[User] Please transcribe this text

[Assistant] So when I go to my local, uh, the closest city to me, it has residential areas that have lots of houses or two story houses or small apartment buildings. And then every few blocks, there will be a commercial area where there's a grocery store and a Walmart and a few fast food restaurants.


[User] Please transcribe this text

[Assistant] They've designed the city to have specific areas. So once again, residential is where people live. Commercial is where you find lots of stores.


[User] Please transcribe this text

[Assistant] On the edge of the city you might have an industrial area or an industrial park. This is where you'll find factories and places where they do manufacturing. You might also find office buildings depending on the type of city that it is.


[User] Please transcribe this text

[Assistant] But a lot of cities have been divided up into distinct areas. If you've ever played the game Sim City


[User] Please transcribe this text

[Assistant] It might have a financial district. So if your city is a very large city like Toronto or Montreal or New York City.


[User] Please transcribe this text

[Assistant] There might be a specific area of the city where there are a lot of banks, where there's a lot of, um, uh, money investing people, stock brokers, and those types of things, mortgage companies, and we would call that the financial district. I think this is the Bank of England by the way.


[User] Please transcribe this text

[Assistant] Uhm, it's not a financial area. For some reason we now use the word district. So it's residential area, commercial area, industrial park or industrial area, and then financial district. So a place where you will find a lot of banks. And then we have a new


[User] Please transcribe this text

[Assistant] Newish word. Relatively new. And that word is green space.


[User] Please transcribe this text

[Assistant] Green space, uhm, is any place in the city where there's grass and trees. So it includes parks.


[User] Please transcribe this text

[Assistant] But it can also include small walkways and pathways. It can include having, uhm, like small parks or trees along the side of the road and it's a little wider than normal. Uh, it can include like a skate park with a lot of trees and grass around it. A skate park is where kids go to skateboard.


[User] Please transcribe this text

[Assistant] Uhm, in Canada we are trying to include more and more green space, uh, when we design our cities because green space is just really healthy for people.


[User] Please transcribe this text

[Assistant] So, uh, hopefully your city has a lot of green space and you enjoy it a lot. Um, living in a city with very little green space means you have to travel really far to get to a park or an outdoor area.


[User] Please transcribe this text

[Assistant] The theater district. If your city is big enough, you might have an area called the theater district. This is a place where you would go to see a play, not a movie, you would go to see a


[User] Please transcribe this text

[Assistant] Cities like Toronto have a theatre district. Uhm, this is the Royal Alexandra Theatre. I think I've actually been there. I think I saw Mamma Mia there when I was younger.


[User] Please transcribe this text

[Assistant] I think so. Uhm, and if you go to a city like London, it might have a theater district, a really cool place to go if you want to see a play.


[User] Please transcribe this text

[Assistant] Theaters have been closed of course for a very long time, but they are just starting to open again. I think in New York, uh, the theater district is starting to open. I think you can go see a show on Broadway now, uh, if you're vaccinated, I think. And in Toronto, I think things are starting to open up a little bit too.


[User] Please transcribe this text

[Assistant] You also have what's called the city limits. So a city actually has a border. I'm not sure if you knew that.


[User] Please transcribe this text

[Assistant] There is a spot where the city ends and the countryside begins or another city begins if there's two cities beside each other.


[User] Please transcribe this text

[Assistant] And we would call that the city limits. When you're driving to Toronto or when you're driving to a city like Hamilton, you will see a sign that says welcome to Toronto or welcome to Hamilton.


[User] Please transcribe this text

[Assistant] And that would be the city limits. It's the edge of the city. It's the border. There's no border guards. There's no line on the road. There's just a sign when you're driving along that says, you know, welcome to the city or it says you are now leaving the Toronto city limits or you're leaving Toronto. Uh, you would see that at the city limits.


[User] Please transcribe this text

[Assistant] There is something called a city block. I'm not sure this is the best picture, but you can see that, I don't think I can move my mouse on this picture, sorry. But there is a square design to this city, where you have a road, a road, another road, and another road, and everything in the middle would be called a city block.


[User] Please transcribe this text

[Assistant] We often use the word block when we're giving directions in a city. If someone says, uhm, is the museum that way?


[User] Please transcribe this text

[Assistant] The response, the response might be, yes, it's about four blocks. So go four blocks this way and then go two blocks to the right and you will arrive at the museum. So again, a city block is


[User] Please transcribe this text

[Assistant] That they have very geometric designs to them.


[User] Please transcribe this text

[Assistant] That style and so you have city blocks. A city block can also be a triangle, like you could call that a block, but usually it's a square.


[User] Please transcribe this text

[Assistant] And then neighborhood. So a neighborhood is just a word to describe an area.


[User] Please transcribe this text

[Assistant] Uhm, someone who knows the city. What's a nice neighborhood to move into? And they'll say, oh, there's a really nice neighborhood down by the lake. Uhm, you should move there. So a neighborhood...


[User] Please transcribe this text

[Assistant] Uhm, a neighborhood might have a lot of people who all came from one country. Uhm, sometimes that happens as well. Uhm, but definitely, uhm, if you move to the city, ask around and find out what's a good neighborhood to move into.


[User] Please transcribe this text

[Assistant] Uptown. So here, this is gonna be a little fun to define.


[User] Please transcribe this text

[Assistant] Some cities, most cities have a downtown.


[User] Please transcribe this text

[Assistant] Some cities have an uptown, but uptown means something different maybe in each city, okay? So uptown, you need to ask why it's called uptown, because it will be different. Uptown might mean the northern part of the city.


[User] Please transcribe this text

[Assistant] Uptown might mean the part of the city that's higher than the rest of the city. Uptown might mean where the rich people live.


[User] Please transcribe this text

[Assistant] So you really need to ask people in the city why it's called Uptown. In Toronto, Uptown is just the northwest part of the city. We call that Uptown, uh, Toronto.


[User] Please transcribe this text

[Assistant] Uhm, but you need to find out. So again, uptown can be the northern part of the city. Uptown might mean the part of the city that's higher than the rest of the city. It's kind of on a hill. Uptown might be the part of the city where all the rich people live.


[User] Please transcribe this text

[Assistant] Then we have something called Midtown. Midtown is usually similar to downtown.


[User] Please transcribe this text

[Assistant] In Toronto, Midtown refers to kind of the business area of the town. So if you go to Midtown Toronto,


[User] Please transcribe this text

[Assistant] The east end, the west end, the north end, the south end. So as a city gets larger, people might say, oh, you're looking for that. Oh, it's in the north end of the city or it's in the west end of the city. They might even say,


[User] Please transcribe this text

[Assistant] The side like the east side, the west side, the south side. Again, these are unique to each city.


[User] Please transcribe this text

[Assistant] In London, England, I think they use East End, West End, and those types of terms. Uhm, I think in New York, they might use East Side, West Side, they might even say Upper West Side. So you need to kind of learn your compass directions and the word end as well.


[User] Please transcribe this text

[Assistant] Things you might find in the city. So I'm gonna just go over a few things you'll find in the city that you probably won't see in the country.


[User] Please transcribe this text

[Assistant] Like one-way streets once I know where they are. I don't like one-way streets if I'm unfamiliar with a city because it can get confusing. You're, you, you wanna turn and then it's, you're not allowed to turn because it's a one-way street. You'll see mass transit.


[User] Please transcribe this text

[Assistant] So mass transit is the general term for things like the subway, the metro, uhm,


[User] Please transcribe this text

[Assistant] Let's see here, uh, streetcars, um, trams, uh, I think even taxis and Uber might fall under that, but I think mostly you're talking about city buses, the subway, the metro, maybe it's called, um,


[User] Please transcribe this text

[Assistant] I'm trying to think of, it might just call it the train, uhm, definitely street cars and those kinds of things. Uhm, and then in, I think in Quebec they had a funiculaire, which was like a little train elevator that went up.


[User] Please transcribe this text

[Assistant] Uhm, I don't, I said it included taxis and ubers, but I don't think it does. I think mass transit refers to definitely things like buses and the subway.


[User] Please transcribe this text

[Assistant] You'll definitely see skyscrapers in the city. A skyscraper is a very, very tall building. Uhm, one of the things I like about going to the city is I like looking at the skyscrapers.


[User] Please transcribe this text

[Assistant] Uh, it's really cool to see buildings that are that tall. You don't see those in the country. They're called skyscrapers because it looks like they touch the sky, like they're scraping against the sky. Very, very cool.


[User] Please transcribe this text

[Assistant] The shape of the city from far away that lets you identify it. If you see the skyline of Toronto, you can recognize it because you see the CN Tower. You might also see the Rogers Center. I think that's what it's called now. The Sky Dome, which is the white thing in the middle.


[User] Please transcribe this text

[Assistant] So when you look at a city from the outskirts of the city, especially at night, sometimes during the day, you can see the shape of the city, uh, and we would call that the skyline.


[User] Please transcribe this text

[Assistant] Uhm, your city might have a bike share program. A bike share program. These are becoming quite popular. In fact, there are even things uhm,


[User] Please transcribe this text

[Assistant] So you can rent a bike. You have an app on your phone and you can punch in a code and then you can rent a bike. I think in some cities it might even be free. Don't quote me on that. Uhm, and some cities do this with scooters as well. A scooter, a little electric scooter might be kind of cool to use if you are exploring a city.


[User] Please transcribe this text

[Assistant] But definitely you will find bike share programs in some cities.


[User] Please transcribe this text

[Assistant] You will find City Hall. Almost every city in their downtown area will have City Hall. It might also be called Town Hall. Even if it's a city it might be referred to as Town Hall.


[User] Please transcribe this text

[Assistant] Uhm, but city hall or town hall is the place where


[User] Please transcribe this text

[Assistant] I think a lot of people get married in churches still, but you, I think, can go to City Hall if you want to get married as well. And the justice of the peace will marry you.


[User] Please transcribe this text

[Assistant] And then of course city lights. One of the coolest things about cities, I think this is a city in China by the way, I'm not 100% sure.


[User] Please transcribe this text

[Assistant] Is the city lights. At night, a city is beautiful. I'll talk about light pollution a little bit later, but at night, a city has a beautiful glow to it and it is lit up and the city lights can just be really, really cool to look at. I, in particular,


[User] Please transcribe this text

[Assistant] Really like city lights. I don't live in the city, so I, I could appreciate them when I visit the city.


[User] Please transcribe this text

[Assistant] Conveniences. We've talked a little bit about this during the lesson, but one of the things that you have in the city is you have a lot of conveniences. I did a lesson a few months ago or a few weeks ago on conveniences.


[User] Please transcribe this text

[Assistant] Conveniences are things that make life nicer or easier. There are certainly a lot of conveniences in the city. You have taxis. You can get an Uber. You can get pizza delivered. Uhm, you can, let me see, you can go for a walk.


[User] Please transcribe this text

[Assistant] To almost anything, if you want to go to a restaurant or a bar or a theater or to go see a movie or to do some shopping, you can walk most likely.


[User] Please transcribe this text

[Assistant] That goes right through the city or maybe it goes around each side of the city.


[User] Please transcribe this text

[Assistant] I'm gonna go to the south end of the city. I'm gonna jump on the highway or I'm gonna jump on the bypass or I'm gonna jump on the beltway or belt line. Uhm, and it's just a way to quickly drive at highway speeds to get to another part of the city. Cities that have


[User] Please transcribe this text

[Assistant] A landmark is any recognizable feature of the city.


[User] Please transcribe this text

[Assistant] It might be natural or it might be something that was built there. Uhm, but a landmark is something that usually you can see from far away. So it could be a tower. Uhm, and it, it's just something that is unique to that city. You can also use it when you're walking around the city so you know where you are.


[User] Please transcribe this text

[Assistant] And like I said when I was saying how to give directions, you could say, uhm, look at this, walk toward the CN Tower, and then when you get to the bottom of it, you know, head north or something. So you can use it to give directions as well.


[User] Please transcribe this text

[Assistant] A lot of cities, big cities, will have what's called a convention center.


[User] Please transcribe this text

[Assistant] That can be used for different kinds of shows. What I mean by show though, isn't like a TV show, uhm, a show like, uh, in Toronto they'll have a car show.


[User] Please transcribe this text

[Assistant] And you can go there and see all the new cars. So Nissan and Lamborghini and, uhm, every car maker you can think of comes and shows their new cars. So you can go to the convention center for a car show. They might have a boat show. They might have a home and garden show.


[User] Please transcribe this text

[Assistant] Before COVID, there were a lot of shows that you could go to and almost all of them are at what's called a convention center. There might be a station in your city.


[User] Please transcribe this text

[Assistant] Main station in your town where you would go if you wanted to travel somewhere.


[User] Please transcribe this text

[Assistant] You might find a lot of parking garages, uhm, out in the country. We don't need parking garages, but in the city, there are buildings where you can drive your car and go all the way to the top or it might even go underground and you will park your car there. Uhm, in Toronto, there are a lot of parking garages. It's quite expensive.


[User] Please transcribe this text

[Assistant] To park your car in a parking garage. But uhm, it's sometimes, uh,


[User] Please transcribe this text

[Assistant] Yeah, I was gonna say it's it's sometimes shocks me how expensive it is to park in the city. It's quite a bit more expensive where I live. You don't pay to park anywhere, but in the city, you often have to pay for parking.


[User] Please transcribe this text

[Assistant] Your city, and a lot of cities are trying to do this, might have a bike path on the same road where cars go.


[User] Please transcribe this text

[Assistant] Uhm, sorry, it might have a bike lane on the same road where cars drive, or it might have special bike paths where only bikes are allowed to go. So I kind of messed that up, so let me re-explain. A bike path is a path. It might be paved or it might be graveled just for bikes.


[User] Please transcribe this text

[Assistant] A bike lane is a part of an existing road where bikes are allowed to drive on. A lot of cities are trying to increase the number of bike paths and bike lanes that they have because a city that is bikeable


[User] Please transcribe this text

[Assistant] In the world with the most bikes and they have really cool designs for their cities so that you can bike everywhere.


[User] Please transcribe this text

[Assistant] Okay, I'm gonna end with three negatives. Are you ready? Three things that I don't like about the city. Number one, the smell. When I go to the city, you might think that Canada is this beautiful country and everything is wonderful, but when you go to the city, the city has


[User] Please transcribe this text

[Assistant] It smells a little bit like garbage. It has certain odors and smells. Not everywhere, but sometimes you're walking down the street and you'll just smell like maybe there's a dumpster full of garbage and maybe the garbage is rotting a little bit.


[User] Please transcribe this text

[Assistant] Uhm, but so the first negative thing about cities, sometimes they smell.


[User] Please transcribe this text

[Assistant] Number two, sometimes there is a lot of pollution. Cities sometimes have smog. Where I live, the air is always clear. There's never any smog here. Sometimes it gets a little humid.


[User] Please transcribe this text

[Assistant] Earlier this summer there were forest fires, so there was some pollution, but generally there's no pollution in the country. In the city, depending on your city, there might be a lot of smog, there might be a lot of pollution in the air.


[User] Please transcribe this text

[Assistant] And then light pollution. I mentioned how much I love the city lights. I love the look of the city at night. Especially when it rains. Because it makes everything have like a really cool look to it. Uhm, but some people don't like the light pollution. If you were someone who liked


[User] Please transcribe this text

[Assistant] Astronomy and you had a telescope and you liked looking at the stars. If you live too close to the city, the city has so much light coming out of it. It makes it hard to do that. So, three things I don't like about the city, smell, pollution, and light pollution. That's it. Other than that, I like most things about the city.


[User] Please transcribe this text

[Assistant] My siblings and I were waiting for my mother to come back from the hospital. She was there because my grandmother had cancer surgery that day.


[User] Please transcribe this text

[Assistant] She started sobbing and immediately said, we must make arrangements. Your grandmother's dying wish was to be buried back home in Korea.


[User] Please transcribe this text

[Assistant] I was barely 12 years old, and when the shock wore off, my mother's words were ringing in my ears. My grandmother wanted to be buried back home.


[User] Please transcribe this text

[Assistant] We had moved from Korea to Argentina six years prior.


[User] Please transcribe this text

[Assistant] I felt very much Argentinian, but my looks betrayed me at times. I remember on the first day of middle school, my Spanish literature teacher came into the room, she scanned all my classmates and she said,


[User] Please transcribe this text

[Assistant] You have to get a tutor, otherwise you won't pass this class. But by then, I was fluent in Spanish already, so it felt as though I could be either Korean or Argentinian, but not both.


[User] Please transcribe this text

[Assistant] It felt like a zero-sum game, where I had to give up my old identity to be able to gain or earn a new one.


[User] Please transcribe this text

[Assistant] Hoping that finally I could find a place to call home. But there, people ask me, why do you speak Korean with a Spanish accent?


[User] Please transcribe this text

[Assistant] And you must be Japanese because of your big eyes and your foreign body language.


[User] Please transcribe this text

[Assistant] And so it turns out that I was too Korean to be Argentinian, but too Argentinian to be Korean.


[User] Please transcribe this text

[Assistant] I have failed to find that place in the world to call home, but how many


[User] Please transcribe this text

[Assistant] Japanese-looking Koreans who speak with a Spanish accent or even more specific, Argentinian accent, do you think are out there?


[User] Please transcribe this text

[Assistant] So with this realization in mind, I decided to embrace all of the different versions of myself, even allow myself to reinvent myself at times. So, for example, in high school,


[User] Please transcribe this text

[Assistant] I have to confess, I was a mega nerd. I had no sense of fashion, thick glasses, and cool hairstyle. You can get the idea, I think. Actually, I only had friends because I shared my homework. That's the truth.


[User] Please transcribe this text

[Assistant] But once at university, I was able to find a new identity for myself. And the nerd became a popular girl.


[User] Please transcribe this text

[Assistant] But it was MIT, so I don't know if I can take too much credit on that. As they say over there, the odds are good, but the goods are odd.


[User] Please transcribe this text

[Assistant] I switched majors so many times that my advisors joked that I should get a degree in random studies.


[User] Please transcribe this text

[Assistant] I tell this to my kids. And then over the years, I have gained a lot of different identities. Uhm, I started as an inventor, entrepreneur, social innovator, then I became an investor, uhm, a woman in tech, a teacher, and most recently, I became a mom.


[User] Please transcribe this text

[Assistant] Or as my toddler says repeatedly,


[User] Please transcribe this text

[Assistant] Reinventing yourself can be very hard. You can face a lot of resistance at times. When I was nearly done with my PhD,


[User] Please transcribe this text

[Assistant] Uhm, I got beaten by that entrepreneurial bug. I was in Silicon Valley. And so, writing a thesis


[User] Please transcribe this text

[Assistant] In the basement didn't seem as interesting as starting my own company, so I went to my very traditional Korean parents who are here today with the task of letting them know that I was going to drop out from my PhD program.


[User] Please transcribe this text

[Assistant] But fortunately, I had a secret weapon with me, which was a chart that had


[User] Please transcribe this text

[Assistant] The average income of all of the graduates from Stanford PhD programs, and then the average income of all of the dropouts from Stanford graduate programs.


[User] Please transcribe this text

[Assistant] My identity quest is no longer to find my tribe. It's more about allowing myself to embrace all of the possible permutations of myself.


[User] Please transcribe this text

[Assistant] I should mention now that my husband


[User] Please transcribe this text

[Assistant] It's actually from Denmark. Just in case I don't have enough culture shocks in my life, I decided to marry a Danish guy. In fact, I think my kids will be the first Vikings who will have a hard time growing a beard when they become older. Yeah, we'll have to work on that. But I really hope


[User] Please transcribe this text

[Assistant] That they will find that their multiplicity


[User] Please transcribe this text

[Assistant] I hope that instead of feeling anxious and worried that they don't fit in that one box or that their identity will become irrelevant someday,


[User] Please transcribe this text

[Assistant] That they can feel free to experiment and to take control of their personal narrative and identity.


[User] Please transcribe this text

[Assistant] I also hope that they would use their unique combination of values and languages, uh, and cultures and skills to help create a world where identities are no longer used to alienate what looks different, but rather to bring together people.


[User] Please transcribe this text

[Assistant] And most importantly, I really hope that they find tremendous joy in going through this


[User] Please transcribe this text

[Assistant] Uncharted territories, because I know I have.


[User] Please transcribe this text

[Assistant] Now as for my grandmother, her last wish was also her last lesson to me. It turns out that


[User] Please transcribe this text

[Assistant] It was about resting next to her son, who had died long before she moved to Argentina. What mattered to her


[User] Please transcribe this text

[Assistant] Was not the ocean that divided her past and new world.


[User] Please transcribe this text

[Assistant] There are four types of loops in Patrell. This module discusses the four types. Start off again, creating a new workflow. We'll call this one loop types.


[User] Please transcribe this text

[Assistant] Move it down here. Again, when we click apply, it's now stored down here at the bottom of the input paint. Start off the same way. Comments. Empty line.


[User] Please transcribe this text

[Assistant] A discussion of loop types with workflows. The first type is called a for loop. All of the loops are in the utilities tab up here in this statement area. For loop we can bring in. Once again, we need to make sure it's in the right location.


[User] Please transcribe this text

[Assistant] And this again has a variable, here it's called $L, I'm just going to call it $loop to make it a little bit bigger. And it goes from, for example, 1 through 7. I'm going to put up here a clear all messages, so we can again see things in the message log. After our loop starts, we can put a message in that just says


[User] Please transcribe this text

[Assistant] Loop counter, dollar, loop. And all loops have to end with an end loop, like right here. No matter what type it is, they all end with this end loop. So if we put that in and run this one simple loop type, move it over a little bit again so we can see. Here's got loop counter, one through seven.


[User] Please transcribe this text

[Assistant] A second type of loop is for all icons in. So I'm going to put that one down here below this one.


[User] Please transcribe this text

[Assistant] Now with this one, we can give it a list of things in a folder and it can be any folder. It can be, for example, the surfaces depth folder. We put that loop in here and then a child reference. This refers to each time through the loop, what it refers to. And that's described by a variable. We can use, for example, variable D here.


[User] Please transcribe this text

[Assistant] Now, if this recursive box is checked,


[User] Please transcribe this text

[Assistant] That means that if there are folders inside this folder that we checked, it will also go down into them. This of type, if we would put something in here like


[User] Please transcribe this text

[Assistant] Use the blue arrow to input something, then it would only work on the things that are of that certain type. But it will go through this loop however many times there are things in this folder.


[User] Please transcribe this text

[Assistant] You know, if there's 100 things in the folder, it will go through it 100 times. The first time through variable D will refer to the first thing in the folder.


[User] Please transcribe this text

[Assistant] The second time, the second thing in the folder, and so on.


[User] Please transcribe this text

[Assistant] So what we can do is get the name each time through the loop. And so let's put a get name in there.


[User] Please transcribe this text

[Assistant] And we'll call it dollar name D, and since this is variable D here, we can tell it we want to refer to variable D. Put a message in.


[User] Please transcribe this text

[Assistant] Now in between these, I'm just going to put a blank message.


[User] Please transcribe this text

[Assistant] So that we get a blank line in our message log between all these.


[User] Please transcribe this text

[Assistant] So here we're looping through dollar name D. Once again, an end loop. You can either bring it over from the utilities tab. You can copy.


[User] Please transcribe this text

[Assistant] And paste as you want. So now we have two loops in here. And if we apply test and run,


[User] Please transcribe this text

[Assistant] We get our loop counter one through seven. We also get looping through each of these. I'm going to actually put this in quotes so it sets it off a little bit better. And just as an example,


[User] Please transcribe this text

[Assistant] Show you that it can also loop, for example, through this folder, the stratigraphy folder, and well tops. So I can put that in here. And when I apply test and run.


[User] Please transcribe this text

[Assistant] Now we can see it's again gone through our loop counter and now it's looping through all the things in the stratigraphy folder. Now, what you do between the for all icons in loop and the in loop depends on what it's a loop through. You can do a variety of different operations in there.


[User] Please transcribe this text

[Assistant] Again, I'm going to put a space in here to, uh,


[User] Please transcribe this text

[Assistant] Make sure we can see it in our message log a little bit better. It's called a while loop. And this loop works while something is true.


[User] Please transcribe this text

[Assistant] So I'm going to do a numeric expression and put it right above that and say while dollar count, or I'm going to say dollar count equals one.


[User] Please transcribe this text

[Assistant] Starting and then here I'll say do while, dollar count.


[User] Please transcribe this text

[Assistant] Is less than five. Then after I start my loop, I need to increment my counter.


[User] Please transcribe this text

[Assistant] So I'll say dollar counter, dollar count equals dollar count plus one.


[User] Please transcribe this text

[Assistant] Print a message out saying what the count is.


[User] Please transcribe this text

[Assistant] So this will work while dollar counts less than five and it will print out the number of dollar count. And I'm going to put this up above the incremental. So here, apply, test, run. Our first two are the same. It did dollar count one, two, three, and four. It helps if I spell count correctly. So we can try that again. Apply, test, and run.


[User] Please transcribe this text

[Assistant] That count is spelled correctly and it did it until it was less than five or until it did it while it was less than five. So.


[User] Please transcribe this text

[Assistant] Once again, I'm going to put spacers in here for our fourth type of lube.


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[Assistant] Now before we can use this, we have to set up the list and it can be done anywhere.


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[Assistant] We can have more columns in here if we want for different counters.


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[Assistant] For different variables, so I'm going to use variable M here.


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[Assistant] Use this blue arrow to put in as variable M. Then down here, however many times there are things in the list. So, I'm going to create four items in this list.


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[Assistant] And the first one I'm going to put from the well tops, base Cretaceous from the well tops. For the third one, I'm going to put top Ness from the well tops. And then I can come down and get things like top Tarbert out of this folder and top Ediv out of this folder. And that sets up my list for variable M. When I hit okay, now down here for for all icons in list, I need to tell it that I want to use


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[Assistant] Paste down here, but now I'm going to call it dollar name M, since it's coming out of variable M.


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[Assistant] And looping the dollar name M. Now obviously whatever is inside this loop has to be compatible with the things in that list. Also need to put an end loop down here on the end of it.


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[Assistant] And so now I have four types of loops. Let's make sure this last one works.


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[Assistant] I had a problem. Set reference list at line 23. There is a problem in that. I need to tell it which one I'm going to use here. So I can also put variable M in there.


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[Assistant] And run it. Here's our first loop, second loop, third loop, and now the last loop.


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[Assistant] So those are the four different types of loops available in Petrel.


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[Assistant] Then in the input pane, make sure the well tops is highlighted. If we're adding stratigraphy, make sure stratigraphy is highlighted. If we're adding faults, make sure faults is highlighted. And since we're adding an existing one, highlight the horizon that you want added. In this case, horizon two. Now, back in the call out for the tool palette, since tarbert two is highlighted here, we will see tarbert two in this box. That way we and Patrell both know


[User] Please transcribe this text

[Assistant] You can also insert duplicate tops.


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[Assistant] Put that, pull it down, select the columns that you want to edit. In this case, we want to edit Z. So the background.


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[Assistant] As soon as you click apply or okay, it will also disappear in case it's being displayed from any place else in the trail.


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[Assistant] Just straight lines. To patrol, that means these are all conformable horizons.


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[Assistant] When the settings come up, go to the settings tab there, and here we have horizon time. When we change it to erosional and hit the apply button, then the display changes to this squiggly line, which in Patrell means an erosional horizon.


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[Assistant] What happens when technology knows more about us than we do?


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[Assistant] A computer now can detect our slightest facial micro-expressions and be able to tell the difference between a real smile and a fake one. That's only the beginning. Technology has become incredibly intelligent and already knows a lot about our internal states. And whether we like it or not,


[User] Please transcribe this text

[Assistant] We already are sharing parts of our inner lives that's out of our control.


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[Assistant] Studying the circuits in the brain that create the unique perceptual realities that we each have.


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[Assistant] And now I bring that together with the capabilities of current technology to create new technology that does make us better, feel more, connect more. And I believe to do that, we have to be OK losing some of our agency. With some animals, it's really amazing.


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[Assistant] And we get to see into their internal experiences.


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[Assistant] We get this upfront look at the mechanistic interaction between


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[Assistant] This is where evolutionary pressures like eating, mating and making sure we don't get eaten.


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[Assistant] It turns out some spiders tune their webs like violins to resonate with certain sounds.


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[Assistant] And likely the harmonics of my voice, as it went higher, coupled with how loud I was singing, recreated either the predatory call of an echolocating bat or a bird, and the spider did what it should. It predictively told me to bug off. I love this. The spider's responding to its external world.


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[Assistant] In a way that we get to see and know what's happening to its internal world.


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[Assistant] We like to think we have cognitive control over what people see, know and understand about our internal states, our emotions, our insecurities, our bluffs, our trials and tribulations and how we respond. We get to have our poker face. Or maybe we don't. Try this with me. Your eye responds to how hard your brain is working.


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[Assistant] The response we're about to see is driven entirely by mental effort and has nothing to do with changes in lighting. We know this from neuroscience. I promise, your eyes are doing the same thing as the subject in our lab, whether you want them to or not. But first you'll hear


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[Assistant] Some voices, try and understand them and keep watching the eye in front of you.


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[Assistant] Your pupil doesn't lie. Your eye gives away your poker face.


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[Assistant] When your brain's having to work harder, your autonomic nervous system drives your pupil to dilate. When it's not, it contracts.


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[Assistant] We might think we have more agency over the reveal of our internal state than that spider, but maybe we don't. Today's technology is starting to make it


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[Assistant] Really easy to see the signals and tells that give us away.


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[Assistant] Our bodies radiate our stories from changes in the temperature of our physiology.


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[Assistant] The dynamic signature of our thermal response gives away our changes in stress, how hard our brain is working, whether we're paying attention and engaged in the conversation we might be having.


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[Assistant] And even whether we're experiencing a picture of fire as if it were real, we can actually see people give off heat on their cheeks in response to an image of flame.


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[Assistant] But aside from giving away our poker bluffs, what if


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[Assistant] Dimensions of data from someone's thermal response gave away a glow of interpersonal interest.


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[Assistant] Tracking the honesty of feelings in someone's thermal image might be a new part of how we fall in love and see attractions.


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[Assistant] Our technology can listen, develop insights and make predictions about our mental and physical health just by analyzing the timing dynamics of our speech and language picked up by microphones.


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[Assistant] Groups have shown that changes in the statistics of our language paired with machine learning can predict the likelihood someone will develop psychosis.


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[Assistant] I'm going to take it a step further and look at linguistic changes and changes in our voice that show up with a lot of different conditions. Dementia.


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[Assistant] Diabetes can alter the spectral coloration of our voice.


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[Assistant] Changes in our language associated with Alzheimer's can sometimes show up more than 10 years before clinical diagnosis. What we say and how we say it tells a much richer story than we used to think.


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[Assistant] And devices we already have in our homes could, if we let them, give us invaluable insight back. The chemical composition of our breath gives away our feelings. There's a dynamic mixture of acetone, isoprene and carbon dioxide


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[Assistant] The changes when our heart speeds up, when our muscles tense. And all without any obvious change in our behaviors. Alright.


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[Assistant] I want you to watch this clip with me. Some things might be going on on the side screens.


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[Assistant] But try and focus on the image in the front and the man at the window.


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[Assistant] I'm actually tracking the carbon dioxide you exhale in the room right now.


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[Assistant] But let's just measure in real time with high precision the continuous differential concentration of CO2.


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[Assistant] Clouds on the sides are actually the real-time data visualization of the density of our CO2. You might still see a patch of red on


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[Assistant] It's our collective suspense driving a change in carbon dioxide. Alright, now watch this with me one more time.


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[Assistant] You knew it was coming. But it's a lot different when we change the creator's intent. Changing the music.


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[Assistant] And the sound effects completely alter the emotional impact of that scene. And we can see it in our breath. Suspense, fear, joy.


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[Assistant] All show up as reproducible, visually identifiable moments.


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[Assistant] Our spaces, our technology, we'll know what we're feeling. We will know more about each other than we ever have.


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[Assistant] We get a chance to reach in and connect that experience and sentiments.


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[Assistant] That are fundamental to us as humans in our senses, emotionally and socially. I believe it is the era of the empath.


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[Assistant] And we are enabling the capabilities that true technological partners can bring to how we connect with each other and with our technology.


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[Assistant] If we recognize the power of becoming technological empaths, we get this opportunity where technology can help us bridge the emotional and cognitive divide. And in that way, we get to change how we tell our stories.


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[Assistant] We can enable a better future for technologies like augmented reality to extend our own agency and connect us at a much deeper level.


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[Assistant] Imagine a high school counselor being able to realize that an outwardly cheery student really was having a deeply hard time, where reaching out could make a crucial positive difference. Or authorities, being able to know the difference between someone having a mental health crisis and a different type of aggression, and responding accordingly. Or an artist, knowing the direct impact of their work,


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[Assistant] Today's technologies will know and can know what we're experiencing on the other side. And this means we can be closer and more authentic. But ...


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[Assistant] I realize a lot of us have a really hard time with the idea of sharing our data.


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[Assistant] And especially the idea that people know things about us that we didn't actively choose to share. Anytime we talk to someone, look at someone, or choose not to look, data is exchanged, given away, that people use to learn, make decisions about their lives and about ours,


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[Assistant] I'm not looking to create a world where our inner lives are ripped open and our personal data and our privacy given away to people and entities where we don't want to see it go. But I am looking to create a world where we can care about each other more effectively


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[Assistant] And we can have richer experiences from our technology. Any technology can be used for good or bad.


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[Assistant] Transparency to engagement and effective regulation are absolutely critical to building the trust for any of this.


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[Assistant] Benefits that empathetic technology can bring to our lives are worth solving the problems that make us uncomfortable.


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[Assistant] And if we don't, there are too many opportunities and feelings we're going to be missing out on. Thank you.


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[Assistant] Tiffany and Miguel will do whatever it takes. I really need to win the new challenge that's coming up and maybe with that, that will bring confidence.


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[Assistant] Tiffany's very headstrong. I wish she would just listen more. Everything that I kind of would bring up just kept getting poo-pooed.


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[Assistant] Running the front of the house and really making the experience a pleasure. That was where I excelled in this challenge. Tiffany had her vision and everything and that's great, but you can't be everything in an equation when there's a team of three.


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[Assistant] Leanne, what do you think went wrong for your team? I was not necessarily comfortable with the team that I had.


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[Assistant] Miguel did not really know that much about traditional Spanish cuisine. But he was happy to sit back and play the sous chef.


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[Assistant] I'll be honest with you, I have yet to be like wowed by one of your dishes. So, uh, I'll buy you a beer when we get back to New York.


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[Assistant] The progress bar is just an indicator on a computer that something's happening inside the device.


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[Assistant] Something happened in the 70s that is sometimes referred to as the software crisis, where suddenly computers were getting more complicated more quickly than anyone had been prepared for from a design perspective.


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[Assistant] People were using percent done indicators in different ways, so you might have a graphical countdown clock, or they would have a line of asterisks that would fill up from left to right on the screen.


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[Assistant] This graduate student named Brad Myers in 1985 decided he would study this. He found that it didn't really matter if the percent done indicator was giving you the accurate percent done.


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[Assistant] What mattered was that it was there at all. Just seeing it there made people feel better. And that was the most surprising thing. He has all these ideas about what this thing could do. Maybe it could make people relax effectively.


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[Assistant] Maybe it would allow people to turn away from their machine and do something else of exactly the right duration. They would look and say, oh, the progress bar's half done, that took five minutes or whatever, so now I have five minutes to send this fax from what people were doing in 1985.


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[Assistant] Both of those things are wrong, like when you see that progress bar, it sort of locks your attention in a tractor beam and it turns the experience of waiting into this exciting narrative that you're seeing unfold in front of you and somehow this time that you've spent waiting in frustration for the computer to do something


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[Assistant] Has been reconceptualized as progress. Oh, great stuff is happening.


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[Assistant] But once you start thinking about the progress bar as something that's more about dulling the pain of waiting, well then you can start fiddling around with the psychology. So if you just have a progress bar that just moves at a constant rate, let's say that's really what's happening in the computer.


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[Assistant] Well now you can start trying to enhance it. Make it appear to move more quickly than it really is. Make it move faster at the beginning, like a burst of speed that's exciting. People feel like, oh, something's really happening. And then you can kind of move back into a more naturalistic growth of the progress bar as you go along.


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[Assistant] So the progress bar at least gives you the vision of a beginning and an end and you're working towards a goal. I think in some ways it mitigates the fear of death.


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[Assistant] The first dish that we tried was some sauteed spot prawns with a lemongrass crab salad and an oba filet of fish.


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[Assistant] I just have a good connection with Harold and I would like to work with Harold.


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[Assistant] You could never expect them to come here and sit there and lolly da, you know what I mean? So I'm gonna let you guys down, you know? Nah, it's not about letting us down, man. It's about us picking you up, bro. I promise you.


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[Assistant] Alright y'all, back in my knife. But it was nice, I had a good time hanging with all y'all.


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[Assistant] To creating healthy recipes, because I'm great at it.


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[Assistant] I thought the idea of a gnocchi was neat. I've never seen, uh, stuffed gnocchi, so I thought it was kind of, you know, innovative, although it didn't quite work. Uhm, gummy and pasty, although, you know, I'll give her credit for something a little bit different.


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[Assistant] By 1610, the world's first joint stock company, the Dutch East India Company, was ready to conquer the world.


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[Assistant] This was an extremely difficult challenge. We finally realized how much sight plays a role in the kitchen as far as how we cook. But here are the results.


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[Assistant] There were two of you that only got one right, and those are Tiffany and Miguel.


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[Assistant] If you have the skill and you want to win this, just go like, hell no, I'm doing this. I'm taking this card over, I'm gonna drive. If I'm gonna go down in a burning plane, I'm gonna be at the wheel.


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[Assistant] Today's the day, Beltha. A lick of feet, and you'll be ready for the high seas once more.


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[Assistant] Oh, not the cookie jar. Looks like Bertha will have to wait.


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[Assistant] And I would have thought a fine hen like you would have known better, Hatsy. Yes, so too. But, all in all, it's not so bad.


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[Assistant] And once in a while, this habit plays tricks on me. For example, if I feel unexpected pain in my body that I've not experienced before and that I cannot attribute.


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[Assistant] Then all of a sudden, my mind might turn a tense back into a heart disease or calf muscle pain into deep vein thrombosis.


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[Assistant] But so far, I haven't been diagnosed with any deadly or incurable disease. Sometimes things just hurt for no clear reason. But not everyone is as lucky as me. Every year, more than 50 million people die worldwide.


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[Assistant] Especially in high-income economies like ours, a large fraction of deaths is caused by slowly progressing diseases.


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[Assistant] Heart disease, chronic lung disease, cancer, Alzheimer, diabetes, just to name a few.


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[Assistant] But we are at a stage where further advancement in health cannot be achieved only by developing new treatments. And this becomes evident when we look at one aspect that many of these diseases have in common.


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[Assistant] Because of this, there's a persisting need for new ways.


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[Assistant] By means of tests that can be applied rapidly and easily. That's a long definition, so let me repeat it.


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[Assistant] Identification of unrecognized disease in an apparently healthy person


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[Assistant] By means of tests that can be applied both rapidly and easily. And I want to put special emphasis on the words rapidly and easily, because many of the existing screening methods are exactly the opposite.


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[Assistant] And those of you who have undergone colonoscopy as part of a screening program for colorectal cancer will know what I mean.


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[Assistant] This ranges from imaging techniques, such as radiography or magnetic resonance imaging,


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[Assistant] To the analysis of blood or tissue, we have all had such tests.


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[Assistant] But besides these five, there are hundreds of other components that are present in very low quantity.


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[Assistant] These are called volatile organic compounds, and we release hundreds, even thousands of them, every time we exhale.


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[Assistant] The analysis of these volatile organic compounds in our breath is called breath analysis.


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[Assistant] In fact, I believe that many of you have already experienced breath analysis. Imagine.


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[Assistant] You're driving home late at night, when suddenly there's a friendly police officer who asks you kindly but firmly,


[User] Please transcribe this text

[Assistant] And determine whether driving in your condition is a clever idea. Now I'd say my driving was pretty good, but let me check.


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[Assistant] Now imagine a device like this one that does not only measure alcohol levels in your breath, but that detects diseases like the ones I've shown you and potentially many more.


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[Assistant] The concept of correlating the smell of a person's breath with certain medical conditions, in fact, dates back to ancient Greece.


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[Assistant] But only recently, research efforts on breath analysis have skyrocketed, and what once was a dream is now becoming reality. And let me pull up this list again that I showed you earlier. For the majority of diseases listed here,


[User] Please transcribe this text

[Assistant] There's substantial scientific evidence suggesting that the disease could be detected by breath analysis. But how does it work exactly?


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[Assistant] The essential part is a sensor device that detects the volatile organic compounds in our breath.


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[Assistant] Simply put, when exposed to a breath sample, the sensor outputs a complex signature that results from the mixture of volatile organic compounds that we exhale.


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[Assistant] Now, this signature represents a fingerprint of your metabolism, your microbiome and the biochemical processes that occur in your body.


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[Assistant] If you have a disease, your organism will change. And so will the composition of your exhaled breath.


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[Assistant] And then, the only thing that is left to do is to correlate a certain signature with the presence or absence of certain medical conditions.


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[Assistant] Firstly, the sensor can be miniaturized and integrated into small handheld devices like this alcohol breath tester.


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[Assistant] And thirdly, the technology is so flexible.


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[Assistant] That the same device could be used to detect a broad range of medical conditions.


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[Assistant] Breath analysis could be used to screen for multiple diseases at the same time.


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[Assistant] On the contrary, existing medical tools often lead to rather high costs per test.


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[Assistant] That the test can only be performed on a narrow part of the population, for example, the high-risk population, and B,


[User] Please transcribe this text

[Assistant] That the number of tests per person needs to be kept at a minimum.


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[Assistant] But wouldn't it actually be beneficial if the test was performed on a larger group of people and more often and over a longer period of time for each individual?


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[Assistant] Especially the latter would give access to something very valuable that is called longitudinal data.


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[Assistant] Nowadays, medical decisions are often based on a limited data set, where only a glimpse of a patient's medical history is available for decision making. In such a case,


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[Assistant] Abnormalities are typically detected by comparing a patient's health profile to the average health profile of a reference population.


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[Assistant] Longiturnal data would open up a new dimension and allow abnormalities to be detected based on a patient's own medical history. This will pave the way for personalized treatment. Sounds pretty great, right?


[User] Please transcribe this text

[Assistant] Now you will certainly have a question that is something like, if the technology is as great as he says, then why aren't we using it today?


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[Assistant] And the only answer I can give you is, not everything is as easy as it sounds.


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[Assistant] So that the sampling process itself does not alter the result of the analysis. And there's the need for data.


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[Assistant] Breath analysis needs to be validated in clinical trials, and enough data needs to be collected.


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[Assistant] So that individual conditions can be measured against baseline. Breath analysis can only succeed


[User] Please transcribe this text

[Assistant] If a large enough dataset can be generated and made available for broad use.


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[Assistant] Transform it from a reactive system that treatment is triggered by symptoms of disease to a proactive system.


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[Assistant] That disease detection, diagnosis and treatment can happen at an early stage, way before any symptoms occur.


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[Assistant] Now this brings me to my last point, and it's a fundamental one. What exactly is a disease?


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[Assistant] Imagine that breath analysis can be commercialized, as I describe it, and early detection becomes routine.


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[Assistant] This is called overdiagnosis, and it leads to a dilemma.


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[Assistant] If a disease is identified, you could decide not to treat it, because there's a certain probability that you would never suffer from it.


[User] Please transcribe this text

[Assistant] Your second option is to undergo early treatment with the hope for curing it. But often this would not come without side effects.


[User] Please transcribe this text

[Assistant] The bigger problem is not overdiagnosis, it's overtreatment. Because not every disease has to be treated immediately, just because a treatment is available.


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[Assistant] The increasing adoption of routine screening will raise the question.


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[Assistant] What do we call a disease that can rationalize treatment? And what is just an abnormality that should not be a source of concern?


[User] Please transcribe this text

[Assistant] My hopes are that routine screening using breath analysis


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[Assistant] Can provide enough data and insight so that at some point, we'll be able to break this dilemma and predict with sufficient certainty whether and when to treat at early stage.


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[Assistant] Our breath and the mixture of volatile organic compounds that we exhale hold tremendous amount of information on our physiological condition. With what we know today, we have only scratched the surface.


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[Assistant] Breath analysis should provide us with a powerful tool not only to proactively detect specific diseases, but to predict and ultimately prevent them.


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[Assistant] And this should be enough motivation to embrace the opportunities and challenges that breath analysis can provide.


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[Assistant] Even for people that are not part-time hypochondriacs like me. Thank you.


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[Assistant] I'm so passionate about what I do and I love what I do, you know, and I just, uhm, I wanna stay around and cook a little bit more.


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[Assistant] No wait a minute, did he talk about the gals and who was hot and who was not hot?


[User] Please transcribe this text

[Assistant] I agree with that 100%. I don't think all chefs are competitive. I do think there's a greater, a greater voice with it. And that is that when you love, when you love a thing, they love that each person comes up with their stuff and they can do it together. It's not competitive like Cutthroat like I'm in Wall Street. I gotta beat out this guy for money.


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[Assistant] Wait a minute, Tiffany, I gotta say this honestly. When I- I'll never forget the first thing I made a joke about when I was- I- I think it was like a little artichoke heart there, and you said, What is this mess? I went, Oh, oh honey. I said, Well, it's mine, I'll pick it up. When you said it to me, I wanted to shoot you.


[User] Please transcribe this text

[Assistant] The Mixed Up Chameleon by Eric Carle.


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[Assistant] It moved onto a brown tree and turned brownish.


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[Assistant] Then it rested on a red flower and turned reddish.


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[Assistant] When the chameleon moved slowly across the yellow sand, it turned yellowish. You could hardly see it.


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[Assistant] Down sideways. Until it spotted a fly.


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[Assistant] And then the chameleon's long and sticky tongue shot at it and caught the fly. That was its life. It was not very exciting.


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[Assistant] The chameleon thought, how small I am, how slow, how weak. I wish I could be big and white like a polar bear. And the chameleon's wish came true. But was it happy? No.


[User] Please transcribe this text

[Assistant] I wish I could swim. Like a fish.


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[Assistant] I wish I could run. Like a deer.


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[Assistant] I wish I could see things far away. Like a giraffe.


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[Assistant] I wish I could be like people. Just then a fly flew by.


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[Assistant] Hey guys, thanks for watching The Mixed Up Chameleon by Eric Carle.


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[Assistant] If you did, please click thumbs up. Don't forget to subscribe to the channel. See you soon. Thanks.


[User] Please transcribe this text

[Assistant] Part of being a good chef is accepting where you're weak and being able to do what you can in order to like, do the right thing.


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[Assistant] Stephen was also the only person that only focused on his dish without focusing on any petit fours or the cake.


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[Assistant] Now patrol does not automatically expand the header so you can see all the information. So put your cursor right here on the border of the header and the rest of the well section and the cursor will turn into a two headed arrow with the arrow heads pointing up and down.


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[Assistant] Hold the left mouse button down and drag it till you can again see all the information in the header.


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[Assistant] A B Happy! B A B Sad! My Leslie packed Sally. A B Happy! B A B Sad!


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[Assistant] Good night, buddy. Baby, sob. Sad baby.


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[Assistant] The end. Subscribe to Joshi's Storytime.


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[Assistant] In the morning for the bill, bill, bill.


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[Assistant] And he knocks on the door with a rattatatat.


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[Assistant] He wrote on the paper for a pill, pill, pill. I'll be back in the morning for the pill, pill, pill.


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[Assistant] Llama llama red pajama.


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[Assistant] I'm a bit closer to this guy than you are. You can stand on my back.


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[Assistant] Mmm, wee rabbit's tree house is definitely taller than I am, Lily.


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[Assistant] So, sometimes I get angry. And it took me many years to be able to say just those words. In my work, sometimes my body thrums. I'm so enraged.


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[Assistant] But no matter how justified my anger has been throughout my life,


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[Assistant] I've always been led to understand that my anger is an exaggeration, a misrepresentation, that it will make me rude and unlikable.


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[Assistant] Mainly as a girl, I learned, as a girl, that anger is an emotion better left entirely unvoiced.


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[Assistant] Think about my mother for a minute. When I was 15, I came home from school one day, and she was standing on a long veranda outside of our kitchen, holding a giant stack of plates.


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[Assistant] Now you can see, you can see how a child would look at an incident like this and think that anger is silent and isolating, destructive and even frightening.


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[Assistant] Especially though, when the person who's angry is a girl or a woman. The question is why.


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[Assistant] Anger is a human emotion, neither good nor bad.


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[Assistant] It is actually a signal emotion. It warns us of indignity, threat, insult and harm.


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[Assistant] And yet, in culture after culture, anger is reserved as the moral property of boys and men.


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[Assistant] Now, to be sure, there are differences. So in the United States, for example, an angry black man is viewed as a criminal, but an angry white man has civic virtue.


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[Assistant] Regardless of where we are, however, the emotion is gendered. And so we teach children


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[Assistant] What if we didn't sever anger from femininity? Because severing anger from femininity means we sever girls and women from the emotion that best protects us from injustice. What if instead we thought about developing emotional competence for boys and girls?


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[Assistant] The fact is, we still remarkably socialize children in very binary and oppositional ways. Boys are held to


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[Assistant] Absurd, rigid norms of masculinity told to sort of renounce the feminine emotionality of sadness or fear and to embrace aggression and anger as markers of real manhood. On the other hand, girls learn to be deferential.


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[Assistant] And anger is incompatible with deference.


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[Assistant] In the same way that we learn to cross our legs and tame our hair, we learn to bite our tongues and swallow our pride.


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[Assistant] What happens too often is that for all of us, indignity becomes imminent in our notions of femininity.


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[Assistant] There's a long personal and political tale to that bifurcation.


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[Assistant] In anger, we go from being spoiled princesses and hormonal teens to high-maintenance women and shrill, ugly nags. We have flavors, though. Pick your flavor.


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[Assistant] Are you a spicy hot Latina when you're mad? Or a sad Asian girl?


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[Assistant] But in fact, the effect is that when we say what's important to us, which is what anger is conveying, people are more likely to get angry at us for being angry.


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[Assistant] Whether we're at home or in school or at work or in a political arena,


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[Assistant] Anger confirms masculinity, and it confounds femininity.


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[Assistant] So men are rewarded for displaying it, and women are penalized for doing the same.


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[Assistant] This puts us at an enormous disadvantage, particularly when we have to defend ourselves and our own interests.


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[Assistant] If we're faced with a threatening street harasser, a predatory employer, a sexist, racist classmate, our brains are screaming, are you kidding me? And our mouths say, I'm sorry, what? Right?


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[Assistant] If you have multiple marginalized identities, it's not just mockery. If you defend yourself, if you put a stake in the ground, there can be dire consequences.


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[Assistant] Now we reproduce these patterns, not in big, bold and blunt ways, but in the everyday banality of life.


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[Assistant] When my daughter was in preschool, every single morning, she built an elaborate castle, ribbons and blocks, and every single morning, the same boy knocked it down gleefully.


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[Assistant] His parents were there, but they never intervened before the fact.


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[Assistant] They were happy to provide platitudes afterwards.


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[Assistant] What many girls and women learn to do. I preemptively kept the peace and I taught my daughter to do the same thing. She used her words. She tried to gently body block him.


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[Assistant] She moved where she was building in the classroom to no effect.


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[Assistant] So I and the other adults mutually constructed a particular male entitlement. He could run rampant and control the environment, and she kept her feelings to herself and worked around his needs. We failed both of them.


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[Assistant] By not giving her anger the uptake and resolution that it deserved.


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[Assistant] Now that's a microcosm of a much bigger problem.


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[Assistant] We preference the performance of masculinity and the power and privilege that come with that performance over the rights and needs and words of children and women.


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[Assistant] So it will come as absolutely no surprise, probably, to the people in this room that women report being angrier.


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[Assistant] In more sustained ways and with more intensity than men do.


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[Assistant] But we also have to find socially palatable ways to express the intensity of emotion that we have.


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[Assistant] And the awareness that it brings of our precarity. So we do several things.


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[Assistant] If men knew how often women were filled with white hot rage when we cried, they would be staggered.


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[Assistant] We use minimizing language. We're frustrated. No, really, it's okay.


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[Assistant] We self-objectify and lose the ability to even recognize the physiological changes that indicate anger. Mainly, though, we get sick.


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[Assistant] Anger has now been implicated in a whole array of illnesses that are casually dismissed as women's illnesses. Higher rates of chronic pain. Autoimmune disorders. Disordered eating.


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[Assistant] Mental distress, anxiety, self-harm, depression.


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[Assistant] Anger affects our immune systems, our cardiovascular systems. Some studies even indicate that it affects mortality rates, particularly in black women with cancer.


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[Assistant] I am sick and tired of the women I know being sick and tired.


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[Assistant] There is anger that's acceptable. We can be angry when we stay in our lanes and buttress a status quo as mothers or teachers.


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[Assistant] We can be mad, but we can't be angry about the tremendous costs of nurturing.


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[Assistant] We can be angry at our mothers, let's say as teenagers. Patriarchal rules and regulations, we don't blame systems, we blame them. We can be angry at other women, because who doesn't love a good cat fight?


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[Assistant] And we can be angry at men with lower status in an expressive hierarchy that supports racism or xenophobia.


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[Assistant] But we have an enormous power in this. Because feelings are the purview of our authority, and people are uncomfortable with our anger,


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[Assistant] We should be making people comfortable with the discomfort they feel when women say no unapologetically.


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[Assistant] We can take emotions and think in terms of competence and not gender.


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[Assistant] People who are able to process their anger and make meaning from it are more creative, more optimistic, they have more intimacy, they're better problem solvers, they have greater political efficacy.


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[Assistant] Now, I'm a woman writing about women and feelings, so very few men with power are going to take what I'm saying seriously as a matter of politics.


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[Assistant] We think of politics and anger in terms of the contempt and disdain and fury that are feeding a rise of macho fascism in the world.


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[Assistant] We have an anger of hope and we see it every single day in the resistant anger of women and marginalized people.


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[Assistant] It's related to compassion and empathy and love. And we should recognize that anger as well.


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[Assistant] The issue is that societies that don't respect women's anger don't respect women.


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[Assistant] The real danger of our anger isn't that it will break bonds or plates.


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[Assistant] It's that, it exactly shows how seriously we take ourselves, and we expect other people to take us seriously as well. When that happens,


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[Assistant] Chances are very good that women will be able to smile when they want to.


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[Assistant] I'm going to talk about how AI and mankind can coexist. But first, we have to rethink about our human values. So let me first make a confession about my errors in my values.


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[Assistant] It was 11 o'clock, December 16, 1991. I was about to become a father for the first time.


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[Assistant] My wife, Shen Ling, lay in the hospital bed, going through a very difficult 12-hour labor.


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[Assistant] Our child didn't come, I was going to leave her there and go back to work and make a presentation about AI to my boss, Apple's CEO.


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[Assistant] Sparing me from doing the unthinkable. And to this date, I am so sorry for letting my work ethic take precedence over love for my family.


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[Assistant] Apple loved my work and decided to announce it at TED 1992, 26 years ago on this very stage. I thought I had made one of the biggest, most important discoveries in AI, and so did the Wall Street Journal on the following day.


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[Assistant] But as far as discoveries went, it turned out I didn't discover India or America. Perhaps I discovered a little island off of Portugal.


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[Assistant] But the AI era of discovery continued, and more scientists poured their souls into it. About 10 years ago, the grand AI discovery was made by three North American scientists, and it's known as deep learning.


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[Assistant] Deep learning is a technology that can take a huge amount of data within one single domain and learn to predict or decide at superhuman accuracy. For example, if we show the deep learning network a massive number of food photos, it can recognize food such as hot dog or no hot dog.


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[Assistant] Or if we show that many pictures and videos and censored data from driving on the highway, it can actually drive a car as well as a human being on the highway.


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[Assistant] And what if we showed this deep learning network all the speeches made by President Trump?


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[Assistant] So this network, if given the request to make a speech about AI, he or it might say,


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[Assistant] So, deep learning has become the core in the era of AI discovery, and that's led by the US. But we're now in the era of implementation, where what really matters is execution, product quality, speed and data. And that's where China comes in.


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[Assistant] Chinese entrepreneurs whom I fund as a venture capitalist are incredible workers, amazing work ethic. My example in the delivery room is nothing compared to how hard people work in China.


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[Assistant] As an example, one startup tried to claim work-life balance, come work for us, because we are 996. And what does that mean? It means the work hours of 9 a.m. to 9 p.m., six days a week.


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[Assistant] And the Chinese product quality has consistently gone up in the past decade. And that's because of a fiercely competitive environment.


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[Assistant] In Silicon Valley, entrepreneurs compete very gentlemanly, in a very gentlemanly fashion, sort of like in old wars, in which each side took turns to fire at each other.


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[Assistant] But in the Chinese environment, it's truly a gladiatorial fight to the death.


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[Assistant] In such a brutal environment, entrepreneurs learn to grow very rapidly, they learn to make their products better at lightning speed, and they learn to hone their business models until they are impregnable.


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[Assistant] As a result, great Chinese products like WeChat and Weibo are arguably better than the equivalent American products from Facebook and Twitter.


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[Assistant] And the Chinese market embraces this change and accelerated change and paradigm shifts. As an example, if any of you go to China, you will see it's almost cashless and credit cardless. Because that thing that we all talk about, mobile payment, has become the reality in China.


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[Assistant] In the last year, 18.8 trillion US dollars were transacted on mobile internet.


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[Assistant] And that's because a very robust technology is built behind it. It's even bigger than the China GDP.


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[Assistant] And this technology, you can say, how can it be bigger than the GDP? Because it includes all transactions, wholesale, channels, retail, online, offline, going into a shopping mall or going into a farmer's market like this.


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[Assistant] The technology is used by 700 million people to pay each other, not just merchants. So it's peer-to-peer, and it's almost transaction fee free.


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[Assistant] And it's instantaneous, and it's used everywhere.


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[Assistant] And finally, the China market is enormous. This market is large, which helps give entrepreneurs more users, more revenue, more investment, but most importantly, it gives the entrepreneurs a chance to collect a huge amount of data, which becomes rocket fuel for the AI engine.


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[Assistant] So as a result, the Chinese AI companies have leaped ahead so that today, the most valuable companies in computer vision, speech recognition, speech synthesis, machine translation and drones are all Chinese companies.


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[Assistant] So with US leading the era of discovery and China leading the era of implementation, we're now in an amazing age where the dual engine of the two superpowers are working together.


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[Assistant] To drive the fastest revolution in technology that we have ever seen as humans.


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[Assistant] And this will bring tremendous wealth, unprecedented wealth, 16 trillion dollars according to PWC, in terms of added GDP to the worldwide GDP by 2030. It will also bring immense challenges in terms of potential job replacements. Whereas in the industrial age,


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[Assistant] It created more jobs, because craftsmen jobs were being decomposed into jobs in the assembly line. So more jobs were created. But AI completely replaces the individual jobs in the assembly line with robots. And it's not just in factories.


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[Assistant] But what's more serious than the loss of jobs is the loss of meaning.


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[Assistant] Because the work ethic in the industrial age has brainwashed us into thinking that work is the reason we exist. That work defines the meaning of our lives. And I was a prime


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[Assistant] And willing victim to that type of workaholic thinking. I worked incredibly hard. That's why I almost left my wife in the delivery room. That's why I worked 996 alongside of my entrepreneurs. And that obsession that I had with work


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[Assistant] Ended abruptly a few years ago when I was diagnosed with fourth stage lymphoma.


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[Assistant] The PET scan here shows over 20 malignant tumors jumping out like fireballs, melting away my ambition.


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[Assistant] But more importantly, it helped me reexamine my life. Knowing that I may only have a few months to live caused me to see how foolish it was for me to base my entire self-worth on how hard I worked and the accomplishments from hard work.


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[Assistant] My priorities were completely out of order. I neglected my family.


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[Assistant] My father had passed away, and I never had the chance to tell him I loved him. My mother had dementia and no longer recognized me, and my children had grown up.


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[Assistant] During my chemotherapy, I read a book by Bronnie Ware, who talked about dying wishes and regrets of the people in the deathbed. She found that facing death, nobody regretted that they didn't work hard enough in this life.


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[Assistant] They only regretted that they didn't spend enough time with their loved ones and that they didn't spread their love.


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[Assistant] I moved closer to my mother. My wife usually travels with me. And when my kids have vacation, if they don't come home, I go to them. So it's a new form of life.


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[Assistant] That helped me recognize how important it is that love is for me. And facing death helped me change my life, but it also helped me see a new way of how AI should impact mankind and work and coexist with mankind.


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[Assistant] Really, AI is taking away a lot of routine jobs, but routine jobs are not what we're about. Why we exist is love. When we hold our newborn baby, love at first sight, when we help someone in need, humans are uniquely able to give and receive love. And that's what differentiates us from AI.


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[Assistant] Despite what science fiction may portray, I can responsibly tell you that AI has no love. When AlphaGo defeated the world champion Ke Jie,


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[Assistant] So how do we differentiate ourselves as humans in the age of AI? We talked about the axis of creativity, and certainly that is one possibility. And now we introduce a new axis that we can call compassion, love or empathy.


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[Assistant] Those are things that AI cannot do. So as AI takes away the routine jobs, I like to think we can, we should and we must create jobs of compassion.


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[Assistant] You might ask how many of those there are, but I would ask you, do you not think that we're going to need a lot of social workers to help us make this transition? Do you not think we need a lot of compassionate caregivers to give more medical care to more people?


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[Assistant] Do you not think we're going to need 10 times more teachers to help our children find their way to survive and thrive in this brave new world?


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[Assistant] And with all the newfound wealth, should we not also make labors of love into careers and let elderly accompaniment or homeschooling become careers also?


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[Assistant] This graph is surely not perfect, but it points at four ways that we can work with AI. AI will come and take away the routine jobs, and in due time, we will be thankful.


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[Assistant] AI will become great tools for the creatives, so that scientists, artists, musicians and writers can be even more creative.


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[Assistant] Analytical tools that humans can wrap their warmth around for the high-compassion jobs. And we can always differentiate ourselves with the uniquely capable jobs that are both compassionate and creative, and using and leveraging our irreplaceable brains and hearts.


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[Assistant] AI is serendipity. It is here to liberate us from routine jobs, and it is here to remind us what it is that makes us human. So let us choose to embrace AI and to love one another. Thank you.


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[Assistant] It's bright red and very old. Everything that's ever happened to me at sea is in it.


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[Assistant] Oh, hi shipmates. I've turned my ship house door upside down and can't find my logbook anywhere.


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[Assistant] What? You've stuck all the pages back in and cleaned it up? You did this for me?


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[Assistant] Since we had such a good morning at the market, I made us a treat.


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[Assistant] You can do it, Sultan. Just believe in yourself. I believe in you.


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[Assistant] Lily! It's time to go home now.


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[Assistant] Oh, a clue like that. Mmm, I haven't seen a clue like that. Especially not the one tied to my tail.


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[Assistant] That's okay, Lily. I wonder what else is back there.


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[Assistant] Hiya, Nonna! We're here in a moment, Pet. I'm just getting down my precious treat jar. Look out!


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[Assistant] Oh no, I keep that key here.


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[Assistant] Some pumpkins would eat all my treats if I don't keep them locked away.


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[Assistant] Uhm. Oh, Salty. I've lost your pocket watch.


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[Assistant] I didn't mean to. It just slipped out of my hat onto Hatsy's train. And we've been chasing it all over the place. And now we've lost it.


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[Assistant] So when Andrew Dufresne came to me in 1949 and asked me to smuggle Rita Hayworth into the prison for him, I told him, no problem.


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[Assistant] Andy came to Shawshank Prison in early 1947 for murdering his wife and the fella she was banging.


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[Assistant] On the outside he'd been vice president of a large Portland bank. Good work for a man as young as he was.


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[Assistant] In response to your repeated inquiries, the state has allocated the enclosed funds for your library project. This is $200.


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[Assistant] In addition, the library district has generously responded with a charitable donation of used books and sundries. We trust this will fill your needs. We now consider the matter closed. Please stop sending us letters. I want all this cleared out before the warden gets back. Yes, sir.


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[Assistant] Six eggs, a box of peaches, and one very precious antique vase. Everything seems to be okay, but please, take more care with your kite next time.


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[Assistant] My wife used to say I'm a hard man to know. Like a closed book. Plained about it all the time. She was beautiful.


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[Assistant] Little place on the Pacific Ocean. You know what the Mexicans say about the Pacific?


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[Assistant] Hey, you've done a stretch in Cashman, right? Yeah, now that was an easy piece of time, let me tell you.


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[Assistant] You really mean that? Yes sir, I do. Good. Because if we do this, we do it all the way. A hundred percent. Nothing half-assed. Thing is, he, uh, don't read so good.


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[Assistant] Well, it's for shit. Wasted a little fucking year of my time with this bullshit.


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[Assistant] It was like some beautiful bird flapped into our drab little cage and made those walls dissolve away. And for the briefest of moments, every last man at Shawshank felt free.


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[Assistant] I'm sure I will. Here's your cashier's check, sir. Will there be anything else?


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[Assistant] Who? Randall Stevens. Who? The silent, silent partner. He's the guilty one, your honor. The man with the bank accounts. It's where the filtering process starts. They trace anything. It's just gonna lead to him. But who is he? He's a phantom. An apparition. Second cousin to Harvey the Rabbit. I conjured him out of thin air. He doesn't exist. Except on paper.


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[Assistant] One day, Danny went to the museum. He wanted to see what was inside.


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[Assistant] Dinosaurs. Danny loved dinosaurs. He wished he had one.


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[Assistant] Said a voice. Can you? Said Danny.


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[Assistant] Yes, said the dinosaur. Oh good, said Danny. What can we do?


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[Assistant] I can take you for a ride, said the dinosaur. He put his head down so Danny could get on him.


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[Assistant] Let's go, said Danny. A policeman stared at them.


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[Assistant] He had never seen a dinosaur stop for a red light.


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[Assistant] The dinosaur was so tall, Danny had to hold up the ropes for him. Look out, said Danny.


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[Assistant] I can make a noise like a car, said the dinosaur.


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[Assistant] What big rocks, said the dinosaur. They are not rocks, said Danny. They are buildings.


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[Assistant] I love to climb, said the dinosaur. Down, boy, said Danny.


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[Assistant] The dinosaur had to be very careful not to knock over houses or stores with his long tail.


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[Assistant] Some people were waiting for a bus. They rode on the dinosaur's tail instead.


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[Assistant] All who want to cross the street may walk on my back, said the dinosaur.


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[Assistant] Danny and the dinosaur went all over town and had lots of fun.


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[Assistant] It's good to take an hour or two off after a hundred million years, said the dinosaur. They even looked at the ball game. Hit the ball, said Danny. Hit a home run, said the dinosaur.


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[Assistant] Oh, what lovely green grass, said the dinosaur. I haven't eaten any of that for a very long time. Wait, said Danny. See what it says? They both had ice cream instead.


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[Assistant] Let's go to the zoo and see the animals, said Danny.


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[Assistant] Nobody stayed to see the lions. Nobody stayed to see the elephants.


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[Assistant] And nobody stayed to see the seals, giraffes, or hippos either.


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[Assistant] Please go away so the animals will get looked at, said the zoo man.


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[Assistant] Let's find my friends, said Danny. Very well, said the dinosaur.


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[Assistant] I'd be delighted, said the dinosaur. Hold on tight, said Danny.


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[Assistant] Danny taught the dinosaur how to shake hands.


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[Assistant] He's smart, said Danny, patting the dinosaur.


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[Assistant] Said the children. How do you play it? Said the dinosaur.


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[Assistant] We hide, and you try to find us, said Danny.


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[Assistant] The dinosaur covered his eyes. All the children ran to hide.


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[Assistant] The dinosaur looked and looked, but he couldn't find the children. I give up, he said. Now it was the dinosaurs' turn to hide. The children covered their eyes.


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[Assistant] The dinosaur hid behind a house. The children found him.


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[Assistant] He hid behind a sign. The children found him.


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[Assistant] He hid behind a big gas tank. The children found him.


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[Assistant] I guess there's no place for me to hide, said the dinosaur. Let's make believe we can't find him, Danny said.


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[Assistant] Said the children. Here I am. Said the dinosaur.


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[Assistant] It got late and the other children left. Danny and the dinosaur were alone. Well, goodbye Danny, said the dinosaur. Can't you come and stay with me, said Danny.


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[Assistant] We could have fun. No, said the dinosaur. I've had a good time. The best I've had in a hundred million years. But now I must get back to the museum. They need me there. Oh, said Danny. Well, goodbye.


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[Assistant] Danny watched until the long tail was out of sight. Then he went home alone.


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[Assistant] Make sure that the welltops name is highlighted over here. Its font is darker and bolder than the others. It does not necessarily have to be reverse video. It just has to be a bigger, darker font. That tells Patrell which welltops to use. We then start by going to the stratigraphy tab, in this case in the geology and geophysics perspective.


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[Assistant] And clicking on the edit well tops icon.


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[Assistant] This brings up the tool palette with well correlation active and the call out over here.


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[Assistant] And drag it down below the horizon we're interested in.


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[Assistant] Can also drop multiple markers into one well at a time. Also in the C4 well,


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[Assistant] And then click on the drop ghost markers icon again. And now it's dropped the Tarbert 2 and Tarbert 1 into the C4 wheel.