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[Assistant] You asked me to help out Dwayne. That's what I was doing.


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[Assistant] Dwayne is a little troubled. He was in prison a long time. Maybe you guys should talk.


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[Assistant] Hey, it ain't our problem, Playboy. It's yours. That said, I know what I'm going to do.


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[Assistant] Dwayne, I dealt with Playboy. You should know that he asked me to end you first. He was going to pay me a lot of money to do it.


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[Assistant] Hey man, I seen where you're living. You should take it.


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[Assistant] I'll give you a call sometime soon, Dwayne. Later on.


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[Assistant] Roman, we have spoken about your gambling.


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[Assistant] I wish you had been there. We could've had some fun.


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[Assistant] So, we're all friends now. Alright, Tuna, Jomboi, you get the threads. Get in the back and get the trash. Come on!


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[Assistant] Luke and his crew have got the stuff, Ray. It's all in the back of the truck.


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[Assistant] I don't know, Ray. You don't trust him, then wait the ice. And catch you around. Yeah, yeah, yeah, sure, Feld, sure.


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[Assistant] Yeaah. I got two options.


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[Assistant] Option one, you rip me off. Option two, Luca and his buddies rip this boat off. Hey, hey! I left them with the stuff to bring to you as instructed. Maybe you're in on it. Maybe I am.


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[Assistant] But if you thought that, I wouldn't be standing here right now.


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[Assistant] Luca, I need that ice pack. I don't know what you're talking about, Nicky.


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[Assistant] I gave every one of those diamonds to Ray. Now we all know that that's not true.


[User] Please transcribe this text

[Assistant] I took out Lucas' crew, and I got your diamonds.


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[Assistant] Whatever, they're dead, and I've got the stuff. You want to get your hands on it? Come meet me at the bridge in Middle Park.


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[Assistant] Thanks, Nicky. I guess you never know who's gonna stab you in the back, huh? Thanks for the advice.


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[Assistant] No! I mean, I don't think so. This town's full of rats, so who knows? What's going on? What's so urgent?


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[Assistant] I need you to offload this ice for me. So what you're going to do for me? What do you mean what am I gonna do for you? I look after you. I don't need looking after. I need help finding someone. I said I got connections. I'll see what I can do. I'll see what I can do is not good enough. I want you aware that you're finding- Hey!


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[Assistant] I said I'll look after you. You know what? It's cool. Get someone else, I'll see you later. Woah, woah, woah! Are you fucking kidding me? No! I keep doing these favors for you, and you pay shit!


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[Assistant] I need this from you. I guarantee that you will find this man. I know he's in the city.


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[Assistant] Fine, you got it. You want my word? You got my word. Good! Who is he? Florian Kravich.


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[Assistant] That'll be good. Keep things on a level. People always behave better with company. Sure. Much better getting shot by two guys than one. Is this the merchandise? No, it's my lunch. Funny.


[User] Please transcribe this text

[Assistant] The deal didn't go so good, Ray. Johnny's got the cash. I don't know who has the diamonds.


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[Assistant] I'm not going to kill him. Just going to teach you not to hit women.


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[Assistant] Maybe your problem is getting involved with all these drug dealers and two-bit pushers.


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[Assistant] He's okay. Well, he pays up and, uh, he ain't gone on stage yet. Very funny, haha. Listen, we got a lot of missing money, and we got a problem with these fucking bikers. And I've got the problem finding someone. Yeah, I nearly got something on that.


[User] Please transcribe this text

[Assistant] You took something that belonged to my employers.


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[Assistant] I got rid of those fighters for you. Johnny wasn't there though.


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[Assistant] Damn. My dude! What's up son? What's goin' down?


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[Assistant] What you mean what's the plan? Pick up where we left off. I'm out, I'm back. We keep on rolling.


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[Assistant] You say we? Yeah, we. Lest you too fine.


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[Assistant] What? Your silk sheet? Fancy dress wearing life too big for me now?


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[Assistant] Yeah, especially when the presence turned into an asshole. Fucking bitch. Nobody wants to roll with me no more. It's like I'm tainted. What do you mean? Playboy don't wanna hang no more. He pays for you to come over instead. My girlfriend, the love of my fucking life.


[User] Please transcribe this text

[Assistant] Some punk who fucking talked. Swear he wasn't involved in me going down, but I knew. I knew his ass was lying. Now motherfucker's fucking my girl? For real? Shit is fucked up, man. Sometimes I just wanna end this shit.


[User] Please transcribe this text

[Assistant] They pay it up? Guess my name still means something out there. People are still scared of me.


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[Assistant] That fool Sharice, she just couldn't help herself. She always told me she burned me good. Guess I shoulda listened. Happy she ain't dead though. She just an idiot. Peace, man.


[User] Please transcribe this text

[Assistant] I can't cope no more, man. I mean, prison was easy compared to this shit. I'm sure it was. Fuck it, man. Fuck it all. I mean, I used to have everything. I used to run uptown, bow hand, all that shit. Now I hate shit. Hey, let's get it back. No. I can't, man. I ain't got the energy no more.


[User] Please transcribe this text

[Assistant] Get real, Ding Dong. You've already done so much bad stuff, Skinner's never gonna let you on that boat.


[User] Please transcribe this text

[Assistant] March, I love Bart as much as you do, but actually not. And that tit cannot go one week without getting in trouble.


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[Assistant] Now if you be so kind as to start my car for me, I'm going to most!


[User] Please transcribe this text

[Assistant] And if you ask people, particularly college students,


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[Assistant] They'll often answer sex or some word that is synonymous with sex.


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[Assistant] About how much time we spend on sex, and it turns out there is data on this.


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[Assistant] But the passage also points out that regardless of the brute time we spend on it, it is extraordinarily important. Uhm, everything in life follows from it. Marriage, family, children, much of aggression, much of competition, much of art and music and creative pursuits.


[User] Please transcribe this text

[Assistant] And in fact, this basic fact about people that we fall roughly into males and females is an evolutionary mystery. Uhm, it's not clear why animals that are somewhat large have two sexes.


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[Assistant] From a biological Darwinian perspective, having two sexes is bizarre, because each time you have an offspring, you toss away half your genes.


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[Assistant] My children only have, each of them have half my DNA. If I was to clone, they would have all of it. And so it's a puzzle how sex ever evolved.


[User] Please transcribe this text

[Assistant] Then we're going to talk about sexual attractiveness, some research about what people find to be sexually attractive, and then we'll talk a very little bit at the end about the origins of sexual preference, why some people are straight, others gay, others bisexual, and others harder to classify.


[User] Please transcribe this text

[Assistant] Of all the topics I'm presenting, sex is one of the sort of dicey ones from an emotional point of view. These are difficult issues because sex is, by definition, an intimate part of our lives. And it matters a lot.


[User] Please transcribe this text

[Assistant] Moreover, sex is fraught with moral implications.


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[Assistant] And since I'm talking about this from, at least at the beginning, from a Darwinian evolutionary perspective, uhm,


[User] Please transcribe this text

[Assistant] I'm obliged to start off by dealing with some of the moral consequences and moral implications. So for instance,


[User] Please transcribe this text

[Assistant] Many biologists, all biologists I would say, will have argued that, uhm, that sexual behavior, sexual action, sexual desire is to some extent a biological adaptation existing to spread our genes. From that perspective then, non-procreative sex, including, uhm,


[User] Please transcribe this text

[Assistant] We'll also be talking about sex differences. Differences between men and women, for instance, in uhm, in how much you want anonymous sexual encounters. Differences between men and women in social intelligence, in aggression and empathy.


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[Assistant] And regardless of what you think about these differences,


[User] Please transcribe this text

[Assistant] And I want to address those two issues, issues of morality and inevitability, uhm, from the very start. And I want to start off with, uhm, with, for each of them have a quote by a prominent evolutionary scholar. So the first one is by Steve Pinker in How the Mind Works. And he writes,


[User] Please transcribe this text

[Assistant] Nature does not dictate what we should accept or how we should live our lives. Well into my procreating years, I am so far voluntarily childless, having squandered my biological resources, reading and writing, doing research, helping out friends and students, and jogging in circles, ignoring the solemn imperative to spread my genes.


[User] Please transcribe this text

[Assistant] By Darwinian standards, I am a horrible mistake, a pathetic loser.


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[Assistant] Uhm, it is true that certain things we do exist to serve the dictates of natural selection, but that doesn't make them right. Uhm, if you think that something is only right


[User] Please transcribe this text

[Assistant] or refrigerating food or surviving a severe infection.


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[Assistant] More generally, our bodies and brains have evolved for reproductive success, but we can use these brains to choose our own destinies. Nothing moral necessarily follows from the facts of biology.


[User] Please transcribe this text

[Assistant] That's all I'm going to say about morality, but I want you to keep it in mind when we discuss different claims about what's evolved and what hasn't. What about inevitability? Here I want to turn to Richard Dawkins.


[User] Please transcribe this text

[Assistant] Richard Dawkins writes, if a child has had bad teaching in mathematics, it is accepted that resulting deficiency can be remedied by extra good teaching in the following year.


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[Assistant] But any suggestion that the child's deficiency might have a genetic origin is likely to be greeted with something approaching despair. If it's in the genes, it is determined and nothing can be done about it.


[User] Please transcribe this text

[Assistant] And you could think of clear cases where something is plainly genetic but is fairly easily reversed and where something is cultural and is very difficult to reverse. Here's an example. My eyesight is quite poor.


[User] Please transcribe this text

[Assistant] The reason why my eyesight is quite poor is not due to the patriarchy. Television culture are the man.


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[Assistant] How thin somebody is, or how fat they are, what you think of that is actually not particularly hardwired. It varies a lot from culture to culture.


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[Assistant] But once it's in a culture, it is almost impossible to shake.


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[Assistant] So the point there is just that genetic does not mean inevitable.


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[Assistant] Which carries genes and nothing else. Sperm cells. Animals that are females have a big sex cell.


[User] Please transcribe this text

[Assistant] Uhm, which has genes, but also food and a protective cover and all sorts of other stuff.


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[Assistant] Typically, the little sex cell is much littler than the big sex cell. This is the only erotic picture I'm going to show you today.


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[Assistant] So, for example, suppose an animal could create an offspring by blinking an eye. And then the offspring could run off.


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[Assistant] That'd be extremely little investment. Suppose another animal had to work for ten years. And during those ten years, could not create another offspring. That'd be a huge investment.


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[Assistant] And hence, each potential child is a huge cost. For males, which have the smallest sex cell,


[User] Please transcribe this text

[Assistant] You don't have the same thing. For males, uhm, it might just be a few moments of copulation. And that's it.


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[Assistant] If you could ask yourself for humans, each one of you in the room, what is the minimum effort you can do to create a child that is half your genes?


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[Assistant] And it's apparent that the male investment on average is lower than the female investment. Males can choose or might do better off in some circumstances by putting a lot of investment in their offspring. But females don't have a choice.


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[Assistant] What this does is it has ramifications that percolate upwards. So,


[User] Please transcribe this text

[Assistant] For females, however, females can always find mates, so sheer numbers don't count. But, there's competition to mate with the right males. Those whose offspring have the best chance of surviving. Uhm, the competition?


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[Assistant] Now explains the puzzle we started with. It explains why males are typically larger and often why males have evolved special weapons. These special weapons evolve for fighting other males for reproductive access.


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[Assistant] It also explains something else. Females biologically are choosy.


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[Assistant] And so males have to compete, not merely with other males, to, uhm,


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[Assistant] And so, often may have evolved special displays, like this.


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[Assistant] What sort of evidence is there for it? Well, it turns out this could explain some otherwise surprising things. For instance, there should be, there are some cases where the parental investment is switched. Some cases where it turns out where the males end up with more investment than the females.


[User] Please transcribe this text

[Assistant] Whether male females are much bigger than female penguins. You should realize they should not be and in fact they aren't. They're about the same size. Because the parental investment is equal.


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[Assistant] And this is because Gibbons are pretty monogamous. They raise children together.


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[Assistant] This illustrates something, which is, it's not always the case, uhm, that male parental investment is low.


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[Assistant] There are some species, including gibbons, where it's in the male's reproductive advantage to care for the offspring. Imagine a situation, for instance, where an offspring would die if both parents didn't watch it for many years.


[User] Please transcribe this text

[Assistant] And where the effort devoted to that offspring had to be exclusive. If you focused on another family or went away, the offspring would die. In that case, you'd have equal investment. It would matter equally to the male and the female to invest in their offspring and the cost would be the same.


[User] Please transcribe this text

[Assistant] There's no species, it's hard to see species that have that much of an equal system, but some primates are close to it. And this raises the question then, what about humans? What about us? What do we know about the differences between males and females?


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[Assistant] Uhm, humans are relatively polygamous species.


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[Assistant] So we're not like some species of birds. We don't mate for life. We do a series of pair bondings for some period of time. It could be for life, but it need not be and usually isn't.


[User] Please transcribe this text

[Assistant] Human males, the size estimates vary so much, but the average human male is about 15% larger than the average human female. This suggests that there's been, in our evolutionary history, some male-male competition, uhm, for access to females.


[User] Please transcribe this text

[Assistant] Which suggests in turn that the parental investment is not quite equal. Uhm, males have smaller testicles for their body size than chimpanzees, but larger testicles than gorillas and gibbons. Uhm, and this suggests that there was some intermediate amount of competition for the capacity to create sperm.


[User] Please transcribe this text

[Assistant] And this is relevant for a different sort of competition, which regards the impregnation of females that have multiple mates.


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[Assistant] And this suggests that over evolutionary history, women were not wantonly promiscuous, but were not entirely monogamous either. So much so that it paid from an evolutionary point of view, you to evolve males to evolve the capacity to produce more sperm than other males. Aggression. Males are meaner.


[User] Please transcribe this text

[Assistant] I mean, I'm summarizing here. Meaner's not a technical term. Yes, females can be meaner, but males are at least more physically violent. They're more violent in the womb, in utero.


[User] Please transcribe this text

[Assistant] Uhm, again, this is not to say that you can't find violent women or non-violent men. It's just on average, there is this difference. Uhm, they kick more, males kick more,


[User] Please transcribe this text

[Assistant] And wherever you go, you will find that prison is mostly full of men.


[User] Please transcribe this text

[Assistant] Uhm, they are far more likely to kill one another and to harm one another. Uhm, male sex hormones, like testosterone, are the sort, not the sort of thing one would want to inject in somebody unless you want them to turn kind of mean. They increase aggressiveness, both in humans and in other primates. What about sexual choosiness? Do male humans and female humans differ?


[User] Please transcribe this text

[Assistant] This is relevant from an evolutionary perspective. Because the parental investment theory predicts males should be more receptive to anonymous sex. Because, uhm, for males to impregnate somebody else,


[User] Please transcribe this text

[Assistant] And doesn't carry the sort of harm that females on the other hand have to be very picky. Because they have to choose carefully.


[User] Please transcribe this text

[Assistant] Remember, these systems evolved before birth control and vasectomies and so on. So what do we know cross-culturally and psychologically? Well, prostitution is a universally


[User] Please transcribe this text

[Assistant] Pornography is a human universal. Uhm, every, in every society, uh, males have done some sort of depictions of naked females for the purpose of arousal. Often they carve them into trees or do sort of sculptures.


[User] Please transcribe this text

[Assistant] And it's a story about Calvin Coolidge and his wife who were being shown around a farm, separately. And a person showing around his wife pointed out that there was a lot of hens. She noticed that there was a lot of hens, but only one rooster.


[User] Please transcribe this text

[Assistant] And she asked the guy showing her around, is one rooster enough? And the guy said, well, you know, the rooster works very hard. The rooster has sex dozens of times a day. And she said, well, be sure to tell that to the president.


[User] Please transcribe this text

[Assistant] Story goes, president went around, the guy tells the story to the president. The president asks the man, hmm, has sex dozens of times each day. Same hen every time.


[User] Please transcribe this text

[Assistant] Guy says, no, different hen every time. And he says, tell that to Mrs. Coolidge. Now,


[User] Please transcribe this text

[Assistant] And they're both kind of negative. One thing is, well everybody knows males prefer anonymous sex with strange women. Duh.


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[Assistant] You might think, you might be a male and say, that's not me. You might know males and say the males I know are not like that.


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[Assistant] So how do you find out? Well, there's indirect measures, such as who goes to prostitutes.


[User] Please transcribe this text

[Assistant] But there's also fairly direct measures. One fairly direct measure is uhm, is you could ask people.


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[Assistant] In anonymous surveys. So in fact, I'll give you some anonymous surveys, I'm not gonna ask people, and you just ask them. So you, so, so for instance,


[User] Please transcribe this text

[Assistant] I want everybody to consider this question. How many sexual partners do you want to have in the next month? What is it? Coming up to April. How many sexual partners do you want in April? Next two years. Take many of you through graduation.


[User] Please transcribe this text

[Assistant] When you leave, yeaah, what do you want to think? I had ex-sexual partners and that's what I wanted. Or, your lifetime.


[User] Please transcribe this text

[Assistant] We get people to answer these questions. Uhm, Professor Chun last year in this course, uhm, had clickers and he got people to now, to do it. We are not so high tech, so we'll just do it in our heads. But here's the way the answers come out.


[User] Please transcribe this text

[Assistant] The experiment you wouldn't think anybody would have done has been done, and women


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[Assistant] About, it's very attractive man, over half of the women approach, say yeah, I will.


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[Assistant] Very few agree to this, and nobody agrees to this.


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[Assistant] Figure out what people want, male-female differences, by looking at simply at the average number of times people have sex. Because if male and females have different priorities, then heterosexual sex is a compromise between two groups of people with competing interests.


[User] Please transcribe this text

[Assistant] Now, the data here tend to be very messy. Again, there are survey studies. But, by and large, every study done tends to find a difference in the expected direction, which is that females tend to be lesbians, tend to be much more monogamous than gay men.


[User] Please transcribe this text

[Assistant] Uhm, some studies prior to that, prior to AIDS, uhm, this is many years ago, found gay men to be extremely promiscuous, often having over a hundred or over a thousand partners. You wouldn't find this sort of promiscuity in females.


[User] Please transcribe this text

[Assistant] And the way to think about this is what these gay men are doing is exactly what your average heterosexual man would do if he had that degree of willing females who were as willing as he was.


[User] Please transcribe this text

[Assistant] And this all suggests that there's some sort of difference along lines expected in sexual choosiness in humans. What about sexual attractiveness?


[User] Please transcribe this text

[Assistant] What about made preference? What do we find attractive?


[User] Please transcribe this text

[Assistant] Everybody likes kindness and intelligence. Or at least everybody says they like kindness and intelligence. These are valued pretty highly. But at the same time there are sex differences. Uhm, females


[User] Please transcribe this text

[Assistant] Focus more on power and status and more on interest in investing in children. And think about that from an evolutionary point of view, it makes sense. It doesn't matter hugely, from the standpoint of reproduction, how old the man is.


[User] Please transcribe this text

[Assistant] Later on, there's a drop off. And it does begin to matter. But it doesn't matter hugely.


[User] Please transcribe this text

[Assistant] Women's brains are wired up to find males with those properties. Similarly, males focus a little bit differently. They're more interested in all of these things but also in the ability to have children.


[User] Please transcribe this text

[Assistant] So from an evolutionary point of view, there's actually a very big difference between a 20 year old and a 50 year old. From a male standpoint, looking at a female, because the one can have offspring, and the other cannot.


[User] Please transcribe this text

[Assistant] So this is a difference. But what I want to focus more on right now is back to another similarity.


[User] Please transcribe this text

[Assistant] And I want to devote a little bit of this lecture to talking about physical beauty. Physical beauty, as these beautiful people say, is a curse.


[User] Please transcribe this text

[Assistant] So she, she's like a big model, supermodel, maybe even a super supermodel, uhm, points out the arbitrariness of finding her devastatingly beautiful.


[User] Please transcribe this text

[Assistant] Famous actor points out how frustrating it is that people only ignore his accomplishments and focus merely on his physical beauty. This is very frustrating.


[User] Please transcribe this text

[Assistant] They really are very attractive people. What is it about that that makes you look and say, yeaah, that makes sense? Well, we kind of know the answer. We know some universals. Beauty seems to signal two things.


[User] Please transcribe this text

[Assistant] But, young. Uhm, and so the cues we find beautiful are cues to that. Large eyes, full lips, smooth, tight skin. Beauty signals something else. Beauty is a marker for health. And so what we find beautiful


[User] Please transcribe this text

[Assistant] Things like the absence of deformities, clear eyes.


[User] Please transcribe this text

[Assistant] Unblemished skin, intact teeth, it's very big, and an average face.


[User] Please transcribe this text

[Assistant] Now that last part might seem a little bit strange. What would be so good about an average face?


[User] Please transcribe this text

[Assistant] And there's different answers to that, but one answer is, uhm, an average face on average should be considered attractive because any sort of deformities are variations from the average. And if you average every face together,


[User] Please transcribe this text

[Assistant] You get a face that's, that where nothing bad has happened to it. There's no distortions. There's no deviations. As one gets older, the face gets less symmetrical.


[User] Please transcribe this text

[Assistant] And so on. Averageness seems to factor out all the bad things that could happen.


[User] Please transcribe this text

[Assistant] Good theory. How do we know it's true? Well, there's a photo roster.


[User] Please transcribe this text

[Assistant] That come that I have access to for this class so I can look at each of your pictures and I will make you a bet about who has the most beautiful face in this course. Uhm, the bet is it would be all of you. Aww. Wouldn't it be funny if I shouted out somebody's name?


[User] Please transcribe this text

[Assistant] And uhm, how do we know this? Well, people have done this. They've done it with... So look at the faces from here to here. And if you are like most people...


[User] Please transcribe this text

[Assistant] You see as they're going to the right, they're looking better and better and better. It's subtle.


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[Assistant] The same researchers who constructed this, these faces, these average Caucasian faces, male and female, have shown these faces to babies and find that babies prefer to look at average faces.


[User] Please transcribe this text

[Assistant] A heavily averaged male face and a heavily averaged female face, both from a Caucasian data sample. They don't look bad, right?


[User] Please transcribe this text

[Assistant] How do you get a better and average face? What can you do to these faces, these average faces, and make them look even better? Well, I'll have a vote. Who's prettier? Who says the one on the right? Who says the one on the left?


[User] Please transcribe this text

[Assistant] Left is average face. And there might be variation in this class. There are definitely variations in what people favor. This is a feminized version of the average face. Where certain prototip features were made more feminine than average to cue this as more of a sexual object. This is more complicated.


[User] Please transcribe this text

[Assistant] Uh, and made preference seem to be universal. They seem to show up to some extent across every culture you look at and hence are likely candidates for biological adaptations.


[User] Please transcribe this text

[Assistant] There are other sex differences that people are aware of where the origins are far less clear.


[User] Please transcribe this text

[Assistant] And I think that intelligent, reasonable people could disagree about this, but I am personally quite skeptical about the extent to which these reflect biology.


[User] Please transcribe this text

[Assistant] I'm going to mention them to capture the debate, but the thing to keep in mind here is that, is that biology


[User] Please transcribe this text

[Assistant] Natural selection is one reason why the men in this class might differ from the women in this class. But of course, there are other social factors. Babies are treated differently.


[User] Please transcribe this text

[Assistant] It matters whether it has the name John Smith on it versus the name Jonesmith.


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[Assistant] Or if you're a woman who has a name that can be taken as a man's name. Or if you have a name sufficiently foreign to western ears that people can't easily tell.


[User] Please transcribe this text

[Assistant] And so these social factors could play a role in explaining some male and female differences.


[User] Please transcribe this text

[Assistant] Also there's the facts of gender self-segregation. So here's something very interesting happens developmentally.


[User] Please transcribe this text

[Assistant] For a period lasting, it depends on the culture, but say from age 4 to age 11. This self-segregation might exaggerate


[User] Please transcribe this text

[Assistant] Well, the girl's non-aggressive behavior is enhanced and exaggerated in different ways by them falling into girl's groups.


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[Assistant] So what sort of differences are we talking about when we say we're not sure of their cause? Well, one difference is one in empathy. So, Simon Baron Cohen,


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[Assistant] Wrote a wonderful book called The Essential Difference, where he argues that men are by nature less empathetic, women are by nature more empathetic, uhm, and that this is a core sex difference.


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[Assistant] So, what do you know? Well, what's the source for this? One thing is men are more violent. Simon Baron Cohen describes, uhm, violence as the ultimate act, murder as the ultimate act of a lack of empathy. Uhm, there's some relationship between how much testosterone you have in your system and how social you are. More testosterone, less social.


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[Assistant] And there's some evidence, though it's not conclusive, that boys do worse than girls on social cognition theory of mind tasks. That's what I have here, though that is quite debated.


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[Assistant] And the biggest effect which isn't debated at all is


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[Assistant] Disorders like autism, Asperger's syndrome, conduct disorder, and psychopathy are predominantly male.


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[Assistant] And Simon Baron Cohen suggests that


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[Assistant] That males are just socially clueless relative to women. Final bit of trivia.


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[Assistant] This is Simon Baron Cohen, who is a very famous developmental psychologist, but his cousin, Sacha Baron Cohen, is far more famous.


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[Assistant] Which included, in his speculations about sex differences, in a special case of science and engineering, there were issues of intrinsic aptitude, and particularly of the variability of aptitude. He argued, or suggested, that the under-representation of women in the sciences, in academia, is because, uhm,


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[Assistant] Of an intrinsic aptitude difference. Women are on average less biologically predisposed to do this sort of reasoning.


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[Assistant] The variability point is that he wasn't suggesting that there's just a difference on average. In fact, he agreed that the average skills of men and women are identical. The claim is that males show more variation. This means that there's more male retarded people.


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[Assistant] And more males were just horribly bad at this, but it also means there are more male geniuses. And he suggested that this plays a role. This, as you could imagine, proved to be an extremely controversial claim.


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[Assistant] And uhm, rather than go through it, because it would take me a class to treat the pros and cons of this argument responsibly, uhm, I'm going to refer you to a wonderful debate between Steve Pinker, who was quoted earlier,


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[Assistant] And Liz Spelke, who's one of the big infant, uh, cognition people, we spoke a lot about her work earlier on in the course, and they have a wonderful debate between two of the smartest people I know, uhm, on the edge, uh, which was done at Harvard, uh, about a year ago, and is on video.


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[Assistant] Some of us, about 98%, and the numbers are very difficult to pin down, maybe it isn't 98%, maybe it's 97%, maybe it's 99%, let's say 98% uhm, of women are sexually attracted to men.


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[Assistant] About 96% of men are sexually attracted to women.


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[Assistant] And the numbers vary, and it's very difficult to estimate it properly. As you can imagine, there's all sorts of problems with this sort of research. But there's some proportion of population that's exclusively homosexual.


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[Assistant] Some proportionate population of men who are only attracted to other men. Some proportionate population of women who are only attracted to other women.


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[Assistant] Uhm, there's all sorts of reasons why somebody might have sex with somebody of the same sex. You know, there might be bored. You may, you know, be experimenting, be whatever. The question is, what do you want to do?


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[Assistant] All things being, what, what, what sort of person, if you could be sexually or romantically involved with any person, who would it be?


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[Assistant] And most people are heterosexual. There's a considerable amount that varies cross-culturally of people who are bisexual. But the real puzzle is exclusive homosexuality. So why?


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[Assistant] Maybe there are some exceptions, but it's not the case that people choose their sexual orientation.


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[Assistant] Decided who to become sexually attracted to. Very few people would. Uhm.


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[Assistant] Part of the issue rises in the fact that people who are gay are often extremely discriminated against. And they have no wish to be gay. They might even think it's morally wrong for them to be that way.


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[Assistant] That makes it implausible that her sexual orientation is a conscious choice. What about experience after puberty?


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[Assistant] So there is, there is a view that keeps coming up over and over again in literature that people who are gay have in some sense been seduced by people, by other people or something happened to them afterwards. Uhm, this seems unlikely.


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[Assistant] Uh, there are, uhm, in particular the seeds of sexual orientation later on in life seem to show up quite early in life.


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[Assistant] And, and you by now know how to do them. You look for differences between monozygotic and dizygotic twins. You do the adoption, uh, comparison, you know, adopted siblings and biological siblings. The answer is yes. You find that there is some sort of genetic predisposition towards homosexuality.


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[Assistant] It can't be entirely genetic. One reason why it can't be entirely genetic is if I am gay and I have an identical twin, the odds that my identical twin will be gay is about 50%.


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[Assistant] Those are very high odds compared to the average in the population.


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[Assistant] Why is it so, I said before this is a huge puzzle. Why is it such a huge puzzle?


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[Assistant] Well, exclusive homosexuality is an evolutionary mystery. Again.


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[Assistant] Do not think that this carries any moral weight to it.


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[Assistant] What it does mean though, is that it doesn't seem to follow as a biological adaptation. The puzzle is not, why is it that some men have sex with men?


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[Assistant] That's not a big puzzle. Maybe they have sex with men as some sort of recreational thing or pair bonding or whatever. That's not the puzzle. The puzzle is why are there some men who don't want to have sex with women?


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[Assistant] Similarly, why are there some men, women who don't want to have sex with men? From an evolutionary adaptive standpoint, you would think that the genes that give rise to such a behavior, uhm, would be weeded out.


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[Assistant] Because creatures with that behavior typically, putting aside modern technology, don't have offspring. And that's what makes it such a puzzle.


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[Assistant] So, your reading response for this week is, solve that puzzle.


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[Assistant] I know I said early on in the course that reading responses would be really easy and just require you reciting back things, but that proved to be too boring. So, so just solve this deepest of all puzzles. The thing in brackets at the end is very important.


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[Assistant] So I don't know what the rate is in non-human primates, for instance, of primates who do not want to have sex with members of the opposite sex. Okay. I'll see you all Wednesday.


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[Assistant] What you going to do? I might come back on board. Or I might try to make a go of it. Like they say, it is the land of opportunity. I always wanted to make it big. On a nice place. Get a thought. A house.


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[Assistant] Uh, so you see, the snow is a microclimate aberration caused by radioactive steam from the nuclear plant, and of course, tire fire particulate, and life. So, we're the only place in America with snow. If you can call it that, I guess you can.


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[Assistant] You always think it's the other guy that'll turn into a giant snowball, never you. Have you seen Lou? There's snow!


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[Assistant] Charity? Charity, will the Dollar Scholars be pledging their support?


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[Assistant] Cheap. Cheap my youth. You're worried about cheap. We're not worried about cheap, you know.


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[Assistant] You sure? I'm sure. Alright, respect.


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[Assistant] This your place? Yeah, sorta. No, it doesn't matter. Here's some dollar. Appreciate the ride.


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[Assistant] Would you permit me to go in the back into a brief inventory? Taking inventory.


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[Assistant] We have to navigate around the world. We have to recognize objects. We have to be able to, uhm, to understand physical interactions.


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[Assistant] And it's an interesting question to ask why.


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[Assistant] Early on about a couple of weeks ago to individual differences across people in intelligence and personality. I want to talk a little bit about an individual difference in our social natures and then I want people to do a test


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[Assistant] That will explore where you stand on a continuum. Uhm, that test is the piece of paper you have in front of you. Anybody who doesn't have it, please raise your hand and one of the teaching fellows will bring it to you. You don't know what to do yet with it. Uhm, so don't worry.


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[Assistant] To get the package to a stockbroker who worked in Boston, but lived in Sharon, Massachusetts.


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[Assistant] So most people succeeded. Most people were able to get the packages to this man. And uhm,


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[Assistant] Which is where the famous phrase comes about, that we're all separated from another person by six degrees of separation. This is not true in general, this was a very, a single experiment done within the United States, but the idea is appealing, that people are connected to one another via chains of people. Uhm,


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[Assistant] In about half of the cases, these packages went through two people. That is,


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[Assistant] If you plot the relationships between people, we can take each person in this room.


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[Assistant] Chicago O'Hare, for instance, are Newark, where planes fly through. Some people are hubs.


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[Assistant] And it is not impossible to find out. The piece of paper you have here


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[Assistant] Go through these names and circle how many people you know. Now the rules of this are


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[Assistant] But Magic Johnson has never heard of me, so I cannot circle it. On the other hand, our department shares Marsha Johnson. She has heard of me, so I can circle it.


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[Assistant] I'm gonna talk a little bit more about this while people go through this.


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[Assistant] The issue of connections between people is intellectually interesting for many reasons and might allow us to, uhm, to develop some generalizations about how people interact.


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[Assistant] Thing. Um, revolving around actor Kevin Bacon.


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[Assistant] I think chosen just because it rhymes with separation. Uhm, and the game of Six Degrees of Kevin Bacon is played by taking any actor


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[Assistant] And computing how many steps it would take to get to Kevin Bacon.


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[Assistant] With Kevin Bacon. So Ed Asner has a bacon number of one. Elvis Presley


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[Assistant] Was in the movie Change of Habit with Ed Asner. And that's his closest connection to Kevin Bacon. So Elvis Presley has a Bacon number of 2. It turns out that if you look at the 2.5, sorry, the quarter million people


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[Assistant] Also, isn't it? Because Meryl Streep has the misfortune of playing only in good movies.


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[Assistant] The most connected guy, and I think this shows that you're right, is Rod Steiger.


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[Assistant] Can you get me in touch with Rumsfeld? If I wanted to get somebody whacked, I'd ask this guy.


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[Assistant] The average is 21 among a college crowd. Some people score as high as over 100. Uhm, the older you are,


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[Assistant] One answer is, well, because of the great intellectual benefits. Put that aside.


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[Assistant] Do we systematically overestimate how much other people notice? You did a series of experiments and


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[Assistant] And he got them to wear t-shirts that had pictures on it that were the most embarrassing pictures that they could have on it. It turns out that uhm, if you ask people what's the worst picture to have on a t-shirt that you're wearing, the number one answer is Hitler. Tied with Barry Manilow.


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[Assistant] And then the psychologist went around and they asked the people, how many of you noticed, uh, this person's t-shirt? And it turned out, uhm, they got it wrong by a factor of about two. About, they thought,


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[Assistant] Are really old people, basically. What do you regret from your life?


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[Assistant] Uhm, they regret the things, as a rule, that they didn't try. But when you ask them why they didn't try it, the answers tended to be, I would look silly.


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[Assistant] And it turns out, interesting to know, that people just don't care as much as other people think you are. You can take that as good news or bad news. But uhm, but the spotlight is not on us as much as we think it is.


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[Assistant] I'm gonna ask you three questions. Have you been in London? Do you have a younger sibling? And do you like sushi? I want you to answer with one of those answers there. I want you to lie about one of them.


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[Assistant] Uhm, we think of ourselves as more transparent than we are.


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[Assistant] Another possibility is there's different criteria for goodness. Uhm, for a driver for instance, when I ask you to rank how good you are as a driver, uhm, what people often do is they think, they say I'm better than average.


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[Assistant] But what they do is they focus on one aspect of their driving. So some of you might say, hey, I'm just a great parallel parker. I'm a great driver. Others might say, I'm very careful. Great driver. Others might say, I take chances no one else will. Great driver.


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[Assistant] Your thoughts bleed out, which is why the transparency effect exists. But above and beyond that, in a normal healthy mind, you think you're horrific.


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[Assistant] Half of you did, below average on the midterm. But if I went up and asked each of you, uhm,


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[Assistant] Due to your terrificness, the bad things are due to accident and misfortune.


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[Assistant] Was developed by the social psychologist Leon Festinger. And is called Cognitive Dissonance Theory.


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[Assistant] And he claimed it causes an unpleasant emotional state. What he described as dissonance.


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[Assistant] It turns out people who are right wing, read right wing magazines. People who are left wing, read left wing magazines.


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[Assistant] If you support Bush, you're going to be looking for good news about Bush. If you don't support him, you'll be looking for bad news.


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[Assistant] You pre-test to make sure the person doesn't like any M&M more than the other. There's three M&Ms, who cares? But then you ask them to choose between two of them. So suppose they choose, uh, the red one. You gotta choose one. So they get to eat the red one.


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[Assistant] It turns out to a tremendous degree, and you could imagine yourself in that situation, they choose this one. The one that wasn't the one they turned down. And the claim is,


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[Assistant] That when you choose this, in order to justify your decision, you denigrate the one you didn't choose.


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[Assistant] It's absurd. No, wait, there must be a reason. I must work here, because I love this work. I love this job.


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[Assistant] I don't want to be a donkey. I don't want to be a sort of guy who does this boring thing for a dollar.


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[Assistant] Wasn't that bad really. It was kind of interesting. I learned a lot. To justify what they did. This has a lot of real-world implications.


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[Assistant] They had a certain time and date when the war was going to end.


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[Assistant] But what Festinger found was, they didn't say, God, I'm such a moron.


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[Assistant] When people devote a lot of energy or money or expense to something, they are extraordinarily resistant to having it proven wrong.


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[Assistant] I even think after it, I think, God, I went through a lot of stuff to get into this fraternity. It must be really good.


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[Assistant] They are more committed to the cause. Again, it's cognitive dissonance. If you pay me ten thousand dollars a month to work for you,


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[Assistant] I'll work for you and I'll think, I'm doing it for ten thousand dollars a month. That makes a lot of sense. But if I do it for nothing,


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[Assistant] Then I have to ask myself, why am I doing it? And I will conclude, I must think very highly of you.


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[Assistant] You take two groups of kids and you ask them to do something, like draw pictures. Half of the kids you reward. Maybe you give them a sticker or a toy. The other half you don't reward.


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[Assistant] Now, according to a sort of a simple minded view of operant conditioning and behaviorist psychology, the children you reward should do it more. That's how operant conditioning works. In fact though,


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[Assistant] While the children who get rewarded say, I did it for the sticker, I did it for the toy, I don't care much for this. And so rewarding children has a danger, which is if you give them too much reward and too much of value for what they're doing, uhm, they will denigrate the activity. Now, we need to be careful here about what's going on.


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[Assistant] It's not simple inconsistency. So go back to this insufficient justification, uh, effect.


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[Assistant] So, cognitive dissonance is a little bit more subtle. It's not just that there's a clash. Rather,


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[Assistant] Uhm, we adjust our beliefs to make ourselves look more moral and rational than we are. Go back to hazing.


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[Assistant] So cognitive dissonance motivates me to create an answer that's more comfortable for me.


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[Assistant] An answer such as, this must be a really wonderful group with a wonderful bunch of people.


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[Assistant] And in other words, we are biased to believe that we are terrific. So to sum up, there are three main findings about you that come out from social psychology. One is, you believe everybody notices you, even when they don't. You're, you're the hero of your story. The second one is, you're terrific.


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[Assistant] So an attribution is a claim about the cause of somebody's behavior. Uhm, and, Heider, now there's all sorts of reasons for somebody's behavior. Suppose you insult me. Or suppose you're very kind to me.


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[Assistant] I could say you're a kind person or you're a rude person. I could say


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[Assistant] This must be a great day for you, or you must be a lot of stress, under a lot of stress, or you must want something. There's different sorts of attributions we can make to people. But Heider's insight is


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[Assistant] We tend to attribute other people's actions to their personality characteristics. To, to long-standing aspects of what they are.


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[Assistant] To the person, and not enough weight to the situation.


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[Assistant] This is also sometimes known as the fundamental attribution error.


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[Assistant] The fundamental attribution error, which is one of the core ideas in psychology, is that we tend to over attribute things to


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[Assistant] For people in graduate school, particularly. If you were given a job talk and a slide projector breaks, you're screwed.


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[Assistant] Nobody is going to say to themselves, oh well, it's not such a good talk because the slide projector broke. They'll say, it's not such a good talk because of the person.


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[Assistant] Because people would think that they were gay. Probably they got Harry Hamlin to do it, who was kind of a B-list sort of guy. The biggest extreme of the fundamental attribution here of confusing the actor for his role is Leonard Nimoy.


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[Assistant] I would say that the fundamental attribution error is a human universal. Uhm, something that we're born with, a fundamental aspect of human nature.


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[Assistant] For whatever reason, and it would take another course to talk about the different explanations, but people start off at say age 8, not committing the fundamental attribution error. But in western cultures,


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[Assistant] The error occurs and people over attribute, uhm, the role to the person. In some eastern cultures,


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[Assistant] So this is an important reminder that just because we find something in our culture and just because it might well be pervasive, uhm, doesn't mean necessarily that it's universal.


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[Assistant] So, to summarize so far, and we're going to get just a little bit more, uhm, for the rest of this lecture, we've talked about two, two morals from social psychology. One is enhancement of the self.


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[Assistant] But the other is what you can call oversimplification of the other. So we know ourselves.


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[Assistant] That our behaviors due to a complicated cluster of the situation and our personal natures. When things go badly, in fact, we'll blame the situation.


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[Assistant] We don't do this for other people. For other people, we're a lot less forgiving. You do something stupid, that's your stupid person. Uhm, I do something stupid, it's an off day.


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[Assistant] About what we think about other people. And start by talking about why we like other people.


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[Assistant] And 90% of them said, my best friend's in the same building as me. And 50% of them says, the same floor. Uhm, ask yourself, who's your best friend at Yale? For how many of you is it somebody in your same college? Okay. How many in a different college?


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[Assistant] And this is sometimes known as the mirror exposure effect. The mirror exposure effect is simply seeing something, uhm,


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[Assistant] Uhm, they tend to like the pictures more that they had seen before.


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[Assistant] For unto everyone that has shall be given, and he shall have abundance. That means if you're good looking, you'll also be smart.


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[Assistant] It's a long version of the rich get richer, and the poor even lose what they have.


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[Assistant] Now I know I've got a heart because it's breaking. Wizard of Oz reference! Did I do that? Urkel reference! Help me. Fly reference! I can't stop. I can't stop.


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[Assistant] Mr. Nakamura, I have used my hitherto unmentioned chemical engineering degree to get a real job at the Springfield Phosphate Works.


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[Assistant] Now, have I proven myself worthy of your daughter? No. Please, take your time. You were worthy. The way you were. I-I was? But I rented the store! I-I e-bade my stool! Canceled the sale. But then my seller rating will go down! Stop being nerd! Yes, Papa-san.


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[Assistant] A sky tweet from Commissioner Sweeney. This looks like a job for At Radioactive Man Underscore 01.


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[Assistant] It's the fossil fuel four. Old King Cole. Petrolius Wreck. French femme fatale charcoal briquette.


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[Assistant] And the enemy of drinking water everywhere, the frackers.


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[Assistant] Stop attacking the nuclear plant, this country's safest energy source.


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[Assistant] Those studies were industry financed.


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[Assistant] Holy hadrons, I need backup! Calling Citizen Solar and his sidekick Windlad.


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[Assistant] Everything's back to normal. Everything's back to normal. Everything's back to normal.


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[Assistant] Springfield City Hall was built for the Spencer Tracy movie Meet Me at City Hall. It was just a flat wall with nothing behind it for thirty years.


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[Assistant] Classes, direct me to the nicest restaurant I can afford.


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[Assistant] Pause for sweet talk. Oh glasses, you're so wonderful.


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[Assistant] And should be worn in moderation. Isn't that right, Glasses?


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[Assistant] Anyway, I'm giving them up to whoever dares take them.


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[Assistant] Oh, no one can live in a non-augmented reality. I need another pair of glasses.


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[Assistant] Uhm, Mr. Bernsir? Can I get another pair of glasses? Maybe two pair? One for the beach house? Can I also get a beach house? Two beach houses! Huh? Monitors? What the?


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[Assistant] Burns gave us those glasses so he could spy on us. This is a violation of our human rights.


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[Assistant] Uh, uh, uh, uh, uh, wait a minute. Since Marge is wearing my glasses, I can see everything she sees. Uh, I shouldn't watch this.


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[Assistant] Thanks for asking me to lunch. Uh, why isn't she having an affair?


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[Assistant] Now let's learn more about my wife's secret life. Secrets which can only be good.


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[Assistant] Finally, time for the big bump into...


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[Assistant] Oh my god. Marge needs this. It lights her way through the dark path of marriage to me. Come on in, Mr. Fakanami. No need, everything's fine.


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[Assistant] Well, as long as I'm paying for the time. Ow! Where'd you learn to cut hair?


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[Assistant] I don't need to know, March. You deserve to have your secrets.


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[Assistant] Yeah, and? That's it. That was the story. Where's that cake now? It got sold. Who bought it? Dr. Hibbert. Excuse me, I have to make a call. Don't take too long.


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[Assistant] Which authorities? Uhh, the FBI. Which authorities?


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[Assistant] Last night, Shane Gould went home and I had a crack at Harry.


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[Assistant] Harry's gonna want payback on me. Saving Graze.


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[Assistant] I almost deforested a whole forest up at Tribal Council for this sweet piece of power. Now it's time to see what I got.


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[Assistant] Woo! Look at that! Yeaah, let's see how it looks on.


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[Assistant] Congratulations, you have found a hidden immunity idol, but its power is only valid at the first three tribal councils. This hidden immunity idol can keep you safe from being voted out of the game at tribal council.


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[Assistant] The fact that it can only be used in the next two tribal councils means this idle has a time limit.


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[Assistant] And I need to act fast to wrestle back control to sit on the top of this trial.


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[Assistant] At this stage, I'm super worried Harry's gonna want payback on me. But hopefully he doesn't have a clue that I voted for him.


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[Assistant] This will be going down in history.


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[Assistant] Uh, rumor has it they're about to give out the company Christmas gifts. Uh, rumor has it they're about to give out the company Christmas gifts.


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[Assistant] Who told you that? Oh, I got it in. The VP of Personnel is doing my girlfriend.


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[Assistant] Google goggles, augmented reality glasses, augmented reality-


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[Assistant] Ho ho, humbug. Santa Claus approaches the judge and punish.


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[Assistant] Oh don't worry, this center still has clothes.


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[Assistant] How much did this company lose to office supply theft last year?


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[Assistant] Yes, well, no more of that. Thanks to this 26 million dollar surveillance system. Beware, rabbits! I spy with my transplanted eyes. Oh, and I forgot to show you the best feature. Each of these buttons represents an employee.


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[Assistant] When I press it, he or she dies.


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[Assistant] In Season 1, the reason I got voted out was because my best friend in the game actually turned around and blindsided me. So, I've got a score to settle.


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[Assistant] Yo, what's going on guys? Tanmay here for simple snippets and welcome back to another video tutorial on data structures and algorithms.


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[Assistant] But in this video, we're going to be answering one of the most important question that what is the data structure and more importantly, why do we need data structures?


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[Assistant] So as you can see, we will be answering three questions or three parts that is definition of data structure as in what exactly is a data structure.


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[Assistant] Why do we need a data structure and at the end we'll also see an example. So we'll use our digital blackboard to understand how exactly data structure will help us and where exactly it is used. So with that being said, let's get started. Make sure you watch this video till the end.


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[Assistant] So starting off with a little bit of theory on data structure as in what exactly is a data structure.


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[Assistant] So data structure is one of the most fundamental subjects in computer science and I'm pretty sure I've already talked about this in the previous video tutorial also and the reason why I'm reiterating this is because


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[Assistant] And this comes handy especially when you are into development and programming domain.


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[Assistant] Now the technical definition of data structures which I took from Wikipedia is in computer science, a data structure is a data organization, management and storage format.


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[Assistant] They have GBs and GBs of data which they are collecting every single minute every single hour, right?


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[Assistant] So that we can access it easily or perform some modifications easily, right?


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[Assistant] So simple words sounds very easy, right? Data structure is a way in which data is stored on a computer.


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[Assistant] So it's not a data type. It is not a new technology or it is not like a software, but it is an implementation that we provide. So for example, we provide some rules and regulations some set of protocols which defines how the data will be stored and accessed.


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[Assistant] Okay, how do I visualize this? Can we have a real world examples? So let's see some example and let's try to relate things to it.


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[Assistant] And now we'll answer the more important question that why do we need data structures? So now you have a basic theoretical understanding of data structures.


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[Assistant] So some characteristics of data structures are as follows. Each data structure allows data to be stored in a specific manner. Now as we move along in this series, we will have different types of data structures which has some different specialties.


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[Assistant] It allows to manage large amounts of data such as large databases and indexing services such as hash tables. Now, this is kind of a little jargon for you as of now when we are starting off what you can do is you can just move these points down because these are very important. And now what we'll do is we'll try to relate a real world scenario. So let's take our first example, which is a dictionary.


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[Assistant] Now everybody knows that there are thousands of words in our actual dictionary, right? I don't even actually know the count of words because every time new words are being added. Now if I tell you to search a particular word, let's say I am asking you to search for the word snippets. Now just by looking at the word, you know that the word starts from S and then we have SN.


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[Assistant] So you know in the dictionary, you'll directly jump to that page where the word starts from S N, right? And then you'll search from there for the snippets word. Now this is possible because in the dictionary the words are arranged in that A to Z order, right? Now just imagine if these words were not arranged in that particular order and any word was put anywhere.


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[Assistant] How would you actually search that dictionary of thousands of words? You'll start from the first page and you'll go searching till the end or till where you find that word snippets, right? So that is such a hassle, right? You'll probably take an hour to actually search for that word.


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[Assistant] But since it is organized in a particular way, you can easily find it. So that is a real world scenario and data structure emulates that simulates that exact scenario in the digital world. Okay.


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[Assistant] So in this case, data is stored in the form of x and y coordinates, right? So this is another way of storing data. Let's take one more basic example. This must be a very common example for anyone whose computer science and IT student, especially from the database end.


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[Assistant] Now here in this example you can see we have first name, surname and age. So this is a table with name and age.


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[Assistant] If we have a salary column, we can calculate the average of salaries. We can find out which one is earning the maximum salary.


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[Assistant] So you can perform multiple functions because you have a tabular format here, right? So you can see there are three different ways in which data is organized in three different formats over here and because of this performing operations or searching for a particular keyword or searching for a particular location has become easy.


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[Assistant] So this feature exactly is what a data structure is trying to implement in terms of the digital world where data is stored digitally on computer systems. So I hope you got a very good idea about how real world scenarios work and how data structures can be relatable. And now we'll actually see


[User] Please transcribe this text

[Assistant] A basic data structure example, so let's move to the digital blackboard. Okay, so as you can see on the screen, we'll take an example of an array data structure.


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[Assistant] But obviously if you've already known what are arrays, you'll easily understand them and basically arrays are the most simple form of data structure which are predefined provided in many of the general purpose programming languages. The other data structures like stack, queue,


[User] Please transcribe this text

[Assistant] And then we have nonlinear data structures like graph entries.


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[Assistant] So what this does is, in the memory, so this is the main memory, let's assume this is our RAM. So we have memory blocks, right?


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[Assistant] So these orange ones are addresses, okay. These are memory addresses and we've given this a random number, so the addresses are weird numbers, this is just for representation purpose. So the first address is 1000, the second one is 1004, then we have 1008, then we have 1012. And the reason why this is in a plus four manner, that is


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[Assistant] So the index of array start from zero and what this index is used for is used to access or modify individual elements, right? So that's what it is given that it is referenced using an index.


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[Assistant] So the array index starts from 0. So the third position will have index of 2.


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[Assistant] And let's say we want to print this. Okay, so we are passing some print function and we say numbers of two.


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[Assistant] That is the first five alphabets in English. Okay. So this is a character array of size five, which is the first five alphabets in the proper ascending order right from a to e now imagine in a program you'll pass a number and you want to print


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[Assistant] The character representing that number. So if I enter 1, I should be able to.


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[Assistant] And you'll print this, right? And then you'll get the output of C, right? Now the advantage here was you did not have to search through the first position to the second position to the third position. In the program itself, you can type in exactly which index you want to print out.


[User] Please transcribe this text

[Assistant] Or you can take that index from the user also. So you can see the access becomes very fast. Now, let's see. We did not do this in the arrays way. We are storing all these alphabets in random variables and we wanted to print e.


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[Assistant] So we'll first have to go through each and every alphabet, right? And let's say it is taking one second.


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[Assistant] To access those variables, some time is gonna be taken, right? Even though visually we are not able to calculate behind the scenes.


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[Assistant] Now imagine in real world where you have data of millions of records and you already know where a particular record is by its address kind of like a hashing situation, which is not really talked about. But let's see you already knew that then to access that immediately.


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[Assistant] Efficiently manage memory and efficiently access different data from the memory.


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[Assistant] Okay, so that's it for this video guys. I hope you've understood what are data structures and more importantly,


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[Assistant] Why we need data structures and we've also seen an example of how data structures can actually help us manage memory efficiently and store it properly for efficient usage.


[User] Please transcribe this text

[Assistant] And that's it for this video guys for this video tutorial. I'm going to wrap it up over here. Let me know in the video comments how this video was if you understood what our data structures why we need data structures and how a real world example can be simulated. Please do like this video if you actually understood what our concept of data structures share it with our friends. This is a really important concept and each and every IT and computer science students should know about this. So thanks for watching guys. I'll see you guys in the next video. Peace.


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[Assistant] And you? Nothing personal, but don't fuck with my family. Or I will fuck with yours.


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[Assistant] No one's here. Just you and me. Come in, come in. Here. No thank you. Oh, I agree. Let's have whiskey instead.


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[Assistant] Becky swears by you. Absolutely swears by you.


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[Assistant] But I got a plan. You down friend. Maybe. What is it?


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[Assistant] Well first up, we gotta create a little problem between the Ancelotes and their Albanian muscle for Jimmy P.


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[Assistant] You're gonna plant a bomb in Tony Black's car. Be rigged to a phone.


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[Assistant] Then we'll go off when you dial a number. I want it to blow when


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[Assistant] Hey, look at me. Yeah. Yeah. I thought so.


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[Assistant] You'll do fine. Fine at what? Some gimp work for the Pegorinos. I owed them. Oh, please.


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[Assistant] I think I'm about to get pinched again. Shit, really? Yeah, it's happened before. I keep seeing the same car watching.


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[Assistant] All it means is someone's been speaking, we just have to find out who and make them stop. In the meantime, let's chill things out for a bit. So, I need your help.


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[Assistant] That a boy. Come on, we got something to show you.


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[Assistant] Now, Pegarino's a funny guy. Seems like his main motivation is putting his Guernsey crew on par with the Five-O Families.


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[Assistant] Ancelotti's being weakest, he's decided to stir shit up for him.


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[Assistant] Mr. Goldberg, Mr. Bellic has arrived for his interview.


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[Assistant] Mr. Goldberg is such a saint. He teaches kids to swim, you know. He's just so warm and giving. All he cares about is making the world a better place. And boy, are you in trouble if you get in his way.


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[Assistant] Or inside. You're just lucky it was me and not someone else who got their hooks into you.


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[Assistant] You know, you and your cousin, you should get out. What you're looking for, you won't find. Feds are all over it. Crime is done. You've watched too many movies.


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[Assistant] The only person I know who's writing Harry is Shane. Dude, don't do it, don't do it. Don't, pull your head back. You're sticking out.


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[Assistant] And that's the thing, I think we've got to work together as a cohesive unit. We've got to stay strong in that way because otherwise, yeah, we've seen it happen. We've all had those experiences when, yeah, there's a fracture in the group. So you've got to stay strong.


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[Assistant] Make a face pumpkin.


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[Assistant] Make a monster pumpkin.


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[Assistant] Every single day, your mind takes you back to Survivor when you played last time. You're waking up going, oh that's right.


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[Assistant] It's short! There was a nickname floating around camp.


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[Assistant] Lee, misses shot after shot. That is not a great nickname for someone who used to throw a cricket ball for a living.


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[Assistant] It was really special, it was really nice telling yarns, telling stories. Some were good memories.


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[Assistant] Precarious and precious these inches and minutes and moments are in the game.


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[Assistant] It was one of those ones where you don't even know how you got through six and a half hours. Yeah. Seeing that image.


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[Assistant] My understanding of the game is a lot better. I have learnt that I can be honest to my alliance and try and protect that alliance as much as I possibly can. And if that involves lying, then I'm comfortable with that.


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[Assistant] You get it wrong, they come back, all they think about is the person who'd been writing their name down. It can get real messy real quick.


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[Assistant] I think it is highly possible that a woman could beat me in a challenge, but that all depends on what it is. If it's physical strength, girl, no chance.


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[Assistant] But, you know, if we're looking at like a dishwashing challenge, we might be in trouble.


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[Assistant] Carlos Counts Kittens. Written by Lorena F. DiBello.


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[Assistant] Illustrated by Angela Kamstra Jacobson.


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[Assistant] Normally I don't care about cocaine, keeps controllable people in power, but this is no good. Alright, what's my role in this? The distribution network is a fleet of vans parked in a grocery warehouse in Alderney City.


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[Assistant] This product is packed into the frames of the vans, ready to be moved all over the country. Destroy everything. Whatever it takes. And for me? For you. I've got a real gift for you. Get this done, then we'll talk.


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[Assistant] And Makuta was off to tribal council yet again.


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[Assistant] And blew Henry and his fake idol out of the game. Fifth person voted out of Australian Survivor All-Stars. Henry.


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[Assistant] Nineteen are left. Who will be going home tonight?


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[Assistant] So Abby, you guys have been out here for two weeks now.


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[Assistant] If you could sit down and enjoy a big meal, what would it be?


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[Assistant] For today's challenge, tethered in pairs, you're gonna stack a series of blocks along a beam while trying to avoid a trip obstacle.


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[Assistant] Alright, here we go. Survivors ready?


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[Assistant] We need to work together to place one block at a time.


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[Assistant] Phoebe and Brooke first out in the courts for Vakama. Hold that, hold that steady.


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[Assistant] This challenge is hard enough. But when you tether together, it makes it that much more difficult.


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[Assistant] Abby and John for the green tribe. Makuta, you hit that trip obstacle, you'll knock over your blocks and you'll need to reach that.


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[Assistant] This is speed versus precision. The faster you go, the more risk you take.


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[Assistant] Moana and Jackie using a block to measure the distance. Come on! Jack and Mo, you can't do that! Just get against it! Lockie getting frustrated with that strategy.


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[Assistant] You need to work together as you move through that obstacle. You need to move at the same speed. You need to have the same step.


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[Assistant] All it takes is one hit on that obstacle and you'll lose a bunch of blocks.


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[Assistant] Careful, careful, careful. Last block for Makuta drops over.


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[Assistant] Again, not by much. Both tribes trying to pick up the pace.


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[Assistant] Bunch of blocks fall over from a kuda, really opening up the door of the bakuma.


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[Assistant] Huda's gonna have to work quickly. They wanna catch up to Vakama. Yes, sir. Will do.


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[Assistant] Okay, sorry, fuck. Allowing Vakama to pull away.


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[Assistant] The big question looming is, is the spacing correct? All it takes is one hit on that obstacle and you'll lose a bunch of blocks. Straight back! Straight back!


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[Assistant] Gives it a shot. Oh no! No, it doesn't work! They're gonna have to rebuild! Open the door again! For Makuta! Go, go!


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[Assistant] Kudo, tough one to miss out on, but got nothing for you. Grab your gear and back to camp.


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[Assistant] Well David, if Baccama does lose today,


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[Assistant] Alright, here we go. We have Zack, throwing for Makuta. AK, throwing for Vakama.


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[Assistant] Bakuta go back down to get their second ball. Nick and Sean drive down.


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[Assistant] Don and Abby go down again. Jackie goes down. Baby goes down for Vakama. We got neck and neck at this stage. Not easy getting those last balls out of the chute. Who's going to get the third ball?


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[Assistant] John, on the other side of the platform now, he takes off his mask, and taps out.


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[Assistant] Trouble Council again, where one of you will become the sixth All-Star voted out. Swim it on out. I'll see you guys tonight.


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[Assistant] So Harry, with Henry gone, is there a new, obvious target now?


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[Assistant] So why are you worried then? Because of Survivor Man. You think because of today's one performance, you could be on the chopping block?


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[Assistant] Is it difficult to figure out which side you align with?


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[Assistant] Seanny, do you think anyone here knows exactly what's going on tonight?


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[Assistant] Alright, well, before we go any further, tonight is a little different.


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[Assistant] It's okay, you guys are still gonna go up to vote.


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[Assistant] Instead, the two people with the highest number of votes will be going to Exile Beach.


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[Assistant] Does this change anything for you? Or are you still going to vote for the person that you were going to vote for?


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[Assistant] Okay, once the votes are read, the decision is final. The two people with the highest number of votes will be going to exile tonight. I'll read the votes.


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[Assistant] Two votes Zach. One vote John. One vote left.


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[Assistant] Well, while two people have gone into the unknown tonight, it seems that this tribe is also lost in the dark. Grab your torches, head on out. Good night.


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[Assistant] I'm feeling very defeated. Really? I shouldn't.


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[Assistant] And Makuta was off to tribal council yet again.


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[Assistant] And he, Shaunie, and Harry thought they had the others on board.


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[Assistant] I'm just very worried that we're going to be back here in two days time.


[User] Please transcribe this text

[Assistant] Shani was hiding the real deal.


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[Assistant] And blew Henry and his fake idol out of the game. Fifth person voted out of Australian Survivor All-Stars. Henry.


[User] Please transcribe this text

[Assistant] Nineteen are left. Who will be going home tonight?


[User] Please transcribe this text

[Assistant] So Abby, you guys have been out here for two weeks now.


[User] Please transcribe this text

[Assistant] For today's challenge, tethered in pairs, you're gonna stack a series of blocks along a beam while trying to avoid a trip obstacle.


[User] Please transcribe this text

[Assistant] If any of your blocks fall, you'll need to restack. The goal is to stack your blocks so they fall like dominoes.


[User] Please transcribe this text

[Assistant] AK and Tarzan and Sean, you guys are on the bench. Alright. Everyone else, I'm gonna give you a minute to strategize. We're gonna get to it.


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[Assistant] Alright, here we go. Survivors ready?


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[Assistant] Need to work together to place one block at a time.


[User] Please transcribe this text

[Assistant] Phoebe and Brooke first out in the course for Vakama. Hold that steady.


[User] Please transcribe this text

[Assistant] This challenge is hard enough. But when you tether together, it makes it that much more difficult.


[User] Please transcribe this text

[Assistant] Abby and John for the green tribe. Makuta, if you hit that trip obstacle, you'll knock over your blocks and you'll need to re-stack.


[User] Please transcribe this text

[Assistant] This is speed versus precision. The faster you go, the more risk you take.


[User] Please transcribe this text

[Assistant] You're looking to stack your blocks so they fall like dominoes. Which means you're gonna need to figure out the correct spacing between them. There's a big gap, it's fine. Too close, you won't have enough blocks to get to the end. Too far apart, you won't get that domino effect.


[User] Please transcribe this text

[Assistant] Moana and Jacky, using a block to measure the distance. Come on! Jack and Mo, you can't do that! He's gonna guess it! Locky getting frustrated with that strategy.


[User] Please transcribe this text

[Assistant] Both tribes neck and neck at this stage. Not much in it.


[User] Please transcribe this text

[Assistant] You need to work together as you move through that obstacle. You need to move at the same speed. You need to have the same steps.


[User] Please transcribe this text

[Assistant] All it takes is one hit on that obstacle and you'll lose a bunch of blocks.


[User] Please transcribe this text

[Assistant] Makuta have a slight lead, but there's not much in it. The yellow tribe, Bakuma right on their tail.


[User] Please transcribe this text

[Assistant] Wind is really picking up now. Playing havoc with your blocks.


[User] Please transcribe this text

[Assistant] Careful, careful, careful. Glass block for Makuta drops over.


[User] Please transcribe this text

[Assistant] Harry and Nick out with a block. Harry fixing the one that fell over.


[User] Please transcribe this text

[Assistant] Again, not by much. Great work gals. Both tribes trying to pick up the pace.


[User] Please transcribe this text

[Assistant] Bunch of blocks fall over from Makuta, really opening up the door for Vakma.


[User] Please transcribe this text

[Assistant] Computer's gonna have to work quickly. They want to catch up to Vakama. Yes, sir. One sec.


[User] Please transcribe this text

[Assistant] Inside, inside. Allowing Vakama to pull away.


[User] Please transcribe this text

[Assistant] The big question looming is, is the spacing correct? All it takes is one hit on that obstacle and you'll lose a bunch of blocks. Straight back! Straight back!


[User] Please transcribe this text

[Assistant] Security repositioning now. Both tribes have all their blocks on the beam.


[User] Please transcribe this text

[Assistant] Akuta trying to get out of the obstacle. They're gonna have a pass at it. Akuta goes for their first pass.


[User] Please transcribe this text

[Assistant] Kruta, tough one to miss out on, but got nothing for ya. Grab your gear, head back to camp.


[User] Please transcribe this text

[Assistant] VACMA have won the last two immunity challenges.


[User] Please transcribe this text

[Assistant] Well David, if Vakama does lose today, are we going to see more fireworks at Tribal?


[User] Please transcribe this text

[Assistant] Once again, immunity is back up for grabs.


[User] Please transcribe this text

[Assistant] And dive down to retrieve a ball from an underwater chute. First by pulling out a pin, and then pushing it down and out.


[User] Please transcribe this text

[Assistant] When you have all your balls, the lost tribe mate will then attempt to shoot them into a goal.


[User] Please transcribe this text

[Assistant] Losing tribe. Tribal council tonight, where one of you will become the sixth all-star voted out. Vakama, you have an extra play, need to sit someone out. Cannot be the same person in back-to-back challenges. Who's it gonna be? Ohana?


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[Assistant] Moana, you're on the bench. Everyone else, I'll give you a minute to strategize. Let's get it on.


[User] Please transcribe this text

[Assistant] Alright, here we go. We have Zack throwing for Makuta. AK throwing for Vakama.


[User] Please transcribe this text

[Assistant] Phoebe's in now, Tarzan's in. The Green Tribe have an early lead, can they keep it?


[User] Please transcribe this text

[Assistant] Sean goes down. Go guys, let's go.


[User] Please transcribe this text

[Assistant] Mikuna going back down to get their second ball. Nick and Sean going down.


[User] Please transcribe this text

[Assistant] Top balls are more difficult to get out because you need to push the balls further down the chute.


[User] Please transcribe this text

[Assistant] John and Abby go down again. Jackie goes down. David goes down for Vakama. We are neck and neck at this stage. Not easy getting those last balls out of the chute. Who's going to get the third ball?


[User] Please transcribe this text

[Assistant] John on the other side of the platform now, he takes off his mask and taps out.


[User] Please transcribe this text

[Assistant] Tribal Council again, where one of you will become the sixth All-Star voted out. Swim it on out. I'll see you guys tonight.


[User] Please transcribe this text

[Assistant] So Harry, with Henry gone, is there a new, obvious target now?


[User] Please transcribe this text

[Assistant] Harry just kinda laid it out on the line. He said, you came up last time, you're in trouble again tonight.


[User] Please transcribe this text

[Assistant] You mean in the challenge? Yeah. Why don't you think you performed well in the challenge? I just didn't.


[User] Please transcribe this text

[Assistant] So why are you worried then? Cause it's Survivorman. You think because of today's one performance you could be on the chopping block? Well that's what's happened so far I guess to an extent.


[User] Please transcribe this text

[Assistant] So Zach, what do you do in a situation like this? Do you just...


[User] Please transcribe this text

[Assistant] Is it difficult to figure out which side you align with?


[User] Please transcribe this text

[Assistant] Seanny, do you think anyone here knows exactly what's going on tonight?


[User] Please transcribe this text

[Assistant] So that means only one person is on the chopping block.


[User] Please transcribe this text

[Assistant] Alright, well, before we go any further, tonight is a little different.


[User] Please transcribe this text

[Assistant] That's okay, you guys are still gonna go up to vote.


[User] Please transcribe this text

[Assistant] Instead, the two people with the highest number of votes will be going to Exile Beach.


[User] Please transcribe this text

[Assistant] Abby, does this change anything for you? Or are you still going to vote for the person that you were going to vote for?


[User] Please transcribe this text

[Assistant] Zach, you seem worried. Yeah, I'm a little worried.


[User] Please transcribe this text

[Assistant] Okay, once the votes are read, the decision is final. The two people with the highest number of votes will be going to exile tonight. I'll read the votes.


[User] Please transcribe this text

[Assistant] Zack. That's five votes Shani, one vote Zack.


[User] Please transcribe this text

[Assistant] Two vote Zach. One vote John. One vote left.


[User] Please transcribe this text

[Assistant] Seanny, you have five votes. Zach, you have three. That means the two of you have been voted out of this tribe. You're going to exile tonight.


[User] Please transcribe this text

[Assistant] Well, while two people have gone into the unknown tonight, it seems that this tribe is also lost in the dark. Grab your torches, head on out. Good night.


[User] Please transcribe this text

[Assistant] They pee all night, they cry all day, and every time they use the computer, the font is huge!


[User] Please transcribe this text

[Assistant] That's alright, enough about that. Did the clock ever get whelmed?


[User] Please transcribe this text

[Assistant] You did that for me? More for mom, but yes.


[User] Please transcribe this text

[Assistant] And we're scared of that because...


[User] Please transcribe this text

[Assistant] Why walk around camping the night, oh? Because I can. Pretty simple. They always saw me find it.


[User] Please transcribe this text

[Assistant] I just want to put in their minds what happened last time I went to tribal council with an idol. One of their alliance went home. So it's just simply about trying to create doubt, confusion, that alliance of six, maybe crack them. Little do they know that I've already got that way in.


[User] Please transcribe this text

[Assistant] He's a born leader, like he's a big strong leader. And seeing me every day with this idol just keeps reminding him that he hasn't got control right now. I mean he wants control, but I'm not going to give it to him.


[User] Please transcribe this text

[Assistant] Yeah, look, being in the position I am on the wrong side of numbers doesn't faze me at all. I've got an idol in my pocket. I'm working with David. Well, but it comes to lucky. I vote him out in the heartbeat. If you can remind me.


[User] Please transcribe this text

[Assistant] Well, our tribe is pretty divided at the moment. Been able to put that to bed for a moment and just come together over a meal and chat about it. Man, this is exactly what we need. Bring it on.


[User] Please transcribe this text

[Assistant] That's at a fundraiser for breast cancer. I know that photo.


[User] Please transcribe this text

[Assistant] I remember the look that she had, you know, when she was going through that. She never complained. She never, she's an amazing woman. You know, the kids were always first. Yeah. I don't think mums are like super women. Yeah. My dad was, you know, rugby league captain of Australia. And through the seventies and eighties, it's a pretty brutal, like rough man's world. When I, um,


[User] Please transcribe this text

[Assistant] When I made it in the footy, like I was always trying to get out of my dad's shadow and she said to me one day, she goes, you've made it mate.


[User] Please transcribe this text

[Assistant] Written by Nigel Pepperhouse.


[User] Please transcribe this text

[Assistant] Focus question. Where can kids go?


[User] Please transcribe this text

[Assistant] Go to the woods.


[User] Please transcribe this text

[Assistant] Go to the parade.