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AI is revolutionizing every stage of the agricultural process from planning and planting to crop estimation and harvesting. It might not be a glamorous industry but it has long been at the forefront of technological advancement and the advancements of AI in farming are truly impressive. Artificial intelligence in agriculture is a far cry from generative AI like ChatGPT but it incorporates all sorts of technology from computer vision and machine learning to lasers and robotics. For many, agriculture is an abstract concept. With more and more people living in cities, farming and the production of our food has become quite removed from our daily lives. But agriculture underpins almost everything we do, from the food we eat to the water we drink and it is intimately linked to current affairs like climate change, deforestation and wildfires. Great work is underway to utilize AI across a wide spectrum of agricultural activities. Let’s take a look at some recent developments in the key stages of the farming process. Using AI To Plant Crops and Trees Problem: the Earth loses 26 million hectares of trees each year, with over 30% attributed to wildfires. Solution: AI-powered seed planting drones can help undo this damage by planting trees in previously inaccesible lands. Getting seeds in the ground is where it all begins. Each year farmers and other professional growers have to plant their new seeds. Although there is a science as to where and when you plant, it remains a hugely manual and time-consuming process. Farming and reforestation are undergoing wholesale improvement due to artificial intelligence. Pioneering firms like Flash Forrest and AirSeed have developed incredible futuristic autonomous flying machines to speed up this process. They use drones, computer vision, GIS and mapping technology to fly over inaccessible and hazardous lands and identify appropriate planting locations. Then, using robotics, they drop seed pods to the earth. These drones can plant seeds 25 times faster than manual human planting and are revolutionizing how we approach the planting process. The drones have a camera attached to their base. The machine then flies over the land and scans the ground for appropriate planting locations using computer vision (the same software behind facial recognition). Advanced machine learning algorithms are used to make these determinations. GIS and mapping software then record the desired planting locations, and either the same drone or a partner machine will drop or some cases fire the seed to the ground using advanced, highly accurate robotics. This explainer video from The World Wildlife Federation explains the process. The company AirSeed has a lofty goal of planting 100 million seeds next year. Their focus is on trees rather than crops but the technology is the same. Through the use of computer vision-enabled drones, they can plant as many as 20,000 seed pods every day , to help replenish forests that were destroyed by fires . Crop Fertilization Using AI Problem: waterways in many parts of the world have become contaminated by excessive nitrogen levels and 12% of the world's arrible land is no longer usable. Solution: Artificial Intelligence powered soil sensors allow farmers fertilize far more accurately, boosting yields, reducing pollution and increasing profits. Next up in the growing process is the act of fertilizing, essentially feeding the young plants. Every plant requires the right mix of nutrients and minerals to grow, and some are more sensitive than others. One of the key nutrients is nitrogen, which is produced from ammonia by naturally occurring biomes in the soil. If the nitrogen levels are too low crop yields can be harmed. If nitrogen levels are too high then it can become a pollutant , especially when it runs off into water supplies. Through the use of advanced sensors, known as chemPEGS , or “chemically functionalized paper-based electrical gas sensor,” machine learning algorithms can accurately predict soil nitrogen levels over the next week or so (generally up to 12 days). This allows farmers to fertilize more accurately and sparingly, reducing their costs and minimizing the risk of pollution and run-off. Weeding and Pest Reduction Using AI Problem: the earth loses 40% of annual agricultural crops to pests. Solution: AI-powered technology can remove 200,000 weeds per machine per hour and reduce pesticide useage by 90% . Weeding is the bane of any farmer’s existence and one of the primary applications of artificial intelligence in agriculture. The suite of technology here is quite similar to what we saw during the planting process, with computer vision and machine learning underpinning the automation. However, instead of connecting this equipment to futuristic drones, these AI-powered weeding rigs are connected to normal-looking farm equipment and tugged along behind a tractor. Of course, beneath the surface, this technology is still cutting-edge. One of the pioneers in this space is Blue River Technologies (read our in-depth case study here) with the groundbreaking Sea & Spray product. In simplistic terms, this technology uses facial recognition software to identify weeds and spray them with pesticides. This selective application of chemicals allows for a 90% reduction in pesticide use compared to a more traditional blanket spray approach, saving farmers money and reducing the amount of toxic pesticides being used. Another approach is the laser-zapping technology of Carbon Robotics . The setup is very similar to See & Spray, but instead, the weeds are zapped out of existence by laser. This technology can remove up to 200,000 weeds per hour , something that would take 70 people to achieve using non-AI methods. Harvesting Crops Using AI The final part of the farming process is typically the harvesting of the crop. Some types of produce are easily harvested by machine, and generally the more delicate the product the harder it is to automate its harvesting. Combine harvesters have existed for decades, automating the relatively straightforward harvesting of cereals. Strawberries, kiwis and other such fruits were long considered beyond our technological capabilities as the fruits are so delicate. These are typically still picked by hand. AI is changing all that. Once again computer vision is a key component of this process. A camera is used to scan the plants and identify pickable fruit. GIS is used to map out where this is. Robotics is used to gently grab or hoover up the fruit. This is hard to imagine but the below video from AbundantRobots illustrates the process. Crop Yield Estimation With AI Apart from automating the mechanical aspects of farming, AI is being used to improve the science of agriculture too. Understanding crop yields is a vital component of farming. It is also part of governmental and economic forecasting. It impacts food prices, can help predict and prevent famines and plays a big role in global financial markets. Companies like Gro Intelligence use artificial intelligence to make highly accurate predictions about agricultural output. They utilize over 170,000 data sets in their models, everything from satellite imagery and soil samples to price data and the weather. Their proprietary models are then used to predict yield estimates from crops like Brazilian soy and US corn, measure the amount of arable land in West Africa, or predict if unseasonably high rains will damage European crops or cause urban flooding. This does not change the processes of how we farm, but it does allow farmers, corporations and governments to make better decisions in terms of agricultural planning. This can help tackle some of humanities greatest problems from hunger and famine to flooding and wildfires. AI Advancements In Farming Summary Artificial intelligence is driving some real change in farming, and farming is helping advance AI. It is helping us make better decisions, plant more seeds, access more land, roll back deforestation, use less chemicals and make farming more profitable. Farming has long been at the forefront of technological advancements and in this era of artificial intelligence, agriculture is once again driving human development.

As the world of agriculture and farming undergoes an AI revolution we look at some key statistics. The Need For Artificial Intelligence Solutions in Agriculture 12% of Arable Land that was once viable farmland is no longer usable due to excessive nitrogen levels. The chemical is a necessary fertilizer but through incomplete information and overuse, it has led to water pollution. 30% of deforestation each year comes from wildfires and the need to replant is growing rapidly. Deforestation is a concern in its own right but it can impact farming directly through land destruction and indirectly by releasing gigatons of carbon . 40% of crops are lost to pests each and every year. This staggering number is due in part to farm labor shortages , climate change and pesticide resistance . With an ever-growing requirement for calories to satisfy global populations this problem cannot be underestimated. Food production needs to increase by 70% over the next 50 years to match population growth around the world. Otherwise, we will not produce enough food to satisfy basic human caloric requirements. Current AI Capabilities in Agriculture AI-powered planting drones can plant 40,000 at a rate 25 times faster than humans or non-AI machinery. These airborne machines can reach difficult-to-access lands and make previously unusable terrain arable again. AI-powered technology can remove 200,000 weeds per hour per machine, with one machine replacing the work of 70 people. This is based on laser-zapping technology and computer vision (facial recognition) software. AI-powered technology can reduce pesticide usage by 90% by using computer vision, robotics and machine learning. Rather than blanket spraying entire fields of crops AI-powered machines can spray the right herbicide or pesticide exactly where it is needed. Fruit harvesting machines have a harvesting success rate of 51% for highly difficult and delicate produce such as kiwis. There is obviously plenty of room to improve but it is already a significant improvement in a difficult. Agrobot has developed an AI-powered strawberry picker that can harvest close to 20 acres in three days . This 24-armed robot is vastly more productive than human picking and helps alleviate labor shortages. The Big Numbers With AI expected to contribute to planting a billion of seeds over the next five years it is no surprise the industry is expected to grow. By one estimate the industry will be worth almost $5 billion by next year and could grow exponentially after that. Artificial intelligence is already making a massive impact on agriculture and that is good news for farmers, consumers and the planet. For more information, read our in-depth analysis of the impact of AI on agriculture here .

Known as "The Godfather of AI", Geoffrey Hinton's pioneering work laid the foundations for many of the artificial intelligence (AI) applications we use today. A computer scientist and cognitive psychologist, he holds a PhD in computer science from the University of Edinburgh. With Yoshua Bengio and Yann LeCun, he won the Turing Award, referred to as the Nobel Prize for Computing, in 2018. In the early 1990s, Hinton began working on deep learning, a type of machine learning that uses artificial neural networks to learn from data. His work was initially met with scepticism, but his refusal to alter course proved correct and eventually led to a revolution in AI. Today, deep learning is used in various applications and AI tools , including driverless cars, natural language processing, and facial recognition systems. Hinton worked for Google from 2013 to 2023. He helped create Google Brain, a research team that is dedicated to advancing the state of deep learning. Hinton left Google in 2023 so that he could speak freely about the dangers of AI. He warned not enough guardrails were in place to control the technology and that he has slight regrets about some of his contributions to the field. In a world increasingly reliant on AI, Geoffrey Hinton's quotes, insights, and warnings are more critical than ever. As the debate on the future direction of AI continues, his perspective, grounded in decades of experience and research, serves as a crucial guide as we explore this new frontier. 1. The Dichotomy of Intelligence: Biology vs. Logic "Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals." Back in 2011, Hinton highlighted to the Globe and Mail the two main approaches to artificial intelligence : one based on human logic and the other on biological adaptation. He believes that learning and adaptation, which form the cornerstone of deep learning, will be critical to creating a complex form of artificial intelligence. This is a paradigm shift from traditional hand-programmed AI. 2. A Rocky Road: Hinton's Early Belief in Neural Networks "I had a stormy graduate career, where every week we would have a shouting match. I kept doing deals where I would say, 'Okay, let me do neural nets for another six months, and I will prove to you they work.' At the end of the six months, I would say, 'Yeah, but I am almost there, give me another six months." Looking back on his time in academia with the Globe and Mail in 2017, Hinton mentioned that despite facing scepticism and resistance in the early days of his career, he remained steadfast in his belief that neural networks would eventually outperform logic-based approaches. They had been discredited at that time, but Hinton never doubted that they would one day prove superior to the logic-based approach. This conviction laid the groundwork for the resurgence and widespread adoption of neural networks in modern AI. 3. The Morality Spectrum: The Influence of Human Bias on AI "AI trained by good people will have a bias towards good; AI trained by bad people such as Putin or somebody like that will have a bias towards bad. We know they're going to make battle robots. They're not going to necessarily be good since their primary purpose is going to be to kill people." At the Collision conference in 2023, Hinton underscores the dual nature of AI , highlighting that human decisions ultimately shape its impact on society. He emphasizes the critical need for proactive measures to mitigate the negative consequences of AI. His concerns resonate deeply in a world grappling with the ethical implications of rapidly evolving AI technologies. 4. A Double-Edged Sword: The Unseen Dangers of AI Enhancement "I am scared that if you make the technology work better, you help the NSA misuse it more. I'd be more worried about that than about autonomous killer robots." Speaking with the Guardian in 2015, Hinton played down concerns about the dangers of autonomous AI, directing attention instead to a more immediate problem: the misuse of AI by influential organizations for surveillance and other malicious purposes. His perspective highlights the importance of addressing not only the long-term risks of AI but also the immediate threats posed by its integration into existing power structures. 5. The Promise of Progress: Sharing the Benefits of AI "In a sensibly organized society, if you improve productivity, there is room for everybody to benefit. The problem isn't the technology, but the way the benefits are shared out." In a Daily Telegraph interview in 2017, Hinton expressed a measured optimism about the potential of AI to revolutionize fields like medicine and contribute to economic progress. However, he noted that the key challenge lies in ensuring the benefits of these advancements are equitably distributed across society. 6. The Inevitability of Progress: A Global Race for AI Advancement "The research will happen in China if it doesn't happen here because there's so many benefits of these things, such huge increases in productivity." In an interview with National Public Radio (NPR) in 2023, Hinton commented on why he did not sign a letter signed by 30,000 AI researchers and academics calling for a pause in AI research. He acknowledged the concerns of the broader AI community but argued that halting research is not a viable solution. His stance highlights the complexities and challenges of regulating AI while development proceeds at breakneck speed. 7. A New Chapter: Hinton's Commitment to Responsible AI "I want to talk about AI safety issues without having to worry about how it interacts with Google's business. As long as I'm paid by Google, I can't do that." On leaving Google in 2023, Hinton commented to the MIT Technology Review that he left so that he could openly express his concerns without the constraints of corporate interests. He intends to contribute to the discussion about responsible AI development and deployment. Hinton's 2023 departure from Google marked a pivotal moment in his career, as he chose to prioritize ethical considerations over corporate allegiance. His decision underscores the importance of open and candid discussions about the responsible development and deployment of AI, free from commercial pressures. Conclusion to Geoffrey Hinton's Quotes Having left Google, Hinton continues to talk about AI publicly. While he believes progress in the field of artificial intelligence is inevitable and probably a good thing, he qualifies this with the warning that we need to ensure AI is used for good and that no existential threat is conceived. As AI continues to evolve and permeate every aspect of our lives, Geoffrey Hinton's quotes, insights and warnings become increasingly important. The safe and ethical development and deployment of AI should be a priority for governments, companies and citizens across the globe.

Artificial Intelligence is changing all sorts of industries from farming to finance, and sport is no different. The AI impact on sports will be profound, particularly when it comes to sports data science analytics. Artificial Intelligence is helping teams in every aspect of their management, from recruiting talent and keeping players fit to improved training and in-game performance. For many people, AI means little more than ChatGPT and funny images but the truth is this is such a thin sliver of AI that it overlooks so many groundbreaking new technologies. Let's take a look at how some of these AI technologies are being applied to sports. AI Performance Analytics in Sports Sports analytics was originally very rudimentary and really only in the last 20 years has the analysis become formalized. Sports analytics was popularized by the movie Moneyball , based on the book of the same name, which detailed a groundbreaking new approach to baseball analytics. The gist of the approach was that The Oakland A’s should not try to recruit the best individual players, but rather recruit players who generated the most runs (even in indirect ways) and therefore wins. The term is now used across sports for coaches and organizations using analytics to underpin new approaches to tactics and recruitment. Rather than old-fashioned “eye tests” or the use of rudimentary statistics like home runs, sports data science is incredibly complex and takes all sorts of metrics into account. A very simple example is how you might measure the performance of a quarterback (QB) in American football. Basing your QB analysis solely on the number of team wins is clearly not insightful. It might capture some aspects of the player's contribution but at a minimum, the analysis will be clouded by the team’s defensive performance. The analysis could be refined by just looking at how many points the team scored and disregarding how many they conceded or allowed. However, that still does not paint an accurate picture of the QB performance and you will see many in sports media discuss a QB based on the number of touchdowns and interceptions thrown (i.e. how many of his passes lead to a score and how many turned over possession to the opposition). This is clearly somewhat accurate. However, more advanced analysis of QB performance will look at metrics like catchable passes (passes that should have been caught, rather than ones that were actually caught) or EPA (Expected Points Added) and CPOE (Completion Percentage Over Expectation). Advancing Sports Analytics With AI As the analysis becomes more and more advanced, the scope for technology to play a role increases in parallel. The primary type of artificial intelligence used in sports analytics is machine learning and the analysis of “big data”. The ability of machines to find patterns is far beyond that of humans, particularly when it comes to analyzing vast amounts of empirical data. Similar to our stylized QB example above, AI-powered machine learning algorithms can help identify if a player scores a lot because he himself is exceptional or if he is reaping the reward of an exceptional player elsewhere on the team or the contribution of others. Learn how computer vision and machine learning can be appied to pickleball AI performance analysis. A good example in Association Football, or soccer, is the evolution of measuring a player’s attacking output . Originally a player would be judged on goals scored. Then the concept of assists was added, to measure not only scoring but facilitating other teammates scoring. However, this did not take into account poor finishing by other players so now teams will measure Expected Assists (xA). This is similar to the catchable pass in American football. If you pass the ball to a teammate and that pass should be scored 100% of the time (which, admittedly is unlikely) you would then receive an xA score of 1.0 - However if that pass should lead to a goal threequarters of the time it would give you an xA score of 0.75. Types of Data in AI Sports Analytics This type of analysis is often referred to as Event Data. You track the key events in a game, such as goals, passes and fouls in soccer, and then give the information to powerful machine learning software and it will identify patterns. Essentially this measures what happens in a game, when it happens, and what the patterns are. However, we can take the analysis even further by adding another AI-powered technology, Computer Vision. This is the same software that underpins facial recognition technology. Essentially this technology converts camera images into analysable data. The application of computer vision in sport science is known as Tracking Data. Sticking with our soccer analogy, let's consider what happens in the build-up to a goal. One player scores, one player gets an assist, and both of these will be measured by metrics such as xG (Excepted Goals) and xA (Expected Assists). However, what about players who contributed without touching the ball or generating an event (i.e. something that would be recorded as part of Event Data)? That is where Tracking Data comes in. Soccer coaches are always training their players to make off-the-ball runs. This creates space for your teammates and can cause confusion among the opposition players. Through the use of cameras and computer vision AI can now track these runs and analyze their contribution to the team’s performance. While one player is scoring all the goals, others may be contributing significantly to this through selfless running or passing earlier in the build-up or pre-scoring phases. This would be practically impossible without artificial intelligence. Even if we could see all 22 players on the field it is far beyond the capacity of a human brain to track and record so much simultaneous movement. With AI it is essentially automatic. Most professional sports games have many cameras and these are now using computer vision to convert the position and movement into Tracking Data and recording it for machine learning analysis. The application is similar to the AI-powered Second Spectrum software used in the NBA. This artificial intelligence uses the same logic as discussed above, with computer vision software tracking the position of players and their actions and recording it to a huge database of games. Machine learning algorithms then analyze the data, giving coaches insights into how plays develop, how to defend better and so much more. The use of AI in sports analytics is still in its infancy but it is already taking the in-game and performance analysis to the next level through computer vision and machine learning. What about other areas of sports science? Lets take a look at some other ways AI is being used to modernize sports analytics. Artificial Intelligence and Injury Prevention in Sports Injury prevention is a key component of sports science and as the value of players continues to increase teams are becoming more and more concerned about keeping them healthy. Using the same suite of technology as in performance analysis teams can predict injuries and make changes before a player is hurt. Obviously, not all injury is predictable but in some cases, there are telltale signs and AI can identify when a player is running an increased risk of injury. One cutting-edge example is using computer vision to see when a player might begin to favor one side of his body over the other. For example, consider a player whose left ankle is weaker than his right. Computer vision will recognize when he starts to favor his right side before the human eye would, and that would be a warning sign that the risk of injury is too high. This is considered to be a leading indicator of an injury and as any sports fan, athlete or coach can tell you, prevention is better than cure. Artificial Intelligence and Player Recruitment Another area of sport being revolutionized by AI is player recruitment. Artificial intelligence helps coaches make better in-game decisions , better injury prevention decisions, and now we can see how it helps make better recruitment decisions. Using similar data and technology, artificial intelligence can help teams in two ways. Firstly, artificial intelligence can show teams what their true need is, and secondly, AI can reveal the true qualities a player has. The Use of AI in The NFL Draft Player recruitment is difficult in any sport but there is nothing quite as intense as the NFL draft . Pro Football in the US enjoys one of the most level playing fields in all of sport, thanks in no small part to the draft system. This sees the best 250 or so university players each year move to the NFL with the weaker teams getting the first choice of picks. Teams need to understand what their weaknesses are and have a plan of how to address those shortcomings by bringing in new talent. Artificial intelligence lets coaches see patterns in performance data, both of their own team and the players in the draft, and make better decisions. Does a college quarterback throw the ball exceptionally well , or does he have receivers who are so good they make his mediocre passes look more impressive than they really are? That is a straightforward question and one that most American football coaches could probably answer themselves without the help of any technology. There are, however, many other similar questions where AI is far more advanced. The performance of fullbacks or running backs could require tracking the position and performance of many other players at the same time and is therefore more suited to computer vision. Off-Field Decisions and On-Field Performance Recruitment technology is now taking into account additional data beyond just game day performance. Just because a player is good it does not necessarily mean he will be good for you. Sport is littered with flops and stars who didn't cut it and that is often down to off-field factors as opposed to any physical or technical attributes. NFL teams are now incorporating player social media into their AI-powered recruitment algorithms to help better understand the players. AI Recruitment in Soccer Soccer teams spend billions of dollars every year buying players and many clubs seem to struggle to find the right fit. The Cristiano Ronaldo-inspired Saudi League is offering clubs a legitimate exit strategy for marquee signings that did not fit their system, but historically, overspending on a big-name player was a disaster for European football clubs. Unlike in American sports, in Europe players are paid whether they play or not, and it can be impossible to get an expensive player off your books. English heavyweights Manchester United are currently suffering in this way having overpaid for the defender Harry Maguire. Maguire led Leicester to a history-making Premier League win in 2016 and he was considered one of the best English centre-backs. Then he made the move to England’s most successful team for a record £80 million pound fee and it looked a perfect move. Except it wasn’t. Almost nothing has gone right for Maguire at United and the club has tried to offload him multiple times but his wages are too high and he is almost unsellable. Could AI have prevented this? Well, the truth is very probably, yes. Just because Maguire was a good defender in one system a Leicester it should not be assumed he would be as effective in Manchester, surrounded by different players, playing a different style, for a different coach. This stands in stark contrast to the approach taken by far smaller clubs like Brentford. Inspired by their gambling and stats-obsessed owner Matthew Benham the London club have used Moneyball techniques to buy diamonds in the rough over and over again. This has allowed them to reach and stay in the English Premier League on a far smaller budget than their rivals. Their AI approach to recruitment would never allow them to waste £80 million on a big-name player who did not fit the system. Artificial Intelligence in Sports Analytics AI is changing every single industry and sport is no different. Computer vision allows us to record far more than the human eye can see, and machine learning identifies patterns hidden in the data. This is helping coaches and teams make better in-game decisions, prevent injuries, and recruit better. Some teams have leaned into this new frontier while others are slowed to adapt and the Rob Thomas quote comes to mind. The IBM Senior Vice President said, "AI is not going to replace managers, but managers who use AI will replace the managers who do not." Artificial Intelligence is not going to win football games, but football teams who use AI will win over teams who do not. Want to learn more? Read how football teams use AI in areas such as recruitment and injury prevention .

The use of Artificial Intelligence is growing at an exponential rate and sports organizations are at the forefront of its practical application. From recruitment to injury prevention and game analysis, football teams are using AI to improve every aspect of their organization. Let's take a look at how some of the world's biggest football clubs are using artificial intelligence today. Click here to read our introduction to the use of artificial intelligence in sports analytics. How Arsenal Uses Artificial Intelligence Arsenal Football Club have long been synonymous with moves to modernize football and are often among the first clubs to adopt new training methods, player welfare systems and scouting models, so much so that Moneyball inspiration Billy Beane has been said to admire long-time Arsenal manager Arsene Wenger. As far back as 2012, Arsenal showed a keen interest in data science and the club bought a company called StatDNA . Arsenal are a notoriously private organisation so it is difficult to know exactly what StatDNA does for them. However, one of the company's few employees with a public profile has stated that she “hates passing percentage” as a metric, which is a typical Moneyball mindset, eschewing top-level statistics in favor of more insightful, harder-to-measure criteria. Although the specifics of StatDNA are proprietary we do know some things about the work they do for Arsenal. Through computer vision and deep learning algorithms, StatDNA requires about 20 hours to analyze a 90-minute football game. This is not because the software is slow, but because it takes in so much data from the game. It uses concepts like Tracking Data to assess the level of defensive pressure players were under during each passage of the game, how they responded to that pressure and what was the impact of that pressure. Another area that StatDNA assesses for Arsenal is the quality of actions recorded as part of Event Tracking . It is not all that useful to record the number of assists a player generates unless you can be confident that the passing player truly assisted the goalscorer. The obvious example is if the goalkeeper passes the ball to a center-back, who then dribbles the entire length of the pitch and scores a wondergoal into the top corner. The goalkeeper would be awarded an assist for this even though he played no part in the goal. This example is of course very stylized, but StatDNA does analyze many factors involved in an assist. For instance, they examine if an assist allowed the goalscorer to shoot with his favoured foot , or to shoot without breaking stride. They assess if the build-up play moved the goalkeeper out of position leaving the goalscorer with a bigger target. This information is hugely valuable at removing randomness or viewership bias from analysis and allows the club to make better-informed decisions. Arsenal uses this information to enhance their recruitment, training and performance analysis. It has allowed the club to improve its recruitment with diamond-in-the-rough singing like Gabriel Martinelli from the fourth tier of Brazilian football or William Saliba from France. How Manchester United Uses Artificial Intelligence Man United are one of the biggest sports organizations on the planet and it is no surprise they are looking to utilize artificial intelligence firms to improve their operations. Unlike Arsenal, they have not bought or developed in-house capabilities but rather the club is partnering with leading global AI providers. One of the primary partners in this effort is a company called Catapult Sports and their focus is on squad management. Squad management is an area of the game that United have historically excelled in. Legendary manager Alex Ferguson could build and manage title-winning squads year in, year out, and did it in an analogue world. Since his retirement he has talked with Havard about his season preparation and the result were plain to see in his relentless success. The game has changed somewhat since then and managers are happy to leverage AI to make this task easier. This is where Catapult comes in. Tony Strudwick , formerly the head of performance at Manchester United, who works closely with Catapult Sports, sums up this modernization of approach perfectly: “Any way of tracking performance through technology allows you to make smarter decisions and gives you an objective measure of performance. Twenty years ago we didn’t have the technology and you were reliant upon intuition alone and the coach’s eye." You can read the rest of his thoughts on the Catapult Sports website . Catapult Sports have partnered with over 3,000 football team around the globe and their data sets and network are unrivaled. Each club has adopted its own approach to AI, with Arsenal keeping it in-house and United partnering externally, but both clubs are leaning into this new era with enthusiasm. How Brentford FC Uses Artificial Intelligence Brentford is the pace-setter in terms of AI and data-driven recruitment policies. The London club is probably the most modern of all major football teams when it comes to data science thanks in no small part to their owner. Matthew Benham’s background was in sports betting before he moved into football ownership, and that is where he honed his data modeling and analysis skills. Benham's story is fascinating in its own right and for anyone with even a passing interest in football, recruitment, or even data science it is worth reading about how he revolutionized his club and football recruitment in general. Here we will look at some aspects of AI football analysis that Brentford does a little bit differently to everyone else. Where Brentford really come into their own is in terms of recruitment. As we mentioned above when looking at Arsenal, the real value of data and analysis often comes from doing things a little bit differently than everyone else. Assists are a commonly used metric but assists where the pass allows the goalscorer to shoot with his good foot without breaking stride is far more valuable. For recruitment of elite football talent, Brentford uses their own proprietary yardsticks for measuring players. Rather than analyzing players against the standard eleven positions on a football pitch, Brentford breaks their analysis down into 16 positions . Each position is evaluated against six criteria, which are not made public, and this is how the club asses its own squad and transfer targets. The club has a database of over 85,000 players globally and each player is assessed in either one or multiple positions. This AI-driven approach has allowed Brentford to recruit far more efficiently than other clubs and maintain top-flight status on a relatively small budget. They can unearth players in leagues many other clubs do not even consider, due to their data-driven AI-powered approach. How Barcelona Uses Artificial Intelligence Barcelona are one of the most iconic football clubs in the world and are known for their commitment to innovation. The club has embraced artificial intelligence, particularly for analyzing gameplay and developing strategies, as part of the club's leading program, The Barca Innovation Hub . As we have discussed previously , AI algorithms are particularly adept at analyzing vast quantities of difficult data. One such example is sifting through hundreds or thousands of hours of match footage to identify repeated patterns of play. This is one of the areas where Barca use AI the most. Computer vision is deployed to identify the repeated movement sequences and patterns that lead up to goal-scoring opportunities. Deep learning algorithms use both Tracking Data and Event Data to understand flowing moves and how direct actions, such as passes, and indirect actions, such as defensive pressure, combine into goals. Once identified, Barcelona can incorporate these patterns into their training regime. The same software is also used to better understand and prepare for opposition formations, tactics, and even set-piece play. There are far more formations and nuances to modern football than many pundits realize and the more complex a dataset the more useful AI technology becomes. There is even a very real chance that these AI technologies will identify patterns that the most advanced coaching minds may have failed to notice. Like most clubs Barcelona play their cards close to their chest but Kognia Sports is one of their artificial intelligence partners. Kogina is a global leader in the use of computer vision and deep learning algorithms to analyze football tactics. How Real Madrid Uses Artificial Intelligence Los Blancos are the biggest name in football so it should be no surprise to learn the club has invested heavily in artificial intelligence. One area that Real Madrid stands out in terms of their AI use is analyzing player decision-making. Using the same suite of tools as the other clubs on this list, particularly computer vision and deep learning algorithms, Madrid leverages AI to understand how players make decisions and what they can do to make better ones. The technology can track where a player looks prior to receiving the ball, his head movements, any changes in direction and ultimately asses if he made the best decision or not. It is possible that a coach could assess this intuitively over time, but it is not possible for a human or team of humans to do this at the speed or scale of AI. Remember, elite football clubs are using AI to track every action and movement of every player on the pitch in every moment of the game. Scanning and decision-making like this were long thought to be the reserve of the most elite players and with AI is it being digitalized. This means it can be understood faster and coached more easily and this could significantly change the game. Set-piece strategy is another area in which Madrid uses AI extensively. They use artificial intelligence to make decisions in everything from free kicks to corners. Should a player shoot directly at goal or pass the ball out wide? Against this particular opposition, is a corner better targeted at the near post or the far one? How far and aggressively will this keeper come out to catch a cross? These are all questions that AI is helping Real Madrid to answer more accurately and more quickly. How Football Teams Use Artificial Intelligence (AI) We have looked at the various ways some of the biggest clubs across Europe utilize AI in their operations, from recruitment and training to injury prevention and decision-making analysis. The use of these technologies in real-time has not yet been confirmed by these clubs that is certainly the direction the game is going. There is of course a long way to go but it is already clear that having an edge in the AI arms race can translate to on-field success in football. Will Arsenal's in-house approach win out over United's global partnership model? Will Bretnford's recruitment algorithms continue to find hidden gems and can Bayern's key players avoid injury more than their peers? Only time will tell, but we can confidently say that football teams that use AI will win out of those that do not. Click here to see how teams like Bayern Munich and PSG use artificial intelligence to manage player welfare and injury prevention.

As part of our series detailing the use of AI in football , we have looked at the various ways sports organizations are deploying artificial intelligence to improve their performance both on and off the pitch. We have looked at some of the unique ways some of the biggest names in football are using AI in their own ways, from Arsenal’s in-house approach to United’s global partnerships. Here we will see how AI is being used to prevent injuries at some of football’s biggest clubs. How Bayern Munich Uses Artificial Intelligence The Bavarian juggernaut is nicknamed FC Hollywood for a reason. Bayern enjoy the lion's share of resources in the Bundesliga and have long been at the forefront of drives to modernize the game. They use artificial intelligence in many of the same ways as the other clubs discussed here, but they excel in one particular application of AI. Bayern Munich are arguably the leaders in terms of deploying AI to prevent injuries and maintain player health. Once again, the technologies are essentially the same as those mentioned above. Computer vision is used to ingest all sorts of game and training data, which is then analyzed by deep learning algorithms. Is a player favoring one foot over the other, or turning more slowly than usual? If so, then it might be time to give him a rest. Artificial intelligence can monitor player biometric data and warn the coaching staff when a player is running at a higher risk of injury than normal. This allows the club to prevent rather than heal injuries, protecting the club's most valuable assets. How PSG Uses Artificial Intelligence One way that PSG uses AI differently to other clubs is their use of "wearables" to gather player data. As we have spoken about previously, ultimately the key differentiator for artificial intelligence technologies will be access to data . PSG see data coming from player-worn devices as key to better understanding their risk of injury. Of course, the Parisian giants use artificial intelligence in many of the same ways as the other clubs. However, as the use of AI proliferates and first-mover advantages become eroded, how clubs use AI will be a more important factor than whether or not they use it at all. How Liverpool Uses Artificial Intelligence The storied English Premier League club have not revealed much about their use of artificial intelligence but they are known to deploy the technology to help prevent injuries. Similar to PSG, Liverpool leverage “wearables” data for measuring performance and output as a way to track player welfare. The club has partnered with Google DeepMind to enhance their data analytics and sports science ventures. As the club continues to enjoy the service of perceived older players, the 31 year old Mo Salah being a prime example, this approach appears to be paying dividends. How Manchester City Uses Artificial Intelligence Man City have undergone change like no other team in football over the last decade or so, both on and off the pitch It is true, of course, that the club’s success has come from massive investment from its generous owner, Sheikh Mansour. However, the club has invested incredibly well. While the hiring of the all-conquering manager Pep Guardiola or superstar Erling Haaland generates plenty of media interest, investment behind the scenes garners far less attention. One such area is the partnership with the multinational technology giant SAP . While SAP might be better known for supply chain management or CRM software, they are also global leaders in AI-driven sports analytics. Again, details are somewhat thin on the ground for exactly how City uses this technology, it is understood that the system offers real-time data analysis and insight into player fitness, fatigue, and potential injury risk. How AI Can Prevent Injuries in Football Artificial Intelligence is being deployed in every sport, from pickleball to the NFL and European football clubs are no different. The same technologies and data being used to improve training methods and decision-making can also help prevent injuries by better understanding player fitness and performance. Want to learn more? Click here to discover how clubs like Brentford use AI in their recruitment process or Real Madrid utilize artificial intelligence to improve their set-piece play.

Football is continually evolving and is now at the forefront of the use of AI in sports. From talent recruitment to injury prevention, and in-game decision-making to set-piece routines, AI is revolutionizing football . The biggest clubs across Europe have incorporated artificial intelligence into every aspect of their operations. Here we look at eight AI quotes from some of the greatest names in football. "Football is not artificial intelligence.” Manchester City manager Pep Guardiola has a reputation for making tongue-in-cheek comments during interviews and this looks like one such remark. Despite making the bold remark against AI, his employer and Manchester City owner City Football Group have just hired Laurie Shaw, an astrophysicist, as their Lead Artificial Intelligence Specialist . City have hired four such AI specialists, so despite what Guardiola may say publicly, the club are leaning into the AI revolution heavily. “In 10-15 years, it will not necessarily be a football specialist who will be a manager for a club. It will be management specialists rather than football specialists, because the football decisions will be made by technology.” Arsène Wenger , the legendary manager of Arsenal Football Club, has long been a revolutionary figure within the world of football. Currently the Chief of Global Football Development for FIFA, Wenger has strong opinions on how far the application of Artificial Intelligence in sports will go. “When you play 50 or 60 games a year - for a club like Manchester United for example - if you’re [going to be] successful, how you manage your squad is so important.” Tony Strudwick, former head of performance at Manchester United, and now at Catapult Sports , is a global leader in sports applications of artificial intelligence. One area of focus for Strudwick is the use of artificial intelligence to manage player resources over the course of a season. This is something Manchester United have always been a leader in, from the analog methods of managers like Alex Ferguson and now with the help of artificial intelligence. "Data gives you the opportunity to be less biased in your decisions." "It’s not that data tells you who to pick, but data can tell you where to look," Rasmus Ankersen, Brentford's co-director of football, is a pioneering advocate for artificial intelligence and his club are at the forefront of the AI football revolution. Brentford have developed a reputation for finding hidden gems in the transfer market, allowing the club to compete far beyond its financial means. Ankersen talks about how data is key to any AI system, something we have looked at previously here . Brentford's Moneyball approach to football has seen the club become a Premier League regular and it all comes from innovative use of data . “Football is the most difficult sport to crack. There are 22 players who make 15-25 micro-decisions per second. Everything is related and it is a low-scoring game.” Giels Brouwer, Founder & CIO of SciSports , discussing the difficulties of advanced analytics in football. Football, despite being the world's most played and watched sport, is quite unusual from an analytical perspective. Scores are typically low and much of the action happens off the ball. SciSports groundbreaking machine learning algorithms to provide players, coaches and clubs with actionable insights. “Kogina provides me and my technical staff with essential information that helps me understand what is happening on the pitch and make the best decisions from the bench.“ Xavi Hernández is one of the greatest footballers of all time. He is also a founding investor in the artificial intelligence and sports analytics firm Kogina. As a player, Xavi epitomized intelligence and good decision-making and it is a real vote of confidence in football AI for him to be among the early adopters. “Zone7 is the perfect partner to help us harmonize the data we’ve been collecting, optimize player performance, and lower injury incident rates.” Victor Orta is the Director of Football at Leeds United and he is another football executive who has seen the benefits artificial intelligence can bring to the game. By monitoring all aspects of player performance AI can identify risk patterns that lead to injury and produce real-time injury threat alerts. This is estimated to reduce player injury incidence rates by 50%. “The insights produced are widely used across our football operations – in scouting and talent identification, in game preparation, in post-match analysis, and in gaining tactical insights.” Ivan Gazidis is one of the top executives in world football and is currently the Chief Executive of the storied Italian club A.C. Milan. The South African-born businessman is an Oxford graduate and a long-term believer in the use of artificial intelligence in football . As discussed previously on Aiifi , artificial intelligence is revolutionizing every aspect of modern football and Gazidis calls out both on and off-field aspects of the sport. Quotes About AI in Football Some of the best minds in football are working to apply AI to the sport and these quotes give us a glimpse into how they are going about it. Artificial intelligence is changing how football clubs recruit, train, play, and even protect their players. If you enjoyed these quotes why not read some of our other collections of quotes from the likes of Geoffrey Hinton and Demis Hassabis .

Football is struggling this season with controversial VAR calls that range from the confusing to the outright incorrect. This has seen coaches come out and claim the technology is not up to scratch, fan outrage, and even calls for games to be replayed. So what has gone wrong and how can artificial intelligence help? What is VAR? VAR is an acronym for Video Assistant Referee and it is a suite of software used in football leagues around the world to help the on-pitch referee. VAR is managed on a game-by-game basis by additional support referees. It is used to overturn “clear and obvious errors” by the referee during the game, to review offside and handball decisions with the aide of slow-motion replays, and to notify the referee of any incident he may have missed. How Does VAR Work? Although there is still a significant human element to VAR, some parts are based on software particularly when analyzing offside decisions. The essence of an offside call is to determine if one player is more advanced than another at a given moment. In a fast-moving sport like football, this is obviously very difficult. VAR has implemented a process of “drawing lines” on the pitch to determine which player is more advanced. VAR And The Offside Rule A player is in an offside position if any part of the head, body or feet is in the opponents’ half (excluding the halfway line) and any part of the head, body or feet is nearer to the opponents’ goal line than both the ball and the second-last opponent. If both of these conditions are met then a player is offside. This becomes an offence when a player in an offside position at the moment the ball is played or touched by a team-mate and then becomes involved in active play. If this rule sounds very technical, that’s because it is. Many aspects of the offside rule are regularly discussed and argued by football fans but two key components are relevant to VAR. Firstly, how is the exact moment of the ball being passed decided? And secondly, how is “nearer to the goal” measured? Unfortunately despite some significant technological advancements, these two steps are manually completed by hand. Could AI Help With VAR Offside Decisions? As you can see in the video, the lines are drawn at the single-pixel level, manually , by a human relying on their own eye. Although VAR gets the vast majority of these calls correct, the consequences of an incorrect decision can be season-defining. Computer Vision and VAR Computer Vision is a branch of artificial intelligence that converts camera feeds into analyzable data. The VAR system already relies on well-established Hawk-Eye computer vision technologies to c alibrate the camerras prior to the game. However, despite this use of computer vision, offside decisions are still made by hand. Computer vision is already used extensively to analyse player movement in football and it should certainly be considered for use by VAR. Wearable Devices and Audio AI Wearables are a relatively new field of AI that could assist referees and VAR. The wearing of highly advanced football boots with tracking devices has recently been approved by the Premier League . The purpose is for more general data gathering and analysis but it is not much of a leap to see it incorporated into the offside check process. Another area of AI that could assist VAR in making offside decisions is the advancement of audio signal processing algorithms. It is essentially the aural version of computer vision, and software such as SoundSee can ingest audio feeds, convert it to analyzable data, run machine learning algorithms and produce usable insights from the sounds. Will Artificial Intelligence Fix VAR And Refereeing? FIFA have already begun testing the use of AI in refereeing but there is a long way to go. As artificial intelligence becomes more mainstream over the coming years it will almost certainly be incorporated into football referring and VAR. However, that does not mean AI will replace human referees . There is a famous quote from Rob Thomas about AI (that you can read here ), that can be easily adapted to the application of AI in football refereeing: AI is not going to replace referees, but referees who use AI will replace the referees who do not.

The AI landscape is continuously, and rapidly, changing. New technologies pop up and disappear all the time. Sources of data are discovered and shut down on a regular basis. Billions of dollars are invested in start-ups, and thousands of job losses are announced seemingly every day. It can be difficult to keep up with all the changes (although following Aiifi is a good start!) but some changes deserve more attention than others. One such example is the recent announcements impacting the legal environment surrounding artificial intelligence. Safe, Secure, and Trustworthy AI On October 30th US President Joe Biden announced a sweeping Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. The goal is to promote the safe development and use of AI that is in the “interests of the American people.” That is a very broad aim and there are obvious questions about how it will work in practice. The AI Bletcherly Accord Meanwhile, UK Prime Minister Rishi Sunak hosted an AI summit, bringing together industry leaders and government officials to discuss the potential of catastrophic harm to humanity from AI. This led to the signing of “ the Bletcherly Accord ” bringing the world’s superpowers into alignment on how to control and monitor “frontier AI”. The EU View on AI Law The EU, not wanting to be left behind in this race to legislate, is looking to bring in sweeping new laws to ensure AI is overseen by people, and not automated systems, to prevent harmful outcomes . Although the European Parliament first introduced AI legislation in 2021 that was essentially a framework for identifying and classifying AI. At the time of writing, there are no EU laws to control the development or use of artificial intelligence. Rather the first push is to register AI and ensure it follows existing legal frameworks within the bloc. Why Legislate Artificial Intelligence So what does all this mean? In truth, it will probably do very little to change the trajectory of artificial intelligence. Business interests have long been happy to skirt their own governments by moving production offshore to lower-legislation locations, utilizing complex legal structures to avail of tax havens or just outright cheating the system. Why then are governments across the globe rushing to legislate AI? There are likely many answers to that question but one thing is certainly true. The threat from “ runaway AI ” is very real. While the public focus of AI has been on generative AI such as ChatGPT and potential job losses, many other applications are far more worrisome. Nefarious actors might use AI to develop biological weapons beyond our current ability to defend against them. Much of our strategic infrastructure (think power grids, air traffic control, and telecommunication networks) runs on software. Where we celebrate the application of AI in these are with developments such as self-mending networks, we are equally vulnerable to autonomous AI designed to attack this same infrastructure. Why Current AI Legislation Will Fail These recent attempts by governments to protect society from harmful applications of AI are too little, too late. The advancements in AI in the last two years have been astounding and these moves are akin to closing the stable door after the horse has bolted. Even if requiring companies in Europe and the US to register their AI, authenticate it, and share it with government works for some companies, almost by definition, groups or individuals with bad intentions will simply ignore the law. Even well-intentioned developers will likely move offshore to avoid the red tape associated with these laws. The development of AI does not require laboratories, heavy equipment or hard-to-obtain supplies that have historically been simple to regulate. Anyone with a laptop and a rudimentary understanding of Python can leverage open-source libraries and build AI models from anywhere on the planet. Even if AI legislation were to succeed, exactly as intended, it would not necessarily be a good thing. One obvious concern is about stifling innovation. This applies to specific nations and humanity as a whole. If the US, for example, becomes a highly regulated jurisdiction for anyone looking to build artificial intelligence systems, the most likely outcome is that other countries become the global leaders in terms of AI development. Already, many Western countries lag behind the likes of Russia and China in terms of technology development. Would more legislation help in that regard? Moreover, some of the potential benefits of artificial intelligence are in hugely complex areas such as biotechnology and healthcare. Research in such fields often requires access to academic institutions, medical equipment and vast funding. This is something that would be much better served with a joined-up, cohesive, global approach. A nation-by-nation approach to legislation will almost certainly not help humanity achieve these AI benefits. How Can We Protect Ourselves From AI Rather than legislating the outcomes of AI, a more holistic approach is urgently needed to address all of the legitimate concerns around artificial intelligence. Can we build strategic infrastructure that is not so vulnerable to AI hacking? Can we de-digitalize some areas that are particularly vulnerable to attack? Whatever we do, to have any chance at preventing catastrophic outcomes a truly global response is needed. The USSR and USA were able to agree to nuclear non-proliferation during the height of the Cold War. A similar approach might be needed to protect humanity from some of the more extreme outcomes of AI. I have no doubt that artificial intelligence will benefit humanity in the long run, but executive orders and registration frameworks will do little to offset any of the downsides.

Google's new AI model, Gemini for Bard, is outperforming ChatGPT, posing a significant challenge to AI applications powered by OpenAI. Recent testing reveals Google's Gemini for Bard as a formidable AI model, surpassing ChatGPT 4 in 30 out of 32 logical tests across various subjects, including law, mathematics, physics, and ethics. This breakthrough could disrupt the AI market, raising concerns for AI companies reliant on GPT4. With Gemini for Bard on the horizon, businesses must adapt to stay competitive in the evolving AI landscape. Watch The Aiifi YouTube Short below for a quick summary: Read the summary of the video below: Recent testing has unveiled a significant development in the field of artificial intelligence, as Google's latest AI model, Gemini for Bard, emerges as a formidable competitor to OpenAI's ChatGPT 4. The results of extensive evaluations demonstrate that Gemini for Bard outperforms ChatGPT 4 in an impressive 30 out of 32 logical tests across a wide range of subjects, including law, mathematics, physics, and even ethics. Remarkably, in 90 percent of the test cases conducted, Gemini for Bard surpasses human performance. This revelation has sent shockwaves throughout the AI community, particularly among companies that rely on OpenAI's GPT4 to power their applications. The impending arrival of Google's Gemini for Bard has raised critical questions about the future competitiveness and viability of these AI applications. With the potential for Gemini for Bard to outshine existing AI models, businesses are now faced with the urgent need to adapt and innovate to stay relevant in this rapidly evolving AI landscape. The threat posed by Gemini for Bard extends not only to AI companies but also to various industries relying on AI-powered tools that produce text or image outputs. The question that arises is, why would the market choose an inferior product powered by GPT4 when a superior alternative backed by Google's Gemini is readily available? As the AI landscape continues to evolve , companies must navigate this new paradigm and explore strategies to remain competitive. Whether through collaboration with Google's Gemini for Bard or by enhancing their own AI offerings, businesses are compelled to adapt to this transformative shift, ensuring their continued relevance and success in the dynamic world of artificial intelligence. Keywords for SEO: Google Gemini, Bard AI model, OpenAI-powered applications, AI market disruption, ChatGPT, AI model comparison, AI landscape evolution, AI-powered tools.