History of Artificial Intelligence (AI) and neural networks in the online poker industry

Artificial Intelligence and neural network in the online poker industry

Pretty much since we have had computers, programmers and researchers have been trying to figure out ways to use them to play games. First that was tic tac toe, then checkers, and moving on to chess. They were relatively successful in all of these, and by the 1980s, the focus has shifted to more complex games.

Here, Poker was the mount Everest of challenges. In all those other games, you could see everything going on the game, but in poker, there are unknowns. These are the cards in the deck and the cards your opponents have. That makes it much more difficult to create software that can play poker, probability calculations can only go so far. But things changed in the poker bot world when researchers began incorporating AI into their systems.

Early Developments in AI Algorithms and Neural Networks

Most of us think of a neural network as something very modern, however the basic concept of a network of processors working together to solve a problem goes all the way back to the 1940s. Called a neural network because the structure mimics how the brain operates, the concept was first published by Warren McCulloch and Walter Pitts in 1943. This was built on by Joseph Erlanger and Herbert Spencer Gasser, among others.

However, it was not until 1982 and the work of Kohonen, Hopfield and later Rumelhart that the concept of a neural network transitioned into a physical reality.

Significance of Using AI in various industries

Ai has been embraced across a wide range of industries, but perhaps the most impactful, and in some ways controversial, has been in content creation. Today a program like ChatGPT can create entire web pages or blog posts from a single prompt. Although the output cannot really compete with a professional writer, for most people who run their own websites, it is better than anything they could create themselves, and has become a go-to solution for many.

Photography has seen a similar trend, AI driven photo editing helps adjust photographs so they look great, even if you are inexperienced, and generative AI solutions can create images from nothing. However, while AI has changed the way those industries work, it is arguable that their impact in other areas is even greater.

Take healthcare, where AI is used to abalyse patient data and create personalized treatment schedules that deliver better outcomes for each individual. AI also distills the millions of words written about a given illness or treatment in thousands of research papers into an easily digestible summary. This provides medical professionals access to detail and trends without eating into their hectic hands on treatment schedules, increasing awareness and again, boosting outcomes for patients.

Perhaps the application of AI we most notice is in retail. Here, AI driven customer service management systems analyse our shopping habits and deliver tailored ad campaigns, offers and more. SO, if you are spending more recently, its probably down to AI!

History of AI and neural networks

The idea of neural networks, and the AI that followed, was first put forward in the mid-20^th century. The idea began from the desire to create a problem-solving machine that operated like our brains, with relatively simple decision makers working together to solve complex problems very quickly.

As technology has progressed, the research and thinking behind neural networks, and subsequently AI, has continued to evolve too. Today, we have technology that can drive powerful AI algorithms that can deal with the most complex type of tasks, adjusting on the fly to react to new scenarios and data inputs.

Initial applications of AI Research in gaming

While early bots such as Orac in the 19802 were hardware-based systems that were easily beaten by a human player. However, the challenge of a game with incomplete data like Poker has been a draw for AI researchers from the start, and throughout the 90s and onwards, Poker was the game of choice for AI development. In particular, Canadian professor Darce Billings and his team at the Massachusetts institute of technology, who worked tirelessly from the early days of AI.

Early attempts to create poker-playing AI

The most notable of these early poker bots was Vexbot, which was one of the first that went beyond logic algorithms to make use of proto-AI to try and understand game strategy. It was a weak poker player that could be easily beaten by a human player with a few weeks of game experience. But poker bots continued to advance, and in 2005 the world’s first poker bot tournament was held in Las Vegas. But the same problem continued, these bots used very basic intelligence algorithms, and could be easily beaten by a human player.

The idea of a new poker bot that could be successful at online betting and revolutionize the gaming industry is not new. While researchers in game development have always been primarily focused on the technology itself, others know AI can help create a lucrative income from automated play. So when the next advancement came, many could see it shaping the future of the gaming experience.

In 2007 a poker bot known as Polaris appeared, and this was the first time a bot had AI capable of modelling individual player’s behaviour. It didn’t have the game understanding to really leverage that ability, but this was the start of what we know as poker AI today.

Evolution of AI in Online Gambling Bets

While researchers continued to develop their AI models, it was only in 2015 that a breakthrough appeared. This bot, another created by the Billings team, became the first bot to ‘solve’ heads up no limit Texas hold’em. Called Cepheus, it used 200 processors and vast amounts of memory, to calculate all 316 quadrillion possibilities in a game of poker.

Using this knowledge and a strategy based on game theory optimal practice, it played what was mathematically the perfect game of poker. This revealed a new problem for the AI researchers. Cepheus was unbeatable over a game, but it wasn’t a bot that could win money, its strategy simply meant it did not lose any.

But of course, the goal of a poker bot is to win money in games of poker, so having a bot that can actually win pots regularly was the next step. That came in 2017, when the same team brought us Libratus. This bot still required huge resources, hundreds of processors and terabytes of memory, but it could win games of poker. Not a practical bot for people to use, but a winning bot.

The next bot from this team was Pluribus in 2019. Pluribus changed everything. First, it switched from hardware to software power, and could be ran on home computers that has 128GB of memory or more. That makes AI powered bots accessible and usable outside a research lab.

But Pluribus was also a great poker player. It took on, and beat, a full table of 5 professional poker plays at once. It could win, and win consistently. Every poker bot on the market today uses the lessons learned in creating Pluribus.

Initial AI models and their limitations

Early AI relied on hardware resources to operate, so were restricted to research facilities and large companies. The idea of AI in a poker bot you could run on a PC at home, or a cloud node, was simply impossible at that time. They also lacked the ability to learn as the latest algorithms do.

Today we train a poker AI by playing itself. In some cases, the bot doesn’t even have to be taught the rules, it learns as it goes by playing against itself. But early AI was focused on probability for each players hand, and could not learn player strategies in real time at all.

Breakthroughs in Neural Networks and Machine Learning

There have been a number of critical breakthroughs in the advancement of neural networks and machine learning. Often overlooked, but something that has been crucial in the wider adoption of the technology has been the development of efficient neural architectures. Using fewer resources and driving down costs, they have boosted access and with that, sped up further development.

Combining deep learning solutions with AI algorithms is perhaps the biggest step forward in digital intelligence yet made, especially when combined with the other crucial advancement, the introduction of Extended Natural Language Processing (NLP) Capabilities. It is this that makes ChatGPT and other similar platforms so easy to use for people. Until the adoption of the large language models, AI systems were incredibly complex to run, but now through prompts, they can not only understand the language, but nuance and context too, making it much easier to access their full capabilities.

Poker is one example of how AI systems have become more accessible without sacrificing performance. In fact, as the AI has transformed from a system that could be beaten even by new players to one that can regularly beat professionals in no-limit Texas hold’em, the personalized gaming experience those products offer has become easier to use.

Integration of AI and Neural Networks in Online Poker Platforms

Tracking tools have become incredibly popular in the poker community, integrating into the platforms to deliver enhanced functionality. They use AI tools to provide players with a wealth of real-time data analysis as they play. This extends beyond how often a player will fold at the big blind though, and can even provide guidance to help players select the right action every turn. In essence, a poker bot without the ability for automated play, integrated into the poker platform.

Impact on the Online Gambling Industry

But what does that mean for online poker as a whole? There are two things to consider. If these tracker/assistant programs become the norm, then any player who doesn’t have one is at a disadvantage. At that point, if every player is using them, following AI suggestions on what to do throughout the game, who is really playing poker? It is not the humans any more, just the trackers and assistants.

But if everyone is using AI, where are new players coming from? If you can’t get started without facing a table of AI powered players, then it will quickly become a disappointing experience. After all, while most players lose over time, it is the thrill of the challenge that really keeps people coming back. Is it thrilling to be beaten by a computer over and over? Artificial Intelligence could be an issue that drives players away.

FAQ

How did AI technology first get integrated into the online poker industry?

Poker has been used by AI researchers for decades because it has incomplete data in its gameplay. That is, we don’t know what cards other players have, and this represents a learning challenge ideal for AI development. As they progressed, the ideas that were successful have been adopted by poker bot creators.

How do neural networks improve the performance of AI poker bots?

Deep learning systems allow for faster analysis and a significantly improved application of underlying strategies, while leveraging the power of large databases of poker hands.

What impact has AI had on the dynamics of online poker games?

In terms of how the games are played very little. However, overall, players are continually looking for signs of bot or assistant use and will adapt their strategies if they believe there is someone using AI at their table.

What are the future prospects for AI and neural networks in the online poker industry?

It is most likely that AI is used by the industry to identify accounts using bots to automate the banning process.