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He sees an opening on the left wing and immediately punishes them. After rushing down the side, he looks for his teammates in the center and quickly makes the cross in to finish it off!

Turn on any sports channel and youll hear something similar. Chances are you pictured Ronaldo or another star player running down a fresh pitch. In fact, this could actually describe a play from an artificial intelligence bot in a recent international tournament. Its time to shift our thinking as AI becomes the star player.

As we already know, using AI to enhance human athlete performance is becoming a pervasive practice. The next step for AI in sports is introducing AI players. In fact, we currently have AI agents smart enough to mimic high-level human tactics. They have the potential to revolutionize the sports industry while pushing the envelope regarding what AI can really do.

The immediate response from many people is that such a world will never come to be how could we enjoy watching machines? Many claim that playing against traditional AI can often be a repetitive and boring experience. Others cant imagine any joy from beating their machine opponents. To address this, lets start by examining why we like traditional sports and then outline how AI will come to meet these demands.

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Sports fan psychologists have nailed down eight core reasons why people love their sports.

Many of the motivations mentioned above arent unique to traditional sports. For example, getting together with friends and family to bond is about the people, not about the sport. As such, if the conditions are right, a similar variant involving AI could make inroads into the industry.

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The adoption of AI into the world of sports will be slower than other AI and software applications. Many of the motivations of sports relate to how others around an individual think and behave, so its not enough to change a few people; you need to change preconceptions around an entire industry to be truly effective. Here are four ways were already seeing AI infiltrate sports and how those applications appeal to our existing interest in sports:

Firstly, AI must be able to compete with humans for humans to get interested. We can already see AIs competitive edge with some of our most complex board games and e-sports. Here are some key cases:

These are all examples of deep learning AI, where strategies are not pre-programmed, but learned. Deep learning systems consist of up to billions of individual parameters which are layered together to create a complex network. Some goal is defined for the system, such as winning in a simple two-player game, which the system can begin to optimize toward. This optimization process happens through machine-based trial and error. The system plays millions of games with itself, each time learning about what works and what doesnt, and adjusting its parameters. After all these games, the system will have (hopefully) learned to play at or above its human counterparts, which is exactly what weve seen with the games mentioned above. This brings us to the wild world of e-sports.

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Our robotics capabilities are still somewhat limited, as seen in various robotic games such as soccer. It will still be some time before we can apply AI players to most traditional sports (though Boston Dynamics is getting there quickly). Instead, AI is likely to become most common in the world of e-sports.

E-sports is quickly becoming comparable (in terms of market share) to traditional sports. The industry has eclipsed $1 billion in revenue in 2021 and has a projected 15 percent year-over-year growth. The largest team in e-sports, Cloud 9, had a valuation of over $300 million, which equates to five percent of the worlds largest sports franchise, the Dallas Cowboys, at $7 billion. In prize pools, e-sports already exceed many, including the Golf Masters and Confederations Cup, at over $40 million.

The key thing to note is that e-sports are still relatively new. As opposed to traditional sports, some of which have franchises that are over a century old and have been big businesses for over 30 years, e-sports only began 25 years ago and the most popular game, Dota 2, was released just 10 years ago. The size of the prize pools contrasted with the young e-sports shows how quickly the industry has grown. Once this continued growth hits a critical mass and breaks into the mainstream, e-sports may provide similar family and group affiliation motivation that we see in traditional sports.

Consider that FIFA now runs an international tournament of e-sports for their very own games. For fans at home, the experience is largely the same, watching the same match on the same television with the same live commentary. Granted, the animation of the current games still has room for improvement, but it improves every year with new games. The rapidly advancing animations, along with the fact that theyre AI-generated, allow for far greater creativity. For example, you can watch in 3D and experience being in play or maybe even in the referee's shoes. The fact that the worlds most lucrative sport (soccer) is already moving into e-sports, so it wont be long before others follow.

There are other reasons e-sports make a good first choice for those interested in AI games, such as the ability to more efficiently train and improve AI. For a computer game, AI can play millions of games (e.g. 5 million games for AlphaGo) for training as opposed to traditional sports where AI must physically play the game to learn strategy and test its performance (and even this limitation is something OpenAI is working on).

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Right now, if someone asks you to watch two programs compete against each other inside another program, you might think theyre a little weird. This is a reasonable reaction, but like it or not, AI competitions are becoming more and more mainstream.

There are various competitions between AI that garner millions of viewers. Heres a list of various games and AI representations on YouTube which already have large audiences.

Overall, this is on the order of 100 million views on YouTube, which was only around two percent of one day of streaming(as of 2017). However, given the relatively small community this number is significant. Coupling the growth of AI bots with the growth in e-sports will create massive expansion in the genre as a whole. However, this growth wont be sustainable unless the AI stays interesting.

Once watching AI compete becomes common, well need to find new ways to keep viewers involved. In order to achieve this, its critical that we diversify our AI. People dont want to watch the same thing over and over again. As previously mentioned, one of the motivators for watching sport is entertainment which comes from the chance factor of not knowing who will walk out victorious on any given day. In order to achieve this, the agents must be capable of making various high-level, non-straightforward plays (which weve already seen with Dota 2 and Go, to name a few).

In fact, theres a common misconception that watching AI is a boring experience as they unintelligently copy humans or follow pre-described rulesets. Certainly that was true of machines of the past, but for many years now weve had AI that can act in creative and all-together astonishing ways.

One of the most interesting parts about Googles AlphaGo was its creativity and ways it played that game that were unexpected by humans. Along the same line, in the world of chess, when human players make moves that vary from the standard procedure, referees start to suspect players of using artificial intelligence systems as assistants. Put another way, in the game of chess, creativity is no longer the mark of a human, but that of a machine. Its the same in Go and as time passes, it will become true in other sports, too.

During the AlphaStar training, the Deepmind team observed that the bots adopted various good strategies. One might expect that the bots followed a specific strategy and got better and better at it in time. In fact, the bots could be clumped into various groups and each group had a different way to play the game (e.g. aggressive start, focus on a certain type of units, etc.). In a way, each bot had its own player personality. These personalities, with varied play-styles will keep AI sports both interesting and entertaining for human viewers.

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Once AI agents have become a regular part of our sporting experience, advancements in robotics will catch up, allowing them to play all of the games we usually play, not just for us, but with us. Soccer players will be able to practice against full teams of AI bots that are set to challenge them and help them grow. Theyll also be able to compete in human-robot leagues.

While human biology is relatively fixed, robotics will continue to advance. This means that sports can continue to evolve too. Imagine a game of soccer played at double the pace with a magnetic ball and speeds matching that of tennis? Sounds pretty exciting to me.

Finally, new games can be created that only AI can perfect. As previously mentioned, escape and aesthetic are two of the motivators for sports fans. Watching an AI empowered machine conquer and handle complex games will create a feeling of escape weve never experienced before.

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If the above story comes to be, there would naturally be significant impacts on sports and entertainment.

Sports organizations and related companies should start preparing for these changes before its too late. For the rest of us, likely not much will change. We cant hope to imitate Cristiano Ronaldos beautiful strikes or Federers impossible serves and I wont be able to match the feats of our robotic future athletes. If nothing else, it will be interesting to see how sports evolve in the wake of AI development. So for now, Ill sit back, pick a side and enjoy the game with my friends.

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Is AI the Future of Sports? - Built In