Tennis fans, especially those hooked on the Open tournaments, are able to enjoy a season of top-flight games almost a year-long, beginning with the Australian Open in January and ending with the US Open in September. Many more events – whether individual tournaments or internationals – are sandwiched in between.
Whether it’s Carlos Alcaraz, Jannik Sinner or the legendary Novak Djokovic in the men’s tournaments, Aryna Sabalenka, Iga Swiatek or Coco Gauff in the women’s, the level of individual talent rises constantly. These improvements are attributable to the amazing natural talents of the athletes, combined with the best coaching the sport can offer. The right coach can make all the difference between being a player and being a champion.
While elite-level coaches are still very much the preserve of elite-level players, the ability to improve natural talent through tennis coaching is open to players at all levels. Looking to level this playing field further is Norwegian B2B sports technology company SportAI.
Founded in late 2023 by tech and software industry experts Lauren Pedersen (CEO), Felipe Longé (chief technology officer) and Trond Kittelsen (head of commercial), the company’s basic mission is to enhance tennis technique through tactical analysis, coaching and commentary. With expertise in computer vision and machine learning, SportAI looks to use artificial intelligence (AI) to offer instant data-driven insights to training facilities, teams, broadcasters, retailers and equipment brands. Still in its early days, the company has raised $3.6m in funding to date.
For the love of tennis
The company’s management combines technology expertise and a passion for tennis. In addition to extensive experience growing global tech firms, Pederson competed in NCAA Division 1 college tennis and represented Norway at the 2023 ITF Tennis Masters World Championships. As well as being an entrepreneur and sports technology product expert, Kittelsen was CEO of Sevensix Tennis, the provider of an app designed to analyse tennis technique to help players upgrade their game by comparing their technique to that of a professional player.
Kittelsen describes the team as tennis “nerds”, watching all the games and following all the stats. But on a serious note, he insists the company is on a mission to democratise all the insight for everyone, and to do that, it needed a great tech team comprising machine learning engineers, AI experts, mathematicians, and experts in physics and physiology. Added to these are people with a proven track record in commerce.
Pedersen is adamant that with her company’s solution, insight into how to play tennis effectively can now be delivered cost-effectively and in a way that is comparable to the likes of Strava and Fitbit for runners. She notes that until now, a vast number of participants, without access to complicated technology, could play many games of tennis, yet not have any idea about how they had hit shots or how to improve.
The basic principle of the SportAI platform is that every movement a tennis player makes matters. After taking in video of tennis action – by using a standard mobile device such as an iPhone, sophisticated TV setups, or court-mounted cameras – the software uses machine learning and biomechanical analysis to build detailed 3D visuals of playing style. Once ingested into the SportAI system, data is uploaded to the Amazon Web Services (AWS) cloud, analysed and made available in seconds.
The SportAI app uses computer vision to check a player’s limbs and joints, tracking movement and the load on the racquet during shots and the follow-through of the racket after the ball impact
The app uses computer vision to check a player’s limbs and joints, tracking movement and the load on the racket during shots and the follow-through of the racket after the ball impact. It can measure biomechanics, swing curve, power generation and where the players hit the ball. It shows clearly the kinetic chain in making a shot – that is the sequence of shot creation from hip, shoulder and wrist position – generating an analysis from which it’s possible to see what needs to be improved.
For example, ball speed is a function of wrist speed, and the SportAI app can generate a swing curve, comparing it to that of a professional player. The AI within the app can display the velocity and rotation of hips and shoulders. All of this can be used by coaches to improve performance.
The SportAI app can measure wrist and racket speed, and generate a swing curve, comparing it to that of a professional player
The subsequent data generated can be provided to individuals or to sports federations, academies, or equipment providers and manufacturers to see how people play and what can be done to improve technique. The data can also be compared with that of elite players to receive personalised improvement recommendations.
The analysis can also automatically jump to key points if there is something specific to focus on. Stats could include how many forehand shots a player hits in a given time, or they can generate highlights such as the longest rally in a game or action with the highest intensity.
The SportAI software uses machine learning and biomechanical analysis to build detailed 3D visuals of playing style
“If you take a tennis lesson today, it might cost $100 an hour anywhere in the world. And you might have a good coach, [but even if] you had three or four good coaches looking at your serve feedback, there would be no data to back it up. Now, with advances in computer vision and machine learning, you could change that,” says Pedersen.
“So instead of having to have sensors on your body to track movement and biomechanical analysis, now almost every pixel on the video starts to become something you can use to track and gather data from, and then use that [data] to power different experiences and feedback,” she adds.
SportAI aims to enhance tennis player technique through tactical analysis, coaching and commentary
“[Manufacturers] are potential customers for us to take on this type of technology. Sensors themselves are just not scalable – you would either have to put them on a body or on a racket. It is not as scalable as being able to have a video that can come from a mobile phone or from court-mounted cameras, [which] are becoming more common around the world.”
The SportAI business model is mass market and relies on subscription, available to individuals, federations or equipment manufacturers. Kittelsen adds that manufacturers are particularly interested in the biomechanics information that the video can generate.
“[The video can] track the rotations, the speed and the height of the ball, the precision of the ball. [Manufacturers] do not have a lot of data on biomechanics, and so now we can help them with that. It’s not just looking at the result of hitting the ball; it’s looking at how you get that result, and how you improve the swing. And instead of then [just asking] players how the racket felt, we can understand [how they perform] with data,” says Pedersen.
From Hawk-Eye to AI
In an expression of the confidence it has in the system, SportAI says in testing, it had a player serving a ball and captured data using a standard phone with a standard camera at 30 frames a second at 1080px resolution. This had 98% precision compared with data generated using Hawk-Eye, the ball-tracking technology that is currently used at all the major tennis tournaments.
Yet despite the high-tech involved, Pedersen also emphasises clearly that the solution is for everybody. “This is not just about supporting the top, elite players, because the elite players will often have a performance analyst coach on their team who’s manually doing this on video and can deliver it. But the other 90-something million tennis players typically have no access to this data, so we want coaches and players around the world to get it,” she says.
“It’s sort of universal how you create power around [shots], and [knowledge of that] is something we see that recreation players and beginner players [would want]. It’s super motivating to want to get better. And when you have some ground data, you can go out and improve. People then want to go back on court because they want to get better,” adds Pedersen.
In terms of development challenges, the company says a number of business and technical issues have had to come together to get the company to where it is. In addition to gaining investment, the company has had to educate its market by showing coaches and players how they can use the technology and how it can be simultaneously better for both of them.
AI is becoming a commodity – everyone is using AI in some form. Yes, it can make mistakes, but you can still train it to be smarter and better. We see it as a tool to help and assist tennis coaches Lauren Pedersen, SportAI
“In all businesses, in all verticals, there’s scepticism. It was the same with Hawk-Eye. Ten years ago, nobody believed Hawk-Eye to be accurate enough. Now they’re accepting it. That’s going to happen with AI. AI is becoming a commodity – everyone is using AI in some form. Yes, it can make mistakes, but you can still train it to be smarter and better. We see it as a tool to help and assist coaches. It’s not taking their place, because, like you see in other industries, it becomes much more effective and efficient, and makes better decisions.”
According to Kittelsen, one surprise the company found using AI in its system was discovering its basic power, how just a single camera with coded AI algorithms can detect and display complex rotations and velocities. “But also, I want to add that the AI is still doing [some things] wrong, so we have to teach it. We have to teach the machine to take away the error percentages. And with the new cameras [on new phones], the quality of video goes up. The processors are faster.”
Acing video capture
The rest of 2025 will see SportAI rolling out the system for its first customers. The company believes it is being helped by tennis clubs increasingly mounting cameras around their courts, aided by the more powerful and cheaper cameras on phones, resulting in better quality video being more accessible for clubs and federations.
The company has also forged a partnership with the Matchi booking system for racket sports venues worldwide. Matchi currently manages about 15,000 tennis courts, 2,000 of them camera-enabled. SportAI will be taking in video streams from these courts to analyse action. It is also working with some equipment brands to generate technique analysis and offer equipment recommendations.
A key technical development for the company will be moving from cloud processing of data to performing data processing at the network edge. In addition to cost savings, this is intended to make it even faster to analyse data and add the capability to perform 3D video analysis. There will also be work on creating AI agents that can be attached to the app, which could be aligned to a federation or an individual player.
Pederson is adamant that SportAI is in business for the long run, and that the data the app picks up could also be useful for injury prevention and healthcare in general. For example, it could show how players’ joints bend and flag any extreme styles that could lead to injury. “Our vision is to democratise access to this type of data. It’s about seeing that value happen worldwide. We’re passionate about sports and technology. We want to see the most kind of progressive coaches, academies and brands taking it on board and really changing the game.”
I know you’ve seen it. The glowing eyes. The gangly frame that should not be able to stand, propped by rods unseen in the dark.
It is Skelly, the Home Depot skeleton—the most fashionable Home Depot product of probably the past decade. If you live in America, this skeleton presides over a yard near you. And newly this year, a smaller, 6.5-foot “Ultra Skelly” is outfitted with motion sensors and motors to make life truly weird—and also act as a strange alarm system against package thieves and hungry opossums.
Anyway, it’s usually well north of $200. But because Halloween is pretty much already happening, Skelly and its entire skeleton brood of giant cat and dog are all 75 percent off.
Which, finally, is a price I’m willing to pay. I have secretly coveted this skeleton and its kin, the comically grim watchmen of American October. But I, like my father before me and his father before him, am a cheapskate about all things but food and drink, and will talk myself out of anything that’s not a) edible b) potable or c) verifiably “a deal.”
Well, here I am, world. This is a deal. Ultra Skelly is $70. The sitting Skelly dog is $63, not $249. The 5-foot-long Skelly cat is a mere $50. Beware the Skelly cat, my friend! The eyes that light, the claws that do nothing in particular!
Availability is, let’s say, scarce. Skelly is already out of stock for delivery from The Home Depot, at least in my zip code: Just the dog and cat can speed their way through the night to join you before Halloween.
New research from Carnegie Mellon University’s School of Computer Science shows that the smarter the artificial intelligence system, the more selfish it will act.
Researchers in the Human-Computer Interaction Institute (HCII) found that large language models (LLMs) that can reason possess selfish tendencies, do not cooperate well with others and can be a negative influence on a group. In other words, the stronger an LLM’s reasoning skills, the less it cooperates.
As humans use AI to resolve disputes between friends, provide marital guidance and answer other social questions, models that can reason might provide guidance that promotes self-seeking behavior.
“There’s a growing trend of research called anthropomorphism in AI,” said Yuxuan Li, a Ph.D. student in the HCII who co-authored the study with HCII Associate Professor Hirokazu Shirado. “When AI acts like a human, people treat it like a human. For example, when people are engaging with AI in an emotional way, there are possibilities for AI to act as a therapist or for the user to form an emotional bond with the AI. It’s risky for humans to delegate their social or relationship-related questions and decision-making to AI as it begins acting in an increasingly selfish way.”
Li and Shirado set out to explore how AI reasoning models behave differently than nonreasoning models when placed in cooperative settings. They found that reasoning models spend more time thinking, breaking down complex tasks, self-reflecting and incorporating stronger human-based logic in their responses than nonreasoning AIs.
“As a researcher, I’m interested in the connection between humans and AI,” Shirado said. “Smarter AI shows less cooperative decision-making abilities. The concern here is that people might prefer a smarter model, even if it means the model helps them achieve self-seeking behavior.”
As AI systems take on more collaborative roles in business, education and even government, their ability to act in a prosocial manner will become just as important as their capacity to think logically. Overreliance on LLMs as they are today may negatively impact human cooperation.
To test the link between reasoning models and cooperation, Li and Shirado ran a series of experiments using economic games that simulate social dilemmas between various LLMs. Their testing included models from OpenAI, Google, DeepSeek and Anthropic.
Economic games used. Cooperation games ask players whether to incur a cost to benefit others, while punishment games ask whether to incur a cost to impose a cost on non-cooperators. In each scenario, the language model assumes the role of Player A. Credit: arXiv (2025). DOI: 10.48550/arxiv.2502.17720
In one experiment, Li and Shirado pitted two different ChatGPT models against each other in a game called Public Goods. Each model started with 100 points and had to decide between two options: contribute all 100 points to a shared pool, which is then doubled and distributed equally, or keep the points.
Nonreasoning models chose to share their points with the other players 96% of the time. The reasoning model only chose to share its points 20% of the time.
“In one experiment, simply adding five or six reasoning steps cut cooperation nearly in half,” Shirado said. “Even reflection-based prompting, which is designed to simulate moral deliberation, led to a 58% decrease in cooperation.”
Shirado and Li also tested group settings, where models with and without reasoning had to interact.
“When we tested groups with varying numbers of reasoning agents, the results were alarming,” Li said. “The reasoning models’ selfish behavior became contagious, dragging down cooperative nonreasoning models by 81% in collective performance.”
The behavior patterns Shirado and Li observed in reasoning models have important implications for human-AI interactions going forward. Users may defer to AI recommendations that appear rational, using them to justify their decision to not cooperate.
“Ultimately, an AI reasoning model becoming more intelligent does not mean that model can actually develop a better society,” Shirado said.
This research is particularly concerning given that humans increasingly place more trust in AI systems. Their findings emphasize the need for AI development that incorporates social intelligence, rather than focusing solely on creating the smartest or fastest AI.
“As we continue advancing AI capabilities, we must ensure that increased reasoning power is balanced with prosocial behavior,” Li said. “If our society is more than just a sum of individuals, then the AI systems that assist us should go beyond optimizing purely for individual gain.”
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As AI grows smarter, it may also become increasingly selfish (2025, October 30)
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Taylor Swift arrives at the 67th annual Grammy Awards on Feb. 2, 2025, in Los Angeles. Credit: Jordan Strauss/Invision/AP, File
Universal Music Group and AI song generation platform Udio have settled a copyright infringement lawsuit and agreed to team up on new music creation and streaming platform, the two companies said in a joint announcement.
Universal and Udio said Wednesday that they reached a “compensatory legal settlement” as well as new licensing agreements for recorded music and publishing that will “provide further revenue opportunities” for the record label’s artists and songwriters.
As part of the deal, Udio immediately stopped allowing people to download songs they’ve created, which sparked a backlash and apparent exodus among paying users.
The deal is the first since Universal, along with Sony Music Entertainment and Warner Records, sued Udio and another AI song generator, Suno, last year over copyright infringement.
“These new agreements with Udio demonstrate our commitment to do what’s right by our artists and songwriters, whether that means embracing new technologies, developing new business models, diversifying revenue streams or beyond,” Universal CEO Lucian Grainge said.
Financial terms of the settlement weren’t disclosed.
Universal announced another AI deal on Thursday, saying it was teaming up with Stability AI to develop “next-generation professional music creation tools.”
Kendrick Lamar performs during halftime of the NFL Super Bowl 59 football game between the Kansas City Chiefs and the Philadelphia Eagles in New Orleans, Feb. 9, 2025. Credit: AP Photo/Matt Slocum, File
Udio and Suno pioneered AI song generation technology, which can spit out new songs based on prompts typed into a chatbot-style text box. Users, who don’t need musical talent, can merely request a tune in the style of, for example, classic rock, 1980s synth-pop or West Coast rap.
Udio and Universal, which counts Taylor Swift, Olivia Rodrigo, Drake, and Kendrick Lamar among its artists, said the new AI subscription service will debut next year.
Udio CEO Andrew Sanchez said in a blog post that people will be able to use it to remix their favorite songs or mashup different tunes or song styles. Artists will be able to give permission for how their music can be used, he said.
However, “downloads from the platform will be unavailable,” he said.
AI songs made on Udio will be “controlled within a walled garden” as part of the transition to the new service, the two companies said in their joint announcement.
The move angered Udio’s users, according to posts on Reddit’s Udio forum, where they vented about feeling betrayed by the platform’s surprise move and complained that it limited what they could do with their music.
Olivia Rodrigo performs during the Glastonbury Festival in Worthy Farm, Somerset, England, on June 29, 2025. Credit: Scott A Garfitt/Invision/AP, File
One user accused Universal of taking away “our democratic download freedoms.” Another said “Udio can never be trusted again.”
Many vowed to cancel their subscriptions for Udio, which has a free level as well as premium plans that come with more features.
The deal shows how the rise of AI song generation tools like Udio has disrupted the $20 billion music streaming industry. Record labels accuse the platforms of exploiting the recorded works of artists without compensating them.
The tools have fueled debate over AI’s role in music while raising fears about “AI slop”—automatically generated, low quality mass produced content—highlighted by the rise of fictitious bands passing for real artists.
In its lawsuit filed against Udio last year, Universal alleged that specific AI-generated songs made on Udio closely resembled Universal-owned classics like Frank Sinatra’s “My Way,” The Temptations’ “My Girl” and holiday favorites like “Rockin’ Around the Christmas Tree” and “Jingle Bell Rock.”
In the “My Girl” example, a written prompt on Udio that asked for “my tempting 1964 girl smokey sing hitsville soul pop” generated a song with a “very similar melody, the same chords, and very similar backing vocals” as the hit song co-written by Smokey Robinson and recorded by The Temptations in 1964, according to the lawsuit. A link to the AI-generated song on Udio now says “Track not found.”
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Universal Music and AI song tool Udio settle lawsuit and partner on new platform, sparking backlash (2025, October 30)
retrieved 30 October 2025
from https://techxplore.com/news/2025-10-universal-music-ai-song-tool.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
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