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[Assistant] Girls, we can't just brush these powers aside because it doesn't seem like the ideal time to get them.


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[Assistant] But when it found you, you know it was fate. And it might seem scary now, but it can be wonderful too.


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[Assistant] You drink a little dash of magic, and you let it ignite. Mix things up a little bit, and it might


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[Assistant] But when I found you, you know it was fate.


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[Assistant] Not that she has any.


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[Assistant] There's someone who wants us to think she's back. Come on.


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[Assistant] There is one thing I'm still wondering about though. What's that?


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[Assistant] Where did the magic that hid this cave come from?


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[Assistant] Dani is part of our EnglishPod team, and today she's gonna help us with this Chinese New Year's lesson. It's an intermediate lesson, but it has a lot of cultural aspects about Chinese New Year. So that's what Dani's gonna help us out with today.


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[Assistant] Okay, so let's listen to the dialogue now, and then when we come back we'll explain everything.


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[Assistant] Okay, so Chinese New Year. A lot of exciting vocabulary here. Right. Before we get into describing it, let's look at the vocabulary that we saw in Language Takeaway.


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[Assistant] But you can also have fried dumplings. Yeah, yeah, that's right. Which are really good.


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[Assistant] Or in Latin America, it's very similar to maybe an empanada.


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[Assistant] A light, they go up in the air and explode and you see lots of nice colors.


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[Assistant] Okay, so we saw some really great phrases of describing excitement, right? So let's start with Fluency Builder.


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[Assistant] I'm so excited about- I'm so excited about- So when you say, I'm so excited, it means that you are-


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[Assistant] Right, so you could say, I'm so excited about our vacation next month.


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[Assistant] Exactly, so, excited. Now we also saw another really great word, very similar to I'm so excited.


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[Assistant] Right. So you would say, oh, I'm really looking forward to meeting your new girlfriend.


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[Assistant] So it means you're a little bit excited, but not SO excited.


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[Assistant] Now let's look at our next phrase and it's very similar to excited. I can't wait. I can't wait. I can't wait.


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[Assistant] So this is very, very similar to I'm So Excited.


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[Assistant] Alright, let's listen to some more examples of I can't wait.


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[Assistant] So when you say, I bet, it means that you're pretty sure about something.


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[Assistant] Right, so let's listen to some more examples of I bet.


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[Assistant] So Danny, what are these red packets of money about?


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[Assistant] As a gift. Wow. The children get it though.


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[Assistant] And definitely playing with firecrackers is always fun, so...


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[Assistant] That's right, Danny, Erica, and I will be there to answer all your questions, so be sure to send us some comments.


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[Assistant] If people spend time giving you feedback, you want to spend time thinking about


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[Assistant] How to incorporate their feedback and taking their ideas seriously. This is what we'll talk about in this video.


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[Assistant] Please listen and consider what your respondents have said. If you think that you know more than everybody else about your project, then that's not really the right approach to, to


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[Assistant] Workshopping, writing, and getting feedback. What you want to think is that you might have spent more time working on your project, but that they as readers have important ideas and insights to provide for you to make it stronger. So please listen to what people are saying and consider it.


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[Assistant] You can make the choice though. You are the ultimate author of your project. So you can choose to reject or modify or take part of.


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[Assistant] Some of the feedback you've gotten, or you can choose to incorporate it just as someone has said. You can always make the choice. So remember, you don't have to feel kind of defensive or hostile to feedback because you can choose what you want to do with it.


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[Assistant] When needed, rephrase the feedback either so you better understand it or so that it's more palatable for you. If someone says something that is hurtful in feedback to you, like,


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[Assistant] And when possible, I find it very helpful to take a break between reading or hearing someone's feedback and when you're actually doing the revising. Sometimes that distance of 24 hours or 36 hours or two days is really helpful to kind of synthesize the feedback that you got.


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[Assistant] Please work hard at incorporating others' feedback into your writing, even if it means that you have considered and decided against incorporating a particular kind of feedback.


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[Assistant] Hello English learners, welcome back to EnglishPod, my name is Marco. And I'm Erica. Today we're bringing you part two of another great lesson that we had.


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[Assistant] Yeah, and this one is also a little bit strange, a little bit weird.


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[Assistant] Alright, on language takeaway today, we have three great words. Uh, Erica, why don't you start us off with the first one?


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[Assistant] Our second phrase, give me the creeps. Give me the creeps. Give me the creeps.


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[Assistant] Right, so I roped you into lending me your car.


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[Assistant] Alrighty, so I guess we could listen to our dialogue again.


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[Assistant] Well I guess that's a lot more clear and with that we can start putting it together today.


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[Assistant] Okay, so we saw some great examples of appetite. You can lose your appetite. That's right. You have a big appetite.


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[Assistant] Hunger for food. Hunger for food. Okay. Great. What's our second phrase for today?


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[Assistant] For example, I got to know her in Spanish class and she's a really nice person.


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[Assistant] Okay, great stuff, so let's just listen to our dialogue a third time.


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[Assistant] That's right. So you can give it any direction you want. That's right. And of course, we'll publish the name of the author.


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[Assistant] Yes. I'm really anxious to see what ideas come up and how we can end this dialogue.


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[Assistant] That's right, that's right. So be sure to send us your ideas, either a private message or you can post it on the comments section of this lesson. And any other questions or doubts, go to EnglishPod.com and you can leave them there.


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[Assistant] Olive, we've seen a lake, there's salt bush growing.


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[Assistant] I feel responsible for my team being in elimination today. Like, I'm the captain. And if I use my immunity pin, it kind of feels as though I'm just jumping ship.


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[Assistant] As a writer with your readers. So they're so important. I think they're really good to, uhm,


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[Assistant] Think about at the, maybe the, throughout the process but especially to at the, at the end of the writing process because by then you know exactly what you want to accomplish with whatever it is you're writing and you're ready to presumably make a title that accomplishes those ends.


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[Assistant] As you're thinking about crafting effective academic titles, you want to think about the academic writing context. So titles that are effective in other contexts like in


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[Assistant] Fiction, non-fiction, creative writing, poetry.


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[Assistant] Uhm, blogging, online, other kinds of online writing, email titles, subject titles, those are all various different kinds of contexts for academic writing.


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[Assistant] Titles are used for particular purpose.


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[Assistant] And we want to think about what that purpose is. Primarily the purpose is that the title is going to go into an online catalog or a database where people, other researchers are going to be looking through


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[Assistant] A variety of different titles and thinking about which ones are gonna


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[Assistant] Teach them what they need to know or teach them something they didn't know they needed to know. So you're trying to, uhm, to lure in readers.


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[Assistant] But then at the end of the process, you'll want to revisit that title to make sure it's accomplishing exactly what you want.


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[Assistant] Assumptions about titles vary within disciplines and also across disciplines. So if you are


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[Assistant] Writing for a particular journal or for a particular kind of discipline or for a particular teacher, you might want to think about


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[Assistant] What title is he or she or the journal or the editorial board, what they're familiar with so that you can.


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[Assistant] Then make a choice. It's okay to go against the grain, right? If you want to have a title that looks different, that's fine. But at least you know that you're making that choice, right? And you're making it from an uninformed perspective.


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[Assistant] Looking at the text from a position of kind of skepticism or drudgery or, or some negative feelings. And so, so you want, you want the article title to be or your, or you want your titles to be engaging.


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[Assistant] You also though need them to be informative because as I said, scholars use titles in order to decide if a particular work is relevant or not for their research.


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[Assistant] It should be connected. It should be connected to what you actually are arguing. You don't want the title to inform them about something and then when a reader gets within an article to find out that it's about something totally different.


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[Assistant] And it should also be connecting to key terms that are important. It should also be somewhat unique.


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[Assistant] You want your title to reflect how your particular argument is compelling


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[Assistant] Uh, and stands out and builds off of and counters of that great stuff, how, how it's situated within the larger framework of scholarship that has been written on a particular topic.


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[Assistant] Here are some sample titles that we can use to think about what makes for effective titles or not.


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[Assistant] And notice that three of them have this colon. Well this one doesn't have a colon, it has a period, but anyways.


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[Assistant] These, what these three titles have in common is that they have kind of an engaging part at the front, the sweet spot.


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[Assistant] Secrets of Greatness from Tyrone of Virtuoso. I think these authors are trying to be inviting and engaging. And then the second half of the title.


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[Assistant] Uhm, engaged by the first part and then informed by the second part.


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[Assistant] This one is very informative as well. The role of expertise, research and human factors in capturing, explaining and producing superior performance.


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[Assistant] Uh, you can make choices yourself about the length of titles, how long or short you want titles to be, and you can decide how


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[Assistant] How catchy or inviting you want that first part to be. Some people are resistant to the two part title because they feel like it's been used so long in academic writing that it's, it's uhm, just a formula at this point.


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[Assistant] It's still, you know, popular I guess as, as these evidence. Be thinking about the titles for your projects throughout this course. Titles do matter. First impressions with titles do matter. So please deliberately construct and write your title so that it's as effective as possible.


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[Assistant] My round one dish needs to pack a punch. It's really good quality chicken, so I need to make it count.


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[Assistant] I cut through my chicken, I'm thinking please be perfect.


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[Assistant] And then I see all the juices come out and I'm thinking, oh my gosh, great. My spring onion oil is looking great.


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[Assistant] I take a split second, I look around me and there's some really amazing chicken dishes up, so I'm worried.


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[Assistant] It means so much to me to be able to put up a good dish that the judges like and especially Maggie.


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[Assistant] Oh my gosh, this looks so gross. Which one first? Any?


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[Assistant] Yum. Yum. I love prawn toast. Thank you.


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[Assistant] Thanks to Brilliant for supporting this episode of SciShow. Go to Brilliant.org slash SciShow to learn how you can take your STEM skills to the next level this year.


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[Assistant] Leprosy is a disease that has affected humanity for thousands of years, going back to at least 2000 BCE. It inflicts painful skin lesions, and the lack of a cure has led people with leprosy to be shunned and isolated throughout history.


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[Assistant] Even up until the turn of the 20th century. It's sometimes referred to as Hansen's disease after the scientists who discovered its cause, the bacterium Mycobacterium lepre in 1873. But even that didn't lead to a reliable treatment right away.


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[Assistant] A cure might have had to wait until the widespread availability of antibiotics in the 1940s if it weren't for the pioneering chemistry work of Alice Ball. Alice Augusta Ball was born in Seattle in 1892.


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[Assistant] Her grandfather, James Presley Ball Sr., is thought to have been one of the first ever black photographers. Photography at the time was an intricate task.


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[Assistant] requiring a lot of chemicals to develop the photos. Though we don't know for sure, it's possible that observing her grandfather at work led young Alice to develop an interest in chemistry. In 1902, when Alice was 8, the family moved to Honolulu in an attempt to improve her grandfather's poor health. When he passed away in 1904, Ball returned to Seattle,


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[Assistant] Where she studied both pharmaceutical chemistry and pharmacy at the University of Washington. She returned to Hawaii for graduate school and became both the first woman and the first African-American


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[Assistant] A researcher who was studying chalmogra oil. At the time, this was one of the few treatments available for leprosy. The oil derived from the chalmogra tree


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[Assistant] But the oil was too thick to be absorbed by the body, causing painful blisters to erupt on the patient's skin. Which was kind of the problem in the first place. Holman wanted to improve treatments for leprosy patients, but he needed an expert in natural product chemistry.


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[Assistant] Which is why he recruited Ball. During her work with Holman, Ball developed a technique to make chalmugra oil injectable by isolating its active ingredients. Ball's method first started with saponification of the oil. Saponification is the same chemical process used to turn fat and oil into soap.


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[Assistant] Molecules that were more water-soluble than they had been in their oil form. And making them soluble made them easier for our bodies to use, which is exactly what you want in a medicine. So the end result was a form of chalmugra oil that retained its bacteria-fighting abilities while being way easier to administer to patients. Ball's treatment worked!


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[Assistant] Her ethyl esters were both safer and more effective than any other leprosy treatment available at the time. And when her method was used, leprosy patients recovered enough to be discharged from the hospital by the hundreds. Sadly, Ball passed away in 1916 before she could publish her results.


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[Assistant] She was only 24 years old. Her colleague Arthur Dean published her work as his own, claiming to have made improvements to the process and not mentioning her name. Dean's claim went unchallenged until Ball's former colleague Harry Holman published a paper that credited her with the technique.


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[Assistant] In the year 2000, though, the University of Hawaii officially honored her with a plaque next to the school's only chalmugra tree. And in 2007, Ball was posthumously awarded the University of Hawaii's Medal of Distinction, its highest honor. Although Alice Ball died young,


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[Assistant] Her development of the Ball method allowed people afflicted with leprosy to recover and return to their families. And her treatment remained the dominant method for treating leprosy until the 1940s, when antibiotics became widely available. Regrettably, we know very little of her personal life.


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[Assistant] Now, all scientists, especially the pioneers, need a working grasp of statistics. Stats help us draw conclusions when our data is limited, and it's always limited. If you want to build that foundation for yourself, you might be interested in Brilliant's brand new statistics course.


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[Assistant] Which will teach you to make judgments based on limited information. Brilliant lets you learn at your own pace, without tests or grades, and if you make a mistake, there's always an explanation to help you out. So if you're ready to get smarter, you can check out Brilliant.org slash SciShow to get 20% off an annual premium subscription.


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[Assistant] Hello everyone, welcome back to EnglishPod, my name is Marco.


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[Assistant] It's something that everyone has learned before in school, describing people. So we're gonna review that and give you some better phrases.


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[Assistant] Okay great, so let's listen to our dialogue for the first time and then we'll come back and talk about it.


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[Assistant] Alright, so, interesting dialogue. Small world. It is.


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[Assistant] Maybe. Dangerous. Girls are more complicated with that. We, guys, we're not that complicated.


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[Assistant] Our next word, fill me in. Fill me in. Fill me in. Fill me in. So when you tell someone to fill you in,


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[Assistant] I could say, I'm sorry I was late for the meeting, can you fill me in on what happened?


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[Assistant] Exactly. Okay, great word. And our last phrase today, career path.


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[Assistant] Right. All the different jobs and things you do during your career.


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[Assistant] Very good. So now let's listen to our dialogue again and pay special attention to the language used to describe David because when we come back, we're going to talk about this.


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[Assistant] So we found some really great words and phrases used to describe someone.


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[Assistant] Okay, so let's break this down. The first thing she says is, he's really good looking. Good looking. Good looking. Good looking. So good looking is the equivalent of handsome.


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[Assistant] I guess for me, whenever someone says handsome, it sounds like an older person saying that.


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[Assistant] Okay, now let's look at the next thing she said.


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[Assistant] Right. So you can describe a person in different ways.


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[Assistant] Okay, then she talked about his exact height.


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[Assistant] You give a persons height using feet and inches.


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[Assistant] But in other countries you would talk about centimeters or meters, so 6'1 is like, 6'1 is about 180.


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[Assistant] Right. Okay, so the next time you give your, uh, your height to someone, make sure you do it in feet more or less.


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[Assistant] Okay, now the next part is really, really useful. She said he's in his early thirties.


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[Assistant] So this is a way of describing someone's age without actually giving the exact number.


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[Assistant] Right, you wouldn't say like his mid-teens or early teens.


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[Assistant] Okay, great language used to describe people.


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[Assistant] Good looking. Really sexy. Right, it could be a girl or a boy. She's hot.


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[Assistant] It is really informal. Maybe even a little bit offensive. You wouldn't go up to a girl and say, wow, you're hot.


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[Assistant] That's something you would comment with your friends. Okay, so we've looked at some really good language for describing people. Now we can listen to our dialogue a third time and I'm sure you will understand everything.


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[Assistant] Okay, so we looked at really useful language for describing people and we want you to describe your girlfriend, boyfriend.


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[Assistant] Right, so come to EnglishPod.com and leave your questions and comments on our website.


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[Assistant] And, uh, we'll definitely be there to answer any doubts as well.


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[Assistant] Yeah, but they're mostly blue. That's why they're named after the latin word spherus.


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[Assistant] But did you know that Sapphire Tent is the best one?


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[Assistant] Not true. You're in the ruby tent? Ha! That's the worst one! Aww man!


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[Assistant] I'm just joking, buddy. Ruby Tend is great. It's like a Sapphire, but with Chromium.


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[Assistant] But the ruby and coral tents do look an awful lot alike to the discerning eye. I better show you where it is. See you around.


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[Assistant] I know I'm charming, but you don't have to fall for me.


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[Assistant] I dunno, it's an awful lot of work. I wouldn't want you guys to miss out on doing other fun camp stuff because you're so busy building a dock for future campers.


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[Assistant] You think that's crafty? You should see my macaroni art. I'm like the Picasso of noodles.


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[Assistant] It's time I told you about the legend of Gaia Everfree.


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[Assistant] Many years ago, my great-grandparents came to this forest. Seeing its beauty, they decided that it would be the perfect place for a camp.


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[Assistant] But once they started building, strange things started to happen. One pitch black night.


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[Assistant] Her mouth had jagged rock feet and her eyes were pools of black tar. But her aura shimmered like diamonds.


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[Assistant] Anywhere she went, she left a trail of gem dust in her wake, trembling. My great-grandparents asked who she was and what she wanted.


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[Assistant] They begged her to let them stay and build their camp to share this wonderful place with others. Gaia Everfree finally agreed but warned them that they would not be able to keep it forever.


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[Assistant] So if you see a trail of gem dust, you'll know that it is...


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[Assistant] Let me show you the way. I'm kind of an expert at these woods. I've lived here my whole life.


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[Assistant] I better make sure my sister doesn't need anything. See you later.


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[Assistant] Hey, cool lantern. Mine's my face.


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[Assistant] Uh, you okay? Mhm. You don't seem like yourself tonight.


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[Assistant] Axe. I was chopping down firewood.


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[Assistant] What about you two? Why are you hanging out in the woods in the middle of the night?


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[Assistant] Come on, I'll walk you guys back. I'll protect you from...


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[Assistant] Gaia everfree, everfree, ever-


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[Assistant] It's legit. How else would you explain what happened to the docks? And that weird thing where the earth shook? Uh, come on. I know a shortcut back to the tents.


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[Assistant] Whoever you are, you have to let my sister go! Please, Gloriosa, come back! I need you!


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[Assistant] Not to brag or anything, but it's pretty cool how I saved all those campers from those rubeous fruticosis. Blackberry brambles.


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[Assistant] Yeah, but I saved you from falling on the dock so that you could save the campers. So technically, it was all me.


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[Assistant] And here I was hoping we'd still be able to hang out. Maybe get dinner and catch a movie?


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[Assistant] And if there's any group that's gonna forgive you for something that happened in the past, trust me, it's this one.


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[Assistant] Legit, huh? I think we just found our Gaia Everfree.


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[Assistant] There's Equestrian magic here. I can... I can feel it.


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[Assistant] Uhm, Pinkie Pie? What are you doing?


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[Assistant] Hmm, why do these kinds of things always happen to us? What are we going to do? What we always do! Save the day!


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[Assistant] Chapter 8. Where is Christine Dyer?


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[Assistant] Are you still going to love her in 10 or 20 years time? Raoul drank his coffee and did not answer.


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[Assistant] It's a lot of money, Monsieur Fermin said unhappily. He thought for a minute.


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[Assistant] What about some flowers in box five?


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[Assistant] Madame Giry says that OG likes flowers.


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[Assistant] O.G. can bring his own flowers, shouted Monsieur Armand. The evening began well.


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[Assistant] The chandelier was now back in place with new ropes. All Paris was in the Opera House.


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[Assistant] Everybody wanted to hear Christine Daye's voice again. People also knew about the love story between Christine Daye and the Vicomte de Chonny. There are no love secrets in Paris.


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[Assistant] People watch the Comte and the Vicomte in Box 14 with interest. Young men from families like Deschany do not marry opera singers.


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[Assistant] When Christine came onto the stage, her face was white and she looked afraid. But she sang like an angel. Ah, what a voice. All Paris was in love with Christine Daye. She began to sing the famous love song. Suddenly, every light in the opera house went out. For a second, nobody moved or spoke.


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[Assistant] But then a woman screamed, and all the lights came on again.


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[Assistant] But Christine Dayet was no longer on the stage. She was not behind the stage. She was not under the stage. Nobody could find her. The opera house went mad.


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[Assistant] Everybody ran here and there, shouted and called.


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[Assistant] In the director's office, people ran in and out. The police came and asked questions, but nobody could answer the questions. Monsieur Armand got angry and shouted, and Monsieur Fermin told him to be quiet. Then, Madame Girry arrived in the office with her daughter Meg. Go away, woman! Monsieur Armand shouted. Monsieur,


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[Assistant] There are three people missing now, Madame Girry said. Meg, tell the directors your story. This was Meg's story.


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[Assistant] When the lights went out, we were just behind the stage. We heard a scream. I think it was Christine Dyer's voice. Then the lights came back on, but Christine wasn't there. We were very afraid, and we began to run back to our dressing room. There were people running everywhere. Then we saw the Vicomte de Chagny.


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[Assistant] His face was red and he was very angry. Where's Christine? Where's Christine? he shouted. Suddenly, the Persian came up behind him and took his arm. He said something to the Vicomte and they went into Christine Dyer's dressing room. Yes. And then, Monsieur Fermat said quickly, what happened next?


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[Assistant] I investigate data that points to problems in the American economy, problems like rising household debt


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[Assistant] Declining wages and benefits, shortfalls in public revenue.


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[Assistant] I geek out about tax policy and infrastructure investments, and I get really excited by a gracefully designed regulatory regime.


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[Assistant] These are the kinds of topics that I was talking about on a public television live call-in show in August of 2016.


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[Assistant] I was about halfway through the program when a man called in, identified as Gary from North Carolina, and he said, I'm a white male and I'm prejudiced.


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[Assistant] He then went on to detail his prejudice, talking about black men and gangs and drugs and crime. But then he said something that I'll never forget. He said, but I want to change. And I want to know what I can do to become a better American.


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[Assistant] But when I open my mouth to respond to this man on live television, I ...


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[Assistant] The most surprising words came out. I said, thank you.


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[Assistant] I thanked him for admitting his prejudice, for wanting to change and for knowing somehow that that would make him a better American.


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[Assistant] Not long after Gary and my viral moment, uh, we met in person. He said that he had taken my advice,


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[Assistant] He said that my words had been like someone wiped the dust from a window and let the light in. Over the years, Gary and I have become friends.


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[Assistant] And Gary would tell you that I've taught him a lot about systemic racism in America and public policy. But I've learned a lot from Gary, too.


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[Assistant] And the biggest lesson for me has been that Gary's prejudice has caused him to suffer. Fear, anxiety, isolation.


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[Assistant] And it's made me rethink many of the economic problems I've been focusing on my entire career. I wondered, is it possible that


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[Assistant] Our society's racism has likewise been backfiring on the very same people set up to benefit from privilege.


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[Assistant] Driven by this question, I've spent the past few years traveling the country, researching and writing a book. My conclusion?


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[Assistant] Racism leads to bad policymaking. It's making our economy worse.


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[Assistant] And not just in ways that disadvantage people of color. It turns out it's not a zero sum.


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[Assistant] Take, for example, America's underinvestment in our public goods, the things that we all need that we share in common, our schools and roads and bridges.


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[Assistant] Our infrastructure gets a D-plus from the American Society of Civil Engineers, and we invest less per capita than almost every other advanced nation. But it wasn't always this way.


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[Assistant] I traveled to Montgomery, Alabama, and there I saw how racism can destroy a public good and the public will to support it.


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[Assistant] In the 1930s and 40s, the United States went on a nationwide building boom of public amenities funded by tax dollars, which in Montgomery, Alabama, included the Oak Park Pool.


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[Assistant] The Oak Park Pool, though it was funded by all of Montgomery's citizens, was for whites only.


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[Assistant] When a federal court finally deemed this unconstitutional, the reaction of the town council was swift. Effective January 1st, 1959, they decided they would drain the public pool rather than let black families swim too.


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[Assistant] Was replicated across the country in towns, not just in the South.


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[Assistant] Towns closed their public parks, pools and schools, all in response to desegregation orders, all throughout the 1960s.


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[Assistant] In Montgomery, they shut down the entire parks department for a decade.


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[Assistant] They closed the recreation centers. They even sold off the animals in the zoo.


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[Assistant] Today, you can walk the grounds of Oak Park as I did, but very few people do. They never rebuilt the pool. Racism has a cost for everyone.


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[Assistant] I remember having that same thought on September 15, 2008, when I learned the breaking news that Lehman Brothers was collapsing.


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[Assistant] Now, Lehman was, like the other financial firms that would go under in the coming days, done in by overexposure to a toxic financial instrument based on something that used to be simple and safe, a 30-year fixed-rate home loan.


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[Assistant] But the mortgages at the center and the root of the financial crisis had strange new terms.


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[Assistant] And they were developed and aggressively marketed for years in black and brown, middle-class communities, like the one that I visited when I met a homeowner named Glenn.


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[Assistant] Glenn had owned a home on a leafy street in the Mount Pleasant neighborhood of Cleveland for over a decade. But when I met him, he was near foreclosure.


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[Assistant] Like nearly all of his neighbors, he'd received a knock on the door from a broker promising to refinance his mortgage.


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[Assistant] But what the broker didn't tell him was that this was a new kind of mortgage.


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[Assistant] The common misperception then and still today is that


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[Assistant] People like Glenn were buying properties they couldn't afford.


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[Assistant] That they themselves were risky borrowers. I saw how this stereotype


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[Assistant] Made it harder for policymakers to see the crisis for what it was back when we still had time to stop it. But that's all it was, a stereotype.


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[Assistant] The majority of subprime mortgages went to people who had good credit, like Glenn.


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[Assistant] And African Americans and Latinos were three times as likely, even if they had good credit, than white people, to get sold these toxic loans. The problem wasn't the borrower, the problem was the loan.


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[Assistant] Most of the nation's big lenders, from Wells Fargo to countrywide, would go on to be fined for racial discrimination. But that realization came too late.


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[Assistant] These loans, super profitable for the lenders but designed to fail for the borrowers, spread out past the confines of black and brown neighborhoods like Glenn's and into the whiter, whiter mortgage market.


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[Assistant] All of the nation's big Wall Street firms bet on these loans. At its peak, one out of every five mortgages in the country was in this mold. And the crisis ...


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[Assistant] My years of advocating in vain for homeowners like Glenn left me convinced.


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[Assistant] We would not have had a financial crisis if it weren't for racism.


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[Assistant] In 2017, I traveled to Mississippi, where a group of auto factory workers was trying to organize into a union.


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[Assistant] Now, the benefits they were fighting for, higher pay, better health care coverage, a real pension, they would have helped everybody at the plant. But in person after person that I talked to,


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[Assistant] A white man named Joey put it this way. He said,


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[Assistant] White workers think, I ain't voting yes if the blacks are voting yes. If the blacks are for it, I'm against it. A white man named Chip told me,


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[Assistant] The idea is that if you uplift black people, you're down in white people. It's like the world's got this crab-in-a-barrel mentality. Now, the union vote failed.


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[Assistant] Tempting perhaps to focus on the prejudiced attitudes of the men and the workers that I heard in Mississippi.


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[Assistant] But I'm more interested in holding accountable the people who are selling racist ideas for their profit than those who are desperate enough to buy it.


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[Assistant] My travels also took me to places where I saw, however, that it doesn't have to be this way. I went to Maine, the whitest state in the nation, the oldest,


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[Assistant] That is being revitalized by new people, mostly African, mostly Muslim, immigrants and refugees.


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[Assistant] There I met a woman named Cecile, whose parents had been part of the last wave of new people.


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[Assistant] Cecilia was retired, but she had found a new purpose in life by organizing Congolese refugees to join with the white retirees at the Franco Heritage Center. These men and women from the Congo


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[Assistant] And together, these two communities helped each other feel at home.


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[Assistant] You know, for all the political talk about the newcomers being a drain on the town,


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[Assistant] A bipartisan think tank found that the local refugee community there


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[Assistant] This zero-sum thinking that what's good for one group has to come at the expense of another, it's what's gotten us into this mess.


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[Assistant] I believe it's time to reject that old paradigm and realize that our fates are linked. An injury to one is an injury to all.


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[Assistant] Our nation was founded on a belief in a hierarchy of human value.


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[Assistant] But we are about to be a country with no racial majority.


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[Assistant] So we can keep pretending like we're not all on the same team.


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[Assistant] We can keep sabotaging our success and ham-streaming our own players. Or ...


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[Assistant] We can let the proximity of so much difference reveal our common humanity.


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[Assistant] Car, exactly. So we're gonna give you some great language about describing a car or characteristics of a car.


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[Assistant] And also some really interesting language for shopping.


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[Assistant] So before we listen to our dialogue, let's take a look at vocabulary preview.


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[Assistant] On vocabulary preview today, we're bringing you two words. The first one is vehicle.


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[Assistant] So it's important to say that a vehicle could be an airplane.


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[Assistant] That's a vehicle. Our second word is sedan.


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[Assistant] So to make it more clear, most taxis are sedans.


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[Assistant] Right? They have four doors, five people can fit in there, including the driver. Except taxis in Mexico.


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[Assistant] They're Volkswagen Beetles so those are not sedans. But yea, most countries have sedans as taxis. To give you some examples, like a Toyota Corolla or a Toyota Camry is a sedan.


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[Assistant] Alright, a surprising ending to a relatively easy shopping day.


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[Assistant] Let's take a look at some language that we saw in our dialogue on Language Takeaway.


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[Assistant] But yeaah, it's, yeaah, basically how many kilometers or how many miles you can travel on a tank or on a liter or gallon of gas.


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[Assistant] Right. I've heard that Volvo has like 18 airbags now in their cars.


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[Assistant] Okay, our third word today is breaks.


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[Assistant] Brakes are little, what are brakes? Brakes are what make your car stop.


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[Assistant] Our last word today on Language Takeaway, reinforced.


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[Assistant] So basically anything that's reinforced is made stronger.


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[Assistant] Great, so now we can listen to our dialogue again. You can practice all these words that we just talked about, and we'll come back shortly.


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[Assistant] Sales people will come up to you and ask you, can I help you? Are you interested in something?


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[Assistant] So maybe you want to buy this really expensive car.


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[Assistant] Uhm, yeaah, actually I did, I did drive a tank before. Really? Yeaah, a really old one, kinda like a World War 2 type tank. But yeaah, they're really fun and interesting, you have, it's not like driving a normal car, you have pedals and you have...


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[Assistant] What many people don't know is that actually tanks are manual transmissions, you have to change gears.


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[Assistant] Yeah, well, it was, it was fun, definitely, and uh, I hope I get to do it again someday.


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[Assistant] Right, we'll be there to answer all your questions. I'll also be there to maybe post some more crazy stories.


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[Assistant] There's no way he's going to make that shot. He's way too far away from the net.


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[Assistant] I get now to introduce one of the most powerful women in the world. I mean, if we are to


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[Assistant] Escape from the mess that we're in right now. She is going to play a major part in helping us do that.


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[Assistant] Uh, she's the head of the International Monetary Fund. A delight to welcome here Kristalina Georgieva.


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[Assistant] So you just took on this role late last year, and within four months, boom, COVID arrives. That is one heck of an introduction to a new job. How are you doing?


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[Assistant] I mean, you've described this pandemic as a crisis like no other. In what way a crisis like no other?


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[Assistant] Well, I'll be excited to come onto that in a bit. But in this moment of responding to the crisis, the main ...


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[Assistant] A tool that seems to have been executed, at least by the rich countries, has been this uhm, this massive economic stimulus to the tune of trillions of dollars. Is that a wise response?


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[Assistant] So how far can it go? Because it's been described, in a sense, as printing money. Governments are issuing more and more bonds that have to be paid back at some point.


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[Assistant] Uhm, there's this term in economics, you know, the Minsky moment, where things can go very well for a while as everyone believes that, you know, the train can keep running, the cycle can keep turning, you know, that governments have all this money. At some point though,


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[Assistant] Doesn't that break down? Do you worry that we may be nearing a Minsky moment where


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[Assistant] Like Michael and Mary Poppins grabs his tuppence and starts to run on the bank. Is there stress in the international financial system now that concerns you, that makes you feel that we may be running out of headroom?


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[Assistant] I mean, the financial crisis of 2008, uhm,


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[Assistant] Kind of came perilously close to breaking the entire financial system, arguably it did, that by most people's ...


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[Assistant] Calculation, this is a far worse impact to the economy overall. Did the world learn something from 2008 that has helped us so far be resilient this time?


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[Assistant] You're paying special attention to the situation in developing countries. And it does seem that they're facing ...


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[Assistant] Execute the kind of injection, stimulus injections that the rich countries are doing and seems to be the only way out. So that seems like a really dangerous cycle. Is there any way to break that cycle?


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[Assistant] But some developing countries are overdoing the lockdown policy. I mean, if large numbers of your citizens are already struggling to stay alive, isn't it almost like a death sentence to order them not to leave their homes?


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[Assistant] Actually, I'd love to expand on that and, uh, just ask you a bit more about, about leadership, actually. You know, when people think of the nations that have performed best, uh, they often refer to, uhm, when I say best, best in response to the current pandemic.


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[Assistant] Uhm, they often refer to Germany, New Zealand, South Korea, Taiwan, Denmark and Norway.


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[Assistant] When they think of those that have performed worse, they often think of Spain, Italy, the UK, Belgium, Sweden, Iran, Brazil, Russia, and the United States.


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[Assistant] Uhm, all of that second group are run by men, all but one of the first group are run by women. Is that a coincidence?


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[Assistant] I mean, it's obviously hard to make generalizations about gender of any kind, but I mean, is there also almost something about ...


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[Assistant] The embracing of nuance. That women might be better at that than men. Men often, it's like,


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[Assistant] Let's win, let's conquer, and uhm, in a situation like this, where it's all probabilities, it's like, there are so many complex dials to turn on this dangerous pandemic machine that we're trying to wrestle, uhm,


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[Assistant] So perhaps talk a bit more about your own leadership in this moment. I mentioned you've only recently come to this job. Before that, you were European Commissioner, you dealt with ...


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[Assistant] Humanitarian crises in more than one part of the world. And in your own country, Bulgaria, you witnessed the wholesale transformation of the country, both politically and economically. What lessons can you bring from your past experience to this moment?


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[Assistant] Actually, could you talk a bit more about the role of the IMF, especially as we look forward to trying to recover from this? What specifically can your organization do to take us forward?


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[Assistant] I mean, this is a global crisis. A lot of people are worried that, unlike perhaps even in 2008, where it really did seem there was a lot of global cooperation,


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[Assistant] There's actually, in some worrying ways, less this time. Are you worried about how crucial is that to getting us through this?


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[Assistant] Well indeed, and the IMF is dependent on the financing from its members, its key members.


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[Assistant] The, I mean, you spoke of the trillion dollars that you are looking to make available to nations that need it. As I read it, that comes from, you've got this, uhm,


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[Assistant] These units are called special drawing rights. You basically draw currency from members. And hasn't there been pushback, though, from the US to block that effort of raising all that money?


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[Assistant] It's, it's so inspiring actually hearing the energy and stuff, the energy that you're bringing to this. I don't think many people coming into this would have expected to hear from the head of the IMF this emphasis on, you know, let's, let's solve the crime crisis, let's tackle


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[Assistant] Inequality and injustice, uhm, it's uhm,


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[Assistant] Do you really believe that this moment, this crisis could help lead us into a great transformation? I mean, people will feel it's your job to sound positive, you have to do that.


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[Assistant] Do you really see the path forward that we can, uhm, get through this and how, what sort of time scale are we talking about here, Kristalina?


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[Assistant] So if you could inject one idea into the mind of everybody, uhm, onto the world leaders who listen to you, what would that idea be at this moment?


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[Assistant] That sounds like optimism as the stance, not just a naive belief that it will happen, but a determination to make it so. That's what you're calling for, to use that as the motivation to pull us all forward together.


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[Assistant] If you want to say one last thing in one minute? Alright, go.


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[Assistant] Wish you the very best as you use your position to help get us out of this mess. Thank you so much, Kristalina, for spending time here at TED. Thank you.


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[Assistant] Certainly. I can help you with that. What type of account would you like to open? A checking or a saving account?


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[Assistant] With our checking account, you can have unlimited daily transactions for a small monthly fee.


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[Assistant] And our savings account has a higher interest rate, but you must carry a minimum balance of $10,000.


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[Assistant] Alright then, with this checking account you'll be issued a debit card and a checkbook. Will you require overdraft protection? There is an extra fee for that.


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[Assistant] In that case, I'll get you to fill out this paperwork. I'll need your social insurance number and two pieces of government ID.


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[Assistant] If you could just sign here and here and here, we'll be all set. Would you like to make a deposit today?


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[Assistant] The maximum overdraft on my account is only five thousand dollars.


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[Assistant] Oh no, I have to pay fifteen hundred dollars in overdraft fees.


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[Assistant] Certainly, I can help you with that. What type of account would you like to open? A checking or a savings account?


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[Assistant] See, with our checking account, you can have unlimited daily transactions for a small monthly fee.


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[Assistant] And our savings account has a higher interest rate.


[User] Please transcribe this text

[Assistant] But you must carry a minimum balance of $10,000.


[User] Please transcribe this text

[Assistant] With this checking account, you'll be issued with a debit card and a checkbook. Will you require overdraft protection? There is an extra fee for that.


[User] Please transcribe this text

[Assistant] In that case, I'll get you to fill out this paperwork. I'll need your social insurance number and two pieces of government ID. If you could just sign here and here and here, we'll be all set. Would you like to make a deposit today?


[User] Please transcribe this text

[Assistant] I can help you with that. What type of account would you like to open? A checking or a saving account?


[User] Please transcribe this text

[Assistant] With our checking account, you can have unlimited daily transactions for a small monthly fee and our savings account has a higher interest rate.


[User] Please transcribe this text

[Assistant] Alright then, with this checking account you'll be issued a debit card and a checkbook. Will you require overdraft protection? There is an extra fee for that.


[User] Please transcribe this text

[Assistant] And here, and here will be all set. Would you like to make a deposit today?


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[Assistant] I'm trying to think what's under the box. Could it be like eggs, chicken, maybe rice? Ready to find out what it is? Yes.


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[Assistant] The idea of this dish actually came up from watching one of the last community challenges. And I was just thinking about what I'd do in that challenge and this was kinda it.


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[Assistant] I had the toast infusing in the anglaise mixture, but as I sip it out, I realised how much anglaise had been soaked up by the bread and how little I have left. What am I gonna do?


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[Assistant] I didn't take into consideration that the bread would soak up all of the liquid. There's only a tiny amount at the unglazed last.


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[Assistant] Yeah, I'm really annoyed about it, but I made a silly mistake, but I'm just hoping there's enough for at least one quinelle.


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[Assistant] Dammit. I have enough for one single Cornell and that's it.


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[Assistant] I've cooked some really great savory dishes in this competition so far, but you know, I really want to ramp up the dish today and make it really restaurant quality.


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[Assistant] My dish is meat and three vegetables. It's quite an old fashioned dish, but today I want to amp my dish up and make it really restaurant quality. So I'm adding a charred onion. I think when you peel the petals apart, like they look really pretty and I can use them as a flavour and a garnish on the plate.


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[Assistant] I'd love a second to be in, like, I've been trying to work on it and come close a few times, so uhm, it's definitely a goal of mine.


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[Assistant] I want as much colour on the plate as possible. So at the last minute I decided to add some parsley oil. Really brings out a nice vibrant green. And then I've got the colours of the carrot and the beef and some lemon, so it's gonna be really appealing. The plate looks nice and fresh.


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[Assistant] I'm really happy with the look of my dish and I think I've reinvented it quite well, but I'm hoping I've done enough to get into that top three.


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[Assistant] This is a somewhat more advanced concept, but it's something that I've often been asked about, and so I wanted to talk about it here and address it here. But this is a somewhat more advanced concept, so feel free to consider this optional material.


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[Assistant] And it's a phenomenon that you may run into that's maybe somewhat useful to understand, but even if you don't understand the normal equation and linear regression, you should be able to get that to work okay. Here's the issue.


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[Assistant] For those of you that are maybe somewhat more familiar with linear algebra, uhm, what some students have asked me is when computing this, uh,


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[Assistant] If theta equals x transpose x inverse x transpose y, what if the matrix x transpose x is non-invertible?


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[Assistant] So for those of you that know a bit more linear algebra, you may know that, you know, only some matrices are invertible and some matrices do not have an inverse. We call those non-invertible matrices singular or degenerate matrices.


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[Assistant] The issue of the problem of x transpose x being non-invertible should happen pretty rarely. And uhm, in Octave, if you implement this to compute theta,


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[Assistant] It turns out that this will actually do the right thing.


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[Assistant] I'm getting a little technical now, and I don't want to go into the details, but Octave has two functions for inverting matrices. One is called P-Inf.


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[Assistant] And the differences between these two are somewhat technical. One's called the pseudo-inverse, one's called the inverse. But uhm, you can show mathematically that so long as you use the P in function,


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[Assistant] Then this will, this will actually compute the value of theta that you want, even if x transpose x is non-invertebrate.


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[Assistant] Those of you that know a bit more linear algebra and might be interested.


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[Assistant] I'm not going to prove this mathematically, but if x transpose x is non-invertible,


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[Assistant] There are usually two most common causes for this. The first cause is if somehow in your learning problem you have redundant features.


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[Assistant] Concretely, if you're trying to predict housing prices, and if x1 is the size of the housing feet,


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[Assistant] Rounded up, rounded to two decimals. And so your two features will always satisfy the constraint that x1 equals 3.28 squared times x2.


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[Assistant] And, uh, you can show, for those of you that are somewhat advanced linear algebra now, but if you're an expert in linear algebra, you can actually show that if your two features are related by a linear equation like this, then the matrix X transpose X will be non-invertible.


[User] Please transcribe this text

[Assistant] The second thing that can cause x transpose x to be non-invertible is if you're training, if you're trying to run a learning algorithm with a lot of features. Concretely,


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[Assistant] N plus one dimensional, so there's 101 dimensional, you're trying to fit 101 parameters from just 10 training examples. And uhm,


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[Assistant] This turns out to sometimes work, but to not always be a good idea, because as we'll see later, you might not have enough data, if you have only 10 examples, to fit, you know, 100 or 101 parameters.


[User] Please transcribe this text

[Assistant] We'll see later in this course why this might be too little data to fit these, this many parameters. But uhm, commonly what we do then, if m is less than n, is to see if we can either delete some features or to use a technique called regularization, which is something that we'll talk about later in this course as well.


[User] Please transcribe this text

[Assistant] That will kind of let you fit a lot of parameters, use a lot of features, even if you have a relatively small training set. But this, this, this regularization will be a later topic in this course.


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[Assistant] To summarize, if uhm, ever you find that x transpose x is singular or alternatively you find this non-invertible, what I would recommend you do is first look at your features and see if you have redundant features like this x1, x2, x3.


[User] Please transcribe this text

[Assistant] You know, being linearly dependent or being a linear function of each other like so. And if you do have redundant features and if you just, you know, delete one of these features, you really don't need both of these features. If you just delete one of these features, that would solve your non-invertibility problem.


[User] Please transcribe this text

[Assistant] And uhm, so I would first think through my features and check if any are redundant, and if so, then you know, keep deleting redundant features until they're no longer redundant.


[User] Please transcribe this text

[Assistant] And uh, if your feature is not redundant, I would check if I might have too many features. And if that's the case, I would either delete some features if I can bear to use fewer features, or else I would consider using regularization, which is this topic that we'll talk about later.


[User] Please transcribe this text

[Assistant] So that's it for the normal equation and what it means for if the matrix X transpose X is non-invertible. But this is a problem that you should run, that hopefully you run into pretty rarely and if you just implement it in octave using P in, using the P in function,


[User] Please transcribe this text

[Assistant] Uh, which is called a pseudo-inverse function. So if you use a different linear algebra library, it's called a pseudo-inverse, uh, but that implementation should just do the right thing, uh, even if x transpose x is non-invertible, which should happen pretty rarely anyway, so this should not be a problem for most implementations of linear regression.


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[Assistant] You now know a bunch about machine learning. In this video, I'd like to teach you a programming language, Octave, in which you'll be able to very quickly implement the learning algorithms we've seen already and the learning algorithms we'll see later in this course.


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[Assistant] In the past, I've tried to teach machine learning using a large variety of different programming languages, including C++, Java.


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[Assistant] What I found is that students were able to learn the most productively, learn the most quickly and prototype your algorithms most quickly using a relatively high level language like Octid.


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[Assistant] In fact, uhm, what I often see in Silicon Valley is that if, even if you need to build, if you want to build a large scale deployment of a learning algorithm, what people will often do is prototype


[User] Please transcribe this text

[Assistant] In a language like Octa, which is a great prototype of a language so you can get your learning algorithms working quickly. And then only if you need a very large scale deployment of it, only then spend your time, uh, re-implementing the algorithms in C++ or Java or some other language like that.


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[Assistant] Because one of the lessons we've learned is that programmer time or developer time, that is your time, the machine learning experience time, is incredibly valuable.


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[Assistant] And, uh, uh, if you can get your learning algorithms to work more quickly in Octave, then overall you have a huge time savings by first developing the algorithms in Octave and then implementing in like maybe C++ Java only after we have the ideas working.


[User] Please transcribe this text

[Assistant] The most common prototyping languages I see people use for machine learning are Octave, Matlab, Python, NumPy, and R.


[User] Please transcribe this text

[Assistant] Uhm, Octave is nice since it's free open source and, uh, Matlab works well too, but it's expensive, uh, to many people. But if you have access to a copy of Matlab, you can also use Matlab for this class.


[User] Please transcribe this text

[Assistant] If you know Python Numpy or if you know R, I do see some people use it, but uhm, what I see is that people usually end up developing somewhat more slowly in Python.


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[Assistant] These languages, uh, because the Python non-Py syntax is just slightly clunkier than the octave syntax.


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[Assistant] And so because of that and because we are releasing starter code in Octave, I strongly recommend that you not try to do the programming exercises in this class in NumPyRR, but that I do recommend that you instead do the programming exercises for this class in Octave instead.


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[Assistant] What I'm going to do in this video is go through a list of commands very, fairly quickly. And the goal is to quickly show you the range of commands and the range of things you can do in Octave.


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[Assistant] The course website will have a transcript of everything I do and so after watching this video you can refer to the transcript posted on the course website when you want to find a command.


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[Assistant] Concretely, what I recommend you do is first watch the tutorial videos.


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[Assistant] And after watching, you know, to the end, then, uh, install Octave on your computer.


[User] Please transcribe this text

[Assistant] And finally, go to the course website, download the transcripts of the things you see in the session, and type in whatever commands seem interesting to you into Octave, so running on your own computer so that you can see it work for yourself. And with that, let's get started.


[User] Please transcribe this text

[Assistant] Here's my Windows desktop, and I'm gonna start up Octave.


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[Assistant] And, uh, I'm now in Octave and there's my Octave prompt. Let me first show you the elementary operations you can do in Octave. So you type in 5 plus 6, that gives you the answer of 11. 3 minus 2, 5 times 8, 1 over 2, 2 to the power of 6 is 64.


[User] Please transcribe this text

[Assistant] So those are the elementary, uhm, math operations. You can also do logical operations. So 1 equals 2, this evaluates to false. The percent command here is, uhm,


[User] Please transcribe this text

[Assistant] Uh, means a comment. So 1 equals 2. You evaluate it as false, which is represented by 0. 1 not equals to 2.


[User] Please transcribe this text

[Assistant] This is true, so that returns 1. Note that the not equal sign is this tilde equals symbol and not bang equals, which is what some other programming languages use. Let's see, logical operations 1.


[User] Please transcribe this text

[Assistant] And zero, use a double ampersand sign to denote the, uh, logical and, and that evaluates the false. One or zero is the or operation, and that evaluates the true, and I can x or one and zero, and that evaluates the one.


[User] Please transcribe this text

[Assistant] And so on, that uhm, you can use to change the prompt. And I guess this uh, quoted string in the middle, you know, quote greater than greater than space, that's what I prefer my octave prompt to look like. So if I hit enter, uhm, oops, excuse me.


[User] Please transcribe this text

[Assistant] Like so, PS1, like so, now my octave prompt has changed to the greater than greater than sign, which, you know, looks quite a bit better.


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[Assistant] Next let's talk about octave variables. I can take the variable a and assign it to 3 and hit enter and now a is equal to 3.


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[Assistant] You want to assign a variable, but you don't want it to print out the result. If you put a semicolon, the semicolon suppresses.


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[Assistant] The print output, so if I do that, enter, it doesn't print anything, whereas a equals 3, you know, makes it printed out, whereas a equals 3, semicolon, doesn't print anything. I can do string assignment, b equals high,


[User] Please transcribe this text

[Assistant] Uhm, now, if I just enter b at the principle of the variable b, so b is the string high, c equals 3 greater than or equal to 1.


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[Assistant] Uhm, I want to print out or display a variable. Here's how you go about it. Let me set a equals pi. And if I want to print a, I can just type a like so and print it out.


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[Assistant] For more complex printing, there's also the DISP command, which, uh, stands for display. So when I display A, it just prints out A like so.


[User] Please transcribe this text

[Assistant] Like so. And, uh, this will print out the string 2.14. This is kind of a,


[User] Please transcribe this text

[Assistant] This is essentially the syntax you use to print strings. So the sprintf generates a string that is this, you know, 2 decimals, 3.14 strings.


[User] Please transcribe this text

[Assistant] This %0.2f means substitute a into here, showing it with, you know, two digits off the decimal point. And this takes the string that's generated by this printf command, sprintf, or the string printf command.


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[Assistant] And, uh, this actually displays a string. And to show you another example, sprintf, 6 decimals.


[User] Please transcribe this text

[Assistant] Percent 0.6f comma a. And, uh, this should print pi with 6 decimal to 6 decimal faces.


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[Assistant] Finally, I was explaining A like so, and it looked like this. You can actually, there are, there are useful shortcuts to type formats long.


[User] Please transcribe this text

[Assistant] It causes strings to by default be displayed to a lot more decimal places and format short is a command that restores the default of just printing a small number of digits. Okay.


[User] Please transcribe this text

[Assistant] That's how you work with variables. Now let's look at vectors and matrices.


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[Assistant] Let's say I want to assign A to a matrix. Let me show you an example. 1, 2, semicolon, 3, 4, semicolon.


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[Assistant] 5, 6. This generates a 3 by 2 matrix A whose first row is 1, 2, second row is 3, 4, third row is 5, 6. What the semicolon does is essentially say go to the next row of the matrix. There are other ways to type this in. Type A equals 1, 2, semicolon,


[User] Please transcribe this text

[Assistant] 3, 4, semicolon, 5, 6, like so. And that's another equivalent way of assigning A to be the values of this 3 by 2 matrix.


[User] Please transcribe this text

[Assistant] Similarly, you can assign vectors, so v equals 1, 2, 3, this is actually a row vector, this is a 3 by 1 vector, right, this is a fat, wide vector, excuse me, this is a 1 by 3 matrix, right, not 3 by 1. If I want to assign this to a


[User] Please transcribe this text

[Assistant] Column vector, what I would do is instead do v equals 1 semicolon 2 semicolon 3 and this will give me a


[User] Please transcribe this text

[Assistant] 3 by 1 instead of 1 by 3 vector. So this is a common vector.


[User] Please transcribe this text

[Assistant] Here's some more useful notation. I can also set v equals 1 colon 0.1 colon 2.


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[Assistant] What this does is it sets v to the bunch of elements that start from 1 and increments in steps of 0.1.


[User] Please transcribe this text

[Assistant] Until you get up to 2. So if I do this, uhm, V is going to be this, you know, row vector, or this is a, what, a 1 by 11 matrix, really. That's 1.1, 1.1, 1.2, 1.3, and so on, until we get up to 2.


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[Assistant] Now, uhm, and uh, I can also set v equals one colon six and that sets v to be, you know, these numbers, one through six. Okay.


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[Assistant] Now, here are some other ways of generating matrices, ones, uh, 2 by 3.


[User] Please transcribe this text

[Assistant] There's a command that generates a matrix that is a 2 by 3 matrix that is the matrix of all 1s. So if I set C equals 2 times 1s, 2 by 3,


[User] Please transcribe this text

[Assistant] This generates a 2 by 3 matrix that is all 2s and, uh, this is, you can think of it as a shorter way of writing this than C equals 2, 2, 2, semicolon 2, 2, 2, which will also give you the same result.


[User] Please transcribe this text

[Assistant] Let's say w equals 1's, 1 by 3, so this is going to be a row vector or a, uhm,


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[Assistant] Just a couple more ways to generate matrices. If I do w equals rand 1 by 3.


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[Assistant] This gives me a 1 by 3 matrix of all random numbers. If I do rand,


[User] Please transcribe this text

[Assistant] Three by three, this gives me a three by three matrix of all random numbers drawn from the uniform distribution between zero and one. So every time I do this, I get a different set of random numbers drawn uniformly between zero and one.


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[Assistant] For those of you that know what a Gaussian random variable is, or for those of you that know what a normal random variable is, you can also say w equals rand n.


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[Assistant] 1 by 3, uhm, and so these are going to be 3 values drawn from a Gaussian distribution with mean 0 and variance of standard deviation equal to 1.


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[Assistant] And, uh, you can set more complex things like w equals minus 6 plus square root 10 times


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[Assistant] Let's say rand n 1 by 10,000. And I'm going to put a semicolon at the end, uhm, because I don't really want this printed out. This is going to be a, well, this is going to be a vector with, uhm,


[User] Please transcribe this text

[Assistant] 100,000, uh, excuse me, 10,000 elements, so.


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[Assistant] Well, actually, you know what, let's print it out. So this will generate a matrix like this.


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[Assistant] Right, we have 10,000 elements, so that's what w is. And, uh, if I now plot a histogram of w with the his command, I can now


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[Assistant] And, uh, octaves print his command, it takes a couple seconds to bring this up, but this is a histogram of my random variable for w, that was minus 6 plus square root 10 times this Gaussian random variable. And I can plot a histogram with more buckets, with more bins, with say, 50 bins, and, uh,


[User] Please transcribe this text

[Assistant] This is my histogram of a Gaussian with mean minus 6 because I have a minus 6 there plus the square root of 10 times this.


[User] Please transcribe this text

[Assistant] Finally, one special command for generator matrix, which is the i command. So i stands for, this is maybe a pun on the word identity, but so if I set i 4, this is the 4 by 4 identity matrix. So i equals i 4, this gives me


[User] Please transcribe this text

[Assistant] That gives me a 6 by 6 identity matrix, and I3 is the 3 by 3 identity matrix.


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[Assistant] Lastly, to wrap up this video, there's one more useful command, which is the help command. So you can type help i, and this brings up the help function for the identity matrix. Hit Q to quit, and you can also type


[User] Please transcribe this text

[Assistant] RAND brings up documentation for the RAND, or the Random Number Generation function, or even Help Help, which shows you, you know, help on the help function.


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[Assistant] So, those are the basic operations in Okta. And with this you should be able to generate a few matrices, multiply, add things.


[User] Please transcribe this text

[Assistant] And, and use the basic operations in Okta.


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[Assistant] In the next video, I'd like to start talking about more sophisticated commands and how to move data around and start to process data in Octave.


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[Assistant] And so we actually have a question from the community that sort of builds on what, um, what you're discussing right now. Um, Yatnika Khanna asks, uh, which countries will prove to be resilient in the Great Transformation? Those with popular leaders or those with sound financial systems?


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[Assistant] Uhm, as the main potential positive shifts and changes in the, in this world from this pandemic, say two to 10 years from now.


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[Assistant] After some time, Tom Morris stopped talking, and Mr White said to his wife and son,


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[Assistant] Tom was a soldier in India for 21 years. India is a wonderful country. Yes, Herbert said, I'd like to go there. Oh, Herbert, his mother cried. She was afraid because she did not want to lose her son. I wanted to go to India too, her husband said, but


[User] Please transcribe this text

[Assistant] It's better for you here, the soldier said quickly.


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[Assistant] But you saw a lot of strange and wonderful things in India. I want to see them too one day, Mr. White said. The soldier put down his whiskey. No, he cried, stay here. Old Mr. White did not stop. But your stories were interesting, he said to Tom Morris.


[User] Please transcribe this text

[Assistant] What did you begin to say about a monkey's paw? Nothing, Morris answered quickly. Well, nothing important. A monkey's paw, Mrs. White said. Come on, Mr. Morris, tell us about it, Herbert said. Morris took his whiskey in his hand, but suddenly he put it down again.


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[Assistant] Slowly he put his hand into the pocket of his coat, and the White family watched him. What is it? What is it? Mrs. White cried. Morris said nothing. He took his hand out of his pocket. The White family watched carefully. And in the soldier's hand, they saw something little and dirty.


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[Assistant] Mrs. White moved back, afraid, but her son Herbert took it and looked at it carefully. Well, what is it? Mr. White asked his friend. Look at it, the soldier answered. It's a little paw, a monkey's paw. A monkey's paw, Herbert said, and he laughed.


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[Assistant] Why do you carry a monkey's paw in your pocket, Mr. Morris? He asked the old soldier. Well, you see, Morris said, this monkey's paw is magic. Herbert laughed again, but the soldier said, don't laugh, boy. Remember, you're young. I'm old now.


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[Assistant] Can do strange and wonderful things.


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[Assistant] An old Indian gave the paw to one of my friends. My friend was a soldier too. This paw is magic because it can give three wishes to three people. Wonderful, Herbert said.


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[Assistant] But these three wishes don't bring happiness, the soldier said.


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[Assistant] The old Indian wanted to teach us something.


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[Assistant] It's never good to want to change things. Well, did your friend have three wishes? Herbert asked the old soldier. Yes, Morris answered quietly. And his third and last wish was to die.


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[Assistant] Mr. and Mrs. White listened to the story and they felt afraid. But Herbert asked, and did he die? Yes, he did, Morris said. He had no family, so his things came to me when he died. The monkey's paw was with his things, but he told me about it before he died.


[User] Please transcribe this text

[Assistant] Tom Morris finished quietly. What were his first two wishes then? Herbert asked. What did he ask for? I don't know. He didn't want to tell me, the soldier answered.


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[Assistant] For a minute or two, everybody was quiet. But then Herbert said, And you, Mr. Morris? Did you have three wishes? Yes, I did, Morris answered. I was young. I wanted many things. A fast car. Money.


[User] Please transcribe this text

[Assistant] Morris stopped for a minute, and then he said with difficulty, My wife?


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[Assistant] And my young son died in an accident in the car. Without them, I didn't want the money. So in the end, I wished to lose it. But it was too late. My wife and my child were dead.


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[Assistant] The White family looked at the unhappy face of the old soldier. Then Mr. White said, why do you want the paw now? You don't need it. You can give it to someone. How can I give it to someone? The soldier said.


[User] Please transcribe this text

[Assistant] The monkey's paw brings unhappiness with it. Well, give it to me, Mr. White said. Perhaps this time it, no, Tom Morris cried. You're my friend. I can't give it to you.


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[Assistant] Then after a minute he said, I can't give it to you. But of course, you can take it from me. But remember,


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[Assistant] This monkey's paw brings unhappiness. Old Mr. White did not listen, and he did not think.


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[Assistant] Quickly he put out his hand and he took the paw. Tom Morris looked unhappy, but Mr. White did not want to wait.


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[Assistant] What do I do now? he asked his friend. Yes, come on father, Herbert said. Make a wish, and he laughed. The soldier said nothing, and Mr. White asked him again. What do I do now?


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[Assistant] At first the old soldier did not answer, but in the end he said quietly, Okay, but remember, be careful. Think before you make your wish.


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[Assistant] Yes, yes, Mr. White said. Take the paw in your right hand, and then make your wish. But, Tom Morris began.


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[Assistant] Yes, we know, Hubbot said. Be careful.


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[Assistant] Just then, old Mrs. White stood up, and she began to get the dinner. Her husband looked at her. Then he smiled and said to her, Come on, help me. What can I wish for? We need money, of course. Mrs. White laughed, but she thought for a minute, and then she said, Well,


[User] Please transcribe this text

[Assistant] I'm getting old now, and sometimes it's difficult to do everything.


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[Assistant] Perhaps I need four hands and not two.


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[Assistant] Yes, ask the paw to give me two more hands. Okay then, her husband said. And he took the monkey's paw in his right hand. Everybody watched him, and for a minute he waited. Then he opened his mouth to make his wish. Suddenly, Tom Morris stood up. Don't do it, he cried.


[User] Please transcribe this text

[Assistant] The old soldier's face was white. Herbert and his mother laughed, but Mr. White looked at Tom's face. Old Mr. White was afraid, and he put the monkey's paw into his pocket.


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[Assistant] After a minute or two they sat down at the table and began to have dinner.


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[Assistant] The soldier told the family many strange and wonderful stories about India. They forgot the monkey's paw. And because the soldier's stories were interesting, they asked him many questions about India. When Tom Morris stood up to leave, it was very late. Thank you for a very nice evening, Morris said to the family. And thank you for a very good dinner.


[User] Please transcribe this text

[Assistant] It was a wonderful evening for us, Tom, old Mr. White answered. Your stories were very interesting. Our life isn't very exciting. And we don't have the money to visit India. So please come again soon. You can tell us some more stories about India. Then the old soldier put on his coat. He said goodbye to the White family.


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[Assistant] Well, goodbye everyone. Are you going? Yes, I must. When are you going? Now. Now? So soon? Don't go. But I must. I need a change. What are you going to do? I'd like to travel for a time. Travel? How wonderful. Yeah, think of us when you're travelling. Yes.


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[Assistant] Think of us, we have to stay here. It's the same old thing, day after day.


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[Assistant] The same old faces. The same old problems. Nothing different for us. But someone has to do it. Yes, that's right. We can't all go. Why not? You can travel too. Us? How can we travel? We have things to do here. What things? He says, what things. What a question.


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[Assistant] He's tired of us. No, I'm not. I'm not tired of you. I'm happy here. Are you? Well, I was happy here. It's a good place. And I... Yes? Well, I like you. I'm one of you. One of the group. That's nice. So where would you like to go? I don't know. Spain, perhaps? Or Greece?


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[Assistant] Maybe even Canada. Canada? Yes, I'm very interested in Canada. Canada? Well, that's interesting. You're very adventurous, Chris. We're not adventurous at all, I'm afraid. But then you're young.


[User] Please transcribe this text

[Assistant] You have your life in front of you. You want to see exciting places. Like Canada.


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[Assistant] The lakes and the forests. The great St Lawrence River. How wonderful. Lucky Chris. It's different for us. We have to stay here.


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[Assistant] We can't go to Canada. I'm one of you, you said. One of the group. But you're not. No. Chris, you're different from us.


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[Assistant] No I'm not. Stop talking like this. I'm not different from you. Sometimes I'm happy, sometimes I'm not.


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[Assistant] I work and eat and sleep like you. Ah. But you are going away. And we are staying here. And that makes you different. I'm not different. But what about Canada? Stop talking about Canada. I'm not going to Canada. I'd like to go there. That's all. So you aren't going there now. I don't know. Perhaps.


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[Assistant] I need some time to think about my life. Oh yes. Everyone does.


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[Assistant] It's usual for people to do that. And there's lots of time. You're young.


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[Assistant] That's right. I have my life in front of me. So, take your time. Right. You can stay here as long as you like. And think about it. Yeaah.


[User] Please transcribe this text

[Assistant] So what are you going to do? Sorry? Are you leaving today? Leaving? Uh, not today.


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[Assistant] I want to think about it first. Of course. When are you going then? Well, not now. Later perhaps. I can stay here a bit longer. There's lots of time. Canada can wait, can't it? Yes it can. It certainly can. You're one of us really, aren't you? One of the group. Yes I am. I think. But, you know,


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[Assistant] You say. Yes? Say what? Well, you say some funny things sometimes. Like what? Well, Canada.


[User] Please transcribe this text

[Assistant] Must go. Me too. I have a lot to do. And me. Let's all go.


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[Assistant] Yes, let's leave Chris with his dreams. About Canada.


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[Assistant] For thousands of years, we've spun spellbinding tales that fit this pattern, and modern storytellers around the world continue to do so.


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[Assistant] Perhaps he is a king, or extraordinary in some other way. But because you and I are neither unusually good, nor unusually bad, neither is the hero.


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[Assistant] And he has one particular tragic flaw, or haemartia, something like ambition, tyranny, stubbornness, or excess pride that causes him to make a critical mistake.


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[Assistant] As an example of these elements in action, let's look to Sophocles' Oedipus Rex, about a man who doesn't know he was adopted and is warned by an oracle that he's destined to murder his father and marry his mother.


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[Assistant] In trying to escape this fate, he kills a man who won't get out of his way at a crossroad.


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[Assistant] Ideally, after watching these tragic events unfold, we experience catharsis, a feeling of relief and emotional purification.


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[Assistant] But regardless of how you feel when you watch Poor Oedipus, never has there been a more salient reminder that no matter how bad things get, at least you didn't kill your father and marry your mother.


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[Assistant] Three days later, in the big new cemetery two miles from their house, the two old people said goodbye to their dead son. Then they went back to their dark old house. They did not want to live without Herbert.


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[Assistant] But they waited for something good to happen, something to help them. The days went by very slowly. Sometimes they did not talk, because there was nothing to say without Herbert. And so the days felt very long.


[User] Please transcribe this text

[Assistant] Then one night, about a week later, Mrs. White got out of bed because she could not sleep.


[User] Please transcribe this text

[Assistant] In the dark her husband heard her and he called, come back to bed. It's cold out there. It's colder for my son, his wife answered. He's out there in the cold cemetery. Mrs. White did not go back to bed. But Mr. White was old and tired and the bed was warm. So in the end he went to sleep again.


[User] Please transcribe this text

[Assistant] Suddenly he heard a cry from his wife. The paw, she cried. The monkey's paw. She came back to the bed and stood there. What is it? What's the matter? Mr. White cried. He sat up in bed. What's the matter? He thought. Why is she excited? What's she talking about? He looked at his wife. Her face was very white in the dark.


[User] Please transcribe this text

[Assistant] I want it. She said quietly. And you've got it. Give it to me. Please. What? Mr. White asked. The monkey's paw. Mrs. White said. Where is it? It's downstairs. Mr. White answered. Why? Mrs. White began to laugh and cry.


[User] Please transcribe this text

[Assistant] We can have two more wishes, she cried. We had one, but there are two more.


[User] Please transcribe this text

[Assistant] Oh no! Not again! Sink, woman! Mr. White cried. But Mrs. White did not listen.


[User] Please transcribe this text

[Assistant] Quickly, she said, go and get the paw.


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[Assistant] We're going to wish for our boy to come back to us. No! Mr. White cried. You're mad! Get it! Get it quickly! Mrs. White cried again. Mr. White said again, Think woman! Think!


[User] Please transcribe this text

[Assistant] Our boy was in the machinery for a long time. They didn't want to show him to us. Think! Do you want to see his body?


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[Assistant] Yes, he's my son. I'm not afraid of him, she answered. You don't understand, Mr. White said sadly, but he went downstairs to look for the monkey's paw.


[User] Please transcribe this text

[Assistant] In the living room it was dark, and Mr. White did not have a candle.


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[Assistant] Slowly he went across the room and he put out his hand for the monkey's paw. He touched it and quickly took his hand away again. No, he thought. I can't. I don't want to see Herbert. His face.


[User] Please transcribe this text

[Assistant] After he was in the machinery, no. Then he thought about his wife, and he put out his hand and took the paw. In the bedroom, his wife waited. She saw the paw in Mr. White's hand and cried, quick, make the wish. I can't, Mr. White answered. Remember, he died in the machinery.


[User] Please transcribe this text

[Assistant] Make the wish. I'm not afraid of my own son. Mrs. White cried again. Mr. White looked sadly at his wife, but he took the paw in his right hand and said slowly,


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[Assistant] I wish for my son Herbert to come back to us. Then he sat down in the nearest chair.


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[Assistant] She stayed there for a long time and she did not move. Nothing happened. The monkey's paw could not do it. Thank God, Mr. White said, and he went back to bed. Soon Mrs. White went to bed too.


[User] Please transcribe this text

[Assistant] Exactly, we're bringing you our very own ER drama here at EnglishPod.


[User] Please transcribe this text

[Assistant] Uhm, well, it's short for cardiopulmonary resuscitation.


[User] Please transcribe this text

[Assistant] Yes, so basically CPR is uhm, you know when someone stops breathing?


[User] Please transcribe this text

[Assistant] You put your mouth on their mouth and you breathe inside their lungs?


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[Assistant] Uhm, basically it's really serious that you stop breathing.


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[Assistant] Yeah, we're gonna come back and teach you some of the important language.


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[Assistant] But in the meantime, let's look at some great language in Language Takeaway.


[User] Please transcribe this text

[Assistant] Exactly. Uhm, when a patient can't breathe properly, sometimes the doctor takes a long tube.


[User] Please transcribe this text

[Assistant] Puts it into their mouth, um, and down their pipes, I guess, down their breathing tube.


[User] Please transcribe this text

[Assistant] So there's a bag attached to this intubation tube and you squeeze it to put air into the lungs.


[User] Please transcribe this text

[Assistant] Right, if a patient is in critical condition, uh, they're either really, really hurt, uhm, or really, really sick, and they could die pretty soon.


[User] Please transcribe this text

[Assistant] Exactly, when a patient is stabilized, uhm, they probably won't die in five minutes.