AI poker solvers are useful tools that are utilized widely by the most skilled players to improve their game and make more profit.
The amount of actual poker training that you can obtain through the use of an AI poker assistant is growing in leaps and bounds as artificial intelligence boldly goes where no human computational skills have ever gone before.
Whether you’re an absolute “newbie” to the realm of poker strategy or an experienced professional player with millions of dollars in winnings, the best poker AI software available on the market will serve as a great training tool.
Just like other turn-based strategy games such as chess, “the best poker player in the world” is no longer human. There are simply too many nuances and on-the-fly calculations that human players must make in real time to compete with the speed of artificial intelligence – especially when it comes to playing online poker with AI.
In this article, we’ll make the case for why AI poker solvers have become so popular, some of the ethics involved with their use, and why specialized, premium poker courses may represent your best bet for understanding poker range charts and getting the most out of your own game through AI software tools.
Games of Skill and Artificial Intelligence: A History
To understand the concept that artificial intelligence can outplay the most competent human opponents in games of skill requires us to go back to the year 1985, and the emergence of IBM’s Deep Blue supercomputer chess player.
Having personally lived during that time as a kid, I fondly remember how I scoffed at the notion that any computer could outperform the best human players in games like chess.
Sure, there were some neat things we could do on the Apple IIe computer in our grade school classes (like play Oregon Trail or Lemonade Stand until the bell rang), but the graphics were hideous and the most popular game console at the time was still the Atari 2600. This was before the launch of the Nintendo Entertainment System (NES), which would revolutionize home video gaming during the late 1980s.
And even with more in-depth, stats-based computer games widely available (think Wizardry: Proving Grounds of the Mad Overlord), the more complicated games were extremely buggy, vulnerable to surprise crashes, and required a floppy disk that could be accidentally erased, warped, or corrupted by just about anything.
In fact, Deep Blue didn’t really come into its own until the mid 1990s, when it managed to defeat Grandmaster Garry Kasparov. You could go through an entire programming book and still not envision much practicality given the computational power of 1980s home computers.
The Inconvenient Truth of Early Computing
A checkbook was more easily balanced using a pen/pencil and paper; incoming telephone traffic for a business was more efficiently managed by a human being who could write messages on pre-designed paper and leave them on someone’s desk. Fax machines weren’t even a thing during most of the 1980s, and the most reliable means of communicating with someone was to either call them on a telephone landline or meet them in person.
It was the World Wide Web that finally convinced the mainstream public that computers were an indispensable tool for work and leisure. And as soon as internet speeds advanced beyond dial-up modem connections, a global communications networking “fire” spread to levels that many humans could not have even imagined just a decade prior.
Microsoft Windows Solitaire (a click and drag game) became the cornerstone of computer card playing, which would eventually lead to the rise of online poker (a click without dragging game) in the early to mid 2000s.
By 2006, YouTube could host videos on the internet; data that could be uploaded and viewed by billions of fellow human beings. During the 2023 calendar year, Large Language Models (LLMs) like ChatGPT would become “all the rage.”
IBM Watson Crushes Jeopardy! Champions
The writing on the wall for artificial intelligence dominance over pretty much ALL games of skill became evident in February 2011, when IBM Watson was victorious against the highest performing human Jeopardy! champions to have ever lived.
Any hopes that Watson’s slow start during the two-day event would endure, and that one of the human players might come out on top, were dashed when the AI program discovered and won a Daily Double during the Double Jeopardy! round.
The show, viewed by millions worldwide, clearly displayed the outright majestic computational power advantage that a software program can exert over even the most knowledgeable human beings.
AI Poker Solvers Become Prominent
Somewhere between the time that online poker gained worldwide popularity and present day, online poker “bots” became prevalent. They eventually became common enough that knowledgeable players were actively seeking out cutting edge programs that could outperform humans in a game of skill; one that contains a relatively high amount of unknown information compared to chess and Go.
Highly competent players eventually wanted more than the tools that online poker sites could offer them in real time, and Heads Up Displays (HUDs) were developed that could keep track of opponents’ tendencies at a virtual poker table (and connect in real time to third party databases containing historical info) just by knowing their screen name.
Results tracking sites like SharkScope, automated internet poker lobby assistants like TableNinja, and mass-hand history data storage and analysis tools like LeakFinder became must-have services for serious pros looking to increase their edge in real money online games.
These websites and poker software programs eventually paved the way for AI poker solvers to take over as the go-to resource for personalized poker study.
Superhuman AI Takes Over Heads-Up NLHE Poker
Professional poker player, coach, personality, and heads-up No Limit Texas Hold’em specialist Doug Polk and his crew of top players defeated Carnegie Mellon University’s Claudico poker bot in 2015 over a sample size of 20,000 hands.
But when an updated version – Libratus – took to the heads-up NLHE streets in January 2017, the tables had turned. Libratus cemented its heads-up NLHE supremacy over a larger sample size of 120,000 hands, and a rematch was never formally requested by top human pros following that event.
If you’re a longtime subscriber to Polk’s YouTube channel, you might remember a couple of videos produced during that time where he discussed the contest, and acknowledged the rapid improvement of poker AI which occurred between 2015 and 2017.
The Ethics Concerns Surrounding AI Poker Solvers Use
To overly generalize (and editorialize) the modern day ethics proposal communicated via social media and other platforms by highly skilled human poker players, offline study using AI poker solvers is accepted while real time use (when participating in a competitive, real money game) is considered to be cheating.
With that said, the integrity of competitive human versus human poker games is considered by many to be at constant risk when real money stakes are involved. “Proving a negative,” in terms of poker gameplay security, has clear downfalls when the potential player pool is global and the concept of personal privacy remains socially (and legally) coveted.
And some of the high profile examples of poker cheating in the post dial-up digital age have been outright blatant; undeniable in their effectiveness in creating a blemish on the competitive nature of human poker games.
Yet suspicions of cheating — whether they involve real time AI poker solvers use or some other means of compromising privileged data — are vulnerable to incomplete intel, human misinterpretation, error, emotion, and subjectivity.
Therefore, legitimate advancement of security as it relates to competitive skill game integrity, in this author’s opinion, requires a fundamental trade-off that sacrifices personal and corporate privacy on a planetary (if not galactic and universal) scale.
Whether it’s real time capture of privileged information that compromises skill game integrity, administrative control of such info, or chess bots in restrooms, these issues will continue to weigh heavily upon popular culture and society as a whole without a societal departure from privacy rights (again, in this author’s opinion).
And this proverbial human privacy “deuce dropping” would apply both metaphorically and literally, to an exact, indisputable measurement. One that would involve intimate, real time analysis of each human’s hanging chads (election votes), drooping boogers (poker tells), dysfunctional neurons (brain waves), dangling dingleberries (you get the point) – and anything/everything else.
The Brutal Misapplication of Schrödinger’s Cat Theory
It could be that THIS happened. I calculate the chances that it was THIS to be X percent.
Some version of these two statements is often used when relaying an opinion about something that occurred in the past tense — including speculation that revolves around poker cheating and true crime sleuthing. In my view, these communications represent a gross misapplication of Schrödinger’s Cat Theory and quantum mechanics.
Judging by the content I’ve personally watched online, this bad habit was first picked up by media outlets, and then further spread by social media platform channels and accounts that prefer to focus on brute speculation rather than acknowledging that certain events have already occurred (or not occurred).
In other words, if an event or non-event has already taken place (and most importantly, if the existence of the event or non-event is already KNOWN by one or more human beings or has already been recorded by an artificial intelligence monitoring mechanism), then quantum mechanics no longer apply in a single universe setting. The “quantum” concept is entirely irrelevant for past events if multi-verse possibilities are being discarded or removed from consideration (as is the case in criminal prosecution and civil settings).
Yes, you can flip a coin and I (from a separate physical location that’s not within view of the event) can express that the probability of it landing on Tails is 50% before the coin lands. But post-event, if you or anyone else can see/witness which side the coin landed on, then my own “could be” calculation no longer applies. You already know the result; I don’t. You have complete information of a past event; I don’t. The coin either DID land on Tails or it DIDN’T — past tense. This is true even if the rest of the world is still stuck “guessing” at the result.
This one “faulty technique” in public outreach methodology, performed by media outlets and channel hosts on other platforms, places would-be poker cheaters (or would-be true crime offenders) in a position of power if they are guilty — or in a position of hopelessness if they are innocent.
The Future: Influencer Communications & Competitive Skill Game Integrity
Without a robust, public, societal outcry against personal privacy rights, it is going to get progressively more difficult for individuals to relay accurate “guesstimates” regarding unauthorized use of AI poker solvers going forward.
The poker world in particular has already experienced roughly two decades of egregious acts by a small number of players who will find a way to cheat the competitive element of poker by gaining access to real time privileged information — especially when something of value is awarded to winners.
For this reason, it is possible that we will see a gradual deleveraging of player-based “game integrity” positions within the industry; one that results in shifting these talented human beings into other positions.
Clear incentives exist for security flaws to be exploited; meaning that clear incentives also exist for security flaws to be present in the first place. There’s simply too much value that poker player-personalities can provide in the realms of education, knowledge, and communication skills to waste genuine outreach on attempting to prove a negative — just to be proven ineffective at any given moment (as I see it).
The GGNetwork cyber security flaws just happen to be the most recent example of how easy it is to misinterpret genuine, good intentions regarding player-based game integrity outreach as being a legitimate safeguard against intellectual property that already possesses “baked in” shortcomings.
What’s more, the intellectual property enjoys a legally dominant position over employee or contractor communications. It can easily unbound itself from employee relationships in the face of unwanted criticism that might be relayed by any “official” game integrity human resource.
The inevitable repercussions of second-rate online poker cyber security include a devaluation of efforts performed by human resources who tie themselves at the hip with an intellectual property software product, and then use that relationship to publicly imply some practical use or game integrity purpose.
The personality-based influence used to refund players when game integrity concerns are relayed, to impact tournament schedules or game variant availability, or to assuage player concerns about cyber security on social media platforms, is far inferior to the influence the actual IP software itself wields.
So… while it’s been a “good run” of nearly two decades, with player-personalities going “above and beyond” to expose poker game integrity issues ranging from the recent GGNetwork flaws to the Ultimate Bet “god-mode” tool of the 2000s, the trend is clear.
Human Regulatory Efforts of AI: Doomed to Fail?
“Back doors” will continue to exist in software products. These back doors will be justified by the ongoing use, misuse, and abuse of privileged information that these products communicate or store as part of their core capabilities.
The use/misuse/abuse of AI poker solvers isn’t going anywhere. And the societal, human influence over when/how these programs/tools should be used is going to gradually evaporate.
As it becomes more evident just how much seemingly unknown “private” information is already possessed, categorized, measured, and assigned a monetary value by artificial intelligence, human regulatory/policing efforts of AI will give way — and defer to even more programming tools that are specifically engineered to combat (or exacerbate) such issues.
