AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: US Tennis Open Partners with IBM's WatsonX for AI Commentary & Analysis

Welcome to the OpenAI podcast, the podcast that opens up the world of AI in a quick and

concise manner.

Tune in daily to hear the latest news and breakthroughs in the rapidly evolving world

of artificial intelligence.

If you've been following the podcast for a while, you'll know that over the last six

months I've been working on a stealth AI startup.

Of the hundreds of projects I've covered, this is the one that I believe has the greatest

potential.

So today I'm excited to announce AIBOX.

AIBOX is a no-code AI app building platform paired with the App Store for AI that lets

you monetize your AI tools.

The platform lets you build apps by linking together AI models like chatGPT, mid-journey

and 11Labs, eventually will integrate with software like Gmail, Trello and Salesforce

so you can use AI to automate every function in your organization.

To get notified when we launch and be one of the first to build on the platform, you

can join the wait list at AIBOX.AI, the link is in the show notes.

We are currently raising a seed round of funding.

If you're an investor that is focused on disruptive tech, I'd love to tell you more

about the platform.

You can reach out to me at jaden at AIBOX.AI, I'll leave that email in the show notes.

So to give a little context on this story here, I wanted to mention that back in May,

IBM kind of reinforced its commitment to AI technology.

They unveiled their Watson X and that's kind of funny because Watson was like one of the

OG AI models.

If you remember, Watson was created many, many years ago and essentially one, I believe,

Jeopardy or something like that.

And so, IBM has had a lot of this AI tech for quite some time.

It kind of fizzled and disappeared, but now that they're doing this whole refresh, they're

bringing it back and now they're calling it Watson X.

So it's not like the old product is dead.

It's just kind of 2.0 version.

So this product, they unveiled the whole product platform at their annual think conference

and the platform aims to serve as a library for fine-tuning pre-existing models for enterprise

applications.

So, however, IBM has recently taken its AI endeavors to an unexpected but exciting frontier

and that is the Tennis Corp.

So for the first time, IBM is offering AI-generated audio tennis highlights for all matches during

the US Open Tennis Championships.

And they're doing this alongside AI-based analysis to kind of evaluate the projected

challenges that players may face against their competition.

Essentially what they're doing is replacing a sports, you know, broadcaster, these, you

know, the people that are sitting up there as a game is going on and they're kind of

making these live calls, live plays.

This is really interesting, right?

Because up until now, we've seen AI obviously pull from a giant data set of data and write

articles or, you know, you can turn that into audio.

This is kind of taking it to a new level where essentially they're having an AI system analyze

all the analytics from the game, all the statistics from the game, everyone that's passing to

everyone else, every yard that's being made, every move that's being played.

And then they're able to make commentary on that.

And my assumption is that they have essentially been able to train a model based off of, you

know, sports games that have happened in the past and what the commenters were saying back

then and in comparison to what was actually happening on the field, right?

So it's kind of interesting because we have all of this in video format.

And then essentially to get that sort of data, you have to kind of convert the video format

into data, into a data format.

But since we have all of the video, you can essentially do that.

You can just convert the video into data, say like this player ran this many yards past

this many past, you know, this and that here.

And essentially you turn the entire video of a game into data, then you feed that data

alongside what the commentators are saying.

And now you have a model that can really powerfully do this.

So I think this is an absolute no brainer.

Sportcasters definitely need to watch out.

I, you know, there's definitely always going to be a place on Fox News and, you know, MSNBC

or wherever sports is being covered for sports broadcasters to kind of be the public face

of the commentary because people like to watch that.

But I think as more sports goes to being watched on phones and on mobile, I mean, you've seen

even the NBA making big investments in this space.

I think that this is going to be an area that is, you know, becomes very, very popular as

it's, you know, essentially cutting down costs and maybe it's getting the analysis in faster

something, right?

So I think we have some sort of reason why this is better than a normal newscaster.

I'm sure a lot of people have think that that's a controversial idea and we'll have strong

opinions on that.

In any case, each year more than 700,000 enthusiasts flock to Flushing Meadows, New York to witness,

you know, it's the creme de la creme of the tennis world and that is the U.S. Open.

So there's 700,000 people that go, but another 10 million fans globally watch the competition

via the U.S. Open app and website.

And IBM has collaborated with the United States Tennis Association, the USTA for three decades

to offer kind of enriched digital experiences for tennis fans.

But this is really, really definitely going to be taken it to another level.

So the magic begins at the IBM data operations center located in the basement of Arthur Ashe

Stadium.

So the hub is where millions of data points are going to get captured and scrutinized,

right? Because as I was mentioning before, obviously you could use an AI to do this on

previous data if you get a video and convert the video to data.

But this you have to do in real time.

So on average, each point in a tennis match generates 56 data points for analysis.

And IBM deploys AI models developed by and deployed via Watson X that run on a hybrid

cloud infrastructure provided by Red Hat OpenShift.

And this enables the company to produce high quality audio narrations and captions for

U.S. Open Highlight videos on really an unprecedented scale.

So IBM's foray into AI goes beyond mere just like highlights.

The company has also introduced an AI draw analysis tool, which is of course powered

by Watson X.

And the tool uses a combination of structured and unstructured data to predict the level

of advantage or disadvantage for each player in the singles draw.

So this is actually really interesting because beyond just like a sports commentator sitting

there and being like, oh, they're, you know, it's, they're looking like this is a tough

time.

And in the past, this player has, you know, had a hard time with their backhand or returning

this type of server that right.

Like the commentators have all sorts of little facts that they can bring out and talk about.

This is interesting because this really has like the predictive power.

They're like predicting how hard in like on a predictive analytical perspective, you

know, something might be so fans can easily dive into individual matches to kind of evaluate

the projected difficulty level levels players might face as the tournament advances.

And Kristen Corio, which is a chief commercial officer at the USDA disclosed the organization's

limitations in covering highlights for, you know, the extensive range of matches.

She said, quote, depending on how many writers you have, you can only do a few matches at

a time.

The other matches would just have stats and scores, but no commentary.

So these stories are untold consequently, IBM and the USDA have been exploring ways

to scale tournament coverage by integrating AI technologies with statistical data and

storytelling elements.

This is really interesting because I'm not sure if all of this is really happening in

real time where they have like, it's not like they've completely replaced sports broadcasters.

But based off of the technology and the, what I was telling you earlier on, I believe it

is fully capable, we're fully, we're at a place we're fully capable of doing that essentially

replacing sports broadcasters.

And I think what's more interesting here, right, we recently did a very interesting podcast

episode with Steven Wasik, who's the CEO of Info Sentience.

And he has a really cool product that essentially it takes all of the scores and analytics from

a game and it writes a really well, really well written article specifically like his

algorithm.

It's kind of interesting because it doesn't just write about all of the data points.

It's not just a template that fills in the data into like an article, but it actually

takes all the data points.

It ranks them on kind of a scale of which ones are the most interesting or unusual.

And then it writes an article and he's has partnerships with some pretty big people in

the sports arena, the sports space to automatically use AI to generate these articles.

And his whole thing is the same as what, you know, they're struggling with right now,

which is there's not enough journalists to cover every single game, like a lot of smaller

university games, a lot of games that are kind of not as big, don't get any coverage,

which, you know, is kind of hard for those teams to, to, you know, get, get coverage

of what they're doing.

And so with these AI technologies, you're able to, even if you don't have a journalist,

you don't have the headcount or the ability, maybe it's not quite financially feasible,

but it would still be a great thing to cover those games.

There's still a lot of fans from those specific areas that would be interested.

You can use AI for those tools.

And so I think that is also an area that we're going to start seeing IBM dive into as well,

just based off of what we're seeing here.

Corio also expressed the USTA's kind of future aspirations to include AI generated highlights

in multiple languages, something that they're not currently doing.

They identified Spanish as an initial focus to kind of broaden engagement.

And then I think when it comes to employing advanced AI applications in this kind of long-standing

partnership, Corio emphasized the critical, you know, the, the, the criticality of data

control really.

So the USTA relies on its own vetted official data, but Corio cautioned that there are others

who quote, pedal in the unofficial data to kind of navigate these waters, the USTA plans

to establish several task forces to assess the benefits and risks involved.

So that's very interesting.

One immediate concern is the issue of AI hallucination instances where the AI systems may

produce incorrect or misleading information.

Addressing this and IBM spokesperson, you know, reassured that human quality checks are in

place for their AI commentary.

They said quote, we're hoping over time we can reduce the need of human QA.

But we do check each highlight clip to make sure that the commentary is solid.

All in all, I think IBM's efforts significantly kind of play to a need in the arena.

I think they kind of signify a groundbreaking fusion of sports and AI technology.

And I think they're really pushing the boundaries of what's possible in delivering a rich data

driven fan experience.

I think this is great overall, especially for a lot of games that, you know, just weren't

able to get coverage before.

And now you can get coverage of all the games.

I think it's a lot more equitable for everyone and for the fans, you know, they definitely

want as much info on their favorite players, their favorite teams as possible.

And so I think this is a really great move in that direction.

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Machine-generated transcript that may contain inaccuracies.