WEBVTT - Smart Talks with IBM: An AI advantage for the US Open

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<v Speaker 1>Hey everyone, it's Robert and Joe here. Today we've got

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<v Speaker 1>something a little bit different to share with you. It

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<v Speaker 1>is a new season of the Smart Talks with IBM

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<v Speaker 1>podcast series.

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<v Speaker 2>Today we are witnessed to one of those rare moments

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<v Speaker 2>in history, the rise of an innovative technology with the

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<v Speaker 2>potential to radically transform business and society forever. The technology,

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<v Speaker 2>of course, is artificial intelligence, and it's the central focus

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<v Speaker 2>for this new season of Smart Talks with IBM.

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<v Speaker 1>Join hosts from your favorite Pushkin podcasts as they talk

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<v Speaker 1>with industry experts and leaders to explore how businesses can

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<v Speaker 1>integrate AI into their workflows and help drive real change

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<v Speaker 1>in this new era of AI. And of course, host

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<v Speaker 1>Malcolm Gladwell will be there to guide you through the

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<v Speaker 1>season and throw in his two cents as well.

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<v Speaker 2>Look out for new episodes of Smart Talks with IBM

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<v Speaker 2>every other week on the iHeartRadio app, Apple Podcasts, or

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<v Speaker 2>wherever you get your podcasts, and learn more at IBM

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<v Speaker 2>dot com slash smart talks.

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<v Speaker 3>Hello, Hello, Welcome to Smart Talks with IBM, a podcast

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<v Speaker 3>from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This

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<v Speaker 3>season we're diving back into the world of artificial intelligence,

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<v Speaker 3>but with a focus on the powerful concept of open

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<v Speaker 3>its possibilities, implications, and misconceptions. We'll look at openness from

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<v Speaker 3>a variety of angles and explore how the concept is

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<v Speaker 3>already reshaping industries, ways of doing business, and our very

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<v Speaker 3>notion of what's possible. I'm particularly excited for today's guest,

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<v Speaker 3>Brian Ryerson. He's Senior Director of Digital Strategy at the

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<v Speaker 3>US Tennis Association, helping to oversee one of the most

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<v Speaker 3>iconic events in the world of sports, the US Open.

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<v Speaker 3>Brian sat down with Pushkin's own Jacob Goldstein, host of

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<v Speaker 3>the podcast What's Your Problem. A veteran business journalist, Jacob

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<v Speaker 3>has reported for The Wall Street Journal, the Miami Herald,

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<v Speaker 3>and was a longtime host of the NPR program Planet Money.

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<v Speaker 3>IBM has been the official technology partner of the US

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<v Speaker 3>Tennis Association for more than thirty years, and the more

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<v Speaker 3>recent evolution into generative AI has enhanced the world class

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<v Speaker 3>digital experiences that help more than fifteen million fans from

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<v Speaker 3>all over the world enjoy the US Open Tennis Championships.

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<v Speaker 3>In this episode, we will explore how generative AI is

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<v Speaker 3>being used to generate match insights, spoken commentary for match highlights,

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<v Speaker 3>and postmatch summaries at scale for fans to enjoy through

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<v Speaker 3>the US Open app and website. We'll explore how these

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<v Speaker 3>AI solutions enable the editorial team to cover more of

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<v Speaker 3>the tournament than ever before, bringing fans even closer to

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<v Speaker 3>the game they love, and will learn more about one

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<v Speaker 3>of the engines behind this AI powered content creation, a

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<v Speaker 3>large language model from the ib M Granite family, which

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<v Speaker 3>is trained and maintained using the watsonex AI and data platform. Okay,

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<v Speaker 3>let's dive in.

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<v Speaker 4>Brian, Welcome to the show.

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<v Speaker 5>Thanks for having me. I'm excited to be here.

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<v Speaker 4>Can you say your name and your job?

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<v Speaker 5>Yeah, I'm Brian Ryerson. I'm Senior director of Digital Strategy

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<v Speaker 5>at the USTA.

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<v Speaker 4>Some question, what's the USTA.

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<v Speaker 5>The US Tennis Association.

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<v Speaker 4>And tell me about the USTA, Like, what is it?

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<v Speaker 5>Yeah? So the USTA is the governing body of tennis

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<v Speaker 5>in the US. Or mission is to grow the sport

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<v Speaker 5>of tennis across the US at all levels. Really, I

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<v Speaker 5>would say we're more like a health and wellness company

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<v Speaker 5>where tennis is the means to health and wellness. And

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<v Speaker 5>then the US Open is kind of our tenth pole

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<v Speaker 5>event that happens everyear and Flushing Meadows and is really

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<v Speaker 5>our chance to showcase the support of tennis at its

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<v Speaker 5>highest level to fans all around the world.

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<v Speaker 4>Yeah, I mean the US Open. I assume most people

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<v Speaker 4>know this, but it's Grand Slam. It's one of the

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<v Speaker 4>what four biggest tennis tournaments in the world.

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<v Speaker 5>Yes, yeah, every year, we especially the past couple of years,

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<v Speaker 5>we've seen immense growth and you know, we are very

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<v Speaker 5>hopeful this year and our big goals that have over

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<v Speaker 5>a million fires on site during the three week window

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<v Speaker 5>this year, So it's an amazing event. I always say

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<v Speaker 5>it's a food and wine festival where tennis is the

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<v Speaker 5>main attraction and it's a really fun, unique atmosphere.

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<v Speaker 4>How did you get into the tennis business.

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<v Speaker 5>It's a great question. It's not where I thought i'd

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<v Speaker 5>end up for especially being there for fourteen years. So

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<v Speaker 5>I was a marketing and technology major in school, and

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<v Speaker 5>I also played college lacrosse and sports was always a

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<v Speaker 5>big part of my life and always wanted to be

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<v Speaker 5>in the sports and entertainment world. I'm here from the

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<v Speaker 5>New York area. This is where I grew up so

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<v Speaker 5>I moved back home and had a few friends who

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<v Speaker 5>worked there, and I started out more on the number

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<v Speaker 5>side of things and really digital analytics. It was really

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<v Speaker 5>the start of when Facebook and Twitter is just starting

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<v Speaker 5>and digital marketing and all of that. And you know,

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<v Speaker 5>I went to my first year so Open not really

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<v Speaker 5>knowing what to expect, and again, I think the atmosphere

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<v Speaker 5>kind of captivated me and hooked me in. And I've

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<v Speaker 5>been there now fourteen years.

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<v Speaker 4>And so your title is Digital Director. What does that mean?

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<v Speaker 4>What's your job?

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<v Speaker 5>Yeah, so it's interesting one because it's tough to explain

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<v Speaker 5>to folks who are not in the weeds on all

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<v Speaker 5>things US open or even in the sports world. But

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<v Speaker 5>really I oversee all of our consumer facing digital property.

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<v Speaker 5>So that's the us open dot org, our website built

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<v Speaker 5>by IBM, as well as our mobile app. I oversee

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<v Speaker 5>our content strategy, our sponsorship integrations. So really anything consumer

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<v Speaker 5>facing that happens on the web is under my purview,

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<v Speaker 5>even some of our new platform extensions and gaming and

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<v Speaker 5>things like that. Anything that you can physically interact with

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<v Speaker 5>is kind of under my purview.

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<v Speaker 4>And so you've been there now for fourteen ISSU years,

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<v Speaker 4>which in the digital world is a long time. How

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<v Speaker 4>has that sort of digital experience of sports changed over

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<v Speaker 4>that time.

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<v Speaker 5>Yeah, it's obviously grown digital now, is what we say

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<v Speaker 5>and what my team says. It's the number one way

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<v Speaker 5>to engage with fans that can't make it to the event,

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<v Speaker 5>as well as those fans who are at the event,

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<v Speaker 5>and how to enrich their stay. So it's really kind

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<v Speaker 5>of you're tackling multiple personas. It's the international fan who's

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<v Speaker 5>staying up late to watch in other countries, to the

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<v Speaker 5>fan here who's maybe watching on broadcasts, and we go

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<v Speaker 5>in a company and enrich that broadcast with new stats

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<v Speaker 5>and insights. To the on site fan who bought a

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<v Speaker 5>ticket and maybe doesn't know what match is happening on

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<v Speaker 5>what court. We do have twenty plus courts happening at

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<v Speaker 5>a time, with all different matches, So we really try

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<v Speaker 5>to help all fans navigate the US Open the best

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<v Speaker 5>way possible.

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<v Speaker 4>And so, like, what are some of the sort of

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<v Speaker 4>problems you're trying to solve. What are some of the

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<v Speaker 4>hard things about your job?

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<v Speaker 5>Yeah, obviously technology changes at a rapid pace, right, So

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<v Speaker 5>I think part of it is how do we stay

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<v Speaker 5>on the forefront of that, and how do we do

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<v Speaker 5>that in the best way and make the best fan

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<v Speaker 5>experiences possible and the best user experiences possible. That's always

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<v Speaker 5>kind of driving factor number one. Then number two, it's

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<v Speaker 5>understanding and listening to our fans and what kind of

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<v Speaker 5>content they want. You hear me talk a lot about storytelling.

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<v Speaker 5>I feel like there's a lot of storytelling that happens

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<v Speaker 5>around the US open that we really want to bring

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<v Speaker 5>to fans. And that can be as simple as storytelling

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<v Speaker 5>of what's happening today and what you should be watching too.

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<v Speaker 5>Maybe it's your favorite players and what's going on behind

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<v Speaker 5>the scenes with them, to even introducing I want to say,

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<v Speaker 5>the casual fans to who they should be watching, why

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<v Speaker 5>they should follow certain players, and more bringing that player's

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<v Speaker 5>story to life.

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<v Speaker 2>Yeah.

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<v Speaker 4>I mean, I feel like almost the whole point of

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<v Speaker 4>sports is to like create stories for us to follow, right,

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<v Speaker 4>Like they're engineered to be stories. It's exactly, this thing

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<v Speaker 4>is happening in front of you and there are two

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<v Speaker 4>antagonists and the stakes are high, and you don't know

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<v Speaker 4>how it's gonna end, Like it's built to be a story.

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<v Speaker 5>Yeah, And that's the main challenge of the job is

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<v Speaker 5>you can plan, plan, plan, but once you get on

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<v Speaker 5>two players on court and you don't know what that

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<v Speaker 5>outcome is going to be, it's now sitting and waiting

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<v Speaker 5>and watching and you become a fan yourself. And then

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<v Speaker 5>it's how do you really captivate that story and how

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<v Speaker 5>do you narrate it and how do you like translate

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<v Speaker 5>up to fans.

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<v Speaker 4>And it's like you kind of have to do it

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<v Speaker 4>in real time, right, Like the whole point of sports

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<v Speaker 4>is you don't know what's gonna.

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<v Speaker 5>Happen exactly, and that's the excitement. And it's also there's

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<v Speaker 5>so many different types of fans. You know, there's the

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<v Speaker 5>fans who want a lot of enriched data and their

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<v Speaker 5>tennis nerds for lack of better of saying it, and

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<v Speaker 5>that they really want to dive deep into the intricacies

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<v Speaker 5>of the game, versus the casual fan who maybe just

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<v Speaker 5>wants more of this high level storyline of what does

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<v Speaker 5>this mean? Why is it important? So it's really trying

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<v Speaker 5>to figure out how to deliver that at scale and

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<v Speaker 5>really help fans get what they're looking for and the

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<v Speaker 5>type of content they're looking for.

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<v Speaker 4>So are there specific examples of you know how fan

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<v Speaker 4>feedback has led to specific features digital features you build.

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<v Speaker 4>Are there, like particularly popular features you've come up with, Like,

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<v Speaker 4>what are some specifics.

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<v Speaker 5>Yeah, some low hanging fruit type things that came from

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<v Speaker 5>fan feedback. Is simple things sometimes like managing time zones

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<v Speaker 5>and when matches start.

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<v Speaker 4>A persistent problem where those of us can work across

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<v Speaker 4>times exactly.

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<v Speaker 5>And we do have, like I've mentioned, twenty plus courts

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<v Speaker 5>happening at a time, So it's a lot to follow,

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<v Speaker 5>and how do you translate that to a fan, whether

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<v Speaker 5>it's to their native language or to their time zone

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<v Speaker 5>or things like that. So that's one thing that came

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<v Speaker 5>through fan feedback, and another one a three to five

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<v Speaker 5>hour match, especially when you're having twenty plus of them

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<v Speaker 5>happening at a time, is there's too much for one

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<v Speaker 5>person to follow. So how do you start from an

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<v Speaker 5>editorial perspective really helping with that storytelling and guiding a

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<v Speaker 5>fan to like, all right, whether there's an upset about

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<v Speaker 5>to happen, or here's your matches to watch, or even

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<v Speaker 5>some of the predictions we're starting to put in, is

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<v Speaker 5>we really want to guide the fan before a match,

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<v Speaker 5>here's where you should tune in to even after a

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<v Speaker 5>match of here's what's happened, Here's what's important, and we're

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<v Speaker 5>really excited with some of the features we've built in

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<v Speaker 5>the last few years that I would say really helps

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<v Speaker 5>us do that at more scale than what we were

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<v Speaker 5>able to do with just writers following a match and

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<v Speaker 5>covering every single match.

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<v Speaker 4>Huh. So I want to talk a little bit about

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<v Speaker 4>the partnership between IBM and the USTA, Like, just tell

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<v Speaker 4>me about the work you do together.

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<v Speaker 5>So IBM is our official digital and technology partner and

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<v Speaker 5>innovation the US Open they predate me. It's a thirty

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<v Speaker 5>year partnership and it truly as a partnership. So I

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<v Speaker 5>view the IBM consulting team as an extension of my

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<v Speaker 5>USTA team, So we work with them year round. They design,

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<v Speaker 5>develop and deliver the digital properties. They help us provide

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<v Speaker 5>the tools to create content to do things at scale,

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<v Speaker 5>They help us from stats and information and really help

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<v Speaker 5>us push from an innovation standpoint to make sure that

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<v Speaker 5>we are staying on that cutting edge of technology. So

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<v Speaker 5>I would truly say it's much more than a sponsorship,

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<v Speaker 5>where it's truly a partnership to deliver that fan experience.

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<v Speaker 4>And so What are some of the specific things that

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<v Speaker 4>you have done with IBM.

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<v Speaker 5>Yeah, so, I mean there's countless ones to talk through. Obviously,

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<v Speaker 5>they thirty years ago. They helped us build our first

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<v Speaker 5>website and it's kind of grown from there over the

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<v Speaker 5>past few years. I would say, I think it was

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<v Speaker 5>twenty eighteen as we started AI Highlights, So that was

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<v Speaker 5>really when we were able to have all twenty matches

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<v Speaker 5>going at a single time. We were able to quickly

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<v Speaker 5>deliver succinct highlights to fans to our digital platform so

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<v Speaker 5>they could see highlights for every single core.

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<v Speaker 4>Is that video highlights? Is that tech summaries? What does

0:11:09.720 --> 0:11:10.160
<v Speaker 4>that mean?

0:11:10.640 --> 0:11:13.160
<v Speaker 5>At the time, it was video highlights, Okay, So it

0:11:13.200 --> 0:11:16.040
<v Speaker 5>was really taking that three to five hour match, let's say,

0:11:16.080 --> 0:11:18.559
<v Speaker 5>and cut it down to a three minute highlight that

0:11:18.720 --> 0:11:21.080
<v Speaker 5>could show up within moments after a match, ending to

0:11:21.160 --> 0:11:23.640
<v Speaker 5>our website and our mobile app, so fans could see

0:11:23.640 --> 0:11:25.280
<v Speaker 5>that all around the world and really kind of get

0:11:25.320 --> 0:11:28.480
<v Speaker 5>that three minute overview what happened in a match, and.

0:11:28.600 --> 0:11:31.720
<v Speaker 4>Was that AI enabled? Was AI a piece of how

0:11:31.720 --> 0:11:32.199
<v Speaker 4>to do that?

0:11:32.320 --> 0:11:34.959
<v Speaker 5>It was? It was probably our first foray into AI.

0:11:35.600 --> 0:11:42.400
<v Speaker 4>Back twenty eighteen is relatively early, Yeah, exactly, for tennis exactly.

0:11:42.480 --> 0:11:45.439
<v Speaker 5>Yeah. It really, I want to say, opened up our

0:11:45.480 --> 0:11:49.439
<v Speaker 5>ability to one again storytell but attract new fans too.

0:11:49.559 --> 0:11:51.800
<v Speaker 5>Is video has actually been our number one growth area

0:11:51.880 --> 0:11:54.040
<v Speaker 5>since twenty eighteen, and I think a lot of that

0:11:54.080 --> 0:11:55.800
<v Speaker 5>has to do with the scale of how we deliver

0:11:55.880 --> 0:11:56.800
<v Speaker 5>that content.

0:11:56.720 --> 0:11:59.640
<v Speaker 4>Using AI and being able to deliver this sort of

0:11:59.720 --> 0:12:02.920
<v Speaker 4>video highlight reels at scale.

0:12:02.760 --> 0:12:05.320
<v Speaker 5>Yeah, and do it quickly. Right. We've always had highlights,

0:12:05.360 --> 0:12:07.319
<v Speaker 5>but it was a manual process where you had a

0:12:07.880 --> 0:12:11.079
<v Speaker 5>video that or cutting through you know, a three hour match,

0:12:11.200 --> 0:12:13.480
<v Speaker 5>selecting the right scene, stitching together, it would have to

0:12:13.520 --> 0:12:16.720
<v Speaker 5>get voiced over, et cetera. We really have used AI

0:12:16.840 --> 0:12:18.400
<v Speaker 5>to make it, i want to say, much more efficient

0:12:18.520 --> 0:12:21.520
<v Speaker 5>and speed up that process and deliver it more quickly

0:12:21.559 --> 0:12:22.160
<v Speaker 5>to our fans.

0:12:22.520 --> 0:12:24.439
<v Speaker 4>I mean, it would be a bummer to get scooped

0:12:24.600 --> 0:12:27.679
<v Speaker 4>by whatever NBC News or Yes Pen or whatever. I'm

0:12:27.679 --> 0:12:29.480
<v Speaker 4>sure there are all your partners and you love that most.

0:12:29.960 --> 0:12:32.240
<v Speaker 4>Obviously you want to have the video first, right, it's

0:12:32.280 --> 0:12:32.760
<v Speaker 4>your match.

0:12:33.000 --> 0:12:35.200
<v Speaker 5>Yeah, And I think it's also important to us as

0:12:35.559 --> 0:12:39.719
<v Speaker 5>being the USTA is ensuring that it's not just you know,

0:12:39.880 --> 0:12:43.440
<v Speaker 5>the main marquee players, that every player and all those

0:12:43.480 --> 0:12:46.960
<v Speaker 5>storylines and that whether it's you know, the main singles

0:12:47.040 --> 0:12:49.679
<v Speaker 5>draw to our mixed doubles, et cetera. They all need

0:12:49.760 --> 0:12:52.200
<v Speaker 5>highlights and they all have their own stories to tell,

0:12:52.200 --> 0:12:53.960
<v Speaker 5>and how do we do that at scale? It was

0:12:53.960 --> 0:12:56.400
<v Speaker 5>something that before we had that product was not something

0:12:56.440 --> 0:12:57.160
<v Speaker 5>you were able to do.

0:12:57.600 --> 0:13:01.040
<v Speaker 4>Great, So let's let's talk in some more detail about

0:13:01.080 --> 0:13:04.480
<v Speaker 4>what you're working on. Let's start with the app. Tell

0:13:04.480 --> 0:13:07.520
<v Speaker 4>me about the us Open app and the Companion website.

0:13:07.640 --> 0:13:10.120
<v Speaker 5>Yeah, so I'll start with the app, and I feel

0:13:10.200 --> 0:13:13.600
<v Speaker 5>like they serve similar needs, but they're a little different

0:13:13.640 --> 0:13:16.720
<v Speaker 5>in their own respective manners. Is the app. Everybody has

0:13:16.760 --> 0:13:18.640
<v Speaker 5>a phone in their hands at this point. The app

0:13:18.720 --> 0:13:20.880
<v Speaker 5>is kind of their guide to when I say a

0:13:20.920 --> 0:13:23.600
<v Speaker 5>million fans on site, we view the app as we

0:13:23.640 --> 0:13:26.160
<v Speaker 5>want that to be their on site guide, and Companion

0:13:26.400 --> 0:13:27.040
<v Speaker 5>a million.

0:13:27.160 --> 0:13:29.880
<v Speaker 4>Let's just pause on a million fans on site, right,

0:13:29.880 --> 0:13:33.640
<v Speaker 4>because like a big professional whatever, an NFL game or

0:13:33.640 --> 0:13:37.120
<v Speaker 4>something that's like one hundred thousand, this is ten x that.

0:13:37.480 --> 0:13:40.520
<v Speaker 5>Yeah, and a three week window and a very succinct,

0:13:40.679 --> 0:13:45.080
<v Speaker 5>tight action packed window. There's a lot of action logistics.

0:13:45.160 --> 0:13:46.880
<v Speaker 4>Okay, so keep going.

0:13:46.960 --> 0:13:49.800
<v Speaker 5>So the app, you know, whether it's finding the schedules,

0:13:49.840 --> 0:13:52.760
<v Speaker 5>the live scores, what's happening on court. That's really the

0:13:52.760 --> 0:13:55.880
<v Speaker 5>focus point of the app, and what we're really focused

0:13:55.880 --> 0:13:58.120
<v Speaker 5>on this year is how do we build in some

0:13:58.160 --> 0:14:00.839
<v Speaker 5>of those map summaries into the app, into our slam

0:14:00.880 --> 0:14:04.200
<v Speaker 5>Tracker experience. So again, before match, that kind of match

0:14:04.240 --> 0:14:06.480
<v Speaker 5>preview of here's maybe if you have a ticket, here's

0:14:06.520 --> 0:14:09.360
<v Speaker 5>what to expect, here's you know are likely to win,

0:14:09.440 --> 0:14:11.920
<v Speaker 5>who we are predicting, so you can kind of get

0:14:11.960 --> 0:14:15.320
<v Speaker 5>some information heading in, and then after the match it's

0:14:15.320 --> 0:14:18.559
<v Speaker 5>more of what just happened, what it means for the

0:14:18.800 --> 0:14:21.560
<v Speaker 5>rest of the draw, who they're playing next, is this

0:14:21.640 --> 0:14:23.800
<v Speaker 5>the first time this has happened, et cetera, and really

0:14:23.920 --> 0:14:26.960
<v Speaker 5>enriching that experience as well. So the app is one

0:14:27.040 --> 0:14:29.360
<v Speaker 5>your guide to what you should be watching, but also

0:14:29.400 --> 0:14:31.960
<v Speaker 5>then giving you that insights and context of what's happening

0:14:32.000 --> 0:14:32.960
<v Speaker 5>on that court as you're.

0:14:32.880 --> 0:14:36.480
<v Speaker 4>Watching, like the commentator in your pocket exactly. So you

0:14:36.640 --> 0:14:39.400
<v Speaker 4>used a phrase in there as if I already knew it,

0:14:40.280 --> 0:14:41.880
<v Speaker 4>and I love the phrase, but I want you to

0:14:41.880 --> 0:14:44.760
<v Speaker 4>talk more about it. That phrase is slam Tracker.

0:14:45.000 --> 0:14:50.720
<v Speaker 5>Yes, So slam Tracker is our long standing live scores

0:14:50.760 --> 0:14:53.040
<v Speaker 5>I want to say Match Center. It is, okay, where

0:14:53.240 --> 0:14:55.960
<v Speaker 5>every single data point for every single match lives, and

0:14:56.000 --> 0:14:59.280
<v Speaker 5>it really it helps showcase what's happening to match. I say,

0:14:59.280 --> 0:15:02.120
<v Speaker 5>it's our brock Cast companions. If you're watching live, it's

0:15:02.120 --> 0:15:04.640
<v Speaker 5>our in stadium companion. And it's also the best thing

0:15:04.720 --> 0:15:06.720
<v Speaker 5>to have if you aren't able to watch.

0:15:06.840 --> 0:15:08.600
<v Speaker 4>And so, like, I'm on the app and there's a

0:15:08.680 --> 0:15:11.520
<v Speaker 4>thing called slam Tracker, and it like taps slam Tracker.

0:15:11.560 --> 0:15:13.320
<v Speaker 4>What do I see on my phone when I tap

0:15:13.360 --> 0:15:16.720
<v Speaker 4>slam Tracker? You know, midday when the tournament's happening.

0:15:16.760 --> 0:15:18.640
<v Speaker 5>So before match, that's where you get a lot of

0:15:18.640 --> 0:15:20.840
<v Speaker 5>pre match content. That's where those live kind of our

0:15:20.880 --> 0:15:24.240
<v Speaker 5>predictions are likelihood to win lives within that So likelihood

0:15:24.280 --> 0:15:26.840
<v Speaker 5>to win essentially pulls in a bunch of data points.

0:15:26.880 --> 0:15:30.000
<v Speaker 5>So pass matches, how many times these players have played

0:15:30.000 --> 0:15:32.680
<v Speaker 5>each other against each other, even some punditry and other

0:15:32.720 --> 0:15:35.520
<v Speaker 5>written articles that maybe our editorial team put out and

0:15:35.560 --> 0:15:37.920
<v Speaker 5>really kind of puts a prediction out there.

0:15:38.040 --> 0:15:40.160
<v Speaker 4>And so it's just a percentage chance.

0:15:40.080 --> 0:15:43.160
<v Speaker 5>Yes exactly, but it uses millions of data points to

0:15:43.200 --> 0:15:45.480
<v Speaker 5>come up with that. Yes, So it really helps you

0:15:45.560 --> 0:15:48.520
<v Speaker 5>kind of understand what you're getting into for that match.

0:15:48.920 --> 0:15:51.640
<v Speaker 5>During a live match, it is every single point, so

0:15:52.000 --> 0:15:55.200
<v Speaker 5>point by point scoring as well as in depth analysis

0:15:55.200 --> 0:15:58.080
<v Speaker 5>in point commentary where also this year have a live

0:15:58.200 --> 0:16:02.080
<v Speaker 5>visualization that accompanies that will really help bring the match together.

0:16:02.520 --> 0:16:04.240
<v Speaker 5>And what I mean by that is it uses our

0:16:04.280 --> 0:16:07.880
<v Speaker 5>ball tracking technology to really showcase the match in near

0:16:07.920 --> 0:16:10.720
<v Speaker 5>real time, so within seconds delay of where the ball's

0:16:10.920 --> 0:16:13.360
<v Speaker 5>being hit, where the players are, and really bring a

0:16:13.480 --> 0:16:16.240
<v Speaker 5>visualization to life and layered stats and data on top

0:16:16.280 --> 0:16:16.400
<v Speaker 5>of it.

0:16:16.520 --> 0:16:16.640
<v Speaker 2>Huh.

0:16:16.960 --> 0:16:18.720
<v Speaker 4>Is that sort of like when I'm watching a match

0:16:18.760 --> 0:16:21.360
<v Speaker 4>on TV and there's like a close call as the

0:16:21.360 --> 0:16:23.120
<v Speaker 4>ball in or out and they do that thing where

0:16:23.120 --> 0:16:25.320
<v Speaker 4>they kind of show a sort of video game version

0:16:25.360 --> 0:16:27.160
<v Speaker 4>of where the ball landed. Does it look like that?

0:16:27.440 --> 0:16:30.120
<v Speaker 5>It's like that before every single shot, So it's not

0:16:30.280 --> 0:16:32.720
<v Speaker 5>just those close ones. It's our first foray to bring

0:16:32.760 --> 0:16:33.760
<v Speaker 5>that match to life.

0:16:34.280 --> 0:16:36.120
<v Speaker 4>Huh. And so what do I see on that kind

0:16:36.120 --> 0:16:38.320
<v Speaker 4>of view that I don't see from whatever watching the video?

0:16:38.440 --> 0:16:41.080
<v Speaker 5>Yeah, So one you'll be able just to see more

0:16:41.120 --> 0:16:44.280
<v Speaker 5>of the ball trajectory and where the ball is being hit,

0:16:44.320 --> 0:16:46.640
<v Speaker 5>but then you can also start layering things in stats

0:16:46.640 --> 0:16:49.120
<v Speaker 5>and insights on top of that, so how many times

0:16:49.160 --> 0:16:52.359
<v Speaker 5>has player A hit the ball on a certain baseline,

0:16:52.360 --> 0:16:55.320
<v Speaker 5>how fast are they hitting it, maybe their serve percentage

0:16:55.320 --> 0:16:57.160
<v Speaker 5>and a certain side of the court, et cetera. So

0:16:57.200 --> 0:16:59.440
<v Speaker 5>you can really start layering in for the ones that

0:16:59.520 --> 0:17:01.920
<v Speaker 5>really want to. I've deep into the for the nerds.

0:17:01.960 --> 0:17:05.240
<v Speaker 4>It's for the information rich exactly.

0:17:05.320 --> 0:17:07.880
<v Speaker 5>It's the strategy of tennis. It really should be an

0:17:07.880 --> 0:17:09.920
<v Speaker 5>interesting way to slice and dice a match.

0:17:10.160 --> 0:17:11.080
<v Speaker 4>Huh.

0:17:11.160 --> 0:17:14.520
<v Speaker 3>It's remarkable how the USTA is leveraging AI to enhance

0:17:14.560 --> 0:17:19.720
<v Speaker 3>fan engagement and deliver immersive experiences both on site and online.

0:17:20.240 --> 0:17:25.800
<v Speaker 3>Brian's emphasis on storytelling really underscores the evolution of sports marketing.

0:17:26.440 --> 0:17:30.240
<v Speaker 3>The slam Chakra feature particularly caught my attention. It's essentially

0:17:30.280 --> 0:17:33.600
<v Speaker 3>bringing the excitement of a tennis match to life in

0:17:33.640 --> 0:17:37.800
<v Speaker 3>your palm, moment by moment. As someone who appreciates the

0:17:37.880 --> 0:17:41.639
<v Speaker 3>narrative intricacies of sports, I find it compelling how AI

0:17:41.760 --> 0:17:45.600
<v Speaker 3>helps predict and analyze matches in real time.

0:17:46.600 --> 0:17:49.000
<v Speaker 4>Tell me about the AI commentary feature.

0:17:49.200 --> 0:17:52.840
<v Speaker 5>Yeah, I know. I mentioned AI highlights back in twenty eighteen.

0:17:52.920 --> 0:17:55.919
<v Speaker 5>It's now progressed for us. And again, if we go

0:17:56.040 --> 0:17:58.960
<v Speaker 5>back to before we had a highlights to have a

0:17:59.040 --> 0:18:02.199
<v Speaker 5>highlight ready for this was a video editor cutting the

0:18:02.280 --> 0:18:05.920
<v Speaker 5>highlight and getting voiced over and then being published aside,

0:18:05.960 --> 0:18:09.600
<v Speaker 5>and it took probably an hour plus for that highlight

0:18:09.640 --> 0:18:13.320
<v Speaker 5>to really be created. Now with AI commentary, not only

0:18:13.320 --> 0:18:16.440
<v Speaker 5>are we creating and cutting the highlights using our AI technology,

0:18:16.480 --> 0:18:18.679
<v Speaker 5>but it's now using all the data points that we

0:18:18.720 --> 0:18:20.920
<v Speaker 5>have around the match, whether it's our live scoring data,

0:18:21.320 --> 0:18:24.679
<v Speaker 5>our ball tra directory data, etc. And it's really creating

0:18:24.720 --> 0:18:28.199
<v Speaker 5>a script that helped storytell around that match. That's all

0:18:28.280 --> 0:18:32.159
<v Speaker 5>using Watson X technology and then using text to speech

0:18:32.240 --> 0:18:34.720
<v Speaker 5>we're able to actually then create the commentary on top

0:18:34.760 --> 0:18:38.040
<v Speaker 5>of that, which all happens now within minutes. So our

0:18:38.040 --> 0:18:40.960
<v Speaker 5>team's able to now create fully voiced highlights for every

0:18:40.960 --> 0:18:44.159
<v Speaker 5>men's and women's singles match to our site within minutes.

0:18:44.960 --> 0:18:47.479
<v Speaker 4>So I know there's a new feature you're working on

0:18:47.520 --> 0:18:51.760
<v Speaker 4>for this year called match reports. What are match reports?

0:18:52.080 --> 0:18:56.679
<v Speaker 5>It's our ability to succicktly tell the story of a match,

0:18:57.160 --> 0:19:00.680
<v Speaker 5>so everything happens in five hours within that match down

0:19:00.720 --> 0:19:04.680
<v Speaker 5>to a couple paragraphs that really helps a user understand

0:19:04.720 --> 0:19:08.320
<v Speaker 5>or a fan understand what just happened. Again, some key

0:19:08.359 --> 0:19:11.919
<v Speaker 5>stats what's upcoming really help us with that storytelling. In

0:19:11.960 --> 0:19:14.600
<v Speaker 5>the past, when we have twenty two courts happening at

0:19:14.600 --> 0:19:16.800
<v Speaker 5>a certain time, we would have to pick and choose

0:19:16.880 --> 0:19:19.320
<v Speaker 5>which stories we think or which matches we think are

0:19:19.320 --> 0:19:21.359
<v Speaker 5>going to have the best stories, and that's a really

0:19:21.400 --> 0:19:24.320
<v Speaker 5>hard thing to predict from an editorial perspective. With our

0:19:24.359 --> 0:19:26.720
<v Speaker 5>match reports now we'll be able to have full coverage

0:19:26.720 --> 0:19:28.680
<v Speaker 5>of every single match during the main draw.

0:19:29.440 --> 0:19:32.080
<v Speaker 4>So, of course I want to talk about jeneritive AI.

0:19:32.320 --> 0:19:35.199
<v Speaker 4>How could we not talk about generative Of course, what

0:19:35.240 --> 0:19:36.600
<v Speaker 4>are you working on with jenitive AI?

0:19:36.960 --> 0:19:39.320
<v Speaker 5>So match reports is the prime example of it. So

0:19:39.440 --> 0:19:42.520
<v Speaker 5>Match Reports will be completely using Watson next genera of

0:19:42.520 --> 0:19:47.080
<v Speaker 5>AI technology, And really again to us, it's how can

0:19:47.119 --> 0:19:50.399
<v Speaker 5>we do that storytelling at scale? Tennis is such a

0:19:50.480 --> 0:19:53.800
<v Speaker 5>data rich sport. All sports have data, but tennis has

0:19:53.840 --> 0:19:55.960
<v Speaker 5>a lot of shots and different shot types and ball

0:19:56.000 --> 0:20:00.560
<v Speaker 5>trajectory and live scoring data and umpire chair data and

0:20:00.560 --> 0:20:03.920
<v Speaker 5>and all that. Factoring in jeneral of AI really helps

0:20:04.000 --> 0:20:06.879
<v Speaker 5>us take some of that structured and unstructured data really

0:20:07.280 --> 0:20:10.720
<v Speaker 5>one organize it in a way, but then help us

0:20:11.000 --> 0:20:14.119
<v Speaker 5>quickly tell that story at scale to all of our fans,

0:20:14.320 --> 0:20:17.040
<v Speaker 5>and I think we're really just starting to scratch at

0:20:17.040 --> 0:20:20.320
<v Speaker 5>some of the capabilities, and we're really excited about where

0:20:20.320 --> 0:20:22.320
<v Speaker 5>we're being, but we also see the opportunity of even

0:20:22.600 --> 0:20:24.840
<v Speaker 5>how we can grow to new fans and new fans

0:20:24.880 --> 0:20:27.320
<v Speaker 5>around the world using jener of AI in the future.

0:20:28.640 --> 0:20:32.400
<v Speaker 4>So I'm curious, and you alluded to this a moment ago,

0:20:32.400 --> 0:20:34.040
<v Speaker 4>but I'd like to talk a little bit more about

0:20:34.040 --> 0:20:37.760
<v Speaker 4>it because it seems interesting as a technical problem. Right,

0:20:37.960 --> 0:20:43.280
<v Speaker 4>is the nature of turning tennis matches into stories, which

0:20:43.280 --> 0:20:45.679
<v Speaker 4>is fundamentally what we're talking about here in different ways

0:20:45.720 --> 0:20:50.960
<v Speaker 4>in different media, is about taking both structured data, right

0:20:51.080 --> 0:20:54.959
<v Speaker 4>like the stats who you know, points stats matches, and

0:20:55.160 --> 0:20:58.840
<v Speaker 4>also unstructured data, right like commentary and articles and the

0:20:58.920 --> 0:21:03.480
<v Speaker 4>kind of fuzzier parts of storytelling. And so I'm curious

0:21:03.600 --> 0:21:06.800
<v Speaker 4>how AI kind of helps you manage both the structured

0:21:06.840 --> 0:21:07.960
<v Speaker 4>and the unstructured data.

0:21:08.440 --> 0:21:12.280
<v Speaker 5>Yeah. So, I think the structured data is pretty self experimentatory,

0:21:12.600 --> 0:21:14.359
<v Speaker 5>but when you get into the unstructured data and some

0:21:14.440 --> 0:21:16.320
<v Speaker 5>of the punditry, that's where you get more of the

0:21:16.359 --> 0:21:19.760
<v Speaker 5>opinion pieces into it. Like a specific player matchup, this

0:21:19.840 --> 0:21:22.679
<v Speaker 5>player always plays well against so and so, or as

0:21:22.680 --> 0:21:24.520
<v Speaker 5>they play always played well at night, or they're a

0:21:24.520 --> 0:21:28.199
<v Speaker 5>fan favorite and the crowd, you know, adrenaline and the

0:21:28.320 --> 0:21:30.640
<v Speaker 5>crowd being behind you can really motivate you to play

0:21:30.680 --> 0:21:34.000
<v Speaker 5>a lot better. So it pulls in all those unstructured

0:21:34.000 --> 0:21:36.800
<v Speaker 5>pieces and helps us really put some more rigor around

0:21:36.800 --> 0:21:39.439
<v Speaker 5>it and help add and enrich our storytelling with it.

0:21:39.840 --> 0:21:43.760
<v Speaker 4>And so I'm curious when you're starting to use generative AI,

0:21:44.080 --> 0:21:46.840
<v Speaker 4>you know, over the past few years, like, what were

0:21:46.880 --> 0:21:48.239
<v Speaker 4>your concerns going into that.

0:21:48.640 --> 0:21:53.119
<v Speaker 5>I think our biggest concern is ensuring that one factually

0:21:53.400 --> 0:21:55.000
<v Speaker 5>it is correct, because it's only as good as the

0:21:55.040 --> 0:21:57.080
<v Speaker 5>data you feed in. And how do you really ensure

0:21:57.119 --> 0:21:59.760
<v Speaker 5>that your model's working right and that the output and

0:21:59.800 --> 0:22:02.600
<v Speaker 5>the data you're feeding it matches the output, and how

0:22:02.600 --> 0:22:04.719
<v Speaker 5>do you do that at scale? So we do have

0:22:04.760 --> 0:22:07.920
<v Speaker 5>a lot of human intervention. That's where the IBM consulting team,

0:22:08.040 --> 0:22:10.320
<v Speaker 5>they're on site with us for those full three weeks

0:22:10.359 --> 0:22:13.760
<v Speaker 5>really helping us review everything and we're constantly learning, especially

0:22:13.760 --> 0:22:16.480
<v Speaker 5>early in the tournament. And I would say the other

0:22:16.760 --> 0:22:19.399
<v Speaker 5>big concern, again it goes around to the data, is

0:22:19.680 --> 0:22:22.359
<v Speaker 5>what data do we have available that is trustworthy? So

0:22:22.560 --> 0:22:24.560
<v Speaker 5>you know, we are feel very confident with the data

0:22:24.600 --> 0:22:26.200
<v Speaker 5>that comes off of court, but when we get into

0:22:26.240 --> 0:22:29.960
<v Speaker 5>that unstructured piece, what are the right data sources? How

0:22:29.960 --> 0:22:32.320
<v Speaker 5>do we validate those data sources and how do we

0:22:32.760 --> 0:22:35.560
<v Speaker 5>ensure that they're accurate Because if the data that has

0:22:35.600 --> 0:22:37.240
<v Speaker 5>to go in has to be accurate for the for

0:22:37.280 --> 0:22:38.240
<v Speaker 5>the output.

0:22:38.240 --> 0:22:40.639
<v Speaker 4>So how do you do that? That's the concern? How

0:22:40.960 --> 0:22:41.800
<v Speaker 4>how do you address it?

0:22:41.960 --> 0:22:44.439
<v Speaker 5>Yeah, so I think there's there's a number of tools

0:22:44.480 --> 0:22:47.159
<v Speaker 5>that we use all within the Watson X umbrella. We

0:22:47.240 --> 0:22:49.919
<v Speaker 5>do a lot of training with the IBM team, so

0:22:49.960 --> 0:22:53.600
<v Speaker 5>we have to constantly train and retrain that model. I

0:22:53.600 --> 0:22:56.480
<v Speaker 5>think the other piece that we're doing is again as

0:22:56.520 --> 0:22:59.240
<v Speaker 5>we're creating that content and we have the IBM consulting

0:22:59.280 --> 0:23:01.719
<v Speaker 5>team on site helping us with that, is as we

0:23:01.760 --> 0:23:04.840
<v Speaker 5>see things and we see outputs, it's refeeding that back

0:23:04.840 --> 0:23:06.840
<v Speaker 5>into the model to make it better for the next time.

0:23:06.960 --> 0:23:10.480
<v Speaker 5>So it's a constantly learning process that we're undergoing.

0:23:11.040 --> 0:23:14.800
<v Speaker 4>So I want to talk about scale. Yes, you have

0:23:14.960 --> 0:23:18.720
<v Speaker 4>like what twenty two different courts with matches going all

0:23:18.760 --> 0:23:22.440
<v Speaker 4>at the same time. You're trying to, you know, approximately

0:23:22.800 --> 0:23:26.199
<v Speaker 4>instantly generate summaries of all these matches in something like

0:23:26.240 --> 0:23:31.080
<v Speaker 4>real time, and I'm curious in particular how the IBM

0:23:31.160 --> 0:23:35.280
<v Speaker 4>models you're using, the IBM Granite models are helping you scale.

0:23:35.840 --> 0:23:38.560
<v Speaker 5>Yeah. So I think one of the big learnings we

0:23:38.640 --> 0:23:42.320
<v Speaker 5>had with IBM granted models too is that we're able

0:23:42.359 --> 0:23:45.199
<v Speaker 5>to run it, you know, against last year's tournaments and

0:23:45.240 --> 0:23:48.520
<v Speaker 5>see what the expected outputs could be and really help

0:23:48.600 --> 0:23:50.800
<v Speaker 5>train that model heading into the tournament. Because as we

0:23:50.840 --> 0:23:53.240
<v Speaker 5>talked about in the beginning, is we can plan, plan,

0:23:53.240 --> 0:23:55.359
<v Speaker 5>and plan, but once two players get on court, the

0:23:55.400 --> 0:23:58.040
<v Speaker 5>outcome is unknown. So how do we really run it

0:23:58.080 --> 0:24:00.879
<v Speaker 5>through its paces and really make sure that whatever that

0:24:00.920 --> 0:24:03.120
<v Speaker 5>outcome could be and whatever that scenario is, whether it's

0:24:03.520 --> 0:24:06.879
<v Speaker 5>a fifth set tie break that happens, or maybe there's

0:24:06.920 --> 0:24:09.359
<v Speaker 5>a you know, a fault in the match or something

0:24:09.400 --> 0:24:12.720
<v Speaker 5>that we're not anticipating, that we have that accounted for

0:24:12.760 --> 0:24:14.800
<v Speaker 5>and that the a won't throw off that output. So

0:24:14.840 --> 0:24:19.000
<v Speaker 5>we really try to think through every scenario, which is

0:24:19.040 --> 0:24:22.520
<v Speaker 5>sometimes difficult, right because again live sports is the unknown

0:24:22.600 --> 0:24:24.800
<v Speaker 5>is the unknown that's what makes it fun. We do

0:24:24.960 --> 0:24:27.639
<v Speaker 5>spend a lot of time thinking through potential scenarios and

0:24:27.720 --> 0:24:29.919
<v Speaker 5>ensuring that we have the right data sets and the

0:24:30.000 --> 0:24:33.080
<v Speaker 5>model to predict that tell.

0:24:32.880 --> 0:24:36.159
<v Speaker 4>Me about match reports and the generative AI model you're

0:24:36.240 --> 0:24:36.720
<v Speaker 4>using for that.

0:24:37.400 --> 0:24:39.880
<v Speaker 5>Yeah, so match reports will be new for us this year,

0:24:39.920 --> 0:24:42.440
<v Speaker 5>So we're in testing right now, so we're really excited

0:24:42.480 --> 0:24:44.800
<v Speaker 5>around it. But the model that we'll be able to

0:24:44.880 --> 0:24:48.280
<v Speaker 5>use using Watson X will use a bunch of different

0:24:48.320 --> 0:24:51.320
<v Speaker 5>parts of the suite of tools A meaning that again

0:24:51.359 --> 0:24:54.000
<v Speaker 5>of taking some of that punditry and the unstructured data

0:24:54.040 --> 0:24:57.359
<v Speaker 5>and the editorial spend, take our structured data as well.

0:24:57.680 --> 0:25:00.800
<v Speaker 5>And really what we're working on right now is figuring

0:25:00.800 --> 0:25:04.080
<v Speaker 5>out the right prompts for the AI to really ensure

0:25:04.440 --> 0:25:09.080
<v Speaker 5>that it tells the right structured story, meaning what just happened. Right,

0:25:09.240 --> 0:25:12.120
<v Speaker 5>So our recap is pretty standard. Here's what the data

0:25:12.160 --> 0:25:14.440
<v Speaker 5>is telling us, who won, who lost, how many sets?

0:25:14.480 --> 0:25:17.160
<v Speaker 4>Here's the score the structured data part, that's the easy part.

0:25:17.320 --> 0:25:20.080
<v Speaker 5>Yeah, and then really where it gets exciting is then

0:25:20.320 --> 0:25:23.960
<v Speaker 5>what does this mean? Meaning what's upcoming? So there's all

0:25:24.000 --> 0:25:26.240
<v Speaker 5>these different scenarios when you get into you know, two

0:25:26.320 --> 0:25:29.000
<v Speaker 5>hundred and fifty four players and a large draw. This

0:25:29.080 --> 0:25:31.680
<v Speaker 5>allows us to distill that down and really tell kind

0:25:31.680 --> 0:25:34.600
<v Speaker 5>of what could happen upcoming. The AI helps us do

0:25:34.680 --> 0:25:35.560
<v Speaker 5>that at scale.

0:25:35.840 --> 0:25:38.160
<v Speaker 4>So I want to sort of generalize for a moment

0:25:38.200 --> 0:25:41.720
<v Speaker 4>to talk about kind of you know, broader challenges with

0:25:41.760 --> 0:25:44.720
<v Speaker 4>AI and how you've solved them. You know a lot

0:25:44.760 --> 0:25:49.640
<v Speaker 4>of generative AI pilots fail because the data quality isn't

0:25:49.720 --> 0:25:53.000
<v Speaker 4>high enough, because the risk controls aren't there, and so

0:25:53.040 --> 0:25:56.360
<v Speaker 4>I'm curious how you dealt with those problems and are

0:25:56.359 --> 0:25:58.120
<v Speaker 4>dealing with them data quality.

0:25:58.359 --> 0:26:01.080
<v Speaker 5>Again, we feel calm with the data that is supplied

0:26:01.359 --> 0:26:04.359
<v Speaker 5>from the US open and from the USTA. Right, So

0:26:04.440 --> 0:26:07.440
<v Speaker 5>we have again that's our structure, scoring data and all that.

0:26:07.840 --> 0:26:10.240
<v Speaker 5>I think what we're constantly looking at is when we

0:26:10.280 --> 0:26:12.800
<v Speaker 5>get outside of our known sources and out to third

0:26:12.840 --> 0:26:15.159
<v Speaker 5>parties is that's where a lot of the testing and

0:26:15.240 --> 0:26:18.960
<v Speaker 5>model work happens. So we pull in different data sources

0:26:19.080 --> 0:26:22.960
<v Speaker 5>and really try to work through how it changes that output. Again,

0:26:23.080 --> 0:26:24.920
<v Speaker 5>some of that comes down to where it's an open

0:26:24.920 --> 0:26:27.639
<v Speaker 5>model and the transparency that we have and the learning

0:26:27.720 --> 0:26:29.880
<v Speaker 5>that comes behind it. That's where a lot of that

0:26:30.119 --> 0:26:32.520
<v Speaker 5>confidence can come from, and it comes from a lot

0:26:32.560 --> 0:26:36.280
<v Speaker 5>of testing and feeding it more data. Your second question

0:26:36.400 --> 0:26:39.040
<v Speaker 5>was a little bit more around the output I believe.

0:26:38.800 --> 0:26:42.000
<v Speaker 4>Right, Yeah, and risks right, So risk, I think of

0:26:42.080 --> 0:26:44.840
<v Speaker 4>risk more in terms of output, right, But the obvious

0:26:44.880 --> 0:26:47.920
<v Speaker 4>sphere is like what if it says something wrong? Yeah,

0:26:48.000 --> 0:26:51.640
<v Speaker 4>inflammatory or whatever like that seems scary?

0:26:51.880 --> 0:26:54.280
<v Speaker 5>Yeah, it definitely is, and it's definitely one of our

0:26:54.359 --> 0:26:57.080
<v Speaker 5>largest concerns when we first took this. FORAY, I would

0:26:57.080 --> 0:26:59.159
<v Speaker 5>say a lot of that comes through our work with

0:26:59.280 --> 0:27:02.840
<v Speaker 5>IBM and I consulting team and really ensuring that again

0:27:02.880 --> 0:27:05.639
<v Speaker 5>they're an extension and the partnership there of our team.

0:27:06.119 --> 0:27:09.119
<v Speaker 5>So whenever we are creating let's say it's the match Report,

0:27:09.200 --> 0:27:11.520
<v Speaker 5>and we're going to be creating these excinct articles for

0:27:11.600 --> 0:27:15.000
<v Speaker 5>every single men's and women's single match that happens, is

0:27:15.080 --> 0:27:18.080
<v Speaker 5>all of those will have manual review and people looking

0:27:18.160 --> 0:27:20.840
<v Speaker 5>through them for accuracy to ensure that the model then

0:27:20.880 --> 0:27:23.359
<v Speaker 5>hallucinate or make up a factor or fill in the

0:27:23.400 --> 0:27:25.919
<v Speaker 5>gaps from things like that. That's the first step. And

0:27:25.920 --> 0:27:28.560
<v Speaker 5>then also when our editorial team goes to publish those

0:27:28.600 --> 0:27:31.000
<v Speaker 5>of the website, they're going to be checking it as well.

0:27:31.040 --> 0:27:34.080
<v Speaker 5>So there are manual interventions throughout that to really check

0:27:34.119 --> 0:27:37.359
<v Speaker 5>that model. But we feel that the ability to do

0:27:37.400 --> 0:27:39.760
<v Speaker 5>it at scale and with us more to check that

0:27:40.119 --> 0:27:42.440
<v Speaker 5>is the efficiency problem that we've been looking to solve.

0:27:43.240 --> 0:27:46.439
<v Speaker 4>So the USTA and IBM have been working together on

0:27:46.720 --> 0:27:49.439
<v Speaker 4>digital innovation for like thirty years from you know, the

0:27:49.480 --> 0:27:54.200
<v Speaker 4>first website, yes for the USTA until now. So that's

0:27:54.240 --> 0:27:57.960
<v Speaker 4>the past thirty years. If you look ahead, what's the next.

0:27:57.680 --> 0:28:00.440
<v Speaker 5>Thirty thirty years is a really long time?

0:28:00.880 --> 0:28:01.200
<v Speaker 2>Agree?

0:28:01.560 --> 0:28:05.119
<v Speaker 6>Yeah, I think you know where I get excited, and

0:28:05.320 --> 0:28:07.480
<v Speaker 6>I think I alluded to it in the beginning about

0:28:07.480 --> 0:28:09.760
<v Speaker 6>how I feel like we're just scratching at the surface,

0:28:09.840 --> 0:28:12.000
<v Speaker 6>especially with Journati of Ai, and where I see it

0:28:12.080 --> 0:28:15.200
<v Speaker 6>going is there's a lot of different fans out there,

0:28:15.480 --> 0:28:17.280
<v Speaker 6>and we're also very kindness in the us OP and

0:28:17.280 --> 0:28:19.679
<v Speaker 6>that we're a worldwide event, and that there's a lot

0:28:19.720 --> 0:28:23.240
<v Speaker 6>of different fans that were not necessary creating content for

0:28:23.560 --> 0:28:27.359
<v Speaker 6>bespoke meaning in their native language or maybe it's in

0:28:27.359 --> 0:28:29.360
<v Speaker 6>that native players language and things like that.

0:28:29.480 --> 0:28:32.760
<v Speaker 5>Is where I get excited is we've seen immense growth

0:28:32.760 --> 0:28:35.080
<v Speaker 5>with a Highlights and the ability to now do highlights

0:28:35.080 --> 0:28:38.040
<v Speaker 5>at scale. Is the ability for us to start creating

0:28:38.120 --> 0:28:42.320
<v Speaker 5>content in different languages, maybe covering different parts of the match.

0:28:42.360 --> 0:28:44.760
<v Speaker 5>So maybe you do have that stats junkie you really wants,

0:28:44.880 --> 0:28:47.880
<v Speaker 5>just it's the fastest serve and here's the deep insights

0:28:47.960 --> 0:28:50.520
<v Speaker 5>versus the casual fan who's looking for more of the

0:28:50.600 --> 0:28:54.120
<v Speaker 5>storytelling around how a player trains and what leading up

0:28:54.160 --> 0:28:56.040
<v Speaker 5>to it was like and what it means for them

0:28:56.480 --> 0:28:59.320
<v Speaker 5>afterwards and things like that. A lot of that takes

0:28:59.320 --> 0:29:01.440
<v Speaker 5>a lot of time. Now we're able to solve that

0:29:01.480 --> 0:29:04.560
<v Speaker 5>efficiency problem and do it in multiple languages, we can

0:29:04.600 --> 0:29:08.200
<v Speaker 5>really create I want to say, personalized content to a

0:29:08.240 --> 0:29:11.480
<v Speaker 5>lot more fans all around the world, which again helps

0:29:11.560 --> 0:29:14.040
<v Speaker 5>us grow the sport of tennis great.

0:29:14.880 --> 0:29:18.000
<v Speaker 4>So I want to finish with a speed round. Okay,

0:29:18.200 --> 0:29:18.840
<v Speaker 4>are you ready?

0:29:18.960 --> 0:29:19.840
<v Speaker 5>I am ready?

0:29:19.880 --> 0:29:23.400
<v Speaker 4>Okay, first thing that comes to mind, complete this sentence.

0:29:24.040 --> 0:29:26.160
<v Speaker 4>In five years, AI will.

0:29:26.600 --> 0:29:29.120
<v Speaker 5>Transform many parts of the business.

0:29:29.360 --> 0:29:33.600
<v Speaker 4>What is the number one thing that people misunderstand about AI?

0:29:34.160 --> 0:29:38.320
<v Speaker 5>That it's supplemental, not replacing, meaning that it helps it

0:29:38.360 --> 0:29:42.440
<v Speaker 5>with efficiencies, but it doesn't necessarily replace the creativity.

0:29:43.120 --> 0:29:46.800
<v Speaker 4>Right now, what advice would you give yourself ten years

0:29:46.840 --> 0:29:49.680
<v Speaker 4>ago to better prepare you for today?

0:29:50.760 --> 0:29:54.280
<v Speaker 5>I think it would have been, especially now that we're

0:29:54.320 --> 0:29:56.800
<v Speaker 5>able to take so much of that unstructured data and

0:29:57.280 --> 0:29:59.840
<v Speaker 5>pass content that we were created to help tell st

0:30:01.000 --> 0:30:03.840
<v Speaker 5>was to I want to say archive more of that

0:30:03.920 --> 0:30:05.800
<v Speaker 5>in a way that we could be using that to

0:30:05.880 --> 0:30:09.360
<v Speaker 5>help pull from that now. So you know, we've seen

0:30:09.440 --> 0:30:11.480
<v Speaker 5>kind of a change in the guard from some of

0:30:11.520 --> 0:30:14.440
<v Speaker 5>our star players to now new and up and comers,

0:30:14.440 --> 0:30:16.480
<v Speaker 5>and it would be really fascinating to me if there

0:30:16.520 --> 0:30:19.239
<v Speaker 5>was a way to to cross sections some of that

0:30:19.360 --> 0:30:22.120
<v Speaker 5>and saying like what tra directories are certain up and

0:30:22.160 --> 0:30:25.840
<v Speaker 5>coming players maybe filing from others. So it's more I

0:30:25.880 --> 0:30:27.760
<v Speaker 5>wish we kept more of the content.

0:30:27.480 --> 0:30:32.400
<v Speaker 4>We created back fave the data exactly. Well are you

0:30:32.440 --> 0:30:33.360
<v Speaker 4>saving it all now?

0:30:33.640 --> 0:30:35.760
<v Speaker 5>Oh yeah, one hundred percent learned our lesson?

0:30:35.880 --> 0:30:36.640
<v Speaker 2>Yes, yes.

0:30:37.280 --> 0:30:39.640
<v Speaker 4>So on the business side of AI, what do you

0:30:39.680 --> 0:30:40.880
<v Speaker 4>think is the next big thing?

0:30:41.520 --> 0:30:44.800
<v Speaker 5>I alluded to it earlier. I think it's personalization and

0:30:44.840 --> 0:30:48.440
<v Speaker 5>getting content that's catered to you at scale, whether you

0:30:48.480 --> 0:30:51.480
<v Speaker 5>know that's across the sports sphere or or any type

0:30:51.520 --> 0:30:54.720
<v Speaker 5>of written content or or news content. I feel like

0:30:55.480 --> 0:30:58.800
<v Speaker 5>the ability to really get contentated to the type of

0:30:58.880 --> 0:31:01.600
<v Speaker 5>fan you are and the insight you have is where

0:31:01.600 --> 0:31:02.200
<v Speaker 5>we're all headed.

0:31:03.280 --> 0:31:07.120
<v Speaker 4>And in terms of your non work life, how do

0:31:07.160 --> 0:31:08.760
<v Speaker 4>you use AI day to day?

0:31:09.000 --> 0:31:11.320
<v Speaker 5>It's funny, I was just having this conversation with a

0:31:11.360 --> 0:31:14.640
<v Speaker 5>friend the other day, and we were talking about sometimes

0:31:14.640 --> 0:31:17.720
<v Speaker 5>when you're starting something new, the hardest thing to do

0:31:17.840 --> 0:31:20.120
<v Speaker 5>is you have a blank piece of paper or a thought,

0:31:20.160 --> 0:31:24.280
<v Speaker 5>and how do you get started. Sometimes with these generative models,

0:31:24.600 --> 0:31:26.200
<v Speaker 5>the easiest thing and the best thing you can do

0:31:26.320 --> 0:31:28.960
<v Speaker 5>is it helps you get started. Meaning it may not

0:31:29.000 --> 0:31:30.920
<v Speaker 5>be one hundred percent with that first prompt, but it's

0:31:30.960 --> 0:31:34.280
<v Speaker 5>that efficiency of whether it's an outline for a new idea,

0:31:34.480 --> 0:31:36.680
<v Speaker 5>or it's a marketing brief you have to write, or

0:31:36.880 --> 0:31:39.000
<v Speaker 5>sometimes even if it's an email you have to write

0:31:39.160 --> 0:31:41.360
<v Speaker 5>for a personal something and you're not sure how to

0:31:41.400 --> 0:31:43.520
<v Speaker 5>word it the right way. It allows you to have

0:31:43.720 --> 0:31:45.680
<v Speaker 5>a start and then you can edit from there. So

0:31:45.720 --> 0:31:48.720
<v Speaker 5>again going back to my efficiency point, it helps you

0:31:48.800 --> 0:31:49.360
<v Speaker 5>become more.

0:31:49.240 --> 0:31:51.600
<v Speaker 4>Efficient, solves the blank page problem.

0:31:51.800 --> 0:31:52.200
<v Speaker 5>It does.

0:31:53.560 --> 0:31:55.280
<v Speaker 4>Brian, it was great to talk with you. Thank you

0:31:55.280 --> 0:31:56.120
<v Speaker 4>so much for your time.

0:31:56.240 --> 0:31:57.720
<v Speaker 5>Yeah, this was fun. Thanks for having me.

0:32:00.120 --> 0:32:02.400
<v Speaker 3>Huge thanks to Jacob and Brian for the deep dive

0:32:02.720 --> 0:32:06.280
<v Speaker 3>into the cutting edge innovations transforming the game of tennis.

0:32:06.880 --> 0:32:09.800
<v Speaker 3>Brian shed light on how the US opens partnership with

0:32:09.840 --> 0:32:15.120
<v Speaker 3>IBM is harnessing data driven insights to reshape storytelling in sports,

0:32:15.520 --> 0:32:21.040
<v Speaker 3>from AI generated commentary to Match reports. As we look ahead,

0:32:21.120 --> 0:32:25.880
<v Speaker 3>I'm excited about the possibilities for personalizing content and reaching

0:32:25.960 --> 0:32:30.000
<v Speaker 3>fans in new ways. The future of AI promises more

0:32:30.040 --> 0:32:38.360
<v Speaker 3>than just efficiency. It's about enhancing fan experiences worldwide. Smart

0:32:38.360 --> 0:32:42.080
<v Speaker 3>Talks with IBM is produced by Matt Romano, Joey Fishground,

0:32:42.280 --> 0:32:46.320
<v Speaker 3>and Jacob Goldstein. We're edited by Lydia jen Kott. Our

0:32:46.360 --> 0:32:51.040
<v Speaker 3>engineers are Sarah Bruger and Ben Tolliday. Theme song by Gramascow.

0:32:52.000 --> 0:32:54.880
<v Speaker 3>Special thanks to the eight Bar and IBM teams, as

0:32:54.880 --> 0:32:58.400
<v Speaker 3>well as the Pushkin marketing team. Smart Talks with IBM

0:32:58.640 --> 0:33:02.960
<v Speaker 3>is a production of Pushkin Indie and Ruby Studio at iHeartMedia.

0:33:03.480 --> 0:33:07.680
<v Speaker 3>To find more Pushkin podcasts, listen on the iHeartRadio app,

0:33:07.960 --> 0:33:13.720
<v Speaker 3>Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell.

0:33:14.040 --> 0:33:17.760
<v Speaker 3>This is a paid advertisement from IBM. The conversations on

0:33:17.800 --> 0:33:36.800
<v Speaker 3>this podcast don't necessarily represent IBM's positions, strategies, or opinions.