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[Assistant] I've taught to public school teachers, to, uh, people in public safety, to, uh, people who are in, uh, healthcare organizations, uhm, and I challenge them, and I'm challenging you, to think of a metaphor that works for you. Something thoughtful, something that embodies, uhm,


[User] Please transcribe this text

[Assistant] Who you are and what you are at your best. I think a good metaphor for success helps you remember your goals because you're able to, uhm, think to yourself, uhm, uh, am I in the right spot? Am I doing what I should be doing?


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[Assistant] Because I can feel the coherence between my activities today and the metaphor for success that I've created for myself.


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[Assistant] Uh, some, some people have, uh, suggested an image of a compass, uh, to help people, uh, find their direction. Uh, a high school, uh, coach, uh, track coach once, uh, suggested that a compass was the metaphor he liked to use. Uh, another person who was a preschool teacher.


[User] Please transcribe this text

[Assistant] Uh, said that hermage of success was a, uh, big shade tree in Africa where the village can come beneath the shade tree and create community and renew itself and be sheltered from the sun.


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[Assistant] And so that was what she was, uh, identifying as, uh, the place she ought to be and why she did what she did. So, you know, your challenge? Find your own metaphor. Uh, uh, write it down, think about it, talk to your friends about it, talk to your loved ones about it and, and see if you can refine it a little.


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[Assistant] Uhm, that actually segues to the second tool, uh, for remembering and renewing your theory of success. And that is, uh, your social experience. Uh, the people that you choose to surround yourself with.


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[Assistant] And if you choose to surround yourself with people who don't hold your values, who resist or compete with your values or who minimize them, it's easy, much easier to forget what those values are and basically get lost


[User] Please transcribe this text

[Assistant] It's a reinforcing experience for your values and not


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[Assistant] Uh, something that compromises your values. You know, when you were a kid, your parents probably wanted to be sure you were hanging out with the right people. And...


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[Assistant] Part of the reason they cared about that was not just your safety.


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[Assistant] But they wanted you to hang out with people who would have values that they endorsed as the leaders in the family.


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[Assistant] And now that you know, you're outside your family circle, you get to make those choices.


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[Assistant] Uh, and it's important that you pick your social circles wisely. Aristotle.


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[Assistant] In, uhm, one of his books, and it's a subject that we talk about in the success course, actually defined three kinds of friendships. And I think, uh, this can be a pretty good diagnostic for how you're doing, uh, with your friends. Uh, the three types of friends that Aristotle, uh, mentioned were friends of pleasure,


[User] Please transcribe this text

[Assistant] Uhm, basically these are people that you like to have a good time with. Uhm, friends of utility.


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[Assistant] And these are friends that, uh, you engage with to, uh, get your work done or to, uhm, you know, advance some, uh, outer goal that you have.


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[Assistant] Uh, and both of those kinds of friendships are very helpful. Uh, they can, you know, provide you with, uh, a good social network. But the real payoff, uh, as Aristotle saw it for,


[User] Please transcribe this text

[Assistant] Uh, they're the people who can gently offer you, uh, reminders of your most important values, uh, give you


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[Assistant] You know, how many Friends of Virtue do you have? Uh, not very many.


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[Assistant] They tend to be people who, uh, you went through a very important part of life with, who know you, uh, when


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[Assistant] Uh, you had a crisis or a conflict and they've stuck with you, uh, through a transition and


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[Assistant] Uh, there are people who you renew with, uh, and it's a good test of a friend of virtue in my experience is, uh, you haven't seen them for a while, you call them,


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[Assistant] And as soon as the conversation starts, you pick it up right where you left it off.


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[Assistant] Uh, you understand each other that well. Uh, there's no need to go back and do chit chat and feel the formality of renewing a friendship of utility and asking how their jobs are or something like that. Uh, you each know what the most important issues are that you're working on in your lives and you pick up


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[Assistant] Uh, with uhm, a piece of that as part of the background of how you're relating to one another.


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[Assistant] Different people have different social capacity, but maybe you have three or four, uh, friends of virtue in your life.


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[Assistant] Uh, they could be your parents or one of your parents. They could be a childhood friend or...


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[Assistant] A classmate from college that you happened to have room next to your freshman year and then you've stayed in touch with him, uh, ever since.


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[Assistant] Uh, maybe they're your spouse. Uh, I think if, uh, you have a friend of virtue who's your life partner, uh, that's a pretty special combination, uh, to go through life with. So surround yourself with, uh, people that renew and remind you of the values. That's a second very important tool.


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[Assistant] Uhm, third is to remember and, uh, renew your sources of personal self-confidence. You know, we all have days when we go up and days when we go down, and when we're down, we lose our self-confidence.


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[Assistant] Uh, we are basically losing confidence in the underlying success values that we have come to endorse. And we begin to feel that, you know, life doesn't have the same


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[Assistant] Uh, meaning or direction or energy as it did. Uh, and so everybody has this happen. How do you renew?


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[Assistant] That source of self-confidence that will get you back, uh, uh, and in motion, uh, in the direction that you know is best for you.


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[Assistant] Uhm, I've got an exercise that we use in the success course.


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[Assistant] That I want to make available. It's going to be available for you to download and take. And it's really a pretty simple, uh, uh, thoughtful thing to engage in. Uhm, in the course we talk about a Roman, uh,


[User] Please transcribe this text

[Assistant] Emperor named, uh, Marcus Aurelius. And Marcus Aurelius, uh, you know, lived, uhm, about 200 or 300 A.D.


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[Assistant] He was a great general. He spent a lot of his time, uh, fighting wars up in France, uh, for the Roman, uh, for the Roman people. But he was also a philosopher.


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[Assistant] And, uh, one day, he started a journal.


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[Assistant] Uh, and he kept writing it and ended up being a document he called, uh, To Myself.


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[Assistant] And uhm, and it was basically a diary. And the very first entrance in this diary, he laid out all the people in his life so far who had endowed him with a legacy that had contributed to his success.


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[Assistant] He talked about an uncle, he talked about a teacher, he talked about his father, he talked about his mother, and each of the, each of the important qualities that each of these people had


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[Assistant] Uhm, sort of handed off to him and that had allowed him to become, uh, a thoughtful person, uh, who, uh, was able to sort of consider his own life and to have a meaningful one.


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[Assistant] We call it the legacy exercise, and I think if you'll take a look at it, uhm, in between the sessions that we're sharing together,


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[Assistant] What you'll see is a whole set of lessons about life. Uhm, how to love and be loved. Uh, how to uhm, set goals. Uh, how to express gratitude.


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[Assistant] Uhm, all these different things and what I challenge you to do is think of the person in your life who endowed you.


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[Assistant] I feel a little grateful for what it is that they've given you.


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[Assistant] And cumulatively, when you think about all of the assets that these people have given you, what tends to happen is your self-confidence begins to return.


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[Assistant] Because you realize that, uh, you've got a lot more going for you and others have, uh, basically given you a lot of gifts to help you get along. And, uh, so the legacy exercise can be a great way to renew your self-confidence, uh, by thinking about how you came to be the person you are.


[User] Please transcribe this text

[Assistant] It's just an extra credit step on this, which I advise my students to do. After you've done it, you may discover that there's some people on this list that you haven't thanked.


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[Assistant] Uh, recently or adequately, and uhm, there's nothing that will make you feel better.


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[Assistant] Then to, uh, send an email, pick up a phone, uh, schedule a call.


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[Assistant] Uhm, connect with these people who have endowed you with the legacies that they have and express your gratitude to them and that also will help you renew your self-confidence as a final step. The second tool for self-confidence renewal is to just remember the rites of passage, the difficult moments that have


[User] Please transcribe this text

[Assistant] Come in your life that you've persevered through and, uh, and come beyond.


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[Assistant] Basically, what doesn't kill you often makes you stronger. People look back on periods of their lives when they were in the military or when they went through a particularly rough, uh, personal crisis in a school or in a relationship, uhm, and they somehow managed to come through it, uh, and they remember, uhm,


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[Assistant] Uh, the feeling of capability of...


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[Assistant] For some people, uhm, a reminder that they have a faith and a power that's greater than they are.


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[Assistant] However you define that, whether it's in religious terms or whether it's in terms of your place in the great scheme of nature, the continuity, the sort of wheel of life, if you have a way to renew that faith, that is often a way to


[User] Please transcribe this text

[Assistant] Your metaphor, your friends of virtue, the various tools to renew self-confidence.


[User] Please transcribe this text

[Assistant] The final thing that I find, uh, useful and that my students report is very helpful is to have some, uh, renewal rituals, uh, some, uh, uh, song that embodies, uh,


[User] Please transcribe this text

[Assistant] Uh, your values. Uh, at the end of a semester, very often the students will put together


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[Assistant] A, uh, song list, uh, uh, uh, they'll burn a CD or collect songs, uh, on a website that are their, uh, signature anthems for, uh, what each of them means by success.


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[Assistant] Uh, this can be a really fascinating collection of music, because for some people it's a rap song, for some people it's a sentimental ballad.


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[Assistant] Some people it's a ritual song from a religion.


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[Assistant] Uhm, so uhm, things that renew your energy, uhm, uh, songs, uhm, symbols, uhm, uh, for, for some it's a, I, I had a student who,


[User] Please transcribe this text

[Assistant] Uh, always wore a necklace around his neck and there was a small wooden


[User] Please transcribe this text

[Assistant] And he spent the whole summer with this group and they built, uh, the school.


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[Assistant] Uh, during the summer he'd gotten to know some of the children in the village very well who were going to actually use the school. And one of them had made him.


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[Assistant] This little symbol, uh, which was, uh, related to the country and, uh, and an animal that is native to the country and, uh, had given it to him as a gift and he had decided to wear it, uh, all the time ever since to remind him of


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[Assistant] The overarching goal he had in life, which was to help others in developing countries,


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[Assistant] Uh, and to bring opportunities and education to people who don't have them.


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[Assistant] Now here's a student, he's at the Wharton School, uh, obviously surrounded by all kinds of power symbols and success status symbols and wealth symbols.


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[Assistant] Success values was literally to wear it around his neck as a way.


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[Assistant] Uh, to remember it. So that can be, uh, a very powerful, ritual-like way to remember.


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[Assistant] So uhm, so whatever, whatever it is that works for you, personally, I, I, uh,


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[Assistant] I have turtles. I have turtles all over my office. I have turtles on my briefcase. Uh, I'm very fond of turtles. That's a symbol because turtles are.


[User] Please transcribe this text

[Assistant] Uh, they're long lived, uh, they are patient, uh, they are wise.


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[Assistant] My wife is very fond of elephants, so we have lots of elephants in our home. Uh, elephants are also patient, uh, also gentle, uh, and, uh, very loyal, uh, and very family-oriented.


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[Assistant] So we try to embody our values with little symbols around us. So, uhm, success, very difficult to, uh, understand once you grasp it, uh, sometimes difficult to remember.


[User] Please transcribe this text

[Assistant] So these tools that we just talked about, uh, each of them may have a different role in your life, but a very important, uhm, uh, exercise, uh, is to identify them, uhm, bring them close to you, uh, cherish them to help you remember, uh, who you are and the direction that you're going in.


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[Assistant] You have all traveled far and faced many challenges. You have done well.


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[Assistant] Take the true scriptures back to the Tang Empire.


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[Assistant] The scriptures will teach the people there to be good and to treat each other kindly.


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[Assistant] But take good care of the scriptures. They're extremely valuable.


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[Assistant] Once you have delivered them, you shall come back here to receive your rewards.


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[Assistant] Hello Sam, have a cigarette. No thanks, Tom. Have a glass of whiskey then. Okay, thanks.


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[Assistant] Sam and I had lunch together today. We went to a restaurant. What did you have? We had roast beef and potatoes.


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[Assistant] Bajie, I thought you were going to check the mountain. Instead you went to look for a feast? You lied to me. You should be ashamed of yourself.


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[Assistant] You knew there were demons and a monster ahead? Said the monk. And you didn't say anything?


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[Assistant] To become a Buddha, he needs to face one hundred challenges!


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[Assistant] I was just looking at the list of challenges he's faced.


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[Assistant] There are only 99 challenges on the list!


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[Assistant] He must face one more challenge to become a Buddha.


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[Assistant] vada vada water vada And the last one, piva.


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[Assistant] To listen. To listen.


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[Assistant] Come on. Come on.


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[Assistant] Hi, welcome to statistics one. This is the first lecture and the topic for this lecture is experimental research. As I mentioned in the introduction video,


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[Assistant] It's important to know as either a consumer of, of statistics or as someone engaged in statistical analysis, what type of research are you doing? What kinds of questions are you asking? And, and what kinds of questions might you ask of your data? So in this first week,


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[Assistant] We'll talk about first, experimental research here in lecture one, and then, correlational research in lecture two. Because they have a very different feel, uh, and they, they typically work with, uh, very different kinds of data. Uh, so let's, here in this first lecture, talk about experimental research.


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[Assistant] This first lecture is broken up into three segments, and that'll be common as we go through the course. Uh, each lecture will be typically broken into either two or three segments, uh, hopefully around ten minutes each, but, uh, that'll fluctuate a bit.


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[Assistant] Uhm, so in this first lecture, we have three segments. The first one, I'll give you just an example of experimental research in action. Uh, the polio vaccine trials. Uh, another, in the second segment, I'll give you another example. Uh, more recent research on working memory training.


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[Assistant] And then in the third segment we'll talk more about this concept of randomness. Uh, you'll see an important aspect to experimental research is random selection from a population and random assignment to conditions. So let's go into this first segment.


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[Assistant] In the first half of the twentieth century, uh, there were approximately 20,000 cases of polio per year in the USA.


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[Assistant] In 1952 was a particularly bad year. There were 58,000 cases just in that one year alone.


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[Assistant] Polio was a very debilitating disease. It was very frightening. Uh, it, it, it was, uh, happened among children of all, uh, backgrounds. Uhm, and some years, uh, as I noted here in 1952 were particularly bad and, and, and, and worrying. Uhm,


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[Assistant] So in 1952, the first effective polio vaccine was developed by Dr. Jonas Salk.


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[Assistant] And how do we know in retrospect, uh, you know, over sixty years later, how do we know that it was effective? Well, through experimental research, more specifically through randomized controlled experiments.


[User] Please transcribe this text

[Assistant] And this set of studies is now known as the Salk vaccine trials. Uh, one of the greatest examples of experimental research put to use to solve a very critical problem facing, uh, modern society.


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[Assistant] Again, go back to the introductory lecture. We need a sample and we need a population. So what was the population?


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[Assistant] At first, the population was all children in the USA.


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[Assistant] Actually the population really is all children worldwide, uh, but these initial studies were funded by the U.S. government.


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[Assistant] Uh, the Salk vaccine trial. So I'll define the population as all children in the U.S. That's, that's who Salk was, uh, hoping he could generalize his vaccine results to.


[User] Please transcribe this text

[Assistant] Uh, is all children in the U.S. The sample, of course he couldn't get his hands on all children in the U.S. So the sample initially was just 4,000 children from the state of Virginia.


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[Assistant] Uh, that turned out to be pretty, uh, uh, pretty promising result from that first sample.


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[Assistant] The US government, uh, funded the research further and they expanded the sample ultimately to include 1.8 million children from 44 of the 50 United States.


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[Assistant] So in this experiment, the independent variable, or what some people may call the treatment, is what the child actually received. So some children received the actual live vaccine, the polio vaccine, and other children received a placebo.


[User] Please transcribe this text

[Assistant] So they got a vaccine, but there was no, uh, live vo- live polio vaccine. It was just, uh, placebo.


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[Assistant] The dependent variable is what you measure after the treatment. So the dependent variable was, you can think of this in one of two ways. One, if you look at the level of the individual child.


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[Assistant] You could think of it as a polio diagnosis. So each individual child is either going to have a polio diagnosis. So we could, we could score that as a one or, uh, no, not be diagnosed with polio. So we score that as a zero.


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[Assistant] Uhm, or if we look at the, the, uh, level of a community of children. So if we look at towns or villages or states, uhm, then we could look at the rate of polio among a certain number of children. So two different ways to score this, uh, dependent variable.


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[Assistant] Cool things about using the SOC vaccine trial.


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[Assistant] As an example to start this course is it was a double blind experiment.


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[Assistant] It's pretty rare in experiment, in experimental psychology or in, in uh, experimental trials, uh, in general.


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[Assistant] Because it's difficult to do. What I mean by double blind is that the experimenter didn't know if the treatment that they were giving to the child was the placebo or was the vaccine. So the person administering, uh, the actual vaccine didn't know if the child was getting the vaccine or getting the placebo.


[User] Please transcribe this text

[Assistant] So no one on site at the time knew which one the child was getting and the child and the parents, they didn't know if they were getting the vaccine or the placebo.


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[Assistant] Final results from several trials can be summarized here. This is the rate per 100,000.


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[Assistant] Children who were given the vaccine, 28 out of 100,000 went on to develop polio versus the control who got a placebo, 71 out of 100,000 developed polio. So you can see almost three times more children


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[Assistant] By 1994, polio had been completely eradicated from all of the Americas. So this is a great success story of how we can see experimental research put to use to solve a problem and eradicate what I call bad stuff.


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[Assistant] So to summarize this segment, the major benefit of experimental research


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[Assistant] Again, experiments allow us to make causal claims about why stuff happens. So, that allows us to predict stuff.


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[Assistant] Which means we can prevent bad stuff and we can promote good stuff. We use it in very basic terms.


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[Assistant] Right? Uhm, so this is an example where society saw we have a lot of bad stuff happening. We have this disease we have to eradicate. And if we can do an experiment to predict, uh, why it's happening and how we can stop it, then indeed we can stop it. And that's what the SOC vaccine trials, uh, were able to do.


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[Assistant] These strong causal claims require a lot of things. So they require what I call true independent variables.


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[Assistant] Uh, a group of, of subjects or in this case a group of children come to a facility and they're randomly assigned to receive one, uh, level of a variable or another. So they were randomly assigned to receive


[User] Please transcribe this text

[Assistant] The vaccine or the placebo, that's a true independent variable. I'll talk in the next lecture about, uh, sort of quasi-independent variables, uh, that aren't as strong as this.


[User] Please transcribe this text

[Assistant] Uhm, it requires a random and representative sample. So initially, the SOC vaccine trials only were done on children from Virginia.


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[Assistant] And maybe it only worked in that subset of, of children. Maybe it wouldn't generalize to the entire United States. They had to see, so they had to broaden their sample, get a more random representative sample of the entire United States to see if it really worked. Fortunately it did.


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[Assistant] Uhm, and finally, experimental research and these strong causal claims depend on


[User] Please transcribe this text

[Assistant] And that's almost impossible to rule out entirely. There might always be confounds. So there could have been confounds in terms of the age of the children or again the geographical location or their socioeconomic status. There could have been lots of confounds in this.


[User] Please transcribe this text

[Assistant] Uh, example, fortunately they ruled out most of those, uh, in the research as they were doing, uh, the, the vaccine trials. Uhm, but it's something to keep in mind as we look at experimental research.


[User] Please transcribe this text

[Assistant] Uhm, these strong causal claims require that there are no confounds lurking behind, uh, lurking in the shadows. Uhm, and we'll address that issue a little bit more directly in the next segment as we look at working memory training.


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[Assistant] That demon had taken over my house and river. Thank you for getting rid of him.


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[Assistant] You don't need to make a boat. I can take the travelers across the river. That's wonderful.


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[Assistant] The Tang Empire and... I know who you are.


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[Assistant] You're traveling to the western paradise to get scriptures from Buddha. I knew you would cross my river eventually. Climb onto my shell!


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[Assistant] Excuse me, but can you do me a favor? Of course.


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[Assistant] I have been a good Buddhist for over a thousand years.


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[Assistant] Can you ask Bootle when I will be reborn as a human?


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[Assistant] Father, our prayers have been answered by a stranger. A stranger? Uh-huh, two pilgrims who swear they can help us.


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[Assistant] Under no circumstances will I pay you a cent for the contract you have clearly violated.


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[Assistant] So with the traps that I've just outlined for what can happen if you, uh, you know, put your chips on the achievement, uhm,


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[Assistant] Number on the Wheel of Fortune. Uhm, I want to conclude by giving you some positive advice because there certainly are things well worth achieving. I think you just have to reframe and reset what it is you're shooting for and you'll find it much more sustainable when you're going for a worthy kind of achievement.


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[Assistant] So for example, instead of untold wealth,


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[Assistant] Seek financial security. Look for the amount of wealth that will give security to your family, to yourself, so that you can lead a fulfilling life. Uh, instead of fame, seek respect.


[User] Please transcribe this text

[Assistant] Seek uhm, people who know you well enough and who are real judges of whether you're excellent at what you do and can help you get better at it. Basically, fame is being known by a lot of people who have very little knowledge of what you are and who you are.


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[Assistant] Respect comes from an inner circle that have reasons to judge you in a positive way and


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[Assistant] So, keeping your goal on the search for respect is more likely to lead you in a better direction.


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[Assistant] Instead of status, worry about finding roles in the lives of others that gives you a chance to contribute to them.


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[Assistant] It could be the role of father. It could be the role of mother. It could be the role of mentor. It could be the role of leader.


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[Assistant] But instead of getting fixated on a title, you know, Chief Financial Officer, ask yourself what are the actual roles that you're playing in the lives of the people around you in the organization and tend to maximize on those roles. That will focus you on the contributions you're making.


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[Assistant] And instead of, and these days this is a really tempting one, instead of a vast social network that follows you on Twitter or is, you know, the number of people that you have on your Facebook page, very relatively shallow relationships based on very little, very episodic information,


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[Assistant] Uhm, seek more, uh, true friends. Friends, uh, who you, uh, can see, touch, smell, feel and


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[Assistant] Who can affect you and you can affect them and you're part of their lives.


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[Assistant] Uh, at the end of the day, maybe some of those will turn into the friends of virtue we talked about that Aristotle thought so much of. So, not wealth, financial security.


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[Assistant] Not fame and recognition, but rather respect. Uh, not status.


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[Assistant] But the worthy roles you play in the lives of others, and not shallow and numerous relationships, but deeper and fewer true friends.


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[Assistant] So, if we're gonna, if we're gonna leave achievement behind for a moment, uh, and anchor on the other side of success, the inner life,


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[Assistant] Uh, let's just see whether when you really begin to think about happiness,


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[Assistant] Uh, that's really got all the answers in it, or if that turns out to be a little complicated as well.


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[Assistant] And I'm trying to complicate this, not to make your life difficult. I'm trying to complicate it because if you have the wrong understanding of what happiness is, or what achievement means, then you'll chase the wrong goal and you'll end up being unsatisfied by the kind of success that you've achieved. So we're looking here for the real deal.


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[Assistant] Uh, the success that, uh, feels deeply satisfying. Uhm, okay, so what about happiness?


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[Assistant] Well, again, I've been privileged to be here at the University of Pennsylvania. I've been in, uh,


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[Assistant] Relationship and interaction with our Positive Psychology Center.


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[Assistant] They are disciplined social scientists who study happiness all over the world and have, uh, programs and projects that they are funded, uh, to measure happiness in many different ways.


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[Assistant] Uh, what they've concluded, uh, is that, uh, there's more than one kind of happiness. And that it's funny, you know, the, uh, the, uh,


[User] Please transcribe this text

[Assistant] Uh, the Inuit people who are the people, the native people of Alaska, uh, live a life that's surrounded by cold weather and, uh, they have many, many names for what we call ice.


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[Assistant] Because in their world, there are many different kinds of ice and they mean different things and they have different roles in the lives of the Inuit people. Uhm, in, in the United States, in Western Europe, in Western countries, we make a lot of this word happiness. It's in the Declaration of Independence that, uh, we're supposed to pursue happiness. Uh, but this one word can often be very confusing.


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[Assistant] And people think they're chasing one thing and they don't get it. They think there's something wrong.


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[Assistant] So I think it's important to at least think about three different kinds of happiness.


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[Assistant] Uh, and uhm, the research helps us think about this. If you want to read some more, uh, about these, some of these forms of happiness, uh, Daniel Kahneman, a Nobel Prize winning psychologist.


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[Assistant] Uh, from Princeton has a wonderful book called Thinking Fast and Slow, has a chapter or two on happiness that distills a lot of this research into a pretty, uh,


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[Assistant] Uh, efficient capsule, but we're gonna talk about it a little bit here. So, the first kind of happiness.


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[Assistant] That most people think about when they think about the word is what I call momentary happiness. Now momentary happiness is an emotion. It's a mood. It's a moment that comes and where you're having a positive feeling. A good example might be


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[Assistant] Uh, you are, uh, at the beach. It's, uh, been a pretty hot day. You go to the boardwalk. There's a soft serve ice cream, uh, vendor there. Uh, they have your favorite flavor. Uh, you get into the shade.


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[Assistant] You've ordered your soft serve ice cream. It's dipped in whatever it is that you love as a little covering chocolate or strawberry or something. And you have that first bite of that ice cream cone. And it just tastes wonderful.


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[Assistant] That's momentary happiness. Could be the same thing if you're a fan of drinking beer and it's the same kind of day and they have the favorite beer that you like and you take that first sip of whatever the drink is that you find most enjoyable and it just fills you with this feeling of happiness. So, momentary happiness is great. I think, uhm,


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[Assistant] Uh, the more of it, uh, you have, uh, the, the better your days are. But there's some problems with momentary happiness, uh, as a measure of success in life.


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[Assistant] The research says that, uh, there are genetic set points that people have that go into their emotional make-ups and some people are kind of positive people and some people are less positive and some people are kind of negative people and


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[Assistant] Your mood state swings on a given day or during a given period of time.


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[Assistant] Sort of above that genetic set point when you take that first bite of the ice cream cone.


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[Assistant] But then somewhere a little later dips below that set point as you begin feeling really hot and sweaty on the beach and you wish that the sun weren't so bright.


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[Assistant] And now you're not in such a happy mood state. Uh, maybe uh, you know, somebody comes along, a dog comes along and puts sand in your


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[Assistant] In your, uh, in your, your drink on the beach, now you're really kind of upset and angry with somebody. So your genetic set point just tells you, whoops, going down here.


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[Assistant] It is a range. And chances are you're not going to have too many moments when you're above the highest point of that range. Uh, you're not going to have too many moments when you're below the low point of that range. And my range is different than yours.


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[Assistant] My best day may be just in the average domain for your day and you get to be a lot happier than me because your genetic set point is different.


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[Assistant] So if, if happiness of this kind, this sort of aggregate number of mood states, is set by our genes, it seems sort of unfair to say that you're more successful than me because you have more positive mood states than I do because you're endowed with a different set of genes than I was.


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[Assistant] So, so there's the problem of a kind of...


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[Assistant] There are a lot of pretty intelligent people who have thought about the question of happiness and kind of come to the conclusion that these positive momentary mood states


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[Assistant] Then something that humans ought to endorse, because if you kind of go by a farm and look out at a herd of cattle, they're happily grazing out there, they're


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[Assistant] You know, in the sun, they're well watered, they're well fed, everything's good. Well, you have a pretty happy cow. But would you really say that that's a successful life?


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[Assistant] Uh, that would be something you would be satisfied with at the end of, uh, your days? I'm not sure.


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[Assistant] So, positive mood states, good. But uhm, this is the only form of happiness that you might call success. Maybe not.


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[Assistant] So, second kind of happiness, and this again is research based. By the way, the way they measure momentary happiness is with, uh, little beepers they put on people. Uh, they'll, uh, put a little beeper, like a cell phone or a clip, and you walk around during the day and,


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[Assistant] The experimenters beep you and at the moment they beep you, you have to take off the little device and, uh, you know, put where, how you feel, you know, excellent, bad, terrible. And you just put the moment when you feel it. And at the end of a week, they measure how many positive moments you have.


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[Assistant] Uh, and that's how they decide whether you're happier or not.


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[Assistant] So, so that's the momentary happiness. Now we go to the second one.


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[Assistant] And I call this overall happiness. And overall happiness is not an emotion.


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[Assistant] It's a thought, it's an evaluation, it's a critique, and you may reach a point


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[Assistant] Either, uh, progression in part of your life, uh,


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[Assistant] Or an anticipation of another part of your life.


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[Assistant] And look back, if you're looking backwards at say your college experience, and someone asks you, were you happy in college?


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[Assistant] And you don't know whether you can really add up all the positive mood states, but for some reason you feel like, yeaah, you know, important things happen. I met my spouse in college, I, you know, I graduated, uh, and so yeaah, overall I was happy in college.


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[Assistant] But actually, if you go back and you had that beeper all the way through college, uh, you would find, as often as not, that you were much more miserable in college than you remember, uh, and you only really remember the peak, the day you met your spouse-to-be, and the ending, which was the


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[Assistant] Uhm, the graduation ceremony when your family came and everybody applauded you and it was a great moment. You got a prize.


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[Assistant] Uh, and all the rest of it, the whole miserable sophomore year, all the hangovers and other disasters that occurred, uh, that


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[Assistant] Uh, a moment in time when you evaluate, but not always accurate. In fact, uh, the research seems to show that


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[Assistant] Uh, if you give someone a cookie or some other thing that they like, and they put them in a positive, momentary mood state,


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[Assistant] Their memories of the past might actually be colored by the fact that they're in a good mood and they'll rate their overall happiness higher.


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[Assistant] And if you put them in a negative mood state, their memories will cast back and select out more negative things and their overall happiness might be a little bit lower. So, it's not that hard to manipulate.


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[Assistant] So we have the problem of selected memory, we have the problem of easy manipulation.


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[Assistant] And uhm, you know, there's always a fundamental problem with any evaluation and that is it's always relative to some baseline expectation. So if you thought college was going to be miserable and it turned out to be okay, that's going to give you a feeling that you're happy.


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[Assistant] If you thought college was gonna be wonderful and it turned out to be okay, then you're gonna evaluate it more negatively. So your overall experience is gonna be cast in a frame


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[Assistant] Manipulatable by what your expectations were and so again, uhm, it's just a cognitive phenomenon, almost a paradox that we can think about the past and even the future in our anticipation.


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[Assistant] Is this really the way you want to measure success? Is it just going to be all about some moment in time when you say my overall happiness was X at time past or I anticipated to be Y in time future? And it seems like that's a pretty flimsy read.


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[Assistant] It has a lot more resonance, it has a lot more depth, a lot more meaning associated with it than eating an ice cream cone does on the hot summer day.


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[Assistant] I call this deep happiness, but it comes with a lot of different names that people have tried to give to it over the thousands of years that humans have tried to grapple with the meaning of success.


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[Assistant] Uh, certainly one word in English that might have captured this is joy.


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[Assistant] Uh, joy is sort of the exultation that you feel, say, uh, on the birth of a, one of your first child or, uh, one of your children.


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[Assistant] It might be a kind of feeling of connection when you are in an amazing place in nature and you simply feel the awe and the inspiration of the vista that's in front of you.


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[Assistant] Uh, it could be the feelings that you have when you fall in love and you just have this mysterious...


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[Assistant] Uh, new, amazing feeling of connection and depth to the relationship that suddenly blossomed.


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[Assistant] So joy is a good word that might capture it. The Greeks had a word for this called eudaimonia. It translates from the Greek as good spirit.


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[Assistant] And Aristotle wrote about it, uh, in his book on ethics and


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[Assistant] It's an interesting Greek concept because it's not a state for them. It's not an emotion for the Greeks. It was a kind of.


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[Assistant] Uh, of itself, you're doing it because you love doing it, you're not doing it for any other purpose. Uh, so eudaimonia is a Greek word that captures this sort of thriving sense that I think deep happiness, uh, may be about.


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[Assistant] There's a Hebrew word, uh, one of my students pointed this out to me one semester, uh, simqah is a,


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[Assistant] Uh, Hebrew word that normally is more like momentary happiness. It's a happiness that...


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[Assistant] Uh, might be the case if you're, uh, celebrating a wedding or a birth. But, uhm, but there was a,


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[Assistant] Uh, a rabbi, uh, that defined it, uh, especially in a way that I resonated with, uh, and


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[Assistant] He translated as the feeling that comes when you're doing what you should be doing.


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[Assistant] The feeling that comes when you're doing what you should be doing.


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[Assistant] So this might be the kind of feeling, uh, when you're sitting with someone and, uhm, you're comforting them.


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[Assistant] Or you're being with them, uh, when they're, uh, in the hospital with an illness and your presence has given them a moment of relief from the suffering that they're feeling.


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[Assistant] Uh, it could be, I had a student once who came to me and he said he was going to have to miss the next class because his grandfather had died.


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[Assistant] So, you know, this is always a good excuse in my world for not being able to come to class at an event like that.


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[Assistant] We were talking about how he felt about it, and he felt nervous, he felt...


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[Assistant] Uh, the sadness that his grandfather, who he was close to, had passed.


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[Assistant] But I predicted for him, I said, when you're at this funeral and you're speaking about your grandfather, I think you're gonna have a moment of something like deep happiness because you're gonna be doing


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[Assistant] What you should be doing. There won't be any other place on the planet.


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[Assistant] Definition of Simcha might be another definition of, uh, of deep happiness.


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[Assistant] And then, uh, the positive psychology community, uh, uh, scholar named Chiksinnihai has come up with a concept called flow. He's written a book called Flow. And flow might be another dimension of this. Flow is the feeling that comes when you're engaged with an activity, you're absorbed in it.


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[Assistant] Uh, it may be the feeling that sometimes comes to runners when they kind of go into the zone.


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[Assistant] Uh, happens to me sometimes when I'm writing and I just lose track of time and completely absorbed and


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[Assistant] The words and, and where they're coming from, where they're going, what I'm saying.


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[Assistant] And, uh, time just stops. You wake up, uh, an hour later and you're not even a, wow, where was I? What was going on? And that sense of engagement is also, uh, very important.


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[Assistant] Maybe component of this thought of what deep happiness means, this feeling.


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[Assistant] So, so this seems like a worthy thing to be identifying with success. Certainly, the more moments of thriving joy, uh, being where you should be, uh, flow, uh, that your life has, uh, you'd count that as a, uh, uh, part of your definition of success. But here's the problem. Uh, are these moments really moments you can engineer?


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[Assistant] Are they moments that you can create or seek and kind of wake up in the morning and say, I think I'm going to go find a little deep happiness today.


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[Assistant] I don't think so. I think these are much more moments of grace. They're more like gifts to us.


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[Assistant] Often, the most important thing is to be slowed down enough to experience them.


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[Assistant] Uh, Nathaniel Hawthorne, the American writer, had a wonderful image for this. He said,


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[Assistant] If you try to catch a butterfly with your hands, it's very difficult.


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[Assistant] But if you'll just sit still, sometimes a butterfly will come and light on your shoulder. And I think that's the case with deep happiness. Sometimes if you just sit still and bring awareness to a moment,


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[Assistant] That's when deep happiness visits you, and it's not really something that you can.


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[Assistant] So, happiness turns out to be a little more complicated than just one thing.


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[Assistant] So let's conclude our discussion of happiness as a success factor by just looking at a few of the things we think we know about what happiness, uh, is and what its importance is based on the research. And as I said, there's extensive research on this.


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[Assistant] So, again, Daniel Kahneman's book, uh, Thinking Fast and Slow, he makes this point really powerfully, and it really


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[Assistant] Kind of simple. What matters most to your happiness is spending time with people you love who love you.


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[Assistant] Uh, being surrounded by the community of people who care the most about you, uh, who you care the most about is just more likely to end up giving you more of all three of the kinds of happiness we talked about.


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[Assistant] It's not always happy. It's not that being around people you love who love you.


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[Assistant] Is a bed of roses all the time, but the odds are very high.


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[Assistant] That, uh, those moments will contain the moments of your


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[Assistant] Uh, most happiness. So that's item number one. Uh, spend time with the people you love who love you. Never underestimate the importance of that to your overall success. Uh, secondly, uh, it turns out happiness much more often comes from experiences, especially experiences you share with others.


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[Assistant] And, you know, oddly, the experience of shopping for something that you're going to purchase with your loved one or your spouse and bring back and bring into the home as one of the decorations


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[Assistant] May actually be the shopping experience itself, the choice, the decision, the variety of things you looked at, what was happening around you when you chose it.


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[Assistant] Those actually sometimes become more important than the object itself. So when it's sitting in your home, it's not the fact that the object is there, it's the story behind the object that gives it meaning and that brings happiness. So think experiences more than material goods. Third, it often comes from your willingness


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[Assistant] To be unhappy, uh, in the pursuit of certain goals that are long-term goals or difficult goals. You think of an Olympic athlete.


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[Assistant] Uh, or someone trying to get through a four-year college experience or a graduate program. Uh, these are difficult long-term commitments to a set of activities, many of which are going to task you.


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[Assistant] I exert efforts that aren't always the happiest moments of your life, but the reward for having been willing to endure some level of unhappiness


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[Assistant] Turns out to be a much deeper and more satisfying happiness when you finally come to achieve those goals.


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[Assistant] When you come to the moments where there's a fulfillment of whatever those efforts were directed toward. So if you only seek happiness and unhappiness is your enemy, the chances are pretty high that you're not going to risk, uh, the


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[Assistant] Effort that it's gonna take because there's some unhappiness built in there and you're trying to avoid that.


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[Assistant] And then that just simply means that you're not going to have the depth of satisfaction and fulfillment that could be yours.


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[Assistant] If you didn't think so simplistically about happiness good, unhappiness bad, that's much more interesting than that, and I think your judgments and decisions will be wiser.


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[Assistant] If you consider the role of happiness, unhappiness, effort, uncertainty in your overall life satisfaction.


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[Assistant] Uh, the fourth and final thing that we know about uh, happiness is that uh, you can reframe your emotions


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[Assistant] By the way you set your own expectations and bring more happiness into your life by having expectations more realistically set.


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[Assistant] Uh, than simply being, uh, over the top with expectations, uh, that a certain party or a certain event in the future is going to bring the answer to all your life's, uh, woes and, uh, and travails.


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[Assistant] Uh, and, and, and moderate a little, and when your expectations are set in a more accurate way, the actual experience that comes, uh, either meets or exceeds them more often, and then that becomes a moment where your emotions, uh,


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[Assistant] Fall in line with a positive set of emotions about what's happening instead of disappointment, uh, you know, didn't meet my expectations, not as good as I expected, and then you're filled with negative emotions.


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[Assistant] So in this part of the course, we looked at the two sides of happiness, the inner life and the outer life, uh, the inner life of happiness and the outer life of achievement, and looked at some of the complications that, uhm, become apparent when you start looking a little more carefully at each of these.


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[Assistant] As a component of your theory of success, it turns out that when it comes to achievement, it's easy to fall into one of several traps and end up achieving things that don't bring any fulfillment or satisfaction, chasing other people's goals.


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[Assistant] More than one form of happiness in that the single word


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[Assistant] That carries some very different meanings and it's very important for you to articulate for yourself.


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[Assistant] What it is you actually are thinking about when you say, I want a life that's going to bring more happiness. Could be momentary happiness, this concept of overall happiness, and then this mysterious but very important feeling of deep happiness. So with all this new understanding, in the next part, in the final part of the course, we're going to look at how you can apply this


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[Assistant] Knowledge that we've gained and your own experiences and memories that you've accessed to begin thinking about


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[Assistant] A success for yourself in a very concrete, very actionable way, uh, thinking about how you can apply these tools, uh, what are some steps you can take to make them concrete so that you can begin advancing toward the values that you've identified in our work together.


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[Assistant] Giraffes are tall. Giraffes have spots.


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[Assistant] Elephants are strong. Elephants have long trunks.


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[Assistant] Zebras have stripes. Zebras live in grassy places.


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[Assistant] Two return tickets to London, please. What time will the next train leave? At 19 minutes past 8. Which platform? Platform 2, over the bridge. What time will the next train leave? At 8.19. We've got plenty of time. It's only 3 minutes to 8.


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[Assistant] Let's go and have a drink. There's a bar. Next door to the station.


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[Assistant] We want to catch the 8.19 to London. You've just missed it. What? It's only 8.15. I'm sorry, sir. That clock's ten minutes slow. When's the next train? In five hours time.


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[Assistant] Lesson 103 The French Test How was the exam, Richard? Not too bad. I think I passed in English and Mathematics. The questions were very easy. How about you, Gary?


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[Assistant] The English and maths papers weren't easy enough for me. I hope I haven't failed. I think I failed the French paper. I could answer sixteen of the questions. They were very easy, but I couldn't answer the rest. They were too difficult for me. French tests are awful, aren't they? I hate them. I'm sure I've got a low mark. Oh, cheer up!


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[Assistant] The guy next to me wrote his name at the top of the paper. Yes?


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[Assistant] Then he sat there and looked at it for three hours. He didn't write a word.


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[Assistant] If it pleased me to do so, I could leave you there inside the folds of my great robe for all eternity! But I won't!


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[Assistant] Take these two upstarts and throw them into the dungeon! They can keep that blubbering monk company!


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[Assistant] I know why you're here, Wukong. I also know who the demon is. You must go to Lausa for help.


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[Assistant] He's my green buffalo. I didn't realize he'd escaped.


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[Assistant] Okay, okay. I can capture the buffalo. Don't worry.


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[Assistant] Hey kids, now we're going to write the letter I as an iguana. E-E-Iguana.


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[Assistant] You'll need a practice sheet and a pencil.


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[Assistant] Go to our website to download a free practice sheet.


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[Assistant] First, we'll write the capital I. That's the big I. Start at the top and make a line down. Go back to the top and make a line across.


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[Assistant] Then, go to the bottom and make another line across.


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[Assistant] Start in the middle and make a line down.


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[Assistant] Then, go above the middle and make a small dot.


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[Assistant] Let's try it again. Down. Make a small dot above the middle. Now you try.


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[Assistant] Next time, we'll write the letter J. Don't forget to subscribe to our YouTube channel.


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[Assistant] Thanks to his cleverness, Pigsy escaped from the yellow giant.


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[Assistant] No one could imagine what news the young hunter had to bring. As far as Chupitaka and the King knew, the princess had always been the giant's prisoner.


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[Assistant] Meanwhile, Pigsy was still enjoying a long restorative nap.


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[Assistant] And Chippy Taka soon discovered he had an unexpected ally.


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[Assistant] And so, Chippitaka's horse, who long ago was turned into the White Dragon Horse by the goddess Quan Yin, flew off on a mission to free his master from the giant spell.


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[Assistant] But we must go back, said Wukong. That woman kidnapped the Tang Monk!


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[Assistant] I'm traveling to the western paradise with the Tang Monk. But he was captured by a demon. Guan Yin said you can help me.


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[Assistant] As long as I'm protecting you, no demon will ever harm you.


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[Assistant] Tripitaka and his companions were continuing their quest for the Books of Wisdom.


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[Assistant] They had entered the desolate and inhospitable Stone Valley in the territory of the evil Jade Witch, and their movements were being watched closely.


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[Assistant] As the evil witch lured Monkey's gullible companions away.


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[Assistant] Hi, I'm Murray Schweitzer, professor at the Wharton School. I teach negotiations and advanced negotiations, and my research interests are in trust, deception, and emotions.


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[Assistant] I'm a co-author of Friend and Foe, and I'm interested in the topics we'll be talking about in this course. The tension between cooperation and competition, how we navigate our social relationships with topics like trust, deception, and apologies, putting those pieces back together.


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[Assistant] And I mentioned communication more generally as well as motivation. We'll talk about goal setting when it goes in the right direction and sometimes when it gets us into trouble. Thanks for joining me and let's get started.


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[Assistant] But I don't understand why it's so hot. It's not summer.


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[Assistant] Which one can make us immortal? At first I thought it was one of these two, but now I believe it's this one.


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[Assistant] My reign is powerless against this fire. Sorry monkey, you're on your own.


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[Assistant] Crazy Cakes Written by Nigel Pepperhouse Focus Question


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[Assistant] What kinds of crazy cakes can bakers make?


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[Assistant] Some artists use paint or marble to make their art.


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[Assistant] Other artists use cake and frosting.


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[Assistant] Some of their cakes just look pretty.


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[Assistant] Other cakes look like something else.


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[Assistant] This cake looks like a pretty peacock.


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[Assistant] Looks like a giant rooster.


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[Assistant] This cake looks like a fancy crown.


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[Assistant] This cake looks like a fish and hook.


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[Assistant] This cake looks like a busy beehive.


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[Assistant] This cake looks like a treasure chest.


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[Assistant] This cake looks like a magic unicorn.


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[Assistant] These cakes look almost too good to eat. They taste


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[Assistant] How would you decorate a cake? Why? Draw a picture of your cake and write about it. Math. Count the cakes in the book. How many look like cakes? How many look like something else?


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[Assistant] Not yet. I must water the garden first.


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[Assistant] I'm afraid I must. Look at it. It's terribly dry. What a nuisance. Last summer it was very dry too. Don't you remember? I had to water it every day.


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[Assistant] That was a pleasant surprise. It means I can have tea instead.


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[Assistant] Hey kids, now we're going to write the letter R, as in robot.


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[Assistant] First, we'll write the capital R. That's the big R. Start at the top and make a line down.


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[Assistant] Then go back to the top and go around to the middle.


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[Assistant] Next, make a line down to the right.


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[Assistant] Now we're going to write the lowercase r. That's the small r.


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[Assistant] Start in the middle and make a line down.


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[Assistant] Then, go up and around to the right.


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[Assistant] Next time, we'll write the letter S. Don't forget to subscribe to our YouTube channel. You can also follow us on Facebook and Twitter. See you next time!


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[Assistant] They're the deadliest thing on earth. They suck your blood and spread disease.


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[Assistant] It may sound unusual that a little bug could be this deadly, but it's true. Mosquitoes spread diseases like malaria, yellow fever, and the dengue virus.


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[Assistant] They do it by biting someone who has a particular disease, and then biting you and injecting their saliva into your body.


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[Assistant] The saliva then enters your bloodstream, causing you to contract whatever virus or bacteria that the mosquito picked up.


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[Assistant] And it's not just humans they affect either. Animals and farm livestock all around the world suffer greatly from mosquitoes. Just a single bite can be fatal. So, how do we get rid of these bugs?


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[Assistant] Well first, we have to figure out exactly how many mosquitoes there are in the world. That's incredibly difficult to find out. It's estimated that there's seven trillion of them, in Alaska alone. Yeah, that's right, Alaska.


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[Assistant] Due to global warming and ocean temperatures rising, mosquitoes can live in places like this. And around the world, there are trillions of other mosquitoes. There's a number of ways we could get rid of them. Bats eat mosquitoes without any risks.


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[Assistant] They can eat 600 mosquitoes in an hour, so if we had enough bats, in theory, they could effectively wipe out mosquitoes all around the world.


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[Assistant] Another thing we need to understand is how mosquitoes reproduce.


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[Assistant] You may think they're sucking our blood because we taste good and they're hungry.


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[Assistant] But instead of feeding themselves, they're actually doing it for their larvae.


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[Assistant] So, if we somehow got rid of all our blood and- Wait, I know where this is going. Yeah, that's not gonna work either.


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[Assistant] Instead of these options, we may need to release genetically modified male mosquitoes.


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[Assistant] Ones that don't bite and can only produce sterile offspring.


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[Assistant] It could take several decades, but with enough of our genetically modified mosquitoes producing sterile offspring, mosquitoes would eventually be eradicated. So now that they're gone, what would happen next?


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[Assistant] Well, millions of people wouldn't be getting sick and dying each year.


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[Assistant] You could enjoy being outside in the summer without any fear of being bitten, and animals would be much safer as well.


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[Assistant] That's right. Not many bad things would happen if we got rid of all the mosquitoes. What about the circle of life and all that? Don't mosquitoes contribute to the food chain?


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[Assistant] Sure, some birds, bats and frogs eat quite a lot of mosquitoes.


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[Assistant] But they're still not a hugely significant part of other creatures' diets.


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[Assistant] And these animals will survive without mosquitoes. And although mosquitoes do pollinate plants, it's not significant enough to justify keeping the species alive.


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[Assistant] But still, is getting rid of mosquitoes the right thing to do?


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[Assistant] After all, we would be getting rid of an entire species.


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[Assistant] We should keep in mind that there are thousands of different species of mosquitoes, but only six of them bite us and spread disease.


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[Assistant] Not only that, but there are some theories that mosquitoes may help to protect the Amazon rainforest.


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[Assistant] That's because they're so deadly and annoying to the people trying to cut down the rainforest that at times the bugs actually prevent them from doing so.


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[Assistant] And, let's be real for a second. The human population is growing incredibly fast.


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[Assistant] Maybe mosquitoes are helping us to keep our population in check.


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[Assistant] Wait, what am I saying? This would most definitely be a good thing. There would be less disease, fewer people dying, and very few negative consequences.


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[Assistant] But what if instead of mosquitoes getting wiped out, half of Earth's population did in an instant? Well, that sounds like a story for another WHAT IF.


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[Assistant] Cowboys ride big bulls. What happens next at the rodeo?


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[Assistant] Cowgirls ride fast horses. What happens next at the rodeo?


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[Assistant] Cowgirls race around barrels. What happens next at the rodeo?


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[Assistant] Cowboys and cowgirls win belt buckles.


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[Assistant] Journey to the West. Chapter 75. Battle of the Wukongs.


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[Assistant] Please stop! Cried the Judge of the Dead. You'll tear this whole palace down! The monkeys stopped.


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[Assistant] Said the other Wukong. He looked worried.


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[Assistant] No, shouted the other Wukong. He swung his iron bar furiously.


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[Assistant] The two Wukongs left the land of darkness.


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[Assistant] A guard rushed out of the thunderclap monastery.


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[Assistant] As the first Wukong started to enter the monastery, the other one backed away.


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[Assistant] The other Wukong hesitated a moment and then bowed also. Greetings Buddha, said the first Wukong.


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[Assistant] When Buddha lifted the bowl, a tiny monkey spirit lay on the table. Wukong leaned closer to see.


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[Assistant] She and the monkey found the Tang monk and the others along the road to the west.


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[Assistant] The Bodhisattva told the Tang monk that it wasn't Wukong who'd attacked him.


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[Assistant] Oh sure, people who tell you where to go and how to dress.


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[Assistant] Oh sure, people who tell you where to go and how to dress.


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[Assistant] Education is the ability to listen to almost anything.


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[Assistant] Without losing your temper or your self-confidence.


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[Assistant] A mother takes 20 years to make a man out of her boy and another woman makes a fool of him in 20 minutes.


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[Assistant] Never be bullied into silence. Never allow yourself to be made a victim.


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[Assistant] There are two kinds of teachers, the kind that fill you with so much quail shot that you can't move and the kind that gives you a little prod behind and you jump to the skies.


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[Assistant] Poetry is when an emotion has found its thought, and the thought has found words.


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[Assistant] We dance round in a ring and suppose, but the secret sits in the middle and nose.


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[Assistant] It starts working the moment you get up in the morning and does not stop until you get into the office.


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[Assistant] A person will sometimes devote all of his life to the development of one part of his body, the wishbone.


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[Assistant] Anything more than the truth would be too much.


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[Assistant] You've got to love what's lovable and hate what's hateable. It takes brains to see the difference.


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[Assistant] Life must be kept up at a great rate in order to absorb any considerable amount of learning.


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[Assistant] Don't tell anyone that you're lonely, because once again, you will convince yourself of this.


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[Assistant] The chief reason for going to school is to get the impression fixed for life, that there is a book side for everything.


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[Assistant] I can't jump. I am very hungry.


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[Assistant] Oh, I know what to do. I can slide and dive.


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[Assistant] I am eating. It's time to work. Stop eating.


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[Assistant] Yes sir. Uh, uuuh.


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[Assistant] Yes sir! Follow me!


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[Assistant] I am working.


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[Assistant] Fill. This. Whole. Space.


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[Assistant] This is no fun. This is taking forever.


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[Assistant] I am so tired. Well done! This is wonderful!


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[Assistant] You did a great job.


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[Assistant] Oh, it's under the rocks. I can't reach it. Huh?


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[Assistant] And I think what I'd add to the original point of like, you know, going to, you know, events or talks is that it turns out what you get from those things aren't necessarily, uhm,


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[Assistant] I remember being at a party, I think it was, uhm, like maybe sophomore year in college when Facebook had just moved out and I ran into Adam D'Angelo who was the CTO at that time at this little party and we kind of kept in touch since


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[Assistant] And Adam spent like 30 minutes on the phone with us, just walking us through the basic things we needed to do to get back up. And like, those little events are the things that matter. So, you know, as much as you're paying attention to the stuff on the slides, make sure to spend some time after the talk getting to know the people around you.


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[Assistant] Now we need to weave more grass into a pretty pattern.


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[Assistant] How many people have, uh, heard of stealth startup, started a stealth startup, feel like they would, you know, go into entrepreneurship and keep their ideas to themselves? I'm gonna assume everyone's going to raise their hand, because like, we've all heard of the stealth startup, how cool it sounds, right? The problem with stealth startups is that you don't get the feedback you need quickly enough.


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[Assistant] In order to test whether you're working on the right thing or not, uhm, you need to put it in front of people. And I think there are certain verticals, like, I'm really talking about consumer internet here specifically. I think there are certain verticals outside of consumer internet where self prob- might make sense if you're doing pharmaceutical or something, right? But for us, like,


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[Assistant] Getting it in front of users was like the most eye-opening experience. I remember putting bourbon in front of people we didn't know and they were just like, what is this thing? Like, what are you doing? Right? We would be in a busy bar and trying to explain to them on our mobile phones and they just like wouldn't get it. And that happened enough.


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[Assistant] And I think that's something to keep in mind as you're going about, you know, starting a startup.


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[Assistant] Uh, building the minimum viable product is super useful.


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[Assistant] Don't build past what you need to build to answer the questions. I think too many people, like I think Eric Reese talks about this all the time and he says he was at his job and he was building this 3D chat client.


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[Assistant] And he said to himself, like, couldn't I have just done one of those platforms to prove that no one used it? So ask yourself, like, how much work do you need to do to actually prove whether or not this thing's gonna sink or swim, right? The Sinker Swim School of Engineering. We should, like, write this book, right?


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[Assistant] It's really true, though. Everything we do at Instagram, we start by saying, what's V1 of this feature? Like, what's V1 of this thing that we're gonna put out in two days and test whether or not it's gonna work? I think that's super, you know, important to remember.


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[Assistant] You need to fail in order to find the right solution. Often the first thing you start off with is, is not the thing you end up with. And you should like assume from the start your first idea is not going to be your last.


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[Assistant] And your job is to fail your way to success. And I think that's what we did, uhm, in Bourbon pretty well. We failed all the time. And finally, we woke up one morning and we were like, we're failing too much, we need to move a little bit to the right.


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[Assistant] And even in Instagram we followed a bunch with different features and things, right? But the idea is you're constantly refining this original idea, right? It's not this wake up one morning and have the brilliant idea and go implement it. You're constantly iterating.


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[Assistant] In the last video, you saw the basic beam search algorithm. In this video, you learned some little changes that will make it work even better.


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[Assistant] Length normalization is a small change to the beam search algorithm that can help you get much better results. Here's what it is.


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[Assistant] We talked about beam search as maximizing this probability.


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[Assistant] But maybe this notation is a bit more scary and more intimidating than it needs to be, but this is that probability, product of probabilities that you've seen previously.


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[Assistant] If you're implementing these, these probabilities are all numbers less than


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[Assistant] In fact, often there are much less than one. And multiplying a lot of numbers less than one will result in a tiny, tiny, tiny number which can result in numerical underflow, meaning that it's too small for the floating part representation in your computer to store accurately.


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[Assistant] And if you insert a log there, then the log of a product becomes the sum of a log and


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[Assistant] Maximizing this sum of log probabilities should give you the same result in terms of selecting the most likely sentence y. So by taking logs, you end up with a more numerically stable algorithm that is less prone to, uhm,


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[Assistant] Rounding errors, numerical rounding errors, or to really numerical underflow. And because the logarithmic function, here's the log function.


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[Assistant] is a strictly monotonically increasing function, we know that maximizing log p of y given x should give you the same result as maximizing p of y given x.


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[Assistant] As in the same value of y that maximizes this should also maximize that.


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[Assistant] So in most implementations, you keep track of the


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[Assistant] Sum of logs of the probabilities rather than the product of probabilities.


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[Assistant] Now, there's one other change to this objective function that makes the machine translation algorithm work even better.


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[Assistant] Which is that, uhm, if you refer to this original objective up here,


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[Assistant] If you have a very long sentence, the probability of that sentence is going to be low because you're multiplying as many terms here. Lots of numbers are less than one.


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[Assistant] So estimate the probability of that sentence. And so if you multiply a lot of numbers or less than one together, you just tend to end up with a smaller probability.


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[Assistant] And so this objective function has an undesirable effect that it maybe unnaturally tends to prefer very short translations, tends to prefer very short outputs.


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[Assistant] Because the probability of a short sentence is determined just by multiplying, fewer of these numbers are less than one.


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[Assistant] And so the product will just be not quite as small.


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[Assistant] And by the way, the same thing is true for this. The log of a probability is always, uhm,


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[Assistant] Less than or equal to 1, you're actually in this range of a log, so the more terms you add together, the more negative this thing becomes.


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[Assistant] So there's one other change to the algorithm that makes it work better, which is instead of using this as the objective you're trying to maximize,


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[Assistant] One thing you could do is normalize this by the number of words in your translation. And so this, uh,


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[Assistant] Takes the average of the log of the probability of each word. Now,


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[Assistant] And this, and, and this significantly reduces the penalty for outputting longer translations. And in practice, uhm, as a heuristic, instead of dividing by TY, by the number of words in the output sentence,


[User] Please transcribe this text

[Assistant] Sometimes you use the softer approach, where you have ty to the power of alpha, where maybe alpha is equal to 0.7. Uhm, so if alpha was equal to 1, then yeaah, completely normalizing by length. If alpha was equal to 0, then, well,


[User] Please transcribe this text

[Assistant] Ty to the zero would be one, then you're just not normalizing at all. And this is somewhere in between full normalization and no normalization. And, uh, alpha is another


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[Assistant] Parameter or hyperparameter of the algorithm that you can tune to try to get the best results.


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[Assistant] And uhm, I have to admit, using alpha this way, this is a heuristic, or this is a hack.


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[Assistant] There isn't a great theoretical justification for it, but people have found that it works, uh, well.


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[Assistant] People have found the worst one in practice, so many groups won't do this. And you can try out different values of alpha.


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[Assistant] Uhm, until, and then see which one gives you the best result.


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[Assistant] So just to wrap up how you run BeamSearch. As you run BeamSearch, you'll see a lot of sentences with length equal one, uhm, long sentences with length equal two, long sentences with length equals three,


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[Assistant] And so on, and maybe you run beam search for 30 steps you'll consider.


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[Assistant] And so you can take your top sentences and just compute this objective function on the sentences that you have seen through the beam search process. And then finally,


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[Assistant] Of all these sentences that you evaluate this way, you would pick the one that achieves the highest value on this, uhm, normalized log probability objective. Sometimes it's called a normalized log likelihood objective.


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[Assistant] And then that would be the final translation you output.


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[Assistant] So that's how you implement Beam Search, and you get to play this yourself in this week's programming exercise.


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[Assistant] Finally, a few implementational details. How do you choose the beam width?


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[Assistant] So here are the pros and cons of setting B to be very large versus very small. If the beam wave is


[User] Please transcribe this text

[Assistant] Very large, then you consider a lot of possibilities, and so you tend to get a better result because you're considering a lot of different options, but it will be slower. And the memory requirements will also grow. It will also be computationally slower.


[User] Please transcribe this text

[Assistant] Uhm, whereas if you use a very small beam width, then you get a worse result because you're just keeping less possibilities in mind. As the algorithm is running, uhm,


[User] Please transcribe this text

[Assistant] But you get a result faster and the memory requirements will also be lower.


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[Assistant] So in the previous video, we used in our running example a beam width of 3, so we're keeping 3 possibilities in mind. In practice, that is on the small side. In production systems, it's not uncommon to see a beam width maybe around 10.


[User] Please transcribe this text

[Assistant] Uhm, and I think being worth of a hundred would be considered very large for a production system, depending on the application.


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[Assistant] Uhm, but for research systems where people want to squeeze out every last drop of performance in order to publish a paper the best possible result, it's not uncommon to see people use beam waves of a thousand or three thousand, but this is very application as well as domain dependent.


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[Assistant] So I would say try out a variety of values of B, uhm, and see what works for your application. But when B gets very large, there is often diminishing returns.


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[Assistant] So for many applications, I would expect to see a huge gain as you go from a beam worth of 1, which is basically greedy search, to 3, to maybe 10, but the gains as you go from 1000 to 3000 in beam worth, uh, might not be as big.


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[Assistant] And for those of you that have taken, uhm, maybe a lot of computer science courses before,


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[Assistant] If you're familiar with computer science search algorithms like


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[Assistant] The way to think about beam search is that unlike those other algorithms, which you might have learned about in a computer science algorithms course, and don't worry about it if you've not heard of these algorithms,


[User] Please transcribe this text

[Assistant] But if you've heard of breadth of search or depth of search, then, uhm, unlike those algorithms, which are exact search algorithms, beam search runs much faster, but does not guarantee to find the exact maximum for this argmax that you like to find.


[User] Please transcribe this text

[Assistant] If you haven't heard of breadth first search or depth first search, don't worry about it. It's not important for our purposes, but if you have, this is how beam search relates to those algorithms.


[User] Please transcribe this text

[Assistant] So that's it for Beam Search, which is a widely used algorithm in many production systems or in many commercial systems.


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[Assistant] Now, in the third course in this sequence of courses on deep learning, we talked a lot about error analysis. It turns out one of the most useful tools I've found is to be able to do error analysis on Beam search.


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[Assistant] So you sometimes wonder, should I increase my beam width? Is my beam width working well enough? And there's some simple things you can compute to give you guidance on whether you need to work on improving your search algorithm. Let's talk about that in the next video.


[User] Please transcribe this text

[Assistant] In this week, you hear about sequence to sequence models, which are useful for everything from machine translation to speech recognition. Let's start with the basic models, and then later this week, you hear about beam search, the attention model, and we'll wrap up the discussion of models for audio data like speech. Let's get started.


[User] Please transcribe this text

[Assistant] Let's say you want to input a French sentence like Jean vissique l'Afrique in September, and you want to translate it to the English sentence Jane is visiting Africa in September.


[User] Please transcribe this text

[Assistant] As usual, let's use x1 through x, in this case, 5 to represent the words in the input sequence, and we'll use y1 through y6 to represent the words in the output sequence.


[User] Please transcribe this text

[Assistant] So how can you train a neural network to input D?


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[Assistant] Well, here's something you could do. And the ideas I'm about to present are mainly from these two papers.


[User] Please transcribe this text

[Assistant] Due to India Saskia, Oyo Vinyals, and Kwok Le, and that one by Qiang Xun Chou, Bart Venn, Marion Boer, Kepler Gausser, Dimitri Badenio, Fethi Bogoros, Hugo Strank, and Yosh Venger.


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[Assistant] First, let's have a network which we're gonna call the encoder network be built as a RNN, and this could be a GRU or an LSTM.


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[Assistant] The RNN then outputs a vector that represents the input sentence. After that, you can build a decoder network, which I'm going to draw here, which takes as input the encoding output by the encoder network shown in black on the left, and then can be trained to output the translation one word at a time.


[User] Please transcribe this text

[Assistant] Say the end of sequence or end of sentence token, uhm, upon which the decoder stops.


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[Assistant] We could take the generator tokens and feed them to the next, uh,


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[Assistant] So that fed in states in the sequence like we were doing before when synthesizing text using the language model.


[User] Please transcribe this text

[Assistant] One of the most remarkable recent results in deep learning is that this model works. Given enough pairs of French and English sentences, if you train a model to input a French sentence and output the corresponding English translation, this will actually work decently well.


[User] Please transcribe this text

[Assistant] And this model simply uses an encoder network whose job it is to find an encoding of the input French sentence and then use a decoder network to then generate the corresponding English translation.


[User] Please transcribe this text

[Assistant] An architecture very similar to this also works for image captioning.


[User] Please transcribe this text

[Assistant] So given an image like the one shown here, maybe you want it to be captioned automatically as a cat sitting on a chair.


[User] Please transcribe this text

[Assistant] So, how do you train a neural network to input an image and output a caption like that phrase up there? Here's what you can do.


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[Assistant] From the earlier calls on confidence, you've seen how you can input an image into a convolutional network.


[User] Please transcribe this text

[Assistant] Network can be the encoder network for the image and you now have a 4000 or 4096 dimensional vector that represents the image.


[User] Please transcribe this text

[Assistant] You can then take this and feed it to an RNN whose job it is to generate decaption one word at a time.


[User] Please transcribe this text

[Assistant] So, similar to what we saw, uhm, with


[User] Please transcribe this text

[Assistant] Machine translation, translating from French to English, you can now input a


[User] Please transcribe this text

[Assistant] Feature vector, describing the input, and then have it generate.


[User] Please transcribe this text

[Assistant] Uhm, an output sequence or output set of words one word at a time.


[User] Please transcribe this text

[Assistant] And this actually works pretty well for image captioning, especially if the captioning you want to generate is not too long.


[User] Please transcribe this text

[Assistant] As far as I know, this model, this type of model was first proposed by Jun Hua Mao, Wei Shu, Yi Yang, Jiang Wang, Zhi Heng Huang, and Alan Yeo. Although turns out there were multiple groups coming up with very similar models independently and at about the same time.


[User] Please transcribe this text

[Assistant] So two other groups that had done, uh, very similar work at about the same time, and I think independently of Mao at all, were Oliver Benioff, Alexander Toschev, Sami Benjo, and Dimitri Erhan, as well as Andrei Kapathe and Fei-Fei Li.


[User] Please transcribe this text

[Assistant] So you've now seen how a basic sequence-to-sequence model works, or how a basic image-to-sequence or image-captioning model works.


[User] Please transcribe this text

[Assistant] You don't want a randomly chosen caption, maybe not, but you might want the best caption, the most likely caption.


[User] Please transcribe this text

[Assistant] So let's see in the next video how you go about generating that.


[User] Please transcribe this text

[Assistant] Okay, one more time, a little faster.


[User] Please transcribe this text

[Assistant] But before they knew it, they were both sound asleep under the magnificent starry skies.


[User] Please transcribe this text

[Assistant] As little nut-brown hair sat with his father, he was tired and happy. He even felt he knew something a little more.


[User] Please transcribe this text

[Assistant] I think of all the co-founders I know, we probably get along best. I've definitely talked to co-founder pairs that like don't get along at all. I think what you need to do in a co-founder relationship is like not necessarily decide who's good at what.


[User] Please transcribe this text

[Assistant] Realize that like, like any relationship, right, your goal is to like figure out the other person and figure out your relationship with them and through I think the last year or so we've really gotten into the groove of like


[User] Please transcribe this text

[Assistant] You know, we own different parts of the day to day stuff, but at the same time, we use each other to kind of like bounce ideas off. Like you do a lot of like the iPhone client stuff. I do a lot of the CEO stuff of like accounting, et cetera, uhm, and do a lot of, you know, the backend coding as well. But what happens is,