, and how to enrich their stay. So it's 97 00:05:03,760 --> 00:05:07,359 Brian Ryerson: really kind of you're tackling multiple fan personas. It's the 98 00:05:07,640 --> 00:05:11,200 Brian Ryerson: international fan who's staying up late to watch in other countries, 99 00:05:11,240 --> 00:05:13,919 Brian Ryerson: to the fan here who's maybe watching on broadcasts, and 100 00:05:13,960 --> 00:05:16,160 Brian Ryerson: we go on in a company and enrich that broadcast of 101 00:05:16,279 --> 00:05:18,560 Brian Ryerson: new stats and insights to the on site fan who 102 00:05:18,640 --> 00:05:20,839 Brian Ryerson: bought a ticket and maybe doesn't know what match is 103 00:05:20,839 --> 00:05:23,640 Brian Ryerson: happening on what court. We do have twenty plus courts 104 00:05:23,680 --> 00:05:26,000 Brian Ryerson: happening at a time with all different matches, so we 105 00:05:26,040 --> 00:05:29,240 Brian Ryerson: really try to help all fans navigate the US Open 106 00:05:29,240 --> 00:05:30,200 Brian Ryerson: the best way possible. 107 00:05:30,560 --> 00:05:32,680 Jacob Goldstein: And so, like, what are some of the sort of 108 00:05:32,720 --> 00:05:34,680 Jacob Goldstein: problems you're trying to solve what are some of the 109 00:05:34,720 --> 00:05:36,040 Jacob Goldstein: hard things about your job? 110 00:05:36,320 --> 00:05:40,440 Brian Ryerson: Yeah, obviously technology changes at a rapid pace, right, So 111 00:05:40,520 --> 00:05:42,400 Brian Ryerson: I think part of it is how do we stay 112 00:05:42,400 --> 00:05:44,560 Brian Ryerson: on the forefront of that, and how do we do 113 00:05:44,600 --> 00:05:46,720 Brian Ryerson: that in the best way and make the best fan 114 00:05:46,839 --> 00:05:50,320 Brian Ryerson: experiences possible and the best user experience as possible. That's 115 00:05:50,360 --> 00:05:53,680 Brian Ryerson: always kind of driving factor number one. Then number two, 116 00:05:53,720 --> 00:05:57,320 Brian Ryerson: it's understanding and listening to our fans and what kind 117 00:05:57,360 --> 00:05:59,520 Brian Ryerson: of content they want. You hear me talk a lot 118 00:05:59,520 --> 00:06:02,040 Brian Ryerson: about storytelling. I feel like there's a lot of storytelling 119 00:06:02,120 --> 00:06:04,039 Brian Ryerson: that happens around the years open that really really want 120 00:06:04,080 --> 00:06:07,080 Brian Ryerson: to bring to fans, and that can be as simple 121 00:06:07,120 --> 00:06:10,400 Brian Ryerson: as storytelling of what's happening today and what you should 122 00:06:10,440 --> 00:06:13,760 Brian Ryerson: be watching too. Maybe it's your favorite players and what's 123 00:06:13,800 --> 00:06:17,359 Brian Ryerson: going on behind the scenes with them, to even introducing 124 00:06:17,560 --> 00:06:20,320 Brian Ryerson: I want to say, the casual fans to who they 125 00:06:20,360 --> 00:06:22,800 Brian Ryerson: should be watching, why they should follow certain players, and 126 00:06:22,839 --> 00:06:25,720 Brian Ryerson: more bringing that player's story to life. Yeah. 127 00:06:25,760 --> 00:06:28,000 Jacob Goldstein: I mean, I feel like almost the whole point of 128 00:06:28,040 --> 00:06:31,280 Jacob Goldstein: sports is to create stories for us to follow, right, 129 00:06:31,400 --> 00:06:33,760 Jacob Goldstein: Like they're engineered to be stories. 130 00:06:33,800 --> 00:06:34,720 Brian Ryerson: It's exactly this. 131 00:06:34,720 --> 00:06:36,920 Jacob Goldstein: Thing is happening in front of you and there are 132 00:06:36,920 --> 00:06:40,600 Jacob Goldstein: two antagonists and the stakes are high, and you don't 133 00:06:40,640 --> 00:06:42,520 Jacob Goldstein: know how it's going to end, Like it's built to 134 00:06:42,560 --> 00:06:43,279 Jacob Goldstein: be a story. 135 00:06:43,520 --> 00:06:45,760 Brian Ryerson: Yeah, and that's the main challenge of the job is 136 00:06:45,760 --> 00:06:48,560 Brian Ryerson: you can plan, plan, plan, but once you get on 137 00:06:48,680 --> 00:06:50,599 Brian Ryerson: two players on court and you don't know what that 138 00:06:50,640 --> 00:06:52,760 Brian Ryerson: outcome is going to be, it's now sitting and waiting 139 00:06:52,800 --> 00:06:55,200 Brian Ryerson: and watching and you become a fan yourself. And then 140 00:06:55,240 --> 00:06:58,640 Brian Ryerson: it's how do you really captivate that story and how 141 00:06:58,680 --> 00:07:01,120 Brian Ryerson: do you narrate it? How do you like translate up 142 00:07:01,160 --> 00:07:02,000 Brian Ryerson: to fans. 143 00:07:01,800 --> 00:07:03,240 Jacob Goldstein: And it's like you kind of have to do it 144 00:07:03,279 --> 00:07:05,320 Jacob Goldstein: in real time, right, Like the whole point of sports 145 00:07:05,360 --> 00:07:06,279 Jacob Goldstein: is you don't know what's going to. 146 00:07:06,279 --> 00:07:09,200 Brian Ryerson: Happen exactly, and that's the excitement. And it's also there's 147 00:07:09,240 --> 00:07:11,120 Brian Ryerson: so many different types of fans. You know, there's the 148 00:07:11,720 --> 00:07:13,800 Brian Ryerson: fans who want a lot of enriched data and their 149 00:07:13,880 --> 00:07:16,040 Brian Ryerson: tennis nerds for lack of better of saying it, and 150 00:07:16,040 --> 00:07:18,920 Brian Ryerson: that they really want to dive deep into the intricacies 151 00:07:18,960 --> 00:07:21,440 Brian Ryerson: of the game, versus the casual fan who maybe just 152 00:07:21,440 --> 00:07:23,520 Brian Ryerson: wants more of this high level storyline of what does 153 00:07:23,560 --> 00:07:26,920 Brian Ryerson: this mean? Why is it important? So it's really trying 154 00:07:26,960 --> 00:07:29,280 Brian Ryerson: to figure out how to deliver that at scale and 155 00:07:29,560 --> 00:07:32,120 Brian Ryerson: really help fans get what they're looking for and the 156 00:07:32,120 --> 00:07:33,400 Brian Ryerson: type of content they're looking for. 157 00:07:33,720 --> 00:07:36,960 Jacob Goldstein: So, are there specific examples of, you know, how fan 158 00:07:37,040 --> 00:07:41,560 Jacob Goldstein: feedback has led to specific features digital features you build. 159 00:07:41,960 --> 00:07:45,000 Jacob Goldstein: Are there, like particularly popular features you've come up with, Like, 160 00:07:45,080 --> 00:07:46,120 Jacob Goldstein: what are some specifics? 161 00:07:46,240 --> 00:07:48,800 Brian Ryerson: Yeah, some low hanging fruit type things that came from 162 00:07:48,800 --> 00:07:52,200 Brian Ryerson: fan feedback. Is simple things sometimes like managing time zones 163 00:07:52,200 --> 00:07:53,480 Brian Ryerson: and when matches start. 164 00:07:53,520 --> 00:07:57,080 Jacob Goldstein: A persistent problem where those of us can work across. 165 00:07:56,800 --> 00:08:00,960 Brian Ryerson: Times exactly, and we do have like a twenty plus 166 00:08:01,000 --> 00:08:03,480 Brian Ryerson: courts happening at a time, so it's a lot to follow, 167 00:08:03,520 --> 00:08:05,960 Brian Ryerson: and how do you translate that to a fan whether 168 00:08:06,000 --> 00:08:08,560 Brian Ryerson: it's to their native language or to their time zone 169 00:08:08,640 --> 00:08:10,840 Brian Ryerson: or things like that. So that's one thing that came 170 00:08:10,840 --> 00:08:13,800 Brian Ryerson: through fan feedback, and another one a three to five 171 00:08:13,880 --> 00:08:16,480 Brian Ryerson: hour match, especially when you're having twenty plus of them 172 00:08:16,520 --> 00:08:18,680 Brian Ryerson: happening at a time, is there's too much for one 173 00:08:18,720 --> 00:08:22,160 Brian Ryerson: person to follow. So how do you start from an 174 00:08:22,280 --> 00:08:25,640 Brian Ryerson: editorial perspective really helping with that storytelling and guiding a 175 00:08:25,680 --> 00:08:28,280 Brian Ryerson: fan to like, all right, whether there's an upset about 176 00:08:28,280 --> 00:08:32,200 Brian Ryerson: to happen, or here's your matches to watch or even 177 00:08:32,240 --> 00:08:33,959 Brian Ryerson: some of the predictions we're starting to put in is 178 00:08:34,000 --> 00:08:36,000 Brian Ryerson: we really want to guide the fan before a match, 179 00:08:36,360 --> 00:08:39,120 Brian Ryerson: here's where you should tune in to even after a match, 180 00:08:39,160 --> 00:08:42,360 Brian Ryerson: of here's what's happened, here's what's important. And we're really 181 00:08:42,400 --> 00:08:43,839 Brian Ryerson: excited with some of the features we built in the 182 00:08:43,920 --> 00:08:46,640 Brian Ryerson: last few years that I would say really helps us 183 00:08:46,679 --> 00:08:48,400 Brian Ryerson: do that at more scale than what we were able 184 00:08:48,400 --> 00:08:50,920 Brian Ryerson: to do with just writers following a match and covering 185 00:08:50,920 --> 00:08:51,720 Brian Ryerson: every single match. 186 00:08:51,920 --> 00:08:54,199 Jacob Goldstein: Uh huh. So I want to talk a little bit 187 00:08:54,320 --> 00:09:00,480 Jacob Goldstein: about the partnership between IBM and the USTA. Tell me 188 00:09:00,480 --> 00:09:01,920 Jacob Goldstein: about the work you do together. 189 00:09:02,320 --> 00:09:05,560 Brian Ryerson: So IBM is our official digital and technology partner and 190 00:09:05,640 --> 00:09:08,600 Brian Ryerson: innovation partner of the US Open. They predate me. It's 191 00:09:08,640 --> 00:09:11,520 Brian Ryerson: a thirty year partnership and it truly as a partnership. 192 00:09:11,559 --> 00:09:14,559 Brian Ryerson: So I view the IBM consulting team as an extension 193 00:09:14,640 --> 00:09:18,120 Brian Ryerson: of my USTA team, So we work with them year round. 194 00:09:18,400 --> 00:09:22,760 Brian Ryerson: They design, develop and deliver the digital properties. They help 195 00:09:22,840 --> 00:09:25,520 Brian Ryerson: us provide the tools to create content to do things 196 00:09:25,520 --> 00:09:27,960 Brian Ryerson: at scale. They help us from stats and information and 197 00:09:28,280 --> 00:09:30,920 Brian Ryerson: really help us push from an innovation standpoint to make 198 00:09:30,960 --> 00:09:33,240 Brian Ryerson: sure that we are staying on that cutting edge of technology. 199 00:09:33,320 --> 00:09:37,800 Brian Ryerson: So I would truly say it's much more than a sponsorship, 200 00:09:37,800 --> 00:09:40,559 Brian Ryerson: where it's truly a partnership to deliver that fan experience. 201 00:09:41,000 --> 00:09:43,640 Jacob Goldstein: And so what are some of the specific things that 202 00:09:43,880 --> 00:09:45,040 Jacob Goldstein: you have done with IBM. 203 00:09:45,320 --> 00:09:49,480 Brian Ryerson: Yeah, so, I mean there's countless ones to talk through. Obviously, 204 00:09:49,520 --> 00:09:52,080 Brian Ryerson: they thirty years ago, they helped us build our first 205 00:09:52,160 --> 00:09:55,640 Brian Ryerson: website and it's kind of grown from there over the 206 00:09:55,800 --> 00:09:57,920 Brian Ryerson: past few years. I would say I think was twenty 207 00:09:57,960 --> 00:10:00,760 Brian Ryerson: eighteen as we started AI Highlights. That was really when 208 00:10:01,120 --> 00:10:04,360 Brian Ryerson: we were able to have all twenty matches going at 209 00:10:04,480 --> 00:10:07,640 Brian Ryerson: a single time. We were able to quickly deliver succinct 210 00:10:07,679 --> 00:10:10,440 Brian Ryerson: highlights to fans to our digital platform so they could 211 00:10:10,600 --> 00:10:12,280 Brian Ryerson: see highlights for every single core. 212 00:10:12,960 --> 00:10:15,920 Jacob Goldstein: Is that video highlights? Is that tech summaries? What does 213 00:10:15,920 --> 00:10:16,360 Jacob Goldstein: that mean? 214 00:10:16,880 --> 00:10:19,360 Brian Ryerson: At the time, it was video highlights, Okay, so it 215 00:10:19,400 --> 00:10:22,280 Brian Ryerson: was really taking that three to five hour match, let's say, 216 00:10:22,320 --> 00:10:24,840 Brian Ryerson: and cut it down to a three minute highlight that 217 00:10:24,920 --> 00:10:27,320 Brian Ryerson: could show up within moments after a match, ending to 218 00:10:27,400 --> 00:10:29,840 Brian Ryerson: our website and our mobile app, so fans could see 219 00:10:29,840 --> 00:10:31,520 Brian Ryerson: that all around the world and really kind of get 220 00:10:31,559 --> 00:10:34,079 Brian Ryerson: that three minute overview what happened in a match? 221 00:10:34,480 --> 00:10:37,360 Jacob Goldstein: And was that AI enabled. Was AI a piece of 222 00:10:37,760 --> 00:10:38,400 Jacob Goldstein: how to do that? 223 00:10:38,559 --> 00:10:41,520 Brian Ryerson: It was it was probably our first foray into AI. 224 00:10:41,800 --> 00:10:45,280 Jacob Goldstein: Back twenty eighteen is relatively early. 225 00:10:45,640 --> 00:10:50,079 Brian Ryerson: Yeah, exactly for tennis exactly. Yeah. It really I want 226 00:10:50,120 --> 00:10:54,400 Brian Ryerson: to say, opened up our ability to one again storyteller, 227 00:10:54,440 --> 00:10:56,720 Brian Ryerson: but attract new fans too. Is video has actually been 228 00:10:56,760 --> 00:10:59,640 Brian Ryerson: our number one growth area since twenty eighteen. I think 229 00:11:00,080 --> 00:11:01,559 Brian Ryerson: of that has to do with the scale of how 230 00:11:01,600 --> 00:11:02,960 Brian Ryerson: we deliver that content. 231 00:11:02,960 --> 00:11:05,880 Jacob Goldstein: Using AI and being able to deliver the sort of 232 00:11:06,000 --> 00:11:09,240 Jacob Goldstein: video highlight reels at scale. 233 00:11:08,960 --> 00:11:11,559 Brian Ryerson: Yeah, and do it quickly. Right. We've always had highlights, 234 00:11:11,559 --> 00:11:13,559 Brian Ryerson: but it was a manual process where you had a 235 00:11:14,080 --> 00:11:17,280 Brian Ryerson: video at or cutting through you know, a three hour match, 236 00:11:17,400 --> 00:11:19,679 Brian Ryerson: selecting the right scene, stitching together. It would have to 237 00:11:19,760 --> 00:11:22,960 Brian Ryerson: get voiced over, et cetera. We really have used AI 238 00:11:23,040 --> 00:11:24,640 Brian Ryerson: to make it, I want to say, much more efficient 239 00:11:24,760 --> 00:11:27,760 Brian Ryerson: and speed up that process and deliver it more quickly 240 00:11:27,760 --> 00:11:28,400 Brian Ryerson: to our fans. 241 00:11:28,760 --> 00:11:30,679 Jacob Goldstein: I mean, it would be a bummer to get scooped 242 00:11:30,840 --> 00:11:33,920 Jacob Goldstein: by whatever NBC News or Yes Pen or whatever. I'm 243 00:11:33,920 --> 00:11:35,439 Jacob Goldstein: sure there are all your partners and you love them 244 00:11:35,520 --> 00:11:38,240 Jacob Goldstein: most likely obviously you want to have the video first, right, 245 00:11:38,280 --> 00:11:39,000 Jacob Goldstein: it's your match. 246 00:11:39,240 --> 00:11:41,720 Brian Ryerson: Yeah, And I think It's also important to us as 247 00:11:41,800 --> 00:11:45,959 Brian Ryerson: being the USTA is ensuring that it's not just you know, 248 00:11:46,120 --> 00:11:49,720 Brian Ryerson: the main marquee players, that every player and all those 249 00:11:49,720 --> 00:11:53,200 Brian Ryerson: storylines and that whether it's you know, the main singles, 250 00:11:53,280 --> 00:11:55,880 Brian Ryerson: draw to our mixed doubles, et cetera. They all need 251 00:11:55,960 --> 00:11:58,400 Brian Ryerson: highlights and they all have their own stories to tell, 252 00:11:58,440 --> 00:12:00,199 Brian Ryerson: and how do we do that at scale? It was 253 00:12:00,240 --> 00:12:02,640 Brian Ryerson: something that before we had that product was not something 254 00:12:02,679 --> 00:12:03,400 Brian Ryerson: you were able to do. 255 00:12:03,840 --> 00:12:07,280 Jacob Goldstein: Great, So let's let's talk in some more detail about 256 00:12:07,320 --> 00:12:10,720 Jacob Goldstein: what you're working on. Let's start with the app. Tell 257 00:12:10,720 --> 00:12:13,760 Jacob Goldstein: me about the us Open app and the Companion website. 258 00:12:13,840 --> 00:12:16,080 Brian Ryerson: Yeah. So, so I'll start with the app, and I 259 00:12:16,120 --> 00:12:19,439 Brian Ryerson: feel like they serve similar needs, but they're a little 260 00:12:19,480 --> 00:12:22,760 Brian Ryerson: different in their own respective manners. Is the app everybody 261 00:12:22,840 --> 00:12:24,560 Brian Ryerson: has a phone in their hands at this point. The 262 00:12:24,600 --> 00:12:27,040 Brian Ryerson: app is kind of their guide to when I say 263 00:12:27,040 --> 00:12:29,680 Brian Ryerson: a million fans on site, we view the app as 264 00:12:29,720 --> 00:12:31,800 Brian Ryerson: we want that to be their on site guide and 265 00:12:31,840 --> 00:12:33,240 Brian Ryerson: Companion a million. 266 00:12:33,400 --> 00:12:36,080 Jacob Goldstein: Let's just pause on a million fans on site, right, 267 00:12:36,120 --> 00:12:39,839 Jacob Goldstein: because like a big professional whatever, an NFL game or 268 00:12:39,880 --> 00:12:43,360 Jacob Goldstein: something that's like one hundred thousand, this is ten x that. 269 00:12:43,720 --> 00:12:47,199 Brian Ryerson: Yeah, and a three week window and a very succinct, tight, 270 00:12:47,280 --> 00:12:51,319 Brian Ryerson: action packed window. There's a lot of action logistics. 271 00:12:51,400 --> 00:12:53,120 Jacob Goldstein: Okay, so keep going. 272 00:12:53,200 --> 00:12:56,000 Brian Ryerson: So the app, you know, whether it's finding the schedules, 273 00:12:56,080 --> 00:12:58,960 Brian Ryerson: the live scores, what's happening on court. That's really the 274 00:12:59,000 --> 00:13:01,640 Brian Ryerson: focus point of the app app and what we're really 275 00:13:01,679 --> 00:13:04,240 Brian Ryerson: focused on this year is how do we build in 276 00:13:04,280 --> 00:13:06,720 Brian Ryerson: some of those match summaries into the app, into our 277 00:13:06,800 --> 00:13:10,200 Brian Ryerson: Slam Tracker experience. So again, before match, that kind of 278 00:13:10,200 --> 00:13:12,440 Brian Ryerson: match preview of here's maybe if you have a ticket, 279 00:13:12,480 --> 00:13:15,559 Brian Ryerson: here's what to expect, here's you know are likely to win, 280 00:13:15,679 --> 00:13:18,160 Brian Ryerson: who we are predicting, so you can kind of get 281 00:13:18,160 --> 00:13:21,520 Brian Ryerson: some information heading in, and then after the match it's 282 00:13:21,559 --> 00:13:24,760 Brian Ryerson: more of what just happened, what it means for the 283 00:13:25,040 --> 00:13:27,800 Brian Ryerson: rest of the draw, who they're playing next, is this 284 00:13:27,880 --> 00:13:30,080 Brian Ryerson: the first time this has happened, et cetera, and really 285 00:13:30,160 --> 00:13:33,199 Brian Ryerson: enriching that experience as well. So the app is one 286 00:13:33,280 --> 00:13:35,600 Brian Ryerson: your guide to what you should be watching, but also 287 00:13:35,640 --> 00:13:38,199 Brian Ryerson: then giving you that insights and context to what's happening 288 00:13:38,240 --> 00:13:39,200 Brian Ryerson: on that court as you're. 289 00:13:39,080 --> 00:13:42,720 Jacob Goldstein: Watching, like the commentator in your pocket exactly. So you 290 00:13:42,880 --> 00:13:45,480 Jacob Goldstein: used a phrase in there as if I already knew 291 00:13:45,480 --> 00:13:47,960 Jacob Goldstein: it and I love the phrase, but I want you 292 00:13:48,040 --> 00:13:51,000 Jacob Goldstein: to talk more about it. That phrase is slam Tracker. 293 00:13:51,200 --> 00:13:56,960 Brian Ryerson: Yes, so slam Tracker is our long standing live scores. 294 00:13:57,000 --> 00:13:59,240 Brian Ryerson: I want to say match Center. It is, okay, where 295 00:13:59,480 --> 00:14:02,200 Brian Ryerson: every single data point, where every single match lives. And 296 00:14:02,240 --> 00:14:05,520 Brian Ryerson: it really it helps showcase what's happening to match. I say, 297 00:14:05,520 --> 00:14:08,360 Brian Ryerson: it's our broadcast companion. So if you're watching live, it's 298 00:14:08,360 --> 00:14:11,000 Brian Ryerson: our in stadium companion. It's also the best thing to 299 00:14:11,200 --> 00:14:13,000 Brian Ryerson: have if you aren't able to watch. 300 00:14:13,080 --> 00:14:14,840 Jacob Goldstein: And so, like, I'm on the app and there's a 301 00:14:14,880 --> 00:14:17,760 Jacob Goldstein: thing called slam Tracker, and it like taps slam Tracker. 302 00:14:17,800 --> 00:14:19,560 Jacob Goldstein: What do I see on my phone when I tap 303 00:14:19,600 --> 00:14:22,960 Jacob Goldstein: slam Tracker? You know, midday when the tournament's happening. 304 00:14:23,000 --> 00:14:24,880 Brian Ryerson: So before match, that's where you get a lot of 305 00:14:24,880 --> 00:14:27,080 Brian Ryerson: pre match content. That's where those live kind of our 306 00:14:27,120 --> 00:14:30,480 Brian Ryerson: predictions are. Likelihood to win lives within that So likelihood 307 00:14:30,480 --> 00:14:33,080 Brian Ryerson: to win essentially pulls in a bunch of data points. 308 00:14:33,120 --> 00:14:36,200 Brian Ryerson: So pass matches, how many times these players have played 309 00:14:36,240 --> 00:14:38,920 Brian Ryerson: each other against each other, Even some punditry and other 310 00:14:38,920 --> 00:14:41,720 Brian Ryerson: written articles that maybe our editorial team put out and 311 00:14:41,800 --> 00:14:44,360 Brian Ryerson: really kind of puts a prediction out there. 312 00:14:44,280 --> 00:14:46,400 Jacob Goldstein: And so it's just a percentage chance. 313 00:14:46,320 --> 00:14:49,400 Brian Ryerson: Yes exactly, but it uses millions of data points to 314 00:14:49,440 --> 00:14:51,720 Brian Ryerson: come up with that. Yes, so it really helps you 315 00:14:51,800 --> 00:14:54,760 Brian Ryerson: kind of understand what you're getting into for that match. 316 00:14:55,120 --> 00:14:57,880 Brian Ryerson: During a live match, it is every single point, so 317 00:14:58,240 --> 00:15:01,400 Brian Ryerson: point by point scoring as well as in depth analysis 318 00:15:01,400 --> 00:15:04,320 Brian Ryerson: in point commentary where also this year have a live 319 00:15:04,400 --> 00:15:07,360 Brian Ryerson: visualization that accompanies that that will really help bring the 320 00:15:07,520 --> 00:15:09,880 Brian Ryerson: match together. And what I mean by that is it 321 00:15:10,000 --> 00:15:13,240 Brian Ryerson: uses our ball tracking technology to really showcase the match 322 00:15:13,560 --> 00:15:16,480 Brian Ryerson: in near real time, so within seconds delay of where 323 00:15:16,520 --> 00:15:19,000 Brian Ryerson: the ball is being hit, where the players are, and 324 00:15:19,040 --> 00:15:21,840 Brian Ryerson: really bring a visualization to life and layered stats and 325 00:15:21,920 --> 00:15:22,640 Brian Ryerson: data on top of it. 326 00:15:22,760 --> 00:15:24,640 Jacob Goldstein: Huh. If that's sort of like when I'm watching a 327 00:15:24,720 --> 00:15:27,480 Jacob Goldstein: match on TV and there's like a close call as 328 00:15:27,520 --> 00:15:29,120 Jacob Goldstein: the ball in or out and they do that thing 329 00:15:29,160 --> 00:15:31,160 Jacob Goldstein: where they kind of show a sort of video game 330 00:15:31,280 --> 00:15:33,400 Jacob Goldstein: version of where the ball landed. Does it look like that? 331 00:15:33,640 --> 00:15:36,360 Brian Ryerson: It's like that before every single shot, So it's not 332 00:15:36,480 --> 00:15:38,960 Brian Ryerson: just those close ones. It's our first foray to bring 333 00:15:39,000 --> 00:15:40,000 Brian Ryerson: that match to life. 334 00:15:40,520 --> 00:15:42,360 Jacob Goldstein: Huh. And so what do I see on that kind 335 00:15:42,360 --> 00:15:44,520 Jacob Goldstein: of view that I don't see from whatever watching the video? 336 00:15:44,640 --> 00:15:47,320 Brian Ryerson: Yeah? So one you'll be able just to see more 337 00:15:47,360 --> 00:15:50,520 Brian Ryerson: of the ball trajectory and where the ball is being hit. 338 00:15:50,560 --> 00:15:52,840 Brian Ryerson: But then you can also start layering things in stats 339 00:15:52,840 --> 00:15:55,360 Brian Ryerson: and insights on top of that, So how many times 340 00:15:55,400 --> 00:15:58,520 Brian Ryerson: has player a hit the ball on a certain baseline, 341 00:15:58,600 --> 00:16:01,560 Brian Ryerson: how fast are they hitting it, maybe their serve percentage 342 00:16:01,560 --> 00:16:03,480 Brian Ryerson: and a certain side of the court, etc. So you 343 00:16:03,520 --> 00:16:05,920 Brian Ryerson: can really start layering in for the ones that really 344 00:16:05,960 --> 00:16:07,600 Brian Ryerson: want to dive deep into the For. 345 00:16:07,560 --> 00:16:11,520 Jacob Goldstein: The nerds, it's for the information rich exactly. 346 00:16:11,560 --> 00:16:14,040 Brian Ryerson: It's the strategy of tennis. It really should be an 347 00:16:14,120 --> 00:16:16,160 Brian Ryerson: interesting way to slice and dice a match. 348 00:16:16,360 --> 00:16:17,200 Jacob Goldstein: Huh. 349 00:16:17,400 --> 00:16:20,720 Malcolm Gladwell: It's remarkable how the USDA is leveraging AI to enhance 350 00:16:20,800 --> 00:16:26,080 Malcolm Gladwell: fan engagement and deliver immersive experiences both on site and online. 351 00:16:26,480 --> 00:16:32,000 Malcolm Gladwell: Brian's emphasis on storytelling really underscores the evolution of sports marketing. 352 00:16:32,640 --> 00:16:36,440 Malcolm Gladwell: The slam Chakra feature particularly caught my attention. It's essentially 353 00:16:36,520 --> 00:16:39,800 Malcolm Gladwell: bringing the excitement of a tennis match to life in 354 00:16:39,840 --> 00:16:44,040 Malcolm Gladwell: your palm, moment by moment. As someone who appreciates the 355 00:16:44,120 --> 00:16:47,840 Malcolm Gladwell: narrative intricacies of sports, I find it compelling how AI 356 00:16:47,960 --> 00:16:51,800 Malcolm Gladwell: helps predict and analyze matches in real time. 357 00:16:52,840 --> 00:16:55,240 Jacob Goldstein: Tell me about the AI commentary feature. 358 00:16:55,440 --> 00:16:59,080 Brian Ryerson: Yeah, I know, I mentioned AI highlights back in twenty eighteen. 359 00:16:59,160 --> 00:17:02,120 Brian Ryerson: It's now progress for us. And again if we go 360 00:17:02,240 --> 00:17:05,200 Brian Ryerson: back to before we had a highlights, to have a 361 00:17:05,240 --> 00:17:08,359 Brian Ryerson: highlight ready for the site was a video editor cutting 362 00:17:08,359 --> 00:17:12,160 Brian Ryerson: the highlight and getting voiced over and then being published aside, 363 00:17:12,200 --> 00:17:15,840 Brian Ryerson: and it took probably an hour plus for that highlight 364 00:17:15,880 --> 00:17:19,520 Brian Ryerson: to really be created. Now with AI commentary, not only 365 00:17:19,560 --> 00:17:22,679 Brian Ryerson: are we creating and cutting the highlights using our AI technology, 366 00:17:22,720 --> 00:17:24,919 Brian Ryerson: but it's now using all the data points that we 367 00:17:24,960 --> 00:17:27,200 Brian Ryerson: have around the match, whether it's our live scoring data, 368 00:17:27,520 --> 00:17:30,919 Brian Ryerson: our ball tra directory data, etc. And it's really creating 369 00:17:30,920 --> 00:17:34,439 Brian Ryerson: a script that helped storytell around that match. That's all 370 00:17:34,520 --> 00:17:38,399 Brian Ryerson: using Watson X technology and then using text to speech 371 00:17:38,480 --> 00:17:40,960 Brian Ryerson: we're able to actually then create the commentary on top 372 00:17:41,000 --> 00:17:44,239 Brian Ryerson: of that, which all happens now within minutes. So our 373 00:17:44,280 --> 00:17:47,160 Brian Ryerson: team's able to now create fully voiced highlights for every 374 00:17:47,200 --> 00:17:50,399 Brian Ryerson: men's and women's singles match to our site within minutes. 375 00:17:51,160 --> 00:17:53,679 Jacob Goldstein: So I know there's a new feature you're working on 376 00:17:53,720 --> 00:17:58,000 Jacob Goldstein: for this year called match reports. What are match reports? 377 00:17:58,320 --> 00:18:02,919 Brian Ryerson: It's our ability to succsickly tell the story of a match, 378 00:18:03,400 --> 00:18:06,920 Brian Ryerson: so everything happens in five hours within that match down 379 00:18:06,960 --> 00:18:10,880 Brian Ryerson: to a couple paragraphs. That really helps a user understand 380 00:18:10,960 --> 00:18:14,560 Brian Ryerson: or a fan understand what just happened. Again, some key 381 00:18:14,600 --> 00:18:18,159 Brian Ryerson: stats what's upcoming really help us with that storytelling. In 382 00:18:18,200 --> 00:18:20,840 Brian Ryerson: the past, when we have twenty two courts happening at 383 00:18:20,840 --> 00:18:23,040 Brian Ryerson: a certain time, we would have to pick and choose 384 00:18:23,080 --> 00:18:25,560 Brian Ryerson: which stories we think or which matches we think are 385 00:18:25,560 --> 00:18:27,600 Brian Ryerson: going to have the best stories, and that's a really 386 00:18:27,600 --> 00:18:30,560 Brian Ryerson: hard thing to predict from an editorial perspective. With our 387 00:18:30,600 --> 00:18:32,960 Brian Ryerson: Match Reports now we'll be able to have full coverage 388 00:18:32,960 --> 00:18:34,920 Brian Ryerson: of every single match during the main draw. 389 00:18:35,680 --> 00:18:38,280 Jacob Goldstein: So, of course I want to talk about jeneritive AI. 390 00:18:38,560 --> 00:18:41,399 Jacob Goldstein: How could we not talk about generative Of course, what 391 00:18:41,480 --> 00:18:42,840 Jacob Goldstein: are you working on with jenerative AI? 392 00:18:43,160 --> 00:18:45,560 Brian Ryerson: So match Reports is the prime example of it. So 393 00:18:45,640 --> 00:18:48,760 Brian Ryerson: Match Reports will be completely using Watson next genera of 394 00:18:48,760 --> 00:18:53,320 Brian Ryerson: AI technology, and really, again to us, it's how can 395 00:18:53,359 --> 00:18:56,639 Brian Ryerson: we do that storytelling at scale? Tennis is such a 396 00:18:56,720 --> 00:18:59,960 Brian Ryerson: data rich sport. All sports have data, but tennis has 397 00:19:00,080 --> 00:19:02,200 Brian Ryerson: a lot of shots and different shot types and ball 398 00:19:02,240 --> 00:19:06,040 Brian Ryerson: trajectory and live scoring data and umpire chair data and 399 00:19:06,080 --> 00:19:10,160 Brian Ryerson: crowd and all that factoring in jenera AI really helps 400 00:19:10,240 --> 00:19:13,119 Brian Ryerson: us take some of that structured and unstructured data really 401 00:19:13,480 --> 00:19:16,960 Brian Ryerson: one organize it in a way, but then help us 402 00:19:17,200 --> 00:19:20,359 Brian Ryerson: quickly tell that story at scale to all of our fans. 403 00:19:20,560 --> 00:19:23,239 Brian Ryerson: And I think we're really just starting to scratch at 404 00:19:23,280 --> 00:19:26,520 Brian Ryerson: some of the capabilities, and we're really excited about where 405 00:19:26,560 --> 00:19:28,560 Brian Ryerson: we're being, but we also see the opportunity of even 406 00:19:28,840 --> 00:19:31,040 Brian Ryerson: how we can grow to new fans and new fans 407 00:19:31,080 --> 00:19:33,560 Brian Ryerson: around the world using jennal of AI in the future. 408 00:19:34,840 --> 00:19:38,600 Jacob Goldstein: So I'm curious, and you alluded to this a moment ago, 409 00:19:38,640 --> 00:19:40,240 Jacob Goldstein: but I'd like to talk a little bit more about 410 00:19:40,280 --> 00:19:44,000 Jacob Goldstein: it because it seems interesting as a technical problem. Right, 411 00:19:44,200 --> 00:19:49,480 Jacob Goldstein: is the nature of turning tennis matches into stories, which 412 00:19:49,520 --> 00:19:51,919 Jacob Goldstein: is fundamentally what we're talking about here in different ways 413 00:19:51,960 --> 00:19:57,200 Jacob Goldstein: in different media, is about taking both structured data, right 414 00:19:57,320 --> 00:20:02,880 Jacob Goldstein: like the stats, you know, points, matches, and also unstructured data, 415 00:20:03,000 --> 00:20:06,800 Jacob Goldstein: right like commentary and articles and the kind of fuzzier 416 00:20:06,840 --> 00:20:10,760 Jacob Goldstein: parts of storytelling. And so I'm curious how AI kind 417 00:20:10,800 --> 00:20:14,200 Jacob Goldstein: of helps you manage both the structured and the unstructured data. 418 00:20:14,680 --> 00:20:18,520 Brian Ryerson: Yeah, so I think the structured data is pretty self experimentatory, 419 00:20:18,840 --> 00:20:20,600 Brian Ryerson: but when you get into the unstructured data and some 420 00:20:20,680 --> 00:20:22,520 Brian Ryerson: of the punditry. That's where you get more of the 421 00:20:22,560 --> 00:20:26,000 Brian Ryerson: opinion pieces into it, like a specific player matchup. This 422 00:20:26,080 --> 00:20:28,879 Brian Ryerson: player always plays well against so and so, or as 423 00:20:28,880 --> 00:20:30,720 Brian Ryerson: they play always played well at night, or they're a 424 00:20:30,760 --> 00:20:34,520 Brian Ryerson: fan favorite, and the crowd, you know, adrenaline and the 425 00:20:34,560 --> 00:20:36,880 Brian Ryerson: crowd being behind you can really motivate you to play 426 00:20:36,880 --> 00:20:40,200 Brian Ryerson: a lot better. So it pulls in all those unstructured 427 00:20:40,240 --> 00:20:43,000 Brian Ryerson: pieces and helps us really put some more rigor around 428 00:20:43,040 --> 00:20:45,680 Brian Ryerson: it and help add and enrich our storytelling with it. 429 00:20:46,080 --> 00:20:50,000 Jacob Goldstein: And so I'm curious when you're starting to use generative AI, 430 00:20:50,280 --> 00:20:53,080 Jacob Goldstein: you know, over the past few years, like what were 431 00:20:53,119 --> 00:20:54,439 Jacob Goldstein: your concerns going into that. 432 00:20:54,840 --> 00:20:59,360 Brian Ryerson: I think our biggest concern is ensuring that one factually 433 00:20:59,600 --> 00:21:01,520 Brian Ryerson: it is because it's only as good as the data 434 00:21:01,520 --> 00:21:03,439 Brian Ryerson: you feed in. And how do you really ensure that 435 00:21:03,480 --> 00:21:06,119 Brian Ryerson: your model's working right and that the output and the 436 00:21:06,200 --> 00:21:08,919 Brian Ryerson: data you're feeding it matches the output and how do 437 00:21:08,960 --> 00:21:11,040 Brian Ryerson: you do that at scale? So we do have a 438 00:21:11,080 --> 00:21:14,160 Brian Ryerson: lot of human intervention. That's where the IBM consulting team, 439 00:21:14,280 --> 00:21:16,520 Brian Ryerson: they're on site with us for those full three weeks 440 00:21:16,560 --> 00:21:19,919 Brian Ryerson: really helping us review everything. And we're constantly learning, especially 441 00:21:20,000 --> 00:21:22,720 Brian Ryerson: early in the tournament and I would say the other 442 00:21:22,960 --> 00:21:25,600 Brian Ryerson: big concern, again it goes around to the data, is 443 00:21:25,920 --> 00:21:28,600 Brian Ryerson: what data do we have available that is trustworthy? So, 444 00:21:28,800 --> 00:21:30,800 Brian Ryerson: you know, we are feel very confident with the data 445 00:21:30,800 --> 00:21:32,440 Brian Ryerson: that comes off of Cork, but when we get into 446 00:21:32,440 --> 00:21:36,160 Brian Ryerson: that unstructured piece, what are the right data sources? How 447 00:21:36,200 --> 00:21:38,560 Brian Ryerson: do we validate those data sources and how do we 448 00:21:39,000 --> 00:21:41,800 Brian Ryerson: ensure that they're accurate Because if the data that has 449 00:21:41,840 --> 00:21:43,440 Brian Ryerson: to go in has to be accurate for the for 450 00:21:43,480 --> 00:21:44,480 Brian Ryerson: the output. 451 00:21:44,480 --> 00:21:46,879 Jacob Goldstein: So how do you do that? That's the concern? How 452 00:21:47,200 --> 00:21:48,000 Jacob Goldstein: how do you address it? 453 00:21:48,200 --> 00:21:50,800 Brian Ryerson: Yeah, so I think there's a number of tools that 454 00:21:50,880 --> 00:21:53,560 Brian Ryerson: we use all within the Watson X umbrella. We do 455 00:21:53,640 --> 00:21:56,280 Brian Ryerson: a lot of training with the IBM team, so we 456 00:21:56,359 --> 00:22:00,119 Brian Ryerson: have to constantly train and retrain that model. I think 457 00:22:00,119 --> 00:22:02,960 Brian Ryerson: the other piece that we're doing is again as we're 458 00:22:02,960 --> 00:22:05,720 Brian Ryerson: creating that content and we have the IBM consulting team 459 00:22:05,760 --> 00:22:08,280 Brian Ryerson: on site helping us with that, is as we see 460 00:22:08,280 --> 00:22:11,280 Brian Ryerson: things and we see outputs, it's refeeding that back into 461 00:22:11,280 --> 00:22:13,080 Brian Ryerson: the model to make it better for the next time. 462 00:22:13,200 --> 00:22:16,680 Brian Ryerson: So it's a constantly learning process that we're undergoing. 463 00:22:17,280 --> 00:22:21,040 Jacob Goldstein: So I want to talk about scale. Yes, you have 464 00:22:21,200 --> 00:22:24,960 Jacob Goldstein: like what twenty two different courts with matches going all 465 00:22:25,000 --> 00:22:28,640 Jacob Goldstein: at the same time, you're trying to you know, approximately 466 00:22:29,000 --> 00:22:32,399 Jacob Goldstein: instantly generate summaries of all these matches in something like 467 00:22:32,480 --> 00:22:37,320 Jacob Goldstein: real time. And I'm curious in particular how the IBM 468 00:22:37,359 --> 00:22:41,560 Jacob Goldstein: models you're using, the IBM Granite models are helping you scale. 469 00:22:42,040 --> 00:22:44,800 Brian Ryerson: Yeah. So I think one of the big learnings we 470 00:22:44,880 --> 00:22:48,560 Brian Ryerson: had with IBM granted models too is that we're able 471 00:22:48,600 --> 00:22:51,399 Brian Ryerson: to run it, you know, against last year's tournaments and 472 00:22:51,440 --> 00:22:54,760 Brian Ryerson: see what the expected outputs could be and really help 473 00:22:54,840 --> 00:22:57,000 Brian Ryerson: train that model heading into the tournament. Because as we 474 00:22:57,080 --> 00:22:59,440 Brian Ryerson: talked about in the beginning, is we can plan play 475 00:22:59,440 --> 00:23:02,040 Brian Ryerson: and play. Once two players get on court, the outcome 476 00:23:02,080 --> 00:23:04,480 Brian Ryerson: is unknown. So how do we really run it through 477 00:23:04,480 --> 00:23:07,479 Brian Ryerson: its paces and really make sure that whatever that outcome 478 00:23:07,520 --> 00:23:09,840 Brian Ryerson: could be and whatever that scenario is, whether it's a 479 00:23:10,040 --> 00:23:13,760 Brian Ryerson: fifth set tie break that happens, or maybe there's a 480 00:23:13,760 --> 00:23:17,560 Brian Ryerson: a fault in the match or something that we're not anticipating, 481 00:23:17,720 --> 00:23:19,359 Brian Ryerson: that we have that accounted for and that the a 482 00:23:19,600 --> 00:23:22,240 Brian Ryerson: won't throw off that output. So we really try to 483 00:23:22,280 --> 00:23:26,840 Brian Ryerson: think through every scenario, which is sometimes difficult, right because 484 00:23:27,000 --> 00:23:29,560 Brian Ryerson: again live sports is the unknown is the unknown that's 485 00:23:29,560 --> 00:23:31,840 Brian Ryerson: what makes it fun. We do spend a lot of 486 00:23:31,840 --> 00:23:34,840 Brian Ryerson: time thinking through potential scenarios and ensuring that we have 487 00:23:34,880 --> 00:23:38,119 Brian Ryerson: the right data sets and the model to predict that. 488 00:23:38,920 --> 00:23:42,240 Jacob Goldstein: Tell me about match reports and the generative AI model 489 00:23:42,240 --> 00:23:42,919 Jacob Goldstein: you're using for that. 490 00:23:43,640 --> 00:23:46,080 Brian Ryerson: Yeah, so match reports will be new for us this year, 491 00:23:46,160 --> 00:23:48,680 Brian Ryerson: so we're in testing right now, so we're really excited 492 00:23:48,720 --> 00:23:51,000 Brian Ryerson: around it. But the model that we'll be able to 493 00:23:51,119 --> 00:23:54,520 Brian Ryerson: use using Watson X will use a bunch of different 494 00:23:54,520 --> 00:23:57,560 Brian Ryerson: parts of the suite of tools A, meaning that again 495 00:23:57,600 --> 00:24:00,560 Brian Ryerson: of taking some of that punditry and the unstructured and 496 00:24:00,600 --> 00:24:04,040 Brian Ryerson: the editorial spend, take our structured data as well. And 497 00:24:04,119 --> 00:24:07,240 Brian Ryerson: really what we're working on right now is figuring out 498 00:24:07,280 --> 00:24:10,880 Brian Ryerson: the right prompts for the AI to really ensure that 499 00:24:11,040 --> 00:24:15,320 Brian Ryerson: it tells the right structured story, meaning what just happened. Right, 500 00:24:15,480 --> 00:24:18,360 Brian Ryerson: So our recap is pretty standard. Here's what the data 501 00:24:18,400 --> 00:24:20,680 Brian Ryerson: is telling us, who won, who lost? How many sets? 502 00:24:20,680 --> 00:24:23,360 Jacob Goldstein: Here's the story the structured data part, that's the easy part. 503 00:24:23,560 --> 00:24:26,280 Brian Ryerson: Yeah, and then really where it gets exciting is then 504 00:24:26,560 --> 00:24:30,200 Brian Ryerson: what does this mean? Meaning what's upcoming? So there's all 505 00:24:30,240 --> 00:24:32,440 Brian Ryerson: these different scenarios when you get into you know, two 506 00:24:32,520 --> 00:24:35,239 Brian Ryerson: hundred and fifty four players and a large straw. This 507 00:24:35,280 --> 00:24:37,880 Brian Ryerson: allows us to distill that down and really tell kind 508 00:24:37,920 --> 00:24:40,840 Brian Ryerson: of what could happen upcoming. The AI helps us do 509 00:24:40,920 --> 00:24:41,800 Brian Ryerson: that at scale. 510 00:24:42,040 --> 00:24:44,359 Jacob Goldstein: So I want to sort of generalize for a moment 511 00:24:44,400 --> 00:24:47,960 Jacob Goldstein: to talk about kind of you know, broader challenges with 512 00:24:48,000 --> 00:24:50,960 Jacob Goldstein: AI and how you've solved them. You know a lot 513 00:24:51,000 --> 00:24:55,880 Jacob Goldstein: of generative AI pilots fail because the data quality isn't 514 00:24:55,920 --> 00:24:59,199 Jacob Goldstein: high enough, because the risk controls aren't there, and so 515 00:24:59,280 --> 00:25:02,560 Jacob Goldstein: I'm curious how you dealt with those problems and are 516 00:25:02,600 --> 00:25:03,200 Jacob Goldstein: dealing with them. 517 00:25:03,560 --> 00:25:06,479 Brian Ryerson: Data quality, again, we feel common with the data that 518 00:25:06,640 --> 00:25:10,320 Brian Ryerson: is supplied from the US open and from the USTA, right, 519 00:25:10,440 --> 00:25:13,239 Brian Ryerson: so we have again that's our structure, scoring data and 520 00:25:13,280 --> 00:25:16,159 Brian Ryerson: all that. I think what we're constantly looking at is 521 00:25:16,160 --> 00:25:18,639 Brian Ryerson: when we get outside of our known sources and out 522 00:25:18,680 --> 00:25:20,840 Brian Ryerson: to third parties is that's where a lot of the 523 00:25:20,880 --> 00:25:24,320 Brian Ryerson: testing and model work happens. So we pull in different 524 00:25:24,359 --> 00:25:27,879 Brian Ryerson: data sources and really try to work through how it 525 00:25:27,960 --> 00:25:30,159 Brian Ryerson: changes that output. Again, some of that comes down to 526 00:25:30,280 --> 00:25:32,600 Brian Ryerson: where it's an open model and the transparency that we 527 00:25:32,680 --> 00:25:35,480 Brian Ryerson: have and the learning that comes behind it. That's where 528 00:25:35,520 --> 00:25:37,720 Brian Ryerson: a lot of that confidence can come from, and it 529 00:25:37,760 --> 00:25:40,800 Brian Ryerson: comes from a lot of testing and feeding it more data. 530 00:25:41,600 --> 00:25:44,000 Brian Ryerson: Your second question was a little bit more around the 531 00:25:44,040 --> 00:25:45,280 Brian Ryerson: output I believe. 532 00:25:45,040 --> 00:25:48,240 Jacob Goldstein: Right, yeah, and risks, Right, so risk I think of 533 00:25:48,320 --> 00:25:51,080 Jacob Goldstein: risk more in terms of output, right, But the obvious 534 00:25:51,119 --> 00:25:54,160 Jacob Goldstein: sphere is like what if it says something wrong? Yeah, 535 00:25:54,200 --> 00:25:57,840 Jacob Goldstein: inflammatory or whatever like that seems scary. 536 00:25:58,119 --> 00:26:01,480 Brian Ryerson: Yeah, it definitely is one of our largest concerns when 537 00:26:01,480 --> 00:26:04,040 Brian Ryerson: we first took this fora I would say a lot 538 00:26:04,040 --> 00:26:06,440 Brian Ryerson: of that comes through our work with IBM and IBM 539 00:26:06,600 --> 00:26:10,000 Brian Ryerson: consulting team and really ensuring that again they're an extension 540 00:26:10,000 --> 00:26:13,520 Brian Ryerson: and the partnership there of our team. So whenever we 541 00:26:13,560 --> 00:26:15,680 Brian Ryerson: are creating let's say it's the Match Report, and we're 542 00:26:15,720 --> 00:26:18,520 Brian Ryerson: going to be creating these extinct articles for every single 543 00:26:19,040 --> 00:26:21,560 Brian Ryerson: men's and women's single match that happens, is all of 544 00:26:21,560 --> 00:26:24,679 Brian Ryerson: those will have manual review and people looking through them 545 00:26:24,720 --> 00:26:27,840 Brian Ryerson: for accuracy to ensure that the model then hallucinat or 546 00:26:27,840 --> 00:26:30,080 Brian Ryerson: make up a fact or fill in the gaps from 547 00:26:30,119 --> 00:26:32,720 Brian Ryerson: things like that. That's the first step. And then also 548 00:26:32,800 --> 00:26:35,600 Brian Ryerson: when our editorial team goes to publish those of the website, 549 00:26:35,760 --> 00:26:37,520 Brian Ryerson: they're going to be checking it as well, So there 550 00:26:37,560 --> 00:26:40,879 Brian Ryerson: are manual interventions throughout that to really check that model, 551 00:26:41,320 --> 00:26:43,920 Brian Ryerson: but we feel that the ability to do it at 552 00:26:43,960 --> 00:26:46,520 Brian Ryerson: scale and with us more to check that is the 553 00:26:46,520 --> 00:26:48,680 Brian Ryerson: efficiency problem that we've been looking to solve. 554 00:26:49,480 --> 00:26:52,679 Jacob Goldstein: So the USTA and IBM have been working together on 555 00:26:52,920 --> 00:26:55,679 Jacob Goldstein: digital innovation for like thirty years from you know, the 556 00:26:55,720 --> 00:27:00,600 Jacob Goldstein: first website, yes for the USTA until now that's the 557 00:27:00,640 --> 00:27:04,120 Jacob Goldstein: past thirty years. If you look ahead, what's the next. 558 00:27:03,920 --> 00:27:06,960 Brian Ryerson: Thirty thirty years is a really long time. 559 00:27:06,840 --> 00:27:07,640 Jacob Goldstein: At A three. 560 00:27:07,800 --> 00:27:11,399 Brian Ryerson: Yeah, I think you know where I get excited, and 561 00:27:11,560 --> 00:27:13,680 Brian Ryerson: I think I alluded to it in the beginning about 562 00:27:13,720 --> 00:27:15,959 Brian Ryerson: how I feel like we're just scratching at the surface, 563 00:27:16,080 --> 00:27:18,240 Brian Ryerson: especially with journat of Ai, and where I see it 564 00:27:18,320 --> 00:27:21,440 Brian Ryerson: going is there's a lot of different fans out there, 565 00:27:21,720 --> 00:27:23,600 Brian Ryerson: and we're also very kindness in the US open that 566 00:27:23,600 --> 00:27:26,000 Brian Ryerson: we're a worldwide event and that there's a lot of 567 00:27:26,000 --> 00:27:30,240 Brian Ryerson: different fans that were not necessary creating content for bespoke 568 00:27:30,359 --> 00:27:33,679 Brian Ryerson: meaning in their native language or maybe it's in that 569 00:27:33,760 --> 00:27:35,560 Brian Ryerson: native players language and things like that. 570 00:27:35,720 --> 00:27:38,960 Brian Ryerson: Is where I get excited is we've seen immense growth 571 00:27:39,000 --> 00:27:41,280 Brian Ryerson: with A Highlights and the ability to now do highlights 572 00:27:41,320 --> 00:27:44,280 Brian Ryerson: at scale is the ability for us to start creating 573 00:27:44,359 --> 00:27:48,560 Brian Ryerson: content in different languages, maybe covering different parts of the match, 574 00:27:48,560 --> 00:27:51,040 Brian Ryerson: So maybe you do have that stats junkie really wants. 575 00:27:51,119 --> 00:27:54,120 Brian Ryerson: Just it's the fastest serve and here's the deep insights 576 00:27:54,200 --> 00:27:56,760 Brian Ryerson: versus the casual fan who's looking for more of the 577 00:27:56,840 --> 00:28:00,480 Brian Ryerson: storytelling around how a player trains and what up to 578 00:28:00,560 --> 00:28:03,400 Brian Ryerson: it was like and what it means for them afterwards 579 00:28:03,440 --> 00:28:05,639 Brian Ryerson: and things like that. A lot of that takes a 580 00:28:05,640 --> 00:28:08,320 Brian Ryerson: lot of time. Now we're able to solve that efficiency 581 00:28:08,400 --> 00:28:11,080 Brian Ryerson: problem and do it in multiple languages, we can really 582 00:28:11,119 --> 00:28:14,680 Brian Ryerson: create I want to say, personalized content to a lot 583 00:28:14,760 --> 00:28:17,960 Brian Ryerson: more fans all around the world, which again helps us 584 00:28:18,040 --> 00:28:20,280 Brian Ryerson: grow the sport of tennis great. 585 00:28:20,840 --> 00:28:24,240 Jacob Goldstein: Uh So I want to finish with a speed round. Okay, 586 00:28:24,440 --> 00:28:25,080 Jacob Goldstein: are you ready? 587 00:28:25,200 --> 00:28:26,000 Brian Ryerson: I am ready? 588 00:28:26,119 --> 00:28:29,640 Jacob Goldstein: Okay, first thing that comes to mind complete this sentence. 589 00:28:30,240 --> 00:28:31,960 Jacob Goldstein: In five years, AI. 590 00:28:31,840 --> 00:28:35,360 Brian Ryerson: Will transform many parts of the business. 591 00:28:35,600 --> 00:28:39,840 Jacob Goldstein: What is the number one thing that people misunderstand about AI? 592 00:28:40,400 --> 00:28:44,560 Brian Ryerson: That it's supplemental, not replacing, meaning that it helps it 593 00:28:44,600 --> 00:28:48,640 Brian Ryerson: with efficiencies, but it doesn't necessarily replace the creativity. 594 00:28:49,360 --> 00:28:53,040 Jacob Goldstein: Right now, what advice would you give yourself ten years 595 00:28:53,080 --> 00:28:55,920 Jacob Goldstein: ago to better prepare you for today? 596 00:28:57,000 --> 00:29:00,520 Brian Ryerson: I think it would have been especially now that we're 597 00:29:00,560 --> 00:29:03,000 Brian Ryerson: able to take so much of that unstructured data and 598 00:29:03,520 --> 00:29:06,640 Brian Ryerson: pass content that we were created to help tell stories 599 00:29:07,240 --> 00:29:10,080 Brian Ryerson: was to I want to say, archive more of that 600 00:29:10,440 --> 00:29:12,040 Brian Ryerson: in a way that we could be using that to 601 00:29:12,120 --> 00:29:15,560 Brian Ryerson: help pull from that now. So you know, we've seen 602 00:29:15,680 --> 00:29:17,720 Brian Ryerson: kind of a change in the guard from some of 603 00:29:17,760 --> 00:29:20,680 Brian Ryerson: our start players to now new and up and comers, 604 00:29:20,680 --> 00:29:22,680 Brian Ryerson: and it would be really fascinating to me if there 605 00:29:22,760 --> 00:29:25,440 Brian Ryerson: was a way to to cross sections some of that 606 00:29:25,600 --> 00:29:28,680 Brian Ryerson: and saying like what trajectories are certain up and coming 607 00:29:28,720 --> 00:29:32,360 Brian Ryerson: players maybe filing from others. So it's more I wish 608 00:29:32,360 --> 00:29:34,480 Brian Ryerson: we kept more of the content we created. 609 00:29:34,200 --> 00:29:39,120 Jacob Goldstein: Back fave the data exactly. Well are you saving it 610 00:29:39,160 --> 00:29:39,600 Jacob Goldstein: all now? 611 00:29:39,880 --> 00:29:42,880 Brian Ryerson: Oh yeah, one hundred percent learned our lesson? Yes, yes. 612 00:29:43,480 --> 00:29:45,880 Jacob Goldstein: So on the business side of AI, what do you 613 00:29:45,880 --> 00:29:47,120 Jacob Goldstein: think is the next big thing? 614 00:29:47,760 --> 00:29:51,040 Brian Ryerson: I alluded to it earlier. I think it's personalization and 615 00:29:51,080 --> 00:29:54,680 Brian Ryerson: getting content that's catered to you at scale, whether you 616 00:29:54,720 --> 00:29:57,720 Brian Ryerson: know that's across the sports sphere or or any type 617 00:29:57,720 --> 00:30:01,800 Brian Ryerson: of written content or or new I feel like the 618 00:30:01,880 --> 00:30:05,280 Brian Ryerson: ability to really get contentated to the type of fan 619 00:30:05,360 --> 00:30:07,959 Brian Ryerson: you are and the insights you have is where we're 620 00:30:08,000 --> 00:30:08,440 Brian Ryerson: all headed. 621 00:30:09,480 --> 00:30:13,320 Jacob Goldstein: And in terms of your non work life, how do 622 00:30:13,360 --> 00:30:15,000 Jacob Goldstein: you use AI? Day to day. 623 00:30:15,200 --> 00:30:17,560 Brian Ryerson: It's funny. I was just having this conversation with a 624 00:30:17,600 --> 00:30:20,840 Brian Ryerson: friend the other day and we were talking about sometimes 625 00:30:20,840 --> 00:30:23,920 Brian Ryerson: when you're starting something new, the hardest thing to do 626 00:30:24,080 --> 00:30:26,360 Brian Ryerson: is you have a blank piece of paper or a thought, 627 00:30:26,400 --> 00:30:30,520 Brian Ryerson: and how do you get started. Sometimes with these generative models, 628 00:30:30,840 --> 00:30:32,440 Brian Ryerson: the easiest thing and the best thing you can do 629 00:30:32,520 --> 00:30:35,200 Brian Ryerson: is it helps you get started. Meaning it may not 630 00:30:35,200 --> 00:30:37,160 Brian Ryerson: be one hundred percent with that first prompt, but it's 631 00:30:37,200 --> 00:30:40,520 Brian Ryerson: that efficiency of whether it's an outline for a new idea, 632 00:30:40,720 --> 00:30:42,920 Brian Ryerson: or it's a marketing brief you have to write, or 633 00:30:43,120 --> 00:30:45,280 Brian Ryerson: sometimes even if it's an email, you have to write 634 00:30:45,400 --> 00:30:47,600 Brian Ryerson: for a personal something and you're not sure how to 635 00:30:47,640 --> 00:30:49,760 Brian Ryerson: word it the right way. It allows you to have 636 00:30:49,960 --> 00:30:51,880 Brian Ryerson: a start and then you can edit from there. So 637 00:30:51,920 --> 00:30:54,960 Brian Ryerson: again going back to my efficiency point, it helps you 638 00:30:55,000 --> 00:30:55,920 Brian Ryerson: become more efficient. 639 00:30:56,200 --> 00:30:57,840 Jacob Goldstein: Solve's the blank page problem. 640 00:30:58,040 --> 00:30:58,400 Brian Ryerson: It does. 641 00:31:00,200 --> 00:31:01,600 Jacob Goldstein: It was great to talk with you. Thank you so 642 00:31:01,680 --> 00:31:02,360 Jacob Goldstein: much for your time. 643 00:31:02,440 --> 00:31:03,920 Brian Ryerson: Yeah, this was fun. Thanks for having me. 644 00:31:06,080 --> 00:31:08,320 Malcolm Gladwell: A huge thanks to Jacob and Brian for the deep 645 00:31:08,400 --> 00:31:12,520 Malcolm Gladwell: dive into the cutting edge innovations transforming the game of tennis. 646 00:31:13,080 --> 00:31:16,040 Malcolm Gladwell: Brian shed light on how the US opens partnership with 647 00:31:16,080 --> 00:31:21,360 Malcolm Gladwell: IBM is harnessing data driven insights to reshape storytelling in sports, 648 00:31:21,760 --> 00:31:27,280 Malcolm Gladwell: from AI generated commentary to match reports. As we look ahead, 649 00:31:27,360 --> 00:31:32,120 Malcolm Gladwell: I'm excited about the possibilities for personalizing content and reaching 650 00:31:32,200 --> 00:31:36,240 Malcolm Gladwell: fans in new ways. The future of AI promises more 651 00:31:36,280 --> 00:31:44,600 Malcolm Gladwell: than just efficiency. It's about enhancing fan experiences worldwide. Smart 652 00:31:44,600 --> 00:31:48,320 Malcolm Gladwell: Talks with IBM is produced by Matt Romano, Joey Fishground, 653 00:31:48,520 --> 00:31:52,560 Malcolm Gladwell: and Jacob Goldstein. We're edited by Lydia Jean Kott. Our 654 00:31:52,600 --> 00:31:57,240 Malcolm Gladwell: engineers are Sarah Bruguiere and Ben Tolliday. Theme song by Gramoscope. 655 00:31:58,240 --> 00:32:01,280 Malcolm Gladwell: Special thanks to the EightBar and IBM teams, as well 656 00:32:01,320 --> 00:32:04,920 Malcolm Gladwell: as the Pushkin marketing team. Smart Talks with IBM is 657 00:32:04,960 --> 00:32:09,200 Malcolm Gladwell: a production of Pushkin Industries and Ruby Studio at iHeartMedia. 658 00:32:09,720 --> 00:32:13,880 Malcolm Gladwell: To find more Pushkin podcasts, listen on the iHeartRadio app, 659 00:32:14,200 --> 00:32:19,960 Malcolm Gladwell: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell. 660 00:32:20,240 --> 00:32:24,000 Malcolm Gladwell: This is a paid advertisement from IBM. The conversations on 661 00:32:24,040 --> 00:32:41,800 Malcolm Gladwell: this podcast don't necessarily represent IBM's positions, strategies or opinions.