Different Ways To Say Poker Face

1. Different Ways To Say Poker Face Mask
2. Different Ways To Say Poker Faces

Poker is one of the many games involving the use of a 52-card deck of playing. A hand consisting of one pair and a three-of-a-kind of a different rank than the pair 5) Flush – all five cards are of the same suit but not all sequential in rank. There are 4 5 = 1024 possible ways. Another word for poker face. Find more ways to say poker face, along with related words, antonyms and example phrases at Thesaurus.com, the world's most trusted free thesaurus.

Bullies are a fairly common subspecies of poker player. You can’t spend too long at the tables without running into one. They bet and raise with reckless abandon. Playing against them can be scary, like riding a bucking bronco.

Let’s talk about three general strategies for playing against the poker bully, two of them wrong, one of them right.

One temptation is to try to out-bully the bully, to punch back even harder than he is hitting. The problem is that you are pretty much reduced to flailing away wildly, kind of like Ralphie in A Christmas Story when he finally loses it and goes nuts on the bully Scut Farcus. It might work, but it’s risky and costly. In the poker setting, it also leaves you wide open to being exploited by other players who are smart enough to wait to trap both of you with a monster when you’re trying to out-bully each other with mediocre holdings.

A second temptation is to go into highly defensive mode — that is, to tighten up even further, waiting for the rare premium hand before playing back at the bully. There are two problems with this. First is that you’re missing out on lots of money that the bully is putting into pots with weak hands which you could win if you were braver. The second is that even minimally intelligent bullies will figure out that the guy who punches back once an hour is doing so only when he has a big hand. He’ll just fold, giving you one pathetic little pot for all your patience and consternation.

The third option — and the correct one — I learned from Mike Caro’s writings. In fact, I think it’s the single most profitable piece of poker strategy advice I’ve ever encountered:

A poker bully is by definition too aggressive. In order to be a bully, he must make a fundamental mistake — he must bet and raise too often. When an opponent makes a mistake, there’s always a way to take advantage. Here’s how to take advantage of a poker bully:

Call more often. Because a bully is betting more hands, it’s obvious that he must be betting more than just the ones you would normally bet. This means you can relax your calling standards and still make a profit.

Bet less often. A key to defeating a poker bully is to let him hang himself. Since his major mistake is betting too liberally, you should give him every opportunity to defeat himself by repeating that mistake. You should check and call frequently. You should also bet less often when a poker bully checks into you, because a bully likes to check-raise a lot. Therefore when he foregoes the opportunity to be a betting bully, you should be wary of a check-raising bully. Just check along.

When you do these two simple things, the bully has a losing expectation against you. And, in the long run, he cannot win. Sometimes it’s tempting to “out bull” the bully by being even more aggressive than he is. That’s the wrong answer. You can’t win at poker by exaggerating the same mistake an opponent is making.

Read the whole article on Caro’s web site here.

Can such a simple formula actually work? Yes. Absolutely it can.

I fondly remember one poker session in which a bully figured prominently. I joined the game and watched him raise the first five hands in a row. He was running over the table, cowing everybody into conceding him pot after pot while almost never having to show a hand.

When a seat opened up two to his left, I made a beeline for it. Caro’s formula is much easier to apply when you have position on the bully.

Then I just started calling him down with medium-strength hands. This took some courage, because he tended to bet big when he was bluffing, since he wanted to induce folds, and I usually try to avoid playing big pots with just one-pair kind of hands. But those are often enough to beat a guy who’s betting with nothing.

Eventually he gave up and said, “I’m done trying to bluff you.” But it was too late. In a $1/$2 no-limit hold’em game I had made $399 in under two hours — almost all of it from the bully’s stack. He had lost the stack he’d had when I sat down, plus two more buy-ins.

I was not the only one to profit. The other players caught on to what I was doing, and copied it. Somebody was calling down the bully every time he tried to win a pot by hammering at it with big bets. Once a bully has been shown to be just a weakling in a scary disguise, all of his former victims are eager to get their licks in.

Different Ways To Say Poker Face Mask

I love the simplicity of the strategy of just calling the bully. Of course, sometimes he gets lucky and hits something big, and you look pretty foolish calling three times with not much of a hand. But in the long run, those instances are far outweighed by both the monetary effect of his too-frequent bluffing and by the psychological effect that you have on him by not backing down to his attempts at intimidation.

In a Card Player magazine column a few years ago, Bob Ciaffone penned a wonderful apothegm that neatly sums up the same concept in a different way:

“We know you can fight fire with fire, but what is wrong with fighting it with water sometimes?”

Robert Woolley lives in Asheville, NC. He spent several years in Las Vegas and chronicled his life in poker on the “Poker Grump” blog.

Different Ways To Say Poker Faces

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  Mike Caro

Our emotions influenceevery aspect of our lives, from our health and how we learn,to how we do business and make decisions, big ones and small. Our emotions also influencehow we connect with one another. We've evolved to livein a world like this, but instead, we're livingmore and more of our lives like this — this is the text messagefrom my daughter last night — in a world that's devoid of emotion. So I'm on a mission to change that. I want to bring emotionsback into our digital experiences.

I started on this path 15 years ago. I was a computer scientist in Egypt, and I had just gotten accepted toa Ph.D. program at Cambridge University. So I did something quite unusual for a young newlywed Muslim Egyptian wife: With the support of my husband,who had to stay in Egypt, I packed my bags and I moved to England. At Cambridge, thousands of milesaway from home, I realized I was spendingmore hours with my laptop than I did with any other human. Yet despite this intimacy, my laptophad absolutely no idea how I was feeling. It had no idea if I was happy, having a bad day, or stressed, confused, and so that got frustrating. Even worse, as I communicatedonline with my family back home, I felt that all my emotionsdisappeared in cyberspace. I was homesick, I was lonely,and on some days I was actually crying, but all I had to communicatethese emotions was this. (Laughter) Today's technologyhas lots of I.Q., but no E.Q.; lots of cognitive intelligence,but no emotional intelligence. So that got me thinking, what if our technologycould sense our emotions? What if our devices could sensehow we felt and reacted accordingly, just the way an emotionallyintelligent friend would? Those questions led me and my team to create technologies that can readand respond to our emotions, and our starting point was the human face.

So our human face happens to beone of the most powerful channels that we all use to communicatesocial and emotional states, everything from enjoyment, surprise, empathy and curiosity. In emotion science, we call eachfacial muscle movement an action unit. So for example, action unit 12, it's not a Hollywood blockbuster, it is actually a lip corner pull,which is the main component of a smile. Try it everybody. Let's getsome smiles going on. Another example is action unit 4.It's the brow furrow. It's when you draw your eyebrows together and you create allthese textures and wrinkles. We don't like them, but it'sa strong indicator of a negative emotion. So we have about 45 of these action units, and they combine to expresshundreds of emotions.

Teaching a computer to readthese facial emotions is hard, because these action units,they can be fast, they're subtle, and they combine in many different ways. So take, for example,the smile and the smirk. They look somewhat similar,but they mean very different things. (Laughter) So the smile is positive, a smirk is often negative. Sometimes a smirkcan make you become famous. But seriously, it's importantfor a computer to be able to tell the differencebetween the two expressions.

So how do we do that? We give our algorithms tens of thousands of examplesof people we know to be smiling, from different ethnicities, ages, genders, and we do the same for smirks. And then, using deep learning, the algorithm looks for all thesetextures and wrinkles and shape changes on our face, and basically learns that all smileshave common characteristics, all smirks have subtlydifferent characteristics. And the next time it sees a new face, it essentially learns that this face has the samecharacteristics of a smile, and it says, 'Aha, I recognize this.This is a smile expression.'

So the best way to demonstratehow this technology works is to try a live demo, so I need a volunteer,preferably somebody with a face. (Laughter) Cloe's going to be our volunteer today.

So over the past five years, we've movedfrom being a research project at MIT to a company, where my team has worked really hardto make this technology work, as we like to say, in the wild. And we've also shrunk it so thatthe core emotion engine works on any mobile devicewith a camera, like this iPad. So let's give this a try.

As you can see, the algorithmhas essentially found Cloe's face, so it's this white bounding box, and it's tracking the mainfeature points on her face, so her eyebrows, her eyes,her mouth and her nose. The question is,can it recognize her expression? So we're going to test the machine. So first of all, give me your poker face.Yep, awesome. (Laughter) And then as she smiles,this is a genuine smile, it's great. So you can see the green bargo up as she smiles. Now that was a big smile. Can you try a subtle smileto see if the computer can recognize? It does recognize subtle smiles as well. We've worked really hardto make that happen. And then eyebrow raised,indicator of surprise. Brow furrow, which isan indicator of confusion. Frown. Yes, perfect. So these are all the differentaction units. There's many more of them. This is just a slimmed-down demo. But we call each readingan emotion data point, and then they can fire togetherto portray different emotions. So on the right side of the demo —look like you're happy. So that's joy. Joy fires up. And then give me a disgust face. Try to remember what it was likewhen Zayn left One Direction. (Laughter) Yeah, wrinkle your nose. Awesome. And the valence is actually quitenegative, so you must have been a big fan. So valence is how positiveor negative an experience is, and engagement is howexpressive she is as well. So imagine if Cloe had accessto this real-time emotion stream, and she could share itwith anybody she wanted to. Thank you. (Applause)

So, so far, we have amassed12 billion of these emotion data points. It's the largest emotiondatabase in the world. We've collected itfrom 2.9 million face videos, people who have agreedto share their emotions with us, and from 75 countries around the world. It's growing every day. It blows my mind away that we can now quantify somethingas personal as our emotions, and we can do it at this scale.

So what have we learned to date? Gender. Our data confirms somethingthat you might suspect. Women are more expressive than men. Not only do they smile more,their smiles last longer, and we can now really quantifywhat it is that men and women respond to differently. Let's do culture: So in the United States, women are 40 percentmore expressive than men, but curiously, we don't see any differencein the U.K. between men and women. (Laughter) Age: People who are 50 years and older are 25 percent more emotivethan younger people. Women in their 20s smile a lot morethan men the same age, perhaps a necessity for dating. But perhaps what surprised usthe most about this data is that we happento be expressive all the time, even when we are sittingin front of our devices alone, and it's not just when we're watchingcat videos on Facebook. We are expressive when we're emailing,texting, shopping online, or even doing our taxes.

Where is this data used today? In understanding how we engage with media, so understanding viralityand voting behavior; and also empoweringor emotion-enabling technology, and I want to share some examplesthat are especially close to my heart. Emotion-enabled wearable glassescan help individuals who are visually impairedread the faces of others, and it can help individualson the autism spectrum interpret emotion, something that they really struggle with. In education, imagineif your learning apps sense that you're confused and slow down, or that you're bored, so it's sped up, just like a great teacherwould in a classroom. What if your wristwatch tracked your mood, or your car sensed that you're tired, or perhaps your fridgeknows that you're stressed, so it auto-locks to prevent youfrom binge eating. (Laughter) I would like that, yeah. What if, when I was in Cambridge, I had access to my real-timeemotion stream, and I could share that with my familyback home in a very natural way, just like I would've if we were allin the same room together?

I think five years down the line, all our devices are goingto have an emotion chip, and we won't remember what it was likewhen we couldn't just frown at our device and our device would say, 'Hmm,you didn't like that, did you?' Our biggest challenge is that there areso many applications of this technology, my team and I realize that we can'tbuild them all ourselves, so we've made this technology availableso that other developers can get building and get creative. We recognize thatthere are potential risks and potential for abuse, but personally, having spentmany years doing this, I believe that the benefits to humanity from having emotionallyintelligent technology far outweigh the potential for misuse. And I invite you all to bepart of the conversation. The more people who knowabout this technology, the more we can all have a voicein how it's being used. So as more and moreof our lives become digital, we are fighting a losing battletrying to curb our usage of devices in order to reclaim our emotions. So what I'm trying to do insteadis to bring emotions into our technology and make our technologies more responsive. So I want those devicesthat have separated us to bring us back together. And by humanizing technology,we have this golden opportunity to reimagine how weconnect with machines, and therefore, how we, as human beings, connect with one another.

Thank you.

(Applause)