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Can AI improve soccer teams’ success from corner kicks? Liverpool and others are betting it can

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Can AI improve soccer teams' success from corner kicks? Liverpool and others are betting it can


Can AI improve football teams’ success from corner kicks? Liverpool and others are betting it can
Credit: Google DeepMind

Last Sunday, Liverpool faced Manchester United in the quarter finals of the FA Cup—and in the final minute of extra time, with the score tied at three-all, Liverpool had the crucial opportunity of a corner kick. A goal would surely mean victory, but losing possession could be risky.

What was Liverpool to do? Attack or play it safe? And if they were to attack, how best to do it? What kind of delivery, and where should players be waiting to attack the ball?

Set-piece decisions like this are vital not only in soccer but in many other competitive sports, and traditionally they are made by coaches on the basis of long experience and analysis. However, Liverpool has recently been looking to an unexpected source for advice: researchers at the Google-owned UK-based artificial intelligence (AI) lab DeepMind.

In a paper published March 19 in Nature Communications, DeepMind researchers describe an AI system for soccer tactics called TacticAI, which can assist in developing successful corner kick routines. The paper says experts at Liverpool favored TacticAI’s advice over existing tactics in 90% of cases.

What TacticAI can do

At a corner kick, play stops and each team has the chance to organize its players on the field before the attacking team kicks the ball back into play—usually with a specific prearranged plan in mind that will (hopefully) let them score a goal. Advice on these prearranged plans or routines is what TacticAI sets out to offer.

Can AI improve football teams’ success from corner kicks? Liverpool and others are betting it can
TacticAI represents a corner-kick setup as a ‘graph’ of player positions and relationships, which it then uses to make predictions. Credit: Wang et al. / Nature Communications

The package has three components: one that predicts which player is most likely to receive the ball in a given scenario, another that predicts whether a shot on goal will be taken, and a third that recommends how to adjust the position of players to increase or decrease the chances of a shot on goal.

Trained on a dataset of 7,176 corner kicks from Premier League matches, TacticAI used a technique called “geometric deep learning” to identify key strategic patterns.

The researchers say this approach could be applied not only to soccer, but to any sport in which a stoppage in the game allows teams to deliberately maneuver players into place unopposed, and plan the next sequence of play. In soccer, it could also be expanded in future to incorporate throw-in routines as well as other set pieces such as attacking free kicks.

Vast amounts of data

AI in soccer is not new. Even in amateur and semi-professional soccer, AI-powered auto-tracking camera systems are becoming commonplace, for example. At the last men’s and women’s World Cups in 2022 and 2023, AI in conjunction with advanced ball-tracking technology produced semi-automated offside decisions with an unprecedented level of accuracy.

Professional soccer clubs have analytical departments using AI at every level of the game, predominantly in the areas of scouting, recruitment and athlete monitoring. Other research has also tried to predict players’ shots on goal, or guess from a video what off-screen players are doing.

Bringing AI into tactical decisions promises to offer coaches a more objective and analytical approach to the game. Algorithms can process vast amounts of data, identifying patterns that may not be apparent to the naked eye, giving teams valuable insights into their own performance as well as that of their opponents.

A useful tool

AI may be a useful tool, but it cannot make decisions about match play alone. An algorithm might suggest the optimal positional setup for an in-swinging corner or how best to exploit the opposition’s defensive tactics.

What AI cannot do is make decisions on the fly—like deciding whether to take a corner quickly to exploit an opponent’s lapse in concentration.






Sometimes the best move is a speedy reaction to conditions on the ground, not an elaborate prearranged set play.

There’s also something to be said for allowing players creative license in some situations. Once teams are using AI to suggest the optimal corner strategy, opponents will doubtless counter with their own AI-prompted defensive setup.

So while the tech behind TacticAI is very interesting, it remains to be seen whether it can evolve to be useful in open play. Could AI get to the stage where it can recognize the best tactical player substitution in a given situation?

DeepMind researchers have advanced decision-making like this in their sights for future research, but will it ever reach a point where coaches would trust it?

My sense from discussions with people in the industry is many believe AI should only be used as an input to decision-making, and not be allowed to make decisions itself. There is no substitute for the experience and instinct of the best coaches, the intangible ability to feel what the game needs, to make a change in formation, to play someone out of position.

Smart tactics, but what about strategy?

Coming back to that crucial Liverpool corner in last Sunday’s FA Cup quarter final: we don’t know whether Liverpool’s manager Jürgen Klopp considered AI advice, but the decision was made to play an attacking corner kick, presumably in the hope of scoring a last-minute winner.

The out-swinging delivery into the box may well have been the tactic with the highest probability of scoring a goal—but things rapidly went wrong. Manchester United gained possession of the ball, moved it down the pitch on the counterattack and slotted home the winning goal, sending Liverpool out of the tournament at the last moment.

So while AI might suggest the optimal delivery and setup for a set piece, a coach might decide the wiser move is to play safe and avoid the risk of a counterattack. If TacticAI continues its career progression as a coaching assistant, it will no doubt learn that keeping the ball in the corner and playing for penalties may sometimes be the better option.

More information:
Zhe Wang et al, TacticAI: an AI assistant for football tactics, Nature Communications (2024). DOI: 10.1038/s41467-024-45965-x

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Can AI improve soccer teams’ success from corner kicks? Liverpool and others are betting it can (2024, March 20)
retrieved 24 June 2024
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Virtual reality as a reliable shooting performance-tracking tool

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Virtual reality as a reliable shooting performance-tracking tool


target practice
Credit: Pixabay/CC0 Public Domain

Virtual reality technology can do more than teach weaponry skills in law enforcement and military personnel, a new study suggests: It can accurately record shooting performance and reliably track individuals’ progress over time.

In the study of 30 people with a range of experience levels in handling a rifle, researchers at The Ohio State University found that a ballistic simulator captured data on the shooters’ accuracy, decision-making and reaction time—down to the millimeter in distance and millisecond in time—on a consistent basis.

In addition to confirming that the simulator—called the VirTra V-100—is a dependable research tool, the findings could lead to establishing the first-ever standardized performance scores for virtual reality ballistics training.

“To our knowledge, we’re the first team to answer the question of whether the simulator could be converted to an assessment tool and if it’s credible to use it day-to-day,” said Alex Buga, first author of the study and a Ph.D. student in kinesiology at Ohio State.

“We’ve figured out how to export the data and interpret it. We’ve focused on the three big challenges of marksmanship, decision-making and reaction time to measure 21 relevant variables—allowing us to put a report in a user’s hand and say, ‘This is how accurate, precise, focused and fast you are.'”

The study was published in the Journal of Strength and Conditioning Research.

U.S. military leaders and law enforcement agencies have shown an interest in increasing the use of virtual reality for performance assessment, said Buga and senior study author Jeff Volek, professor of human sciences at Ohio State. Earlier this year, an Ohio Attorney General Task Force on the Future of Police Training in Ohio recommended incorporating virtual reality technology into training protocols.

Volek is the principal investigator on a project focused on improving the health of military service members, veterans and the American public. As part of that initiative, the research team is investigating the extent to which nutritional ketosis reduces detrimental effects of sleep loss on cognitive and physical performance in ROTC cadets—including their shooting ability as measured by the VirTra simulator. Verifying the simulator’s results for research purposes triggered the attempt to extract and analyze its data.

“We were using it as an outcome variable for research, and we found that it has very good day-to-day reproducibility of performance, which is crucial for research,” Volek said. “You want a sensitive and reproducible outcome in your test where there’s not a lot of device or equipment variation.”

Because the lab also focuses on human performance in first responders, researchers’ conversations with military and law enforcement communities convinced Buga that data collected by the simulator could be more broadly useful.

“I created a few programs that enabled us to calculate the shooting data and produce objective training measures,” he said. “This equipment is close to what the military and police use every day, so this has potential to be used as a screening tool across the country.”

Users of the simulator operate the infrared-guided M4 rifle by shooting at a large screen onto which different digitally generated visuals are projected—no headset required. The rifle at Ohio State has been retrofitted to produce the same recoil as a police or military weapon.

The study participants included civilians, police and SWAT officers, and ROTC cadets. Each was first familiarized in a single learning session with the simulator and then completed multiple rounds of three different tasks in each of three study performance sessions.

In the first task, participants fired at the same target a total of 50 times to produce measures of shooting precision. The decision-making assessment involved shooting twice within two seconds at designated shapes and colors on a screen displaying multiple shape and color choices. In the reaction-time scenario, participants shot at a series of plates from left to right as rapidly as possible.

Internal consistency ratings showed the simulator generated good to excellent test-retest agreement on the 21 variables measured.

All participants were well-rested and completed the study sessions at about the same time of day. Self-evaluations showed that participants’ overall confidence about their shooting performance increased from their first to final sessions. They also rated the simulator as a realistic and a low-stress shooting assessment tool.

The low stress and well-rested conditions were important to establishing baseline performance measures, the researchers noted, which then would enable evaluating how injuries and other physical demands of first-responder professions affect shooting performance.

“This simulator could be used to assess the effectiveness of specific training programs designed to improve shooting performance, or to evaluate marksmanship in response to various stressors encountered by the same law enforcement and military personnel,” Buga said. “These novel lines of evidence have enabled us to push the boundaries of tactical research and set the groundwork for using virtual reality in sophisticated training scenarios that support national defense goals.”

Additional co-authors, all from Ohio State, included Drew Decker, Bradley Robinson, Christopher Crabtree, Justen Stoner, Lucas Arce, Xavier El-Shazly, Madison Kackley, Teryn Sapper, John Paul Anders and William Kraemer.

More information:
Alex Buga et al, The VirTra V-100 Is a Test-Retest Reliable Shooting Simulator for Measuring Accuracy/Precision, Decision-Making, and Reaction Time in Civilians, Police/SWAT, and Military Personnel, Journal of Strength & Conditioning Research (2024). DOI: 10.1519/JSC.0000000000004875

Citation:
Virtual reality as a reliable shooting performance-tracking tool (2024, June 11)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-virtual-reality-reliable-tracking-tool.html

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Power cut causes flight chaos at UK’s Manchester airport

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Power cut causes flight chaos at UK's Manchester airport


Manchester Airport, the third-busiest in Britain, cancelled over 100 flights affecting thousands of passengers Sunday following a major power cut
Manchester Airport, the third-busiest in Britain, cancelled over 100 flights affecting thousands of passengers Sunday following a major power cut.

Manchester Airport, the third-busiest in Britain, cancelled over 100 flights affecting thousands of passengers Sunday following a major power cut.

At least 20 percent of all outgoing and incoming flights were cancelled, a Manchester Airport spokesperson said, adding that it expected further disruption.

Earlier aviation analytics firm Cirium said 66 departures and 50 inbound flights had been cancelled with easyJet experiencing the largest number of cancellations.

Manchester Airports Group, which also operates London Stansted and the East Midlands airports, said the airport had been “affected by a major power cut in the area earlier this morning” and passengers at two of the three terminals were told to stay away.

The power cut led to problems with airport security and baggage systems, according to Chris Woodroofe, the airport managing director. Flights resumed in the afternoon.

Woodroofe said on social media he expected flights to be “back to normal operations” on Monday.

The airline Jet2 said that as well as cancelling dozens of flights, it was unable to load bags onto planes as the baggage system remained “inoperable”.

EasyJet warned of “very long queues” for security and said passengers could only board flights with cabin bags.

Some arriving flights were diverted to other airports including London Heathrow and Birmingham.

Some flyers took to X, formerly Twitter, to describe the “chaos”, with one passenger saying they had been waiting for their bags after landing after midnight and another saying they were “stuck on the plane”.

The UK travel industry has been hit by a series of technical and strike disruptions in recent years that have affected rail and air passengers.

Last month, a nationwide outage of immigration e-gates caused long delays for thousands of passengers. In August last year, Britain faced its worst air traffic control disruption in years due to a technical fault.

© 2024 AFP

Citation:
Power cut causes flight chaos at UK’s Manchester airport (2024, June 23)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-power-flight-chaos-uk-manchester.html

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‘Flying taxis’ to be tested during Paris Olympics: Minister

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'Flying taxis' to be tested during Paris Olympics: Minister


The VoloCity air taxi will be authorised for test flights during the Olympics
The VoloCity air taxi will be authorised for test flights during the Olympics.

France’s transport minister said Wednesday that so-called “flying taxis”—large futuristic drones capable of transporting several people—would be authorized for use on an experimental basis during the Paris Olympics.

“We are going to experiment with this world-first during the Olympic Games. It’s a technological advance that could be of use,” Patrice Vergriete told Le Parisien newspaper.

But he also dashed hopes of sports fans hoping to buzz over the City of Light to reach their destinations in July and August, saying that the terms of the authorization would be limited and not include use by the general public.

“I’m not a fan of the name ‘flying taxi’ as it’s been called,” he added before explaining the possible roles for the 18-rotor vehicles which resemble small helicopters.

He said they “could be useful as a future ambulance, so let’s be pragmatic. Let’s analyze the impact and do a cost-benefit analysis.

“There’ll be some test flights during the Games. If we see that they’re not effective and that they make too much noise, then we’ll draw conclusions,” Vergriete added.

“Flying taxis” were once a staple of science-fiction movies but are now a reality—in theory.

Manufacturers have run into regulatory and safety barriers around the world that have prevented their roll-out.

‘Greenwashing’?

Germany manufacturer Volocopter has been conducting test flights in the Paris region for several years of its two-seater VoloCity and has lobbied hard for authorization from European authorities in time for the Olympics.

The Paris Olympics begin on July 26
The Paris Olympics begin on July 26.

The company has partnered with French airport operator ADP, the capital’s metro and bus operator RATP, and the Paris regional government.

Four landing and take-off zones have been built around the capital, including at the Charles de Gaulle airport and the smaller Le Bourget airfield, in addition to a new floating platform on the river Seine in western Paris.

In addition to regulatory hurdles, it is yet to convince French authorities of its environmental credentials or utility as a battery-powered low-carbon transport solution.

Local councilors in Paris have voted unanimously against the concept.

“It’s greenwashing in its purest form, a mode of transport created for the ultra-rich in a hurry because there’s only one space for a passenger,” deputy mayor of Paris, Dan Lert, from the French Greens party told AFP.

A petition demanding a ban has garnered around 15,000 signatures and a collective named “Flying Taxis, No Thanks” has called for a demonstration on June 21.

Volocopter says it has invested around 600 million euros ($650 million) and the group came close to bankruptcy earlier this year.

It is aiming for certification from the European Union Aviation Safety Agency (EASA) “in the autumn”, the company said last month.

With a maximum airspeed of 110 kilometers (68 miles) per hour, the VoloCity has room for a pilot and a passenger.

The Paris Olympics run from July 26-August 11, followed by the Paralympics from August 28-September 8.

© 2024 AFP

Citation:
‘Flying taxis’ to be tested during Paris Olympics: Minister (2024, June 12)
retrieved 24 June 2024
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Researchers develop new, more energy-efficient way for AI algorithms to process data

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Researchers develop new, more energy-efficient way for AI algorithms to process data


Can AI learn like us?
A schematic comparing typical machine-learning models (A) with Daruwalla’s new design (B). Row A shows input or data having to travel all the way through every layer of the neural network before the AI model receives feedback, which takes more time and energy. In contrast, row B shows the new design that allows feedback to be generated and incorporated at each network layer. Credit: Kyle Daruwalla/Cold Spring Harbor Laboratory

It reads. It talks. It collates mountains of data and recommends business decisions. Today’s artificial intelligence might seem more human than ever. However, AI still has several critical shortcomings.

“As impressive as ChatGPT and all these current AI technologies are, in terms of interacting with the physical world, they’re still very limited. Even in things they do, like solve math problems and write essays, they take billions and billions of training examples before they can do them well,” explains Cold Spring Harbor Laboratory (CSHL) NeuroAI Scholar Kyle Daruwalla.

Daruwalla has been searching for new, unconventional ways to design AI that can overcome such computational obstacles. And he might have just found one.

The key was moving data. Nowadays, most of modern computing’s energy consumption comes from bouncing data around. In artificial neural networks, which are made up of billions of connections, data can have a very long way to go.

So, to find a solution, Daruwalla looked for inspiration in one of the most computationally powerful and energy-efficient machines in existence—the human brain.

Daruwalla designed a new way for AI algorithms to move and process data much more efficiently, based on how our brains take in new information. The design allows individual AI “neurons” to receive feedback and adjust on the fly rather than wait for a whole circuit to update simultaneously. This way, data doesn’t have to travel as far and gets processed in real time.

“In our brains, our connections are changing and adjusting all the time,” Daruwalla says. “It’s not like you pause everything, adjust, and then resume being you.”

The findings are published in the journal Frontiers in Computational Neuroscience.






Credit: Cold Spring Harbor Laboratory

The new machine-learning model provides evidence for a yet unproven theory that correlates working memory with learning and academic performance. Working memory is the cognitive system that enables us to stay on task while recalling stored knowledge and experiences.

“There have been theories in neuroscience of how working memory circuits could help facilitate learning. But there isn’t something as concrete as our rule that actually ties these two together. And so that was one of the nice things we stumbled into here. The theory led out to a rule where adjusting each synapse individually necessitated this working memory sitting alongside it,” says Daruwalla.

Daruwalla’s design may help pioneer a new generation of AI that learns like we do. That would not only make AI more efficient and accessible—it would also be somewhat of a full-circle moment for neuroAI. Neuroscience has been feeding AI valuable data since long before ChatGPT uttered its first digital syllable. Soon, it seems, AI may return the favor.

More information:
Kyle Daruwalla et al, Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates, Frontiers in Computational Neuroscience (2024). DOI: 10.3389/fncom.2024.1240348

Citation:
Researchers develop new, more energy-efficient way for AI algorithms to process data (2024, June 20)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-energy-efficient-ai-algorithms.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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