AI News, Hold 'Em or Fold 'Em? This A.I. Bluffs With the Best

1.5 – Artificial Instincts: Playing Poker

And I’m at Carnegie Mellon University to play poker against a world champion AI, and get this, I’ve never even played poker before, so this is going to be real good.

Tuomas Sandholm: Yeah, so unlike, let’s say chess or go, it’s a game of imperfect information, because when you have to make a decision, you don’t know the state of the world and your opponent or opponents might know things that you don’t know.

Jessica Chobot: Hey, I will take all the guesswork out of doc market investment and will correctly predict the outcome of any match after defeating humans in the most complex board games.

Dave Graham: 10 to 15 years of cumulative data analysis, that kind of increased compute capability, the technology advancements, so on and so forth, allow computers to process information even faster than they ever did before.

So here are some of the thoughts of deep learning pioneer, Geoffrey Hinton and what he sees as the difference between playing games with brute force versus intuition.

Geoffrey Hinton: So after they’d managed to beat Kasparov at chess, people said, you’re not going to be able to do the same thing for Go.

He’ll just have a strong intuition about that, and he wouldn’t bother to explore all the other places, whereas a brute force machine would explore everything, and there’s just too many alternatives.

So the neural nets were trained to mimic the moves that a master would have made to begin with, and then the system played against itself, so it got even better.

It’s reaches something we call a threshold, and when that threshold activates, then it activates the next neuron in that series, and continues to go on that neural network, and it continues to go along that path.

An input is received, I recognize a spot on the board, and I know that I can place my object, my player, my chip, whatever it ends up being.

That’s basically letting the entire environment press down on you at one point and saying, I feel this way, therefore I’m gonna act in a way that’s neither rational nor based around anything to do with logic.

So, have you been in one of these meetings where if financial advisors shows you a bunch of pie graphs and charts and then asks you what your risk tolerance is at all?

Jessica Chobot: So, unless you happen to be really up on money markets, I think a lot of us then go with our gut, or like you were saying earlier, kind of an educated guess based on what knowledge we come to the table with and maybe what our advisor is telling us.

And then, even if you do know the markets, like a professional stock trader and broker, I would assume that they also use their intuition as well, just at a more educated level, because they have probably more data that they can base that off on.

Because he doesn’t believe in just unleashing AI on the stock market, he’s come up with a different approach, what he calls IA or intelligence augmentation.

So if you go along these lines, developing machines that help you get smarter faster and therefore make better informed decisions with your investment, then you stand a chance to claw back this huge knowledge deficit that exists between a segment of the investing population, a small one, and the vast majority who’s not.

There is a tendency to be able to say, well we believe that since the Patriots won six out of the last, I don’t know what, 10 Super Bowls, there’s a good chance that they’re probably going to win the next year as well.

He’s a machine learning PhD student in Switzerland, and his team has creativekickoff.ai, a platform to predict the outcome of football matches and when he says football you mean soccer because he is in Europe.

There is an inherent part of randomness in football matches and I think that AI will never be about to predict 100%, with a 100% accuracy, football games and fortunately, because otherwise there is actually no point in watching football anymore.

I’ll tell you this, this particular episode of the podcast series is the hardest one for me to wrap my mind around because it sounds like based on what everybody we’ve heard talk about, that maybe this podcast shouldn’t be about AI learning intuition, but AI proving that intuition doesn’t actually even need to exist, because it’s all just accumulated data and really we should get rid of intuition and just call it educated guesses, I guess.

Dave Graham: Yeah, a lot of it’s all predicated on data, how we feed data into these systems, how we get data out of these systems, how we appreciate that data, how we let it be used.

Jessica Chobot: Well, putting this to the test, I’m actually going to go play Texas hold’em against an AI opponent, and I’ve never even played poker.

So what’s interesting is that the majority of human players use a sort of small set of intuitions to depict this phrase.

The very basic set of institutions, especially in this drawing and guessing game seem to be drawn from a sort of a similar source of common sense knowledge.

Even though we all have our own intuitive sense of how to play, we share to some extent a common set of images of icons, and that’s what the team at Allen is trying to teach their AI.

But here is the thing, they couldn’t use the same kind of reinforcement learning that taught AIs to play chess and Go, where they just played each other until they get really good, because Pictionary is so subjective.

Ani Kembhavi: Because you can imagine if two agents play this game, one is given a phrase like a dog eating a bone, and the other one has to guess the drawing.

You can imagine that these agents might get better over time, but they might decide that they’re going to use a cat icon to depict dog, and a hamburger icon to depict bone.

Even though the AI can see the phrase, family birthday party, and use the icons for people, cake and candles, what if the human partner in the game guesses happy birthday?

Ani Kembhavi: And so, when the human partner makes a wrong guess, how do you adapt its drawing to guide your human partner towards the right guess?

Ani Kembhavi: One of the main reasons was we wanted to get a lot of people playing it because the more people played with Allen AI, the more data we get to push back into the system and help Allen AI improve its algorithms.

Tuomas Sandholm: These AIs have never listened to any human, or read any poker book, and they’ve never seen a single human play poker ,or a single other AI play poker.

Jessica Chobot: Sounds kind of like reading between the lines in layman’s terms, that the AI does so well because it’s a bit of a wild card when it comes up against human players that are used to playing in a certain way.

Tuomas Sandholm: The goal at the end is to make the five card hand out of the seven cards you have, ie, The two private cards, and the five public cards.

And does it get- Tuomas Sandholm: Yeah, well the games theory is that even if the opponent plays in a crazy way, we’re still safe, unlike let’s say, machine learning based approaches.

Instead, it approaches every new game with the same strategy, the strategy it developed using game theory and the rules of poker.

Tuomas Sandholm: You have a decent chance or flush, and if it ends up being in the flush, we’ll have what’s called the nut flush, which is the best flush, because we have the ace.

Jessica Chobot: I just want to point out, because this will probably never happen again, that I played against a super intelligent poker AI and I beat it.

So,, obviously we’re playing poker here, but I’m assuming you didn’t create this AI strictly for poker playing, so what other applications does it have?

In terms of the high level issue in how optimization and planning happens today, in all of these applications, if at all, sometimes it’s just human gut feel and people make decisions, but if there’s optimization, today it’s assuming a strategy for the adversary.

So think about negotiation, pricing, various investment banking, and trade execution situations, video games, coming up with good, smart AI opponents for video games, instead of these simple, boring AI opponents that you see in video games today.

Listen to The machines know when to hold ’em, and when to fold ’em now.

Professionals keep losing to Pluribus, an AI poker player that’s learned a new strategy for a bot: bluffing.

All bets are off: a new AI beats professional players at poker

In particular, games like chess and Go quickly become too complex to solve by brute force alone, leading researchers to develop programs that learn a form of intuition in order to master them.

In this way, Pluribus makes decisions in real time based on intuition gained from its past games instead of simply memorizing a catalog of moves to make for every possible scenario.

Finally, Pluribus consistently beat professional poker players, all of whom had previously won at least $1 million in tournaments.  Pluribus represents a major advance in artificial intelligence that has implications in other game-theory fields, such as cybersecurity and finance (trading stocks is not so fundamentally different from poker).

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