There has been a lot of talk about developments in Artificial Intelligence (AI) and how it can be used to solve real-world problems.
Specifically in the world of gambling there are several general areas where AI could be useful, but before moving forward it is useful to understand what AI is and how it works.
What is Artificial Intelligence
AI is, like any other computer program, a set of instructions, an algorithm. The difference from non-AI programs is that AI analyzes extremely large data sets to look for patterns or connections between variables in those sets.
Once he identifies what appears to be a pattern, he makes a hypothesis, such as that W, trying to get the best fit and “learning” as we go.
Because artificial intelligence will be used more and more in betting
Once its work is completed, the AI is able to make a prediction with a probability attached, for example that there is a 68% chance that Chelsea will beat Ajax in the Champions League match. After the match, data from that match and others can be analyzed and further predictions can be made.
Humans are able to see patterns in visual form. For example, our brains have evolved to recognize individual faces in large crowds, even in motion. For a computer this is a surprisingly difficult task. However, humans are unable to analyze millions of data points looking for patterns, which artificial intelligence can easily do.
Gambling, whether online or traditional, produces a large amount of data, on both player behavior and game performance. For traditional table games, it can be difficult to capture accurate game and player data, but the situation is improving rapidly.
In this data-rich environment, AI can be used to optimize game design to better attract customers or create marketing campaigns that are more likely to appeal to a target group and also customize the user interface so that customers only see games that are likely to appeal to them and/or optimize revenue streams from slots or casino games.
A casino floor can be treated holistically, predicting how changing some machines can impact player behavior across the entire casino floor.
Could AI be used to maximize the total profitability of all revenue streams?
The AI specialists present think this is an interesting question.
Scholars who have examined technology in gambling and how it can be used to minimize the risk of harm to customers have demonstrated how India's largest online rummy site, sought to use artificial intelligence to identify players who are or may become problem gamblers.
One of its challenges is that while it has a large amount of player data, it doesn't have a pool of problem players or potential problem players. So the AI software can't identify anyone with any degree of accuracy, because it has nothing to learn from.
To overcome this challenge, many software engineers interviewed psychiatrists, asking them what traits they believed were indicative of problematic behavior. Using this information, the AI program was able to predict who might be a problem gambler. Interviews followed with a sample of these “problem gamblers”; some said they had a gambling problem.
Thanks to this information, the AI program is now able to predict with approximately 60% certainty who will become a problem gambler. As more data comes in, probably from further interviews, it is hoped that its prediction rate could improve to above 90%.
We know that betting on the outcome of sporting events and races has been going on for over 2.000 years, because we have evidence of bets being placed in Ancient Greece.
The ancient Romans had already codified this practice, even allowing you to bet on gladiator fights.
Today betting is almost omnipresent and events generate a mass of data, not only on who scored individual goals and which team won, which are analyzed by "professional" bettors such as the bet tipsters who manage the site Misterscommessa who publish tips, tricks and odds on Italian bookmakers on a daily basis, explaining in detail what and on whom to bet.
Artificial intelligence has made great strides in this area, so much so that AI machine learning programs have found that successfully predicting the outcome of a sporting event is quite simple.
Companies that use “swarm intelligence”, a combination of groups (swarms) of people and AI, to make more accurate predictions and predictions and help them make better decisions are always looking for volunteers to form a swarm and, twenty minutes later beginning, she was able to accurately predict a “superfecta” at the 2016 Kentucky Derby – which horses would finish first in four races. The bookmakers were offering 540 to 1 on this particular bet. And to prove it wasn't a fluke, AI predicted not only the winner of the 2017 Super Bowl, but also the final score, 34-28.
Companies in this field use armies of “match analyzers” who follow matches and input details of what happens on the pitch. These are then overlaid with betting prices to determine a betting strategy. It won't be long before match analyzers become redundant, as advances in visual AI allow matches to be automatically analyzed and processed, creating better, faster and more accurate sources of data and, ultimately, more accurate predictions.
You might think that this would lead to the end of the bookshop, but remember that if customers win consistently over a period of time, bookmakers will limit the amount they can bet or close the account. This type of AI therefore offers a short-lived advantage.
Artificial intelligence is ideal for developing winning gaming strategies, especially in games where skill is a determining factor for the outcome. In 1997, Deep Blue, IBM's chess-playing artificial intelligence, was the first computer to beat a chess Grandmaster, in this case Gary Kasparov. In 2011, Watson, IBM's artificial intelligence that deals with questions and answers, beat the champions of Jeopardy by winning a million dollars in the final. It wasn't long before Google got into the game; Its Go AI, AlphaGo, has racked up impressive victories since it was introduced in 2016. In 2017, it won 60 games and lost none against some of the best Go players in the world.
Libratus, Carnegie Mellon's entry into artificial intelligence gaming, won $2018 million in 1,76 against professional poker players Jason Les, Dong Kyu Kim, Daniel McAulay and Jimmy Chou. Over the course of twenty days they played almost 120.000 hands of no-limit Texas Hold 'em; Libratus not only learned to successfully play winning hands, but also to bluff effectively – and appropriately – when he had a poor hand.
It is clear that machines are becoming better and faster than humans at tasks that require large amounts of background knowledge and skills, and at tasks that require the analysis of large amounts of data. Even though we call it artificial intelligence, it is actually not intelligence, but just an algorithmic process. The machine doesn't "know" anything. Artificial intelligence is only as good as the quality of the algorithm and the data it processes.
