Monthly Archives: July 2020

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player or much of a poker fan, in fact but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely a view shared years later by Sandholm in his research with artificial intelligence.

WHAT I LEFT OUT is a recurring feature in which book authors are invited to share anecdotes and narratives that, for whatever reason, did not make it into their final manuscripts. In this installment, Maria Konnikova shares a story that was left out of The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win (Penguin Press).

Poker is the main benchmark and challenge program for games of imperfect information, Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.

Tall and thin, with wire-frame glasses and neat brow hair framing a friendly face, Sandholm is behind the creation of three computer programs designed to test their mettle against human poker players: Claudico, Libratus, and most recently, Pluribus. (When we met, Libratus was still a toddler and Pluribus didnt yet exist.) The goal isnt to solve poker, as such, but to create algorithms whose decision making prowess in pokers world of imperfect information and stochastic situations situations that are randomly determined and unable to be predicted can then be applied to other stochastic realms, like the military, business, government, cybersecurity, even health care.

While the first program, Claudico, was summarily beaten by human poker players one broke-ass robot, an observer called it Libratus has triumphed in a series of one-on-one, or heads-up, matches against some of the best online players in the United States.

Libratus relies on three main modules. The first involves a basic blueprint strategy for the whole game, allowing it to reach a much faster equilibrium than its predecessor. It includes an algorithm called the Monte Carlo Counterfactual Regret Minimization, which evaluates all future actions to figure out which one would cause the least amount of regret. Regret, of course, is a human emotion. Regret for a computer simply means realizing that an action that wasnt chosen would have yielded a better outcome than one that was. Intuitively, regret represents how much the AI regrets having not chosen that action in the past, says Sandholm. The higher the regret, the higher the chance of choosing that action next time.

Its a useful way of thinking but one that is incredibly difficult for the human mind to implement. We are notoriously bad at anticipating our future emotions. How much will we regret doing something? How much will we regret not doing something else? For us, its an emotionally laden calculus, and we typically fail to apply it in quite the right way. For a computer, its all about the computation of values. What does it regret not doing the most, the thing that would have yielded the highest possible expected value?

The second module is a sub-game solver that takes into account the mistakes the opponent has made so far and accounts for every hand she could possibly have. And finally, there is a self-improver. This is the area where data and machine learning come into play. Its dangerous to try to exploit your opponent it opens you up to the risk that youll get exploited right back, especially if youre a computer program and your opponent is human. So instead of attempting to do that, the self-improver lets the opponents actions inform the areas where the program should focus. That lets the opponents actions tell us where [they] think theyve found holes in our strategy, Sandholm explained. This allows the algorithm to develop a blueprint strategy to patch those holes.

Its a very human-like adaptation, if you think about it. Im not going to try to outmaneuver you head on. Instead, Im going to see how youre trying to outmaneuver me and respond accordingly. Sun-Tzu would surely approve. Watch how youre perceived, not how you perceive yourself because in the end, youre playing against those who are doing the perceiving, and their opinion, right or not, is the only one that matters when you craft your strategy. Overnight, the algorithm patches up its overall approach according to the resulting analysis.

Theres one final thing Libratus is able to do: play in situations with unknown probabilities. Theres a concept in game theory known as the trembling hand: There are branches of the game tree that, under an optimal strategy, one should theoretically never get to; but with some probability, your all-too-human opponents hand trembles, they take a wrong action, and youre suddenly in a totally unmapped part of the game. Before, that would spell disaster for the computer: An unmapped part of the tree means the program no longer knows how to respond. Now, theres a contingency plan.

Of course, no algorithm is perfect. When Libratus is playing poker, its essentially working in a zero-sum environment. It wins, the opponent loses. The opponent wins, it loses. But while some real-life interactions really are zero-sum cyber warfare comes to mind many others are not nearly as straightforward: My win does not necessarily mean your loss. The pie is not fixed, and our interactions may be more positive-sum than not.

Whats more, real-life applications have to contend with something that a poker algorithm does not: the weights that are assigned to different elements of a decision. In poker, this is a simple value-maximizing process. But what is value in the human realm? Sandholm had to contend with this before, when he helped craft the worlds first kidney exchange. Do you want to be more efficient, giving the maximum number of kidneys as quickly as possible or more fair, which may come at a cost to efficiency? Do you want as many lives as possible saved or do some take priority at the cost of reaching more? Is there a preference for the length of the wait until a transplant? Do kids get preference? And on and on. Its essential, Sandholm says, to separate means and the ends. To figure out the ends, a human has to decide what the goal is.

The world will ultimately become a lot safer with the help of algorithms like Libratus, Sandholm told me. I wasnt sure what he meant. The last thing that most people would do is call poker, with its competition, its winners and losers, its quest to gain the maximum edge over your opponent, a haven of safety.

Logic is good, and the AI is much better at strategic reasoning than humans can ever be, he explained. Its taking out irrationality, emotionality. And its fairer. If you have an AI on your side, it can lift non-experts to the level of experts. Nave negotiators will suddenly have a better weapon. We can start to close off the digital divide.

It was an optimistic note to end on a zero-sum, competitive game yielding a more ultimately fair and rational world.

I wanted to learn more, to see if it was really possible that mathematics and algorithms could ultimately be the future of more human, more psychological interactions. And so, later that day, I accompanied Nick Nystrom, the chief scientist of the Pittsburgh Supercomputing Center the place that runs all of Sandholms poker-AI programs to the actual processing center that make undertakings like Libratus possible.

A half-hour drive found us in a parking lot by a large glass building. Id expected something more futuristic, not the same square, corporate glass squares Ive seen countless times before. The inside, however, was more promising. First the security checkpoint. Then the ride in the elevator down, not up, to roughly three stories below ground, where we found ourselves in a maze of corridors with card readers at every juncture to make sure you dont slip through undetected. A red-lit panel formed the final barrier, leading to a small sliver of space between two sets of doors. I could hear a loud hum coming from the far side.

Let me tell you what youre going to see before we walk in, Nystrom told me. Once we get inside, it will be too loud to hear.

I was about to witness the heart of the supercomputing center: 27 large containers, in neat rows, each housing multiple processors with speeds and abilities too great for my mind to wrap around. Inside, the temperature is by turns arctic and tropic, so-called cold rows alternating with hot fans operate around the clock to cool the processors as they churn through millions of giga, mega, tera, peta and other ever-increasing scales of data bytes. In the cool rows, robotic-looking lights blink green and blue in orderly progression. In the hot rows, a jumble of multicolored wires crisscrosses in tangled skeins.

In the corners stood machines that had outlived their heyday. There was Sherlock, an old Cray model, that warmed my heart. There was a sad nameless computer, whose anonymity was partially compensated for by the Warhol soup cans adorning its cage (an homage to Warhols Pittsburghian origins).

And where does Libratus live, I asked? Which of these computers is Bridges, the computer that runs the AI Sandholm and I had been discussing?

Bridges, it turned out, isnt a single computer. Its a system with processing power beyond comprehension. It takes over two and a half petabytes to run Libratus. A single petabyte is a million gigabytes: You could watch over 13 years of HD video, store 10 billion photos, catalog the contents of the entire Library of Congress word for word. Thats a whole lot of computing power. And thats only to succeed at heads-up poker, in limited circumstances.

Yet despite the breathtaking computing power at its disposal, Libratus is still severely limited. Yes, it beat its opponents where Claudico failed. But the poker professionals werent allowed to use many of the tools of their trade, including the opponent analysis software that they depend on in actual online games. And humans tire. Libratus can churn for a two-week marathon, where the human mind falters.

But theres still much it cant do: play more opponents, play live, or win every time. Theres more humanity in poker than Libratus has yet conquered. Theres this belief that its all about statistics and correlations. And we actually dont believe that, Nystrom explained as we left Bridges behind. Once in a while correlations are good, but in general, they can also be really misleading.

Two years later, the Sandholm lab will produce Pluribus. Pluribus will be able to play against five players and will run on a single computer. Much of the human edge will have evaporated in a short, very short time. The algorithms have improved, as have the computers. AI, it seems, has gained by leaps and bounds.

So does that mean that, ultimately, the algorithmic can indeed beat out the human, that computation can untangle the web of human interaction by discerning the little tactics of deception, of asking yourself what is the other man going to think I mean to do, as von Neumann put it?

Long before Id spoken to Sandholm, Id met Kevin Slavin, a polymath of sorts whose past careers have including founding a game design company and an interactive art space and launching the Playful Systems group at MITs Media Lab. Slavin has a decidedly different view from the creators of Pluribus. On the one hand, [von Neumann] was a genius, Kevin Slavin reflects. But the presumptuousness of it.

Slavin is firmly on the side of the gambler, who recognizes uncertainty for what it is and thus is able to take calculated risks when necessary, all the while tampering confidence at the outcome. The most you can do is put yourself in the path of luck but to think you can guess with certainty the actual outcome is a presumptuousness the true poker player foregoes. For Slavin, the wonder of computers is That they can generate this fabulous, complex randomness. His opinion of the algorithmic assaults on chance? This is their moment, he said. But its the exact opposite of whats really beautiful about a computer, which is that it can do something thats actually unpredictable. That, to me, is the magic.

Will they actually succeed in making the unpredictable predictable, though? Thats what I want to know. Because everything Ive seen tells me that absolute success is impossible. The deck is not rigged.

Its an unbelievable amount of work to get there. What do you get at the end? Lets say theyre successful. Then we live in a world where theres no God, agency, or luck, Slavin responded.

I dont want to live there, he added I just dont want to live there.

Luckily, it seems that for now, he wont have to. There are more things in life than are yet written in the algorithms. We have no reliable lie detection software whether in the face, the skin, or the brain. In a recent test of bluffing in poker, computer face recognition failed miserably. We can get at discomfort, but we cant get at the reasons for that discomfort: lying, fatigue, stress they all look much the same. And humans, of course, can also mimic stress where none exists, complicating the picture even further.

Pluribus may turn out to be powerful, but von Neumanns challenge still stands: The true nature of games, the most human of the human, remains to be conquered.

Maria Konnikova is the author, most recently, of The Biggest Bluff. She is a regularly contributing writer for The New Yorker, the author of two previous New York Times best-sellers, and a professional poker player.

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The Deck Is Not Rigged: Poker and the Limits of AI - Undark Magazine

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Today's lottery news: the Poker Lotto numbers and results for Monday July 20, 2020 are here... - Born2Invest

Author and former professional poker player Maria Konnikova says that U.S. President Donald Trumpcould stand to learn a thing or two about politics from a game of Texas Hold'em.

In her latest book,The Biggest Bluff,Konnikovadetails what poker can teach us about decision making and writing for Politico, she argues Trump's political strategies bear all the hallmarks of a bad poker player.

She spoke to Day 6 guest host Peter Armstrong about how she thinks the skills she learned at the poker table could benefit the president.

Below is part of their conversation.

Trump himself has said that he sees his life as something of a poker match. How do you think he's faring at this point in the game?

I remain convinced that he probably is not quite aware of the rules of poker. And it's so funny for someone who lies as openly and as frequently as Trump does, I'm shocked that he hasn't just said, "Oh, yes, I'm a wonderful poker player."

It makes me think that he does realize that it might be a game that's a little bit above his pay grade.

You've said his whole process of decision-making ... stands in direct contrast to what the game of poker teaches. What are the key lessons that he's missed?

There's a notion in poker that I think just everyone outside the poker world should employ in their vocabulary because it's such a convenient word and that's tilt. And when somebody is 'on tilt' or 'tilted' or when you find something 'tilting' that means that you've let emotions into your decision process so you're no longer thinking logically.

This can be both positive and negative in terms of the valence of the emotions so it can be very angry, you can be really ecstatic.

Tilt makes someone a much worse player because tilt is very exploitable by other opponents. That means that people can take advantage of it.

So what Trump should be doing, or what any good poker player should be doing, is trying to perform that calculus on themselves and figure out, "OK, how am I emotional? What things get to me?"

In the great geopolitical poker game, if Trump is the guy at the table who is on tilt, can you look from a distance and see what other players are taking advantage of that?

Yeah, absolutely. I think someone who is very, very good at taking advantage of it is Vladimir Putin. And that's very disconcerting. You do not want a leader who's that easy to manipulate.

Of course, all of this comes with a huge caveat, which is that Trump has already won the presidency and who's to say he's not going to win the re-election?Sometimes this kind of strategy can be successful, despite everything that you think is going to go down.

Do [Trump's] bluffing skills maybe make up for his bluster and emotion and erraticism on the other?

In some ways, yes. It's funny, even though this book was about poker, my last book was about con artists. And even though I think Trump would be a horrendous poker player, he is an excellent con artist and is able to convince people of just about anything who already want to believe it he's very good at selling people the version of the world that they already want to be true.

And what we know about victims of con artists is that once they've already fallen for a con, almost nothing that you can say or do will convince them otherwise.

But, is he someone who is successful at bluffing people who aren't victims of the con artist Trump and instead are just looking from the outside at the politician Trump? And there I think you might see him falter.

So his bluffs work against the weaker players, but I think the strong opponents will be able to sniff out where he's actually just full of it and take advantage of it. And you actually see Trump walking back some of his biggest bluffs when people fight back.

As poker analogies go, this one's pretty incredibly high stakes. [Trump] just used his presidential powers to commute the sentence of a criminal associate. COVID-19 cases are absolutely surging.

We're heading into an election season. He doesn't show any signs of improving his poker skills. If you were betting on it or if you were sitting at this sort of analogous table, how would you weigh the odds that he might actually win this game this round?

I'm actually still incredibly nervous that Trump's chances are much higher than we think that they are, because, first of all, anything can happen, and he's shown himself to be someone who's very good in the 11th hour.

We also know that his international standing doesn't really matter when it comes to re-election. So it doesn't matter that all these leaders are able to take advantage of a hermit and that he's not really a match for the best leaders on the global scale.

Right now, the betting markets are favouring [Joe] Biden. But until it happens, I'm not going to believe it. And I would actually not put any money against Trump right now, personally.

Written by Kyle Myzuka. Produced by Annie Bender. Q&A edited for length and clarity.

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Trump's approach to politics bears the hallmarks of a bad poker player, author says - CBC.ca

Life goes on with the changing world.Almost everything is made to be changed after some time. Poker is as old as theexistence of man. It was designed to play cards in leisure time. But now it haschanged drastically over many years due to the advent of technology. Manysignificant changes are observed in a poker game during the past few years.Change in the privacy policies, the settings, and the way of playing it haschanged over many years.

Reasons for Changes in Poker

The online poker industry has manypotential reasons which act as a massive catalyst for changing the strategiesin the poker industry. Lets discuss all of these reasons in detail as below.

The strategies of poker werestraightforward in the past. Most players knew every trick to win the game inthe end. There were many traditional resources available to every player,because of this, the competition was quite low. This is the significant reason whythe change was needed in strategies of poker.

There is no doubt in the past 6-10years that the availability and quality of resources to poker users becomequite restricted. In the past, there were many skill books for the training ofnew pokers in the market, which they used to develop more in the industry ofpoker. In the past, players were worried about the offloading of their secrets,but now everything is open to them. They can get all the techniques by usingmany resources on the internet.

Most players realized that there aremany other impressive ways to beat their opponents. This was the most importantreason for the change in strategies of poker. The creativity of players hasbeen increased with the help of many online training sites. These onlinetraining sites play an essential role in enhancing the creativity of playersworldwide.

Ways in Which Poker Has Changed Over the Last Many Years

Poker has made drastic changes inrecent years, which is very prominent in the gaming industry. In past yearspoker has made massive progress in the filed of continuation bet. However,still, there is nothing new in it. Most players played this version many yearsago. Most players used to okay at hands about five years ago, but now they aremade to assign a range of hands so that the opponents could not recognize them.

Poker strategies have also changed, considering the decision support tools. Many decision tools play an essential role. There is a long list of such tools available now, which can give a raft of online support tools, which will allow you to play in a post-mortem of poker for playing at hands. If you are searching to play poker according to new strategies, then you must have to visit Situs sbobet; it will guide you in the best way.

Poker has always been an essentialsports activity for most players. It doesnt matter how it has changed over therecent years; it is still an exciting activity for people.

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How Has Poker Changed Over Many Years? - Loop21

July 21, 2020Chad Holloway

Natural8 is home to the WSOP 2020 Online with 54 Bracelet Events taking place from July 19th till September 6th. Win a Bracelet and join the exclusive Natural8 Winner's Circle where up to $700,000 in Sponsorship rewards are waiting to be claimed. Start your journey by exploring the WSOP Silk Road Micro-Stakes Tournaments or via the $5,000,000 WSOP Giveaway. The opportunities are endless.

The 2020 World Series of Poker (WSOP) online bracelet events are running now through September 6th. The series is comprised of 85 WSOP gold bracelets on the digital felt across two different clients: WSOP.com and GG Poker.

Each week, well highlight a Hand(s) of the Week, which very well could include bad beats, lucky suck outs, and game-changing swings. In the first week of the 2020 WSOP online series, we shared two big hands played by

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This weeks hand comes from Event #12: $500 The Big 500 No-Limit Hold'em, a tournament that attracted 2,427 entries (1,624 unique players who rebought 803 times). Its from that event that arguably the 2020 WSOPs most popular story sprung the victory of poker vlogger Ryan joeyisamush Depaulo, who captured a $159,563 top prize and his first gold bracelet.

What really caught the attention of the poker world was that Depaulo, who was a recent guest on the PokerNews Podcast, was literally playing from inside his car in a New Jersey Whole Foods parking lot. It was also a redeeming victory as last summer he finished third in the 2019 WSOP Event #61: $400 Colossus for $208,643.

Depaulos victory wasnt easy as he had to deal with seasoned veteran Jack "EarlGrey" Salter in heads-up play. Depaulo began the match with a nice chip lead but Salter, who has more than $8.4 million in lifetime earnings, used his experience to battle back and claim it for himself. It looked as if the bracelet was going to slip through Depaulos fingers, but then he got a little lucky.

Arguably the most influential hand of the tournament took place in Level 47 (400,000/800,000/100,000) when Salter limped the button and then called when Depaulo moved all in for 17.2 million.

Ryan "joeyisamush" Depaulo: Jack "EarlGrey" Salter:

The board ran out and Depaulo shipped a big double into the chip lead, which subsequently left Salter with 13.7 million.

Salter dwindled a bit before doubling with ace-eight against king-four, and for a moment it appeared as if he was going to claw back. The comeback was cut short though when Salter opened for 1.6 million from the button, and Depaulo three-bet jammed in the big blind. Salter called off his last 17,000,000 and the cards were tabled.

Jack "EarlGrey" Salter: Ryan "Joeyisamush" Depaulo:

The flop and turn meant Salter had to hit an ace to survive, but instead the bricked on the river. Salter had to settle for second-place while Depaulo earned his first WSOP gold bracelet and made history by doing it in a Whole Foods parking lot.

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In the days that followed, PokerNews caught up with Depaulo to talk about the whole experience:

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Natural8 2020 WSOP Online Hand of the Week: A Wholesome Hand for Ryan Depaulo - PokerNews.com