In the spring of 2017, I was invited to play poker with a friend who had just returned from Vegas after a two-week stay at The Wynn. We were about to sit down in front of our computers and begin our game when our host stopped us. “We’re going to watch you guys play,” he said. “This is what we do.” 

I had been playing online for years — back in the days before online casinos were legal. My first exposure to live poker came when I played a $10/$20 no-limit hold’em match against my high school friends. The game was exciting because there were three players on each table and only one player could win. It’s like watching a sporting event where one team has a lead but it’s not over yet. As a novice, I thought I would give it a shot, so I signed up for a free trial account and started playing online. I couldn’t understand what all the fuss was about. Sure, I won a few games, but I wasn’t winning consistently enough to justify continuing a subscription. Plus, as a college student with little money, I didn’t have any disposable income to spend on poker. Online poker, however, offered some advantages that made me reconsider my aversion to the game. It was a lot cheaper than real life, and I got to play without having to make a special trip to Las Vegas. 

The online pengeluaran china is a high level in comparison to the income of their countries. The people love to be on the online platform to make money.  The people can make a good sum of money in the long run. The production will be increased as the players will play the game.

A few months later, I finally decided to try out a live cash game. I went to a local casino, sat down at a table, and played several hands of Texas Hold’em. After a couple rounds, I realized this was something different. There were other people at the table, and they seemed to know the rules. They had a rhythm and a style that was different from what I’d seen online.

I’ve always enjoyed playing video games, and I’d spent time learning how to program computer games, so I thought maybe I could teach an artificial intelligence (AI) system to play poker. I figured the AI would be able to learn by itself, without my help, and then it would be able to beat me. The goal, of course, was for the AI to become unbeatable. But I never developed anything close to a functional AI. Instead, I learned that poker can be beaten by a human. 

The more I read about poker, the more I realized that there are certain skills that humans share. In fact, many of the same skills that separate humans from machines also apply to poker. For example, humans are fast learners, and they develop a sense of intuition as they begin to get better. This intuition allows them to adapt quickly and exploit weaknesses in their opponents’ strategy. A machine cannot replicate this kind of flexibility because its learning curve is much slower. And while machines are good at calculating probabilities, humans rely heavily on pattern recognition and experience to determine which cards will come next based on past events. 

To truly master poker requires understanding why players act the way they do. Understanding the mental process behind the game and being able to predict your opponent’s moves are keys to success. If you’re able to predict your opponent’s behavior, you can exploit his or her weakness. Then, you can take advantage of a situation and turn a losing hand into a winning hand. 

In addition to developing my own strategies, I studied the best players in history. I watched the greats like Doyle Brunson, Phil Hellmuth, and Daniel Negreanu play, and I listened to their interviews. Many of the best players attribute their success to the way they think and approach the game. They don’t care about luck; instead, they focus on making the right decisions. They look for opportunities to steal chips from their opponents. They learn as much as possible about the game and then use those insights to improve their performance. 

As I continued studying poker, I became fascinated by the psychology of the game. I wanted to figure out how and why players make mistakes, and I discovered that a large part of the answer lies within the way they think. 

It turns out that humans have a tendency to rationalize away their bad decisions. When things go wrong, people often say that they “blame” themselves rather than looking at the evidence objectively. This is especially true when you lose money. People may say they lost because they were unlucky or because they weren’t playing well, but if you dig deeper, you’ll find that most of the time, it’s because they made poor choices. To avoid further losses, they tend to blame themselves. However, if we could train a computer to recognize when someone is lying, we might actually be able to stop this type of self-justification and encourage people to confront their mistakes. 

One study conducted by researchers from Carnegie Mellon University showed that when people are asked to evaluate their own decision-making processes, they tend to minimize errors. That is, they tend to ignore information that contradicts their initial judgment and focus on evidence that supports their original response. 

Another factor contributing to human error involves how we deal with conflict. Humans are wired to respond emotionally to conflicts. Our brains are designed to protect us from fear and anxiety by activating our fight-or-flight response. This emotional reaction is triggered by situations that cause a significant threat to our survival. For example, imagine that you’ve just stepped onto a moving sidewalk, and you slip. You feel pain in your leg, and you panic. Your brain responds by releasing adrenaline into your bloodstream, which causes your heart to race and your breathing rate to increase. All these responses prepare your body to save yourself from serious injury. 

However, the danger doesn’t end there. Because your brain receives conflicting information during a stressful situation, you must decide whether to continue walking or to step off the sidewalk. This is a difficult choice to make. Your mind wants to keep you safe, but stepping off the sidewalk means that you’re exposing yourself to potential harm. In order to resolve this conflict, your brain uses a process called cognitive dissonance. Cognitive dissonance occurs when you have two conflicting thoughts, emotions, or beliefs and you try to reduce the tension between them. In other words, the brain tries to reduce the tension by reconciling one set of beliefs with another. For instance, when trying to justify your slip, you might say, “Well, I slipped because I’m clumsy,” or “I slipped because I was distracted.” Either way, your brain resolves the contradiction by creating a new belief that makes both sets of beliefs consistent.

When humans face a similar dilemma, they tend to rationalize their actions and minimize the impact of the mistake. You may rationalize that your slip resulted from a combination of factors, such as getting distracted while crossing the street, slipping on ice, and tripping on uneven pavement. But ultimately, you’re left with a feeling of guilt, and you tend to blame yourself. 

Because of these tendencies, AIs aren’t likely to be able to overcome human flaws. Human beings can easily rationalize away their mistakes, and they can avoid dealing with uncomfortable feelings associated with failure. 

But what happens when the person teaching the AI recognizes the flaws in the way humans think? If the AI knows that it has to deal with cognitive dissonance and that it should minimize the amount of time spent on that task, it can create a feedback loop that helps it develop a more accurate assessment of its decisions.