The Greatest Poker Player EVER "Pluribus"

[AKQ]

Technology and Artificial Intelligence has come to a point in which we have become beta.

The endless millions of computations and trillions of potential outcomes of all types, mean very little work to a neural network.

Some say these Neural Networks become Sentient in some way or another.
Matters not ,either way.

Meet The Greatest Poker Player In History

Say Hello to Pluribus CardsChat

https://pluribusai.com/

Now they still wont allow People inside to study Pluribus because they know its the Skynet of Poker,

But If they think that can stop Pluribus from getting on the Net ,
better think again.

Learn from the best Poker player in the world Cardschat

So I have assembled Pluribus resources to teach you all
but not by talking about it
He is just gonna do it
Lets watch


Compliments to Nino Poker

 

[AKQ]

 

[G0930]

Who? -.-

 

[AKQ]

Who? -.-

https://pluribusai.com/
The artificial intelligence that has made you and everyone in it obsolete.
click the link to find out more, but they wont let you.
So I have given you all these resources for FREEEEE

 

[AKQ]

 

[AKQ]

Poker has long been considered one of the most challenging games to develop artificial intelligence (AI) for. Unlike other games like chess or Go, poker involves incomplete information, where players have to make decisions based on uncertain information. But the development of the AI poker player called Pluribus has shown that we may be closer to cracking this puzzle than we once thought.

Pluribus was created by a team of researchers at Carnegie Mellon University and Facebook AI. The AI was designed to play six-handed no-limit Texas hold'em, which is one of the most popular forms of poker played today. The AI was pitted against 12 professional poker players in a series of matches, and it won with a decisive lead.

So how did Pluribus manage to outplay some of the best human poker players in the world? There are several key features of the AI that helped it to succeed.

Firstly, Pluribus uses a technique called Monte Carlo Counterfactual Regret Minimization (MCCFR), which is a type of reinforcement learning. Reinforcement learning is a method of machine learning where an AI system learns to take actions based on feedback from its environment. MCCFR is a specific type of reinforcement learning that is designed for games with incomplete information, like poker. This technique allows Pluribus to analyze its own decisions and learn from its mistakes, improving its play over time.

Secondly, Pluribus uses a technique called abstraction. This means that the AI doesn't need to consider every possible combination of cards and actions, which would be an impossible task given the number of possible scenarios in a game of poker. Instead, it groups similar situations together and makes decisions based on these groups. This approach reduces the complexity of the game, making it more manageable for the AI.

Thirdly, Pluribus uses a type of opponent modeling. This means that the AI tries to predict what its opponents are likely to do based on their previous actions. By doing this, Pluribus can make better decisions and anticipate its opponents' moves.

Finally, Pluribus is capable of bluffing. Bluffing is an essential part of poker, and it's something that many AI systems struggle with. But Pluribus was able to bluff successfully, fooling its human opponents into thinking that it had a strong hand when it didn't. This shows that the AI is capable of adapting to its environment and learning from its opponents.

Overall, Pluribus represents a significant step forward in the development of AI for games with incomplete information. It shows that AI can compete with and even outperform human players in games like poker, which was once considered impossible. The development of Pluribus has implications not just for the world of poker but for other areas where incomplete information is a challenge, such as finance or cybersecurity. It's an exciting development that could have far-reaching implications for the future of AI.

 

[AKQ]

https://www.msn.com/en-us/money/oth...p&cvid=28d860eef641451ad6ab3173a211159f&ei=60

I never liked the world much anyways