[User] Please transcribe this text

[Assistant] Bony, if you can see their bones. One word that's often used to describe very thin people that shouldn't really be used is anorexic. It's a medical term, it's not actually an adjective to describe somebody's physical appearance. Uhm, so try to avoid that one. Okay, let's move on to hair.


[User] Please transcribe this text

[Assistant] Now, there are two ways in which you can talk about somebody's hair colour.


[User] Please transcribe this text

[Assistant] You can say to have, adjective, hair.


[User] Please transcribe this text

[Assistant] The second one's a bit of a mouthful actually. To be blonde haired. I am blonde haired.


[User] Please transcribe this text

[Assistant] You can also just say, I am blonde. But that's better to use for colours that are very specific to hair colours, like blonde and ginger and brunette. So I am blonde, I am ginger, I am brunette, that's fine, but if you say, I am white or I am black,


[User] Please transcribe this text

[Assistant] It could be confused with skin colour. So it's best to say I am black haired. I have white hair.


[User] Please transcribe this text

[Assistant] So we've got a huge spectrum of colours that you can be. I'm going to talk about the most common ones, but they are quite specific and you might not have heard of them all before. So let's start with the lightest and move to the darkest.


[User] Please transcribe this text

[Assistant] So we have white, then we have grey, then we have platinum blonde, and this is white blonde hair. It's normally not a natural colour, but some people are lucky enough to have naturally white blonde or platinum hair. It's a really interesting colour.


[User] Please transcribe this text

[Assistant] Then we have blonde, if it's a bit darker it could be called golden.


[User] Please transcribe this text

[Assistant] And then, if it's a bit darker than blonde, there are two ways you can describe it.


[User] Please transcribe this text

[Assistant] If it's blonde almost brown, you can say dirty blonde. If it's blonde almost ginger, you can say strawberry blonde.


[User] Please transcribe this text

[Assistant] Then we have ginger, which is more orange, and red, which is like a darker red colour, obviously.


[User] Please transcribe this text

[Assistant] After that, you have mousy brown, which is a light brown colour. Then brown.


[User] Please transcribe this text

[Assistant] Then brunette as well, which is another way of saying brown hair. It's normally a bit darker. Then dark brown and then black.


[User] Please transcribe this text

[Assistant] Now if you don't want to specify a colour of hair or skin or eyes and you just want to say light or dark, you can say fair for light or dark for the darker colours. So I am fair. I have fair eyes, fair hair and fair skin. This means I'm just light.


[User] Please transcribe this text

[Assistant] Somebody else might have dark hair, dark eyes, dark skin. So we normally use fair or dark.


[User] Please transcribe this text

[Assistant] So what about hair length? Well, if you have no hair, then you are bald.


[User] Please transcribe this text

[Assistant] I am bald. In a video that I did on Ali's channel, Papa Teach Me English, I was bald. If you would like to see me with no hair and, well, no


[User] Please transcribe this text

[Assistant] Feminine makeup, different makeup, then you can click up there. See if you recognise me, I look slightly different. Uhm, then you have short hair, long hair, but then in between you can have a bob,


[User] Please transcribe this text

[Assistant] I have a bob haircut. In my first videos I had very short hair and a bob haircut. I didn't ask for that haircut, it was a surprise.


[User] Please transcribe this text

[Assistant] Shoulder length hair, medium length hair, long hair.


[User] Please transcribe this text

[Assistant] If you want to say how long your hair is, you can say my hair goes down to my, so my hair goes down to my armpit, my hair goes down to my waist.


[User] Please transcribe this text

[Assistant] As far as hair texture, you can have straight hair, wavy hair,


[User] Please transcribe this text

[Assistant] Curly hair, afro hair, which is really really dense curls.


[User] Please transcribe this text

[Assistant] So let's move on to the subject of skin. This again is a more difficult one. So we have the spectrum of white and black.


[User] Please transcribe this text

[Assistant] But different people like to be called different things. So I would call myself white.


[User] Please transcribe this text

[Assistant] I have a lot of friends with darker skin who are often called black, but they would actually prefer to be called brown.


[User] Please transcribe this text

[Assistant] Uhm, I would say when in doubt, use fair or dark to describe somebody's skin tone.


[User] Please transcribe this text

[Assistant] Now in the middle we have tanned and this means that you've been blessed by the sun, you've caught a sun tan, you've gone brown in the sun. And in British English we say tanned and in American English they say tan. If you're not tanned,


[User] Please transcribe this text

[Assistant] Then you are pale, and that is what I am all of the time. I am always pale, even when I go on holiday. Now let's move on to eye colour.


[User] Please transcribe this text

[Assistant] Again, you can use fair or dark. Fair eyes, light eyes, dark eyes.


[User] Please transcribe this text

[Assistant] Most of the colours are pretty simple. Blue, brown, green, black, grey, brown. I said brown, haven't I? But one colour that is used quite frequently is hazel. If you have a sort of browny green eye, it can be called hazel. So they have hazel eyes. Now, lips.


[User] Please transcribe this text

[Assistant] Uhm, you can have thin lips, uh, but if you want to talk about somebody with big lips, you can say they have full lips. So somebody has big


[User] Please transcribe this text

[Assistant] Kissy pouty lips, you can say they have very full lips.


[User] Please transcribe this text

[Assistant] If somebody has really sticky out lips and speaks like this...


[User] Please transcribe this text

[Assistant] You can call them pouty lips. But yes, full or thin, really. Next, you can talk about noses. So...


[User] Please transcribe this text

[Assistant] Big or small, obviously are the basics. If somebody has a bend in their nose, it can be called a crooked nose, a crooked nose. If they haven't got a bend in their nose, it can be a straight nose. If somebody has a small nose, you can call it a button nose. If they've got a hook,


[User] Please transcribe this text

[Assistant] A hooked nose. If somebody's nose is like this, it's a turned up nose, or an upturned nose. Face shape. I looked online, and apparently there are nine different face shapes, but we're going to talk about four today. You have oval, round, heart-shaped, it ends in a point, and square. Somebody has a square jaw.


[User] Please transcribe this text

[Assistant] Some of the ways in which you can compliment people, so that's the positive adjectives. I will cover them in this lesson, but if you'd like to see that, you can click up there. But it's quite good to separate them into male and female, because one adjective that might be really, really flattering and positive for a male,


[User] Please transcribe this text

[Assistant] Might actually be quite insulting for a female. Addictives that can be used for both. The positive ones. Attractive. Beautiful.


[User] Please transcribe this text

[Assistant] Stunning, which means somebody is just amazingly beautiful. But then, just for men, there's handsome, and just for women, there is pretty.


[User] Please transcribe this text

[Assistant] Now if you described a woman as handsome, you might be insinuating that she has manly features, in the same way that if you described a woman


[User] Please transcribe this text

[Assistant] In the same way that if you described a man as pretty, you might be saying he has quite feminine features, which is not always something that somebody wants. Negative ones, you can have ugly or plain.


[User] Please transcribe this text

[Assistant] Other ways of describing people, you can describe them as masculine or feminine, boyish, manly, girly, womanly.


[User] Please transcribe this text

[Assistant] Right, your homework is to describe yourself in as much detail as possible.


[User] Please transcribe this text

[Assistant] Put it in the comments and I can't wait to see your descriptions. Also, if you can add any more vocabulary, then definitely include that in your description as well.


[User] Please transcribe this text

[Assistant] I hope you enjoyed this lesson. I really hope you learnt something. I've given you a lot of information. Don't forget to connect with me on all of my social media. I've got my Facebook, my Instagram, and my Twitter. And I will see you soon for another lesson. Mwah!


[User] Please transcribe this text

[Assistant] In this lesson, I have another super quick challenge for you so you can practice setting up the Selenium WebDriver in a blank project and scraping a different piece of data from a website. This time we're going to work with the Wikipedia main page.


[User] Please transcribe this text

[Assistant] So you can head over to the course resources and find the link to this page, or you can just type it in.


[User] Please transcribe this text

[Assistant] Back in our project I'm going to create a new file and I'm going to call this


[User] Please transcribe this text

[Assistant] Now in this new Python file, we're going to interact with this Wikipedia webpage.


[User] Please transcribe this text

[Assistant] And as a challenge to you, the first thing I want you to do is to figure out how you can get a hold of this particular number and print it out inside our interaction.py.


[User] Please transcribe this text

[Assistant] Remember that you'll need to import Selenium and also use the web driver to get hold of this page and then find this particular number and finally print it out.


[User] Please transcribe this text

[Assistant] And then when you're ready to run it, all you have to do is right click and then run this interaction.py and it'll work and you should see the outcome being printed in your console. So pause the video now and give that a go.


[User] Please transcribe this text

[Assistant] First, we're going to go into the Selenium package, which we've already installed into this project, so we don't have to install it again. And then we're going to import the WebDriver.


[User] Please transcribe this text

[Assistant] Now using the web driver, we're going to create a new driver from the Chrome browser. So we're going to have to navigate to that path again.


[User] Please transcribe this text

[Assistant] Now this is my path and your path might be a little bit different, but this is what we put to initialize a new Chrome driver. Now once we've created our driver, now we can use the driver to navigate


[User] Please transcribe this text

[Assistant] And this is the URL, which we'll copy and paste into here. And once we've gotten hold of this page,


[User] Please transcribe this text

[Assistant] Then we're going to try to narrow down on this particular element. So let's go ahead and inspect it.


[User] Please transcribe this text

[Assistant] And you can see that it's inside an anchor tag with no particular identifiers. There's no ID, there's no name, there's no class.


[User] Please transcribe this text

[Assistant] But this anchor tag lives in a div that has an ID. So this article count is going to be a unique identifier for the div that holds this particular anchor tag.


[User] Please transcribe this text

[Assistant] So we can narrow in on this anchor tag using our CSS selectors. So we can say driver dot find element by CSS selector. Make sure that it's element, not elements. And then inside here, we're going to put our selector, which is first the ID of article count.


[User] Please transcribe this text

[Assistant] And that is going to be proceeded with a pound sign.


[User] Please transcribe this text

[Assistant] And then inside that div with that ID, we're looking for the first anchor tag.


[User] Please transcribe this text

[Assistant] Now notice that inside that div there's actually two anchor tags, but by using this find element by CSS selector, it's only going to give us the first one that matches this criteria. So this is going to be R.


[User] Please transcribe this text

[Assistant] And now what we want to do is we want to print the articleCount.text. So now let's go ahead and right click and run our interaction.py. It should open up our browser to this page.


[User] Please transcribe this text

[Assistant] And now it should have found and printed out that number.


[User] Please transcribe this text

[Assistant] So this is what we've been doing so far, creating our driver, opening web pages, and then finding specific elements and printing some sort of property.


[User] Please transcribe this text

[Assistant] But the next step is to actually form some sort of interaction with the webpage. For example, clicking on a link or typing something into the search bar.


[User] Please transcribe this text

[Assistant] Because after all when we're working with websites, it's often that we'll need to interact with it in order to navigate to new pages and get hold of specific pieces of information that we're interested in.


[User] Please transcribe this text

[Assistant] Now that we've understood a little bit more about the terminal commands, let's come back to our Flask code and understand what the code is doing in a little bit more detail. For example,


[User] Please transcribe this text

[Assistant] What is this name? Because we're creating this app from this Flask class and in order to initialize a new Flask application, there is only one required input and that is the import name.


[User] Please transcribe this text

[Assistant] Let's go ahead and print it out. So if we print this underscore, underscore name and comment out the rest of our code and hit run,


[User] Please transcribe this text

[Assistant] This name is one of the special attributes that's built into Python.


[User] Please transcribe this text

[Assistant] At any given point, you could tap into the name to find out what is the current class function method or descriptor's name.


[User] Please transcribe this text

[Assistant] And when we get main, what it's telling us is basically we're executing the code in a particular module.


[User] Please transcribe this text

[Assistant] So that means it's run as a script or from an interactive prompt, but it's not run from an imported module.


[User] Please transcribe this text

[Assistant] It gives us this code here. If the name is equal to main, then execute something only if it's run as a script.


[User] Please transcribe this text

[Assistant] Also takes us to one of the common ways that you'll see people run Flask apps.


[User] Please transcribe this text

[Assistant] Is double equal to underscore, underscore main as a string. So exactly what was printed just now, when we printed out this name,


[User] Please transcribe this text

[Assistant] Well, if this is the case, then that means we're running the code from within this current file. We're running hello.py. So in that case, we're going to tap into our app and we're going to call the run method.


[User] Please transcribe this text

[Assistant] Now this app.run basically does exactly the same thing as when we went into the terminal and we said flask run.


[User] Please transcribe this text

[Assistant] But notice when we say flask run, firstly, we have to provide the flask app environment variable. And secondly, we have to stop the code using control C instead of using our normal run and stop.


[User] Please transcribe this text

[Assistant] But if we use app.run, we can now use our standard controls. So I can simply hit run to run this hello.py.


[User] Please transcribe this text

[Assistant] And it will start serving up our Flask app at this address, just as it did before when we did Flask run. And instead of using Control C to quit, we can simply use the stop button to stop our Flask application, making it a lot easier. Basically, by providing the name


[User] Please transcribe this text

[Assistant] To Flask, Flask will check that this is the current file where the app code is located and we're not in fact using an imported module. For example, if I was to import the random module,


[User] Please transcribe this text

[Assistant] And I was to print the random dot name.


[User] Please transcribe this text

[Assistant] Then you can see the first thing that gets printed is the name of that module.


[User] Please transcribe this text

[Assistant] But the name that's printed from hello.py, which is the file that's being run,


[User] Please transcribe this text

[Assistant] Is simply underscore underscore main. So this basically denotes the file that is currently being run.


[User] Please transcribe this text

[Assistant] And you say run hello, well then inside hello, name is going to be equal to main.


[User] Please transcribe this text

[Assistant] Wrestling and, you know, when I was playing football and basketball and doing all the other sports, you know, and then I went on to college and was playing basketball. I, uh,


[User] Please transcribe this text

[Assistant] So my coach, between my junior and senior year, my coach is getting all these inquiries from like these European teams.


[User] Please transcribe this text

[Assistant] And, uh, you know, he's telling me like, hey, you're getting a lot of interest. You know, you might get some tryout or something over there. They just want you to get a little bigger. So between my junior and senior year of college, I start training. I was already weight training, but now I'm training to really get big. Cause even at, you know, at six foot eight, six foot nine and two thirty,


[User] Please transcribe this text

[Assistant] In the world of professional wrestling, I look like a giant walking stick. I mean, you know, just, I was thin.


[User] Please transcribe this text

[Assistant] Like eating just stupid some, you know, I mean that's all I did was train and eat and just to the point where it was just like...


[User] Please transcribe this text

[Assistant] I don't want to eat anymore, but you gotta eat and that's, you know, eat and rest and.


[User] Please transcribe this text

[Assistant] Yeaah, they do. And I did, but uhm, I ended up showing up for my senior year of school. Now I'm 280 pounds.


[User] Please transcribe this text

[Assistant] My coach looked at me the first day when I got to school. He looked at me and immediately, and he was an old school coach, right? Like, you know, he equated size with being slow and he just thought everything, you know, he thought all my skills were going to be diminished and so he bans me, day one, bans me from the gym. He said, you cannot work out.


[User] Please transcribe this text

[Assistant] I'm like, cool, but like, I can jump higher, I'm faster. Anyway, we butted heads.


[User] Please transcribe this text

[Assistant] And one day at practice, uhm, you know, now I'm already training too. Now I'm training to wrestle.


[User] Please transcribe this text

[Assistant] I'm playing basketball. I'm trying to do all of this and go to school. I'm trying to do all this stuff, right? But it's in me already. It's just like, this is what I'm going to do.


[User] Please transcribe this text

[Assistant] You know, but you know, I'd made a commitment to my teammates and everything that, you know, I was going to try and make it through the year and just like.


[User] Please transcribe this text

[Assistant] I was struggling and it happened one day at practice. We were, uh, we were doing a drill.


[User] Please transcribe this text

[Assistant] Coach was, like, throws the ball up, tip drill, then you hustle and, you know, everybody was terrified of me. You know, you got a bunch of guys that are...


[User] Please transcribe this text

[Assistant] Everybody, you know, these guys are like 6'9", 6'10", but they all weigh a buck 80. And, you know, here I am at about 280 and just mean and aggressive and just, I was just nasty and everybody was terrified to do drills with me. And this kid, uh, he was a freshman and he was about 6'9", and about a buck 85. And I don't know what he was thinking. Coach throws the ball up and instead of jumping, he cut into me. He took my knees out.


[User] Please transcribe this text

[Assistant] So he was so light in the asshole, like it knocked him down, and then I fell down, and like, I wasn't even thinking, man, I just got up.


[User] Please transcribe this text

[Assistant] No, well, he didn't, not with me, because that was like, my end was coming, like coach throws me out of practice, right? He flips on me. Although the guy cut me, undercut me, coach throws me out of practice, right? I might have been a bit aggressive, but, you know, he's like, alright, Callaway, you're out. And it was at that moment, man. Bang. It was like, I'm done. This is over.


[User] Please transcribe this text

[Assistant] So I'm already into, I'm already trying to, you know, I'm training and I'm trying to get my foot in the door somewhere. And...


[User] Please transcribe this text

[Assistant] I'm solely focused on, you know, trying to train, find people to train me.


[User] Please transcribe this text

[Assistant] Uhm, you know, I didn't have, I wasn't a legacy in the business, right? So I didn't have any ends anywhere. You know, I didn't know anybody in the business, I didn't...


[User] Please transcribe this text

[Assistant] You know, so I mean, I'm starting from, from ground zero and just trying to get in wherever I could and, you know, try and get myself in a spot where people would see me.


[User] Please transcribe this text

[Assistant] And he had a local wrestling promotion there. And I knew on Wednesday...


[User] Please transcribe this text

[Assistant] They would, all the wrestlers and everybody would show up like to get their checks.


[User] Please transcribe this text

[Assistant] And, uh, and we're going back. This is like 80, this is 86, 87, you know.


[User] Please transcribe this text

[Assistant] So, I was like, I'm gonna show up, somebody's gonna see me, and they're gonna be, you know, they're gonna be so impressed with me that, you know, they're gonna give me a job.


[User] Please transcribe this text

[Assistant] So I went every Wednesday for damn near eight months.


[User] Please transcribe this text

[Assistant] I went, and I'd sit down on a couch like this, right, and there was, the guys would come in this way, and like the guy that promoted and booked and


[User] Please transcribe this text

[Assistant] Did everything. His office was right here. These guys, I'd catch them here, they'd walk by. I'd start to stand up, you know, introduce myself. They'd never stop. They just kept walking. And I was like, holy.


[User] Please transcribe this text

[Assistant] Eight months I did that. I didn't know, I didn't know or didn't have anything, you know, I didn't know anything else to do, right? And I know it had to start here, to go anywhere else it had to start here, so.


[User] Please transcribe this text

[Assistant] This is like week after week after week, and I was about to just kind of give it up, just like, well this ain't working, I'm gonna have to try something else.


[User] Please transcribe this text

[Assistant] And one day Fritz walked in. Now here's the, here's the crazy part. So.


[User] Please transcribe this text

[Assistant] I'm a young kid, he had already lost one of his boys.


[User] Please transcribe this text

[Assistant] He'd lost, uh, let's see, which one was it now? Wasn't Kerry, it was, uh, David. No, not David. There's all, there's a ton of them. Uh, David Von Air.


[User] Please transcribe this text

[Assistant] Yeah, Carrie was the last one to die. I think it was David.


[User] Please transcribe this text

[Assistant] Oh my gosh, it's tragic man, tragic story. But he had lost David in uhm, in Japan.


[User] Please transcribe this text

[Assistant] And I just happened to resemble, I mean, I looked almost, I was...


[User] Please transcribe this text

[Assistant] He looked at me. He stopped. Fritz walks in and he stops and he's just staring at me, right? It ain't even the normal kind of stare at somebody like, you know, this kid's a, you know,


[User] Please transcribe this text

[Assistant] And then he turned around, he went to, to, uh, another guy's office, his name was Bronco Lubich, and I could hear,


[User] Please transcribe this text

[Assistant] Fritz had this really deep, gravelly voice and, you know, he's like, who's that kid out there? And he's, uh, he's been coming here for weeks. He's trying to get booked. And then I heard him say it and I was like, he goes, book him Friday night. He looks just like David.


[User] Please transcribe this text

[Assistant] Oh yeaah, I was much bigger, but my hair, my face and everything, you know, I looked like him. He comes back out, I go to get up.


[User] Please transcribe this text

[Assistant] He keeps walking, goes into the office, he never says a word to me. But then the following, that Friday night, they...


[User] Please transcribe this text

[Assistant] It was, uh, it was kind of crazy, but you know, that's how it all got started. It was, uh, yeah.


[User] Please transcribe this text

[Assistant] Yeah, that's, you know, I think that's kind of been one of the things that have helped me.


[User] Please transcribe this text

[Assistant] You know, cause yeah, I had a, there was a lot of obstacles and roadblocks in the way. Like I said, I didn't have anybody.


[User] Please transcribe this text

[Assistant] I knew this is what I wanted to do, but I don't know how to, uh,


[User] Please transcribe this text

[Assistant] This is the only thing I can think of, you know, and even that I was getting at the end of it and I was like, man, this isn't working either. I'm going to have to try and figure something out. And then I, like I said, I was fortunate enough to put myself in the right situation. But, um, yeah.


[User] Please transcribe this text

[Assistant] So then, my first match, right, was with uh, Bruiser Brody.


[User] Please transcribe this text

[Assistant] They say, okay, you're going to work with uh, you're going to work with Rooster Brody. And I'm like, Brody? Okay.


[User] Please transcribe this text

[Assistant] You know, get in there, do what you gotta do. You know, it was strictly to get uhm, you know, it was strictly to get


[User] Please transcribe this text

[Assistant] Yeah, they had a long history. Abdullah and Brody had a long history here and Japan and everywhere. So I, you know, I know the score, I know what's going to happen and I get in the ring and I lose my freaking mind.


[User] Please transcribe this text

[Assistant] I'm standing here now, I'm six foot eight now and by this time I'm three hundred and something pounds and I'm looking at Brody and I'm like...


[User] Please transcribe this text

[Assistant] You know, we're, we're kind of just kind of hand checking each other and like I go to shove him off and somehow another like and I meant to hit him in the chest, but I somehow because our hands are


[User] Please transcribe this text

[Assistant] He turns it around and out of nowhere, he come up with that big furry boot of his, kick me in the head. I'm like, holy.


[User] Please transcribe this text

[Assistant] I'm in a, I'm in a whole different world right now. Now this is, it was time for me to get one of my first lessons in, uh, and don't fuck with the old guys cause they don't play.


[User] Please transcribe this text

[Assistant] Wrestling's come a long way, man. I learned a valuable lesson on my very first match. It's like, yeaah, don't judge a book by its cover.


[User] Please transcribe this text

[Assistant] There's a reason why you're supposed to sit down, shut up, and watch, right? And it was just, I'm sure, you know, when you come into the league, you want to test somebody, but...


[User] Please transcribe this text

[Assistant] Yeah. In fact, they, you know, shortly thereafter, um,


[User] Please transcribe this text

[Assistant] They were trying to ship me off somewhere else to a different territory.


[User] Please transcribe this text

[Assistant] You know, and uh, wi- wi- wi- with Ab- Abdullah and everything, I got a chance to work.


[User] Please transcribe this text

[Assistant] From the Sudan. That's where, yeah, yeah, from the Sudan. But, uh, that's kind of a difference of where we are to now.


[User] Please transcribe this text

[Assistant] Where we're at today with talent and where the guys were back then. The guys always protected their, you know.


[User] Please transcribe this text

[Assistant] Oh yeaah man. Oh yeaah. He scared the shit out of me when I was a little kid. I lived in Houston.


[User] Please transcribe this text

[Assistant] I don't know, maybe 10 or 11 years old. I was living in Houston, Texas. My mom had got some tickets and the sheik was coming in to work with somebody. I don't remember who he was going to wrestle.


[User] Please transcribe this text

[Assistant] When the sheet comes down to the ring, we're going to jump on the barricade, and we're going to, yeaah, we're going to say this, and what's right.


[User] Please transcribe this text

[Assistant] So, so yeaah, he was like, he, he wasn't a big guy, man, but everybody was terrified of him, man. Sometimes he'd come out with a snake, he threw fire, I mean, he was...


[User] Please transcribe this text

[Assistant] Yeah, Sabu's dad. And, uh, so anyway.


[User] Please transcribe this text

[Assistant] He makes just a lunge at us. We damn near took out the whole road trying to run because we thought the sheik was coming to get us. He was going to get us, man. We wiped out this old lady. I mean, we were hauling ass trying to get out of the way.


[User] Please transcribe this text

[Assistant] Yeah, and that's how they, you know, that's how, that's how we were protected our business back then.


[User] Please transcribe this text

[Assistant] You know, if you were from the Sudan, you were from the Sudan and you didn't let anybody know anything different.


[User] Please transcribe this text

[Assistant] Like, they're more concerned now with the shit that's happening.


[User] Please transcribe this text

[Assistant] You know, behind the scene. Not so much what's going on in the ring. They want the dirt.


[User] Please transcribe this text

[Assistant] Right, it's just like anything else in our society and it's all, you know, social media driven.


[User] Please transcribe this text

[Assistant] Uhm, you know, you, everybody puts their life out there for, so it's hard, you know,


[User] Please transcribe this text

[Assistant] It's hard for me watching everything like, okay, you're this guy on TV.


[User] Please transcribe this text

[Assistant] And then you go on social media and then, you know, you're somebody completely different.


[User] Please transcribe this text

[Assistant] I mean, it is, it is what it is and that's, that's where we're at. So, you know, I, I don't.


[User] Please transcribe this text

[Assistant] I don't necessarily like it, but there's nothing I can do about it. And, you know, there's, it's just the way it is. But I think, I think it's pretty, it's, it makes things harder. It really makes things harder to have.


[User] Please transcribe this text

[Assistant] Uh, you know, good storylines and, and people invested like, you know.


[User] Please transcribe this text

[Assistant] Uh, it's so funny. So he joked about big guys. So Andre, Andre didn't like Stud at all. I bet. Andre didn't like, he didn't like big guys. Fortunately for me, he liked me. Um, but Stud, he did not care for. He, he on several occasions, damn near killed Stud. Wow. Oh, Bigelow, all those guys, man, he was,


[User] Please transcribe this text

[Assistant] Yeah, yeah, the Black Jacks, right? So Mulligan was, he was a big dude. He was like 6'8", 6'9", three, three bills.


[User] Please transcribe this text

[Assistant] And just a hair, hair trigger, man. I mean, he would snap just like that.


[User] Please transcribe this text

[Assistant] No, and then there was always, there was always the, uh,


[User] Please transcribe this text

[Assistant] And he had somebody with him, I don't remember who was with him, but Andre ended up beating the both of them.


[User] Please transcribe this text

[Assistant] But Andre, man, Andre, he could be alone if he wanted to. Like, you know, everybody called him the boss, right? And he was. And if he didn't like you, you just need to stay away from him because he's so big.


[User] Please transcribe this text

[Assistant] No, I think it's a, uh, I think it starts in the pituitary gland.


[User] Please transcribe this text

[Assistant] That the pituitary gland never stops growing and, uh,


[User] Please transcribe this text

[Assistant] You know, the guys that hung out with him, his handler and everything like that, bless his heart. He was a little dude. And he had to sit and drink with Andre all night long. And the stories he had man, like there was one time Andre passed out. Andre passed out in the hotel lobby and they couldn't do anything with him. I mean, you can't pick up.


[User] Please transcribe this text

[Assistant] Some linens and blankets and covered him up in the lobby until he decided to wake up and.


[User] Please transcribe this text

[Assistant] I was on, here's a good Andre story. I was on a plane and we were, we were on a red eye coming out of LA headed back to Dallas.


[User] Please transcribe this text

[Assistant] And as guy come, I'd already got, I'd already boarded the plane and I'm sitting there and I see the guy come in, puts his, I don't think anything about it, he, guy puts his,


[User] Please transcribe this text

[Assistant] His bag up there, and then he wasn't even sitting in first, right? He was like two rows back.


[User] Please transcribe this text

[Assistant] And then here comes Andre, right? So Andre carried the Halliburton suitcase, metal suitcase.


[User] Please transcribe this text

[Assistant] He sets it down in his chair, he opens the bin, he looks in there, and he looks, you know, he looks this way, he looks that way, and nobody's claiming it.


[User] Please transcribe this text

[Assistant] Right, right in the middle of the aisle, he takes his bag, sticks it in there, boom, and he sets down. So I'm laughing to myself, I'm like, he just took that bag and just, he don't give a damn, right? So then, I'm sitting there, and I'm kind of just laughing about it, and then I see this guy.


[User] Please transcribe this text

[Assistant] This guy that put the bag up there, crawling. He's crawling up the aisle.


[User] Please transcribe this text

[Assistant] He just, you know, he believed certain things, and he believed if he, that was his seat, that that was his spot, and, you know, he was gonna move it, but that's just the way he was.


[User] Please transcribe this text

[Assistant] But when it comes time to go to work, if I can go to work, right? No excuses, no, you know what, I was out too late, business was business, and that was kind of the way he approached it.


[User] Please transcribe this text

[Assistant] I worked, I wrestled warrior quite a bit when I first came up, and uhm, yeaah, I don't think he would have been able


[User] Please transcribe this text

[Assistant] Oh yeaah, he was, I mean he was real, yeaah, he, he, his physique was kinda ahead of the times.


[User] Please transcribe this text

[Assistant] I used to, you know, he liked me and he wanted to work because, you know, we used to run separate towns, like Hogan would be in one town and Warrior when they were the two top dogs, right? And so I was wrestling against Warrior and I think it was like Sarge of Slaughter and Sheik.


[User] Please transcribe this text

[Assistant] We're wrestling Hogan. And we're out drawing him. Right? So.


[User] Please transcribe this text

[Assistant] So Warrior loved me, but every once in a while I get, I gotta have fun somewhere, right? So like I said, he'd come down the ring, he'd shake the ring, shake the ropes, and I, he's like, he can't breathe.


[User] Please transcribe this text

[Assistant] You know? So, especially my character early on, like, you could hit on me, bang, boom, boom, boom, I don't give a shit, right? I'm just, I'm like, I'm Michael Myers, I'm walking through everybody.


[User] Please transcribe this text

[Assistant] I'm like, alright Jim, alright, come on, let's, let's do this. And he couldn't, he couldn't get it out. He was so out of breath that...


[User] Please transcribe this text

[Assistant] Oh yeah, yeah. He was, he was, he was good for about.


[User] Please transcribe this text

[Assistant] 10 minutes and then, you know, then you didn't know what you were going to get.


[User] Please transcribe this text

[Assistant] He was so hot on stage. He was so big and strong and, but, you know. So much exertion. Yea, I mean, he's, you know, a lot of our...


[User] Please transcribe this text

[Assistant] So, this is, this is the early 90s, right, when a lot of people were taking like denim and airbrushing them.


[User] Please transcribe this text

[Assistant] So you're sitting in an airport at uh, you know, six in the morning.


[User] Please transcribe this text

[Assistant] But then when you have it written all over your body as well, right? You can, you know, like, me, I've got my hat pulled down, my sunglasses on, I'm in a corner somewhere just trying to be, you know, I'm trying to blend in, but here he's like a beacon of light as it says, yes, I'm the warrior. And I remember, I'll never forget this little kid coming up to him. And so he's like, Mr. Warrior?


[User] Please transcribe this text

[Assistant] Any like me, for whatever reason, I think because we, you know, we made money together, but...


[User] Please transcribe this text

[Assistant] It was, uh, he was always, he was always a really interesting dude, man. He was, you know, and that's again back there, that era.


[User] Please transcribe this text

[Assistant] Those guys were good, man. Yeah, you can tell you're a fan, man. You're bringing back some names a lot of people don't even remember anymore.


[User] Please transcribe this text

[Assistant] Yeaah, it's uhm, yeaah, that's, it's funny because I think people, people use it.


[User] Please transcribe this text

[Assistant] Especially they used to use it as an escape, right? Cause you had your, whatever character was out there, you had somebody for, I mean there was somebody for everyone, right?


[User] Please transcribe this text

[Assistant] And, you know, it was cool that people could lose themselves in their reality. It's amazing now.


[User] Please transcribe this text

[Assistant] So now I'm going out and doing, uh, you know, my one man show.


[User] Please transcribe this text

[Assistant] But they're again, yeah, they want to hear all that behind the scenes stuff, but it's amazing when I do my meet and greets, right?


[User] Please transcribe this text

[Assistant] Then the amount of people that come up to me and say, you know, if it hadn't been for you and Monday night raw, you know, I don't think I would have made it.


[User] Please transcribe this text

[Assistant] I was going to do it and it came down to it. He's the only reason I didn't, I didn't follow through with my plan is cause I wanted to see what you were going to do on Monday night on Raw. And then it became, you know,


[User] Please transcribe this text

[Assistant] I struggled all week to get to the next Monday night so I could see what you were going to do. And I'm just, I'm like, I'm sitting there like blown away and like.


[User] Please transcribe this text

[Assistant] That's crazy to have that kind of, that people invest and has that


[User] Please transcribe this text

[Assistant] It, it gives you a different perspective on, on your life and what you do.


[User] Please transcribe this text

[Assistant] Oh yeaah, I mean this is like, yeaah, it's crazy man. But, uh, yeaah, I need to get some, uh,


[User] Please transcribe this text

[Assistant] Yeah. I mean, it was really good. Uh, cause I'm trying to get mine. I've done about eight of my shows now.


[User] Please transcribe this text

[Assistant] I was like, it just dawned on me, even before this came up, I was like.


[User] Please transcribe this text

[Assistant] Yeah, every time that I would say something that they liked, they would, uh, they would turn into a chant, right? So I'm telling a story, uh,


[User] Please transcribe this text

[Assistant] You know, this particular story, this girl back in the day, like she was all in on my creatures of the night and she comes up to me and


[User] Please transcribe this text

[Assistant] And then, I was like, yeaah, it's Taker's Tattooed Titty Girl.


[User] Please transcribe this text

[Assistant] I couldn't help but laugh, right? I made me forget everything that I was going to say because they just, they were everything that they felt like was entertaining or amusing. They just chanted it right back to me. I mean that show went way off the rails and it was one of the best shows that we've done but man, a live


[User] Please transcribe this text

[Assistant] That's that's really good money, but no, I don't, I don't, you know, I don't think so.


[User] Please transcribe this text

[Assistant] It goes against everything that I did for all those years. You know, I protected everything and then now everything's out there. So it's just like.


[User] Please transcribe this text

[Assistant] Yeah, I'm, I'm, I'm getting, you know, I'm starting to get much more comfortable with it, but, uh,


[User] Please transcribe this text

[Assistant] During the course that you, now I'm sitting in this big church, it's gotta be 150 people in there and he throws, uses me as a, you know, like a, as a kind of a joke. And I'm like, I'm looking at my wife and I'm like, tell me he didn't just say that.


[User] Please transcribe this text

[Assistant] Everything, traveling and just, it was, you know, people don't, people don't think of that.


[User] Please transcribe this text

[Assistant] Yeah, yeah, he was obviously, he was treated like a king in France, but uhm, but yeah, just, you know.


[User] Please transcribe this text

[Assistant] Yup. I learned a lot, I learned a lot from Andre, uhm, good and bad, but yeaah, he was, he was, he was a good man, really good man, so.


[User] Please transcribe this text

[Assistant] One of the people that I've worked with, my favorite people to be in the ring with.


[User] Please transcribe this text

[Assistant] You know, I think that's probably one of the things that, that, you know, as a rescue, everybody, you know, will, that...


[User] Please transcribe this text

[Assistant] Well, I think it's one of the reasons why we kind of take exception when people say


[User] Please transcribe this text

[Assistant] It's, it's not fake. It's a different, it's a different genre of entertainment. And it's, it's like you said, it's sports, it's sports opera for men, but you, I mean,


[User] Please transcribe this text

[Assistant] Yeah. They get stepped on and you know, things like that. But my point to that was is like the amount, you know, back in the day, if you didn't work, you didn't get paid, right? So guys would go to the ring.


[User] Please transcribe this text

[Assistant] You know, needing surgery or being injured and just go out and do it. Because what happens is back then, say you're booked in the garden, right?


[User] Please transcribe this text

[Assistant] It's not going to affect that crowd because everybody's bought their ticket, but the next time, you know, they're going to think before they go and buy their tickets, like.


[User] Please transcribe this text

[Assistant] Oh well, the last time he didn't show up, do we want to risk this? I'm not going to be able to see that. So guys end up, end up working and, and just the, I just, I think my point to all of it is, is guys don't get enough credit for how tough


[User] Please transcribe this text

[Assistant] You know, not tough in the sense of whether they're a good fighter or, or, or, you know, but the amount of pain.


[User] Please transcribe this text

[Assistant] Back in the, that day, they were, they were a different breed, man. It really, uhm, and I'm not taking away from any of these guys today either, but it, it, it's just back then.


[User] Please transcribe this text

[Assistant] I'm just saying, I'm going to go back, like you standing there getting the referee's final instructions, not just, just like, oh man, this dude's dead.


[User] Please transcribe this text

[Assistant] Another dude, freaky athlete, man. Don't give two shits either. He is not, Brock is not a people person whatsoever.


[User] Please transcribe this text

[Assistant] He's, uh, Bronx is one of those rare once in a lifetime kind of athlete. Big old country boy.


[User] Please transcribe this text

[Assistant] No, I don't blame you there. It's going to get much more play. WWE is going to do it right. Yeah.


[User] Please transcribe this text

[Assistant] Yeah, yeah, the company just had a huge evaluation.


[User] Please transcribe this text

[Assistant] Business is just off the charts.


[User] Please transcribe this text

[Assistant] And then getting a crossover like this with Mike was really huge.


[User] Please transcribe this text

[Assistant] Yeah, this is, yeah, things really started taking off, you know, after this.


[User] Please transcribe this text

[Assistant] So I think the first couple things you need is just come to, come to the events.


[User] Please transcribe this text

[Assistant] Once football is over with, then you can, you know, you can work your way in there pretty easily.


[User] Please transcribe this text

[Assistant] Like a pure wrestling straight match with Shawn Michaels at WrestleMania 25.


[User] Please transcribe this text

[Assistant] Oh yeah, the, the, this, yeah, the rivals, this was, yeah, Mick was my biggest, biggest rivals in the nineties, man.


[User] Please transcribe this text

[Assistant] As bad as that one was. Look at that young man. Holy hell.


[User] Please transcribe this text

[Assistant] Is just, uh, he almost didn't get up from. That one was bad but the next one is really bad.


[User] Please transcribe this text

[Assistant] He had never, he had never been up there on top of that cell.


[User] Please transcribe this text

[Assistant] His mentality on this thing was, so I'd had the first Hell in a Cell match with, uh, with Shawn Michaels.


[User] Please transcribe this text

[Assistant] And it was good. It was, it was violent and it was everything that the Hell in the Cell match was supposed to be.


[User] Please transcribe this text

[Assistant] And then this would have been the second one. And Mick was afraid that we wouldn't be able to deliver as good a match as the first one. So he had it in his head that he wanted to give him, if he said we can


[User] Please transcribe this text

[Assistant] Almost smoke and mirror people into thinking that they've seen something really, you know, incredible.


[User] Please transcribe this text

[Assistant] And that's what, that's what he decided we needed to do.


[User] Please transcribe this text

[Assistant] In just a second, like a little bit, like five minutes later in this match, we get back up on this. After I throw him, I throw him down, he climbs back up. We continue fighting. And, uh. Over here. He's climbing back up.


[User] Please transcribe this text

[Assistant] Yeah, you need to see this though in actual speed.


[User] Please transcribe this text

[Assistant] Oh! He's out. Oh yeaah, he's dead. He's out right now.


[User] Please transcribe this text

[Assistant] I'm looking down, honestly in my eyes, I'm looking down at a dead guy.


[User] Please transcribe this text

[Assistant] That wasn't supposed to happen. The first one wasn't supposed to happen. That one, two more inches of rotation, he didn't get up.


[User] Please transcribe this text

[Assistant] He, this was, I guess this is our morbid sense of humor. So we continue. He gets up from this again. He gets up and we start fighting again and I'm so distracted. This is kind of gross. Like he's got something in his nostril.


[User] Please transcribe this text

[Assistant] And I'm looking at him and I'm punching him, I'm thinking like, man, he's got a big booger in his nose, right?


[User] Please transcribe this text

[Assistant] Come to find out it wasn't a booger, it was his incisor. When he hit, his tooth went through his lip and lodged in his nose.


[User] Please transcribe this text

[Assistant] There's, there's a point where he's sitting there and he's bleeding and he's all beat up and he sticks his tongue through the hole in his lip. And he goes, and anyway, yeah, his, his, uh, at this point, this is Terry Fung.


[User] Please transcribe this text

[Assistant] And that's him sticking his tongue through his, through his lip. So that was the best part of his night, actually, was the table. That one, that one was really nasty.


[User] Please transcribe this text

[Assistant] Of course, of course I, you know, showed him all kind of compassion and mercy.


[User] Please transcribe this text

[Assistant] Yeaah, doing well. Just the sweetheart of a guy, really. But uhm, man, he took a beating that night.


[User] Please transcribe this text

[Assistant] To kind of pull that curtain back and let people really understand that, you know, I'm a normal guy and, and, uh, you know, I'm not a, you know.


[User] Please transcribe this text

[Assistant] I don't, did graves all the time. You know, all that. So, it was kind of tough, but it was, it was cool. And then obviously, just listening, uh,


[User] Please transcribe this text

[Assistant] To my peers and the people that I've worked with and, you know, their feelings, you know, it's kind of cool to hear all of that and, um, you know, kind of let you know that through my course of my career, you know,


[User] Please transcribe this text

[Assistant] It was, I think my mom stole the show on the, on my A&E deal. She, my mom's 90 and, uh, you know, she was, she talks mad, right? Like, I'd call her.


[User] Please transcribe this text

[Assistant] And man, she would let me have it. She'd be, she'd be on Vince all the time. Does he see the way he walks? Does he, does he, I mean, does he do that on purpose? I'm like, mom, and then she, and Triple H. Why's he got to have that damn hammer all the time? I'm like, mom, it's just part of the deal. I know, but nobody likes that. Nobody likes a cheater.


[User] Please transcribe this text

[Assistant] I had four brothers. Yeah, I was the, I was the youngest and they all kicked the shit out of me growing up.


[User] Please transcribe this text

[Assistant] Yeah, my brothers, I think they kind of set the tone for where I was going in life.


[User] Please transcribe this text

[Assistant] Yeah, as the runt man, I was bigger than everybody, but yeah, they all, they were all quite a bit older.


[User] Please transcribe this text

[Assistant] Salt of the Earth people, man. My dad, middle class, worked, worked his butt off. He worked for the Houston Post, uh, he worked in the, uh, in the press rooms.


[User] Please transcribe this text

[Assistant] And then during the day, he did, he did extra jobs for like, he was a master plumber and did all, you know, he was just, man, he was a grinder.


[User] Please transcribe this text

[Assistant] My in-laws are pretty funny, like, you know, they kind of try and take the, you know, people will get real excited, like,


[User] Please transcribe this text

[Assistant] And I look over at my daughter like, and I'm ready, you know, I'm going to give her the, you know, as you boy kind of deal.


[User] Please transcribe this text

[Assistant] And I see her, and I see her and she's, she's on the rail and she's got like one thumb up and one thumb down.


[User] Please transcribe this text

[Assistant] I'm like, Kaya, what was with the thumb up and the thumb? And she goes, well dad.


[User] Please transcribe this text

[Assistant] I'm really happy that you won, but I can't believe that you beat Cena.


[User] Please transcribe this text

[Assistant] But she was, yeaah, so this is no lie, and this is kind of a testament to the person that Sena is. Sena actually had to go and console my daughter. She's like, well, he was telling her, he's like, you know, I talked a lot of smack about your dad, and you know, I kind of deserved it.


[User] Please transcribe this text

[Assistant] His new eyes. He looks good too. Yeaah, he's a good dude. He's really good.


[User] Please transcribe this text

[Assistant] Sees the big picture of it all. So it's good to see guys like him and rock and.


[User] Please transcribe this text

[Assistant] Batista and those guys be able to cross over and do, do other things because I don't think.


[User] Please transcribe this text

[Assistant] You knew that would rock, even back in the day when he was just wrestling. You knew that one day he was going to be doing something special.


[User] Please transcribe this text

[Assistant] They beat it. I mean, they pretty much beat everybody. They were, they were a pretty hot tag team. Yeah. Yeah.


[User] Please transcribe this text

[Assistant] Yeah, he was one of our, our, our road agents, like he handled the live events. And for years there, you know, because of my character, I mean.


[User] Please transcribe this text

[Assistant] I would get flowers sent to me, black roses and all this stuff. You know, Arnold, Arnold being in the dressing room playing cards, right? I'd bring these flowers and I said, Hey Arnie, here's, here's some flowers.


[User] Please transcribe this text

[Assistant] Yeah, John was a good heel. He was a good bad guy. Yeah, good bad guy. And, and uh, I've been trying to think, I'm trying to think of your character. Oh no.


[User] Please transcribe this text

[Assistant] Yeah, we've been friends for 30 plus years, man. We met, this is a good story, we met in the ring.


[User] Please transcribe this text

[Assistant] In Tennessee, this is 1988, I think.


[User] Please transcribe this text

[Assistant] And I was in Texas, I just finished up in the Texas territory. We used to work in territories that, you know, we didn't have the big...


[User] Please transcribe this text

[Assistant] Mass, um, you know, production like WWE or WCW, we had little territories that we all went to. And, uh, so I just finished up in Texas and I go back to Tennessee and, um, so I'm, I drove and I got there just in time to get dressed and then go to the ring. So I go to the ring.


[User] Please transcribe this text

[Assistant] And who's there waiting on me is the Godfather. His name at the time was the Soul Taker.


[User] Please transcribe this text

[Assistant] And I'm looking, right, and we hadn't had a chance to talk, we ain't met, we just, we're in the ring, and I'm looking at him, and he's looking at me.


[User] Please transcribe this text

[Assistant] Big white boy, I'm gonna have to, you know. So we're kind of sizing each other up and not even, you know, and uh, little did I know, this was like his third match.


[User] Please transcribe this text

[Assistant] It was his third man, and man, so we, we,


[User] Please transcribe this text

[Assistant] When we lock up and I'm like, oh, this dude's strong. And, uh, he ends up.


[User] Please transcribe this text

[Assistant] He ends up taking me and shooting me across from one corner to the next and I hit so hard.


[User] Please transcribe this text

[Assistant] Like the top rope snaps off. It's a big mother. Right? So.


[User] Please transcribe this text

[Assistant] Or we can do this the hard way. I said, I would much rather do this the easy way. And he goes, me too.


[User] Please transcribe this text

[Assistant] Yeah, right. And from that point forward, we've been the best of friends and man, we've been through so much together. Uhm, he just had, he just had hip, had his hip replay and he's got to get his knees done. But, uh, yeah, he's a really good dude.


[User] Please transcribe this text

[Assistant] So much fun, but yeah, he's still in, he's still in Vegas. Godfather is, but man alive, the trouble we got into.


[User] Please transcribe this text

[Assistant] Fortunately not. We, we, we got away before we ever got arrested, but there's probably a couple of cases, chances we should have been arrested, but we got, you know.


[User] Please transcribe this text

[Assistant] Alright, you know, you know what they look big, jacked up, yeah, with the face paint. So me and Danny, I was, I was his partner and we wrestled, you know, we were wrestling the road warriors.


[User] Please transcribe this text

[Assistant] I go in, you know, I do. So we get to the dress rooms in Corpus Christi, Texas.


[User] Please transcribe this text

[Assistant] I don't have any idea what he's talking about. Anyway, we have this match. I don't know what TQ or something like that. Danny gets a chair and he starts beating these two dudes like, like they owed him money. And I am just like, Oh man.


[User] Please transcribe this text

[Assistant] We're about to, we're about to fight the road warriors. We're about to fight them in the dressing room for real.


[User] Please transcribe this text

[Assistant] Old kind of civic auditorium kind of deal, had to walk upstairs and, uh, we're sitting down.


[User] Please transcribe this text

[Assistant] And, uh, and then they kind of walked past me again like, hey kid, good job kid. And I'm like,


[User] Please transcribe this text

[Assistant] What the hell did I just miss? And then I look back down at Danny, right? And Danny just kind of gives me this little shy, like this little, it gives me the wink, right?


[User] Please transcribe this text

[Assistant] You'll, you'll appreciate this. Danny beat the living out of Adrian Adonis.


[User] Please transcribe this text

[Assistant] Danny had come up the first time to WWF and Adrian Adonis was messing with him. Danny knocked him out. Wham, right in the ring.


[User] Please transcribe this text

[Assistant] Out. And then they woke, they woke him up and then he tried to fight him again in the dress room, knocked him out again.


[User] Please transcribe this text

[Assistant] Yeah, he had a, but he was one of those guys like business would get rough or get, he'd just go home and like, screw it man, I'm done. And then he'd just wait. But yeah, he was a big dollar Dan, the left hand man.


[User] Please transcribe this text

[Assistant] Uhm, I hadn't seen him in, uh, I hadn't seen him in years, but he's still, you know, him and Hogan are buddies.


[User] Please transcribe this text

[Assistant] What I want them to know man, uhm, I just want them to know how, uh, how thankful that I am for, for all my fans for all these years that stayed with The Undertaker. And I hope that you guys continue the, uh,


[User] Please transcribe this text

[Assistant] You think you know, you think you know some things about The Undertaker, there's going to be a lot of things that people find out that they're going to be shocked and, uh,


[User] Please transcribe this text

[Assistant] Uh, they're hearing what they're hearing. And I, you know, I think, uhm, you know, come out, if you get a chance to go, it's called the One Dead Man Show.


[User] Please transcribe this text

[Assistant] Uh, we're going to be in Montreal coming up.


[User] Please transcribe this text

[Assistant] In March, March 24th, my birthday, I'm gonna be in Vegas.


[User] Please transcribe this text

[Assistant] Yeah, so yeah, we're gonna do, I'm gonna do a show on my birthday in Vegas. And then we head to LA for WrestleMania.


[User] Please transcribe this text

[Assistant] So far, I mean, it's going to be nuts.


[User] Please transcribe this text

[Assistant] You got a 50-50 chance of actually being me, but.


[User] Please transcribe this text

[Assistant] It is, you know, that's, that's, that's where we're at in this day and age. So I guess I got to play the game.


[User] Please transcribe this text

[Assistant] People that are fans about, you know, what we do. And obviously I'm a huge fan of yours and continued success.


[User] Please transcribe this text

[Assistant] Haystacks man, big haystacks. You're like, you're just a plethora of, uh,


[User] Please transcribe this text

[Assistant] And we'll present, thank you, whoever said yes.


[User] Please transcribe this text

[Assistant] And today we will present a competition that unlike deep traffic.


[User] Please transcribe this text

[Assistant] Teach you about concepts of deeper, deeper reinforcement learning. SegFuse, the deep.


[User] Please transcribe this text

[Assistant] Dynamic driving scene segmentation competition that I'll present today is at the very cutting edge.


[User] Please transcribe this text

[Assistant] Whoever does well in this competition is likely to produce a publication or ideas that would lead the world in the area of perception.


[User] Please transcribe this text

[Assistant] Perhaps together with the people running this class, perhaps on your own. And I encourage you to do so.


[User] Please transcribe this text

[Assistant] Majority of the successes in how we interpret form representations, understand images and videos, utilize to a significant degree neural networks. The very ideas we've been talking about that applies for supervised, unsupervised and reinforcement learning.


[User] Please transcribe this text

[Assistant] And for the supervised case, which is the focus of today, the process is the same.


[User] Please transcribe this text

[Assistant] The data is essential. There's annotated data where the human provides the labels that serves as the ground truth in the training process then the neural network


[User] Please transcribe this text

[Assistant] Ghosts through that data, learning to map from the raw sensory input to the ground truth labels and then generalize over the testing data set. And the kind of raw sensors we're dealing with are numbers. I'll say this again and again, that for human vision,


[User] Please transcribe this text

[Assistant] For us here, we take for granted this particular aspect of our ability, is to take in raw sensory information through our eyes and interpret it.


[User] Please transcribe this text

[Assistant] What, what is the data that it's tasked to work with in order to perform the task you're asking it to do?


[User] Please transcribe this text

[Assistant] Perhaps the data it's given is highly insufficient to do what you want it to do. That's the question that will come up again and again. Are images enough to understand the world around you?


[User] Please transcribe this text

[Assistant] And given these numbers, these set of numbers, sometimes with one channel, sometimes with three RGB, where every single pixel have three different colors, the task is to classify


[User] Please transcribe this text

[Assistant] Or regress, producing continuous variable or one of a set of class labels.


[User] Please transcribe this text

[Assistant] As before, we must be careful about our intuition of what is hard and what is easy in computer vision.


[User] Please transcribe this text

[Assistant] Let's take a step back to the inspiration.


[User] Please transcribe this text

[Assistant] For neural networks. Our own biological neural networks. Because the human vision system and the computer vision system is a little bit more similar in these regards.


[User] Please transcribe this text

[Assistant] The structure of the human visual cortex is in layers, and as information passes from the eyes to the...


[User] Please transcribe this text

[Assistant] To the parts of the brain that make sense of the raw sensory information, higher and higher order representations are formed.


[User] Please transcribe this text

[Assistant] This is the inspiration, the idea behind using deep neural networks for images. Higher and higher order representations are formed through the layers.


[User] Please transcribe this text

[Assistant] The early layers, taking in the very raw sensory information, then extracting edges.


[User] Please transcribe this text

[Assistant] Connecting those edges, forming those edges to form more complex features, and finally into the higher order semantic meaning that we hope to get from these images. In computer vision, deep learning is hard.


[User] Please transcribe this text

[Assistant] I'll say this again, the illumination variability is the biggest challenge, or at least one of the, one of the biggest challenges in driving for visible light cameras. Pose variability. The objects.


[User] Please transcribe this text

[Assistant] As I'll also discuss about some of the advances from Jeff Hinton and the Capsule Networks, the idea with neural networks as they are currently used for computer vision are not good with representing variable pose. These objects in images in this 2D plane of color and texture


[User] Please transcribe this text

[Assistant] Look very different numerically when the object is rotated.


[User] Please transcribe this text

[Assistant] And the object is mangled and shaped in different ways. The deformable truncated cat. Interclass variability.


[User] Please transcribe this text

[Assistant] The classification task, which would be an example today throughout to introduce some of the networks over the past decade that have received success and some of the intuition and insight that made those networks work. Classification.


[User] Please transcribe this text

[Assistant] There is a lot of variability inside the classes and very little variability between the classes.


[User] Please transcribe this text

[Assistant] All of these are cats at the top, all of those are dogs at the bottom. They look very different. And the other, I would say the second biggest problem in driving perception, visible light camera perception, is occlusion. When part of the object is occluded, due to the three dimensional


[User] Please transcribe this text

[Assistant] Nature of our world, some objects in front of others, and they occlude the background object.


[User] Please transcribe this text

[Assistant] And yet we're still tasked with identifying the object when only part of it is visible. And sometimes that part, I told you there's cats, is very hardly visible. Here, we're tasked with classifying a cat when just an ear is visible, just the leg.


[User] Please transcribe this text

[Assistant] And then the philosophical level, as we'll talk about, the motivation for our competition here. Here's a, uh, uh, a cat dressed as a monkey eating a banana. On the philosophical level,


[User] Please transcribe this text

[Assistant] Most of us, uh, understand what's going on in the scene.


[User] Please transcribe this text

[Assistant] And what else is missing is the dynamic information. The temporal dynamics of the scene.


[User] Please transcribe this text

[Assistant] That's what's missing in a lot of the perception work that has been done to date in the autonomous vehicle space, uh, in terms of visible light cameras. And we're looking to expand on that. That's what SegFuse is all about.


[User] Please transcribe this text

[Assistant] Image classification pipeline. There's a bin with different categories inside each class. Cat, dog, mug, hat.


[User] Please transcribe this text

[Assistant] Those bins, there's a lot of examples of each. And you're tasked with, when a new example comes along you've never seen before, to put that image in a bin. It's the same as the machine learning task before. And everything relies on the data that's been ground truth, that's been labelled by human beings.


[User] Please transcribe this text

[Assistant] MNIST is a toy data set of handwritten digits often used as examples.


[User] Please transcribe this text

[Assistant] Coco, Cypher, ImageNet, Places, and a lot of other incredible data sets, rich data sets of a hundred thousands, millions of images out there, represent scenes, people's faces, and different objects. Those are all ground truth data for testing algorithms and for


[User] Please transcribe this text

[Assistant] Almost toy data sets of tiny icons with 10 categories of airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. It's commonly used to explore some of the basic convolutional neural networks we'll discuss. So let's come up with a very trivial classifier to explain the concept of how we could go about it.


[User] Please transcribe this text

[Assistant] In fact this is, maybe if you start to think about how to classify an image if you don't know any of these techniques, this is perhaps the approach you would take. Is you would subtract images. So in order to know that an image of a cat is different than an image of a dog, you have to compare them.


[User] Please transcribe this text

[Assistant] Given those two images, what's the, what's the way you compare them? One way you could do it is you just subtract it.


[User] Please transcribe this text

[Assistant] And then sum all the pixel-wise differences in the image. Just subtract the intensity of the image pixel by pixel, sum it up.


[User] Please transcribe this text

[Assistant] If that inten-, if that difference is really high, that means the images are very different. Using that metric, we can look at CIFAR10 and use it as a classifier, saying,


[User] Please transcribe this text

[Assistant] Based on this difference function, I'm going to find one of the ten bins for a new image that, that is, that has the lowest difference.


[User] Please transcribe this text

[Assistant] Find an image in this data set that is most like the image I have and put it in the same bin as that image is in. So.


[User] Please transcribe this text

[Assistant] Using our image difference classifier, we can actually do pretty good, much better than random, much better than 10%. We can do 35, 38% accuracy. That's the classifier. We have our first classifier.


[User] Please transcribe this text

[Assistant] Let's take our classifier to a whole new level. Instead of comparing it to just


[User] Please transcribe this text

[Assistant] If I try to find one image that's the closest in our data set, we try to find K closest and say, what is, what class do the majority of them belong to? And we take that K and increase it from one to two to three to four to five and see how that changes the problem.


[User] Please transcribe this text

[Assistant] And with convolutional neural networks, we get very close to 100%.


[User] Please transcribe this text

[Assistant] Where neural networks shine, this very task of bending images. It all starts at this basic computational unit. Signal in, each of the signals are weighed, summed, bias added.


[User] Please transcribe this text

[Assistant] And put an input into a nonlinear activation function that produces an output. The nonlinear activation function is key. All of these put together in more and more hidden layers.


[User] Please transcribe this text

[Assistant] For the case of ten examples, the output of the network is ten different values.


[User] Please transcribe this text

[Assistant] And we wanted our network to classify what is in this image of a handwritten digit. Is it one, uh, is zero, one, two, three through nine.


[User] Please transcribe this text

[Assistant] And each of the neurons on the output is responsible for getting really excited when its number is called.


[User] Please transcribe this text

[Assistant] And everybody else is supposed to be not excited. Therefore,


[User] Please transcribe this text

[Assistant] The number of classes is the number of outputs. That's how it's commonly done. And you...


[User] Please transcribe this text

[Assistant] But that's for a fully connected network that we've discussed on Monday.


[User] Please transcribe this text

[Assistant] There is in deep learning a lot of tricks that make things work, that make training much more efficient on large,


[User] Please transcribe this text

[Assistant] Class problems where there's a lot of classes on large data sets, when the representation of the neural network is tasked with learning is extremely complex. And that's where convolutional neural networks step in. The trick they use is spatial invariance. They use the idea that


[User] Please transcribe this text

[Assistant] A cat in the top left corner of an image is the same as a cat in the bottom right corner of an image. So we can learn the same features across the image. That's where the convolution operation steps in.


[User] Please transcribe this text

[Assistant] Instead of the fully connected networks, here there's a third dimension of depth. So the blocks in this neural network


[User] Please transcribe this text

[Assistant] As input, take 3D volumes and as output, produce 3D volumes.


[User] Please transcribe this text

[Assistant] They take a slice of the image, a window, and slide it across.


[User] Please transcribe this text

[Assistant] Applying the same exact weights, and we'll go through an example. The same exact weights as in the fully connected network on the edges that are used to map the input to the output. Here are used to map this slice of an image, this window of an image to the output.


[User] Please transcribe this text

[Assistant] And you can make several, many of such convolutional filters. Many layers, many different options of what kind of features you look for in an image. What kind of window you slide across in order to extract all kinds of things.


[User] Please transcribe this text

[Assistant] All kinds of edges. All kinds of higher order patterns in the images.


[User] Please transcribe this text

[Assistant] The very important thing is the parameters on each of these filters, these subset of the image, these windows, are shared. If the feature


[User] Please transcribe this text

[Assistant] That defines a cat is useful in the top left corner, it's useful in the top right corner, it's useful in every aspect of the image. This is the trick that makes convolutional neural networks save a lot of


[User] Please transcribe this text

[Assistant] Uh, a lot of parameters reduce parameters significantly.


[User] Please transcribe this text

[Assistant] The stride is the skip of the filter, the step size, how many pixels you skip when you apply the filter to the input and the padding.


[User] Please transcribe this text

[Assistant] Is the padding, the zero padding on the outside of the input to a convolutional layer. Let's go through an example. So, on the left here,


[User] Please transcribe this text

[Assistant] And the slides are now available online, you can follow them along. And I'll step through this example.


[User] Please transcribe this text

[Assistant] On the left here is input volume of three channels.


[User] Please transcribe this text

[Assistant] The left column is the input. The three blocks, the three squares there are the three channels.


[User] Please transcribe this text

[Assistant] And there's numbers inside those channels.


[User] Please transcribe this text

[Assistant] Two of them. Two channels of filters. With a bias.


[User] Please transcribe this text

[Assistant] That are to be learned. These are our variables, our weights that we have to learn.


[User] Please transcribe this text

[Assistant] And then we slide it across an image to produce the output on the right, the green.


[User] Please transcribe this text

[Assistant] And you can look, uh, you can pull up the slides yourself now if you can't see the numbers on the screen. But the, the operations...


[User] Please transcribe this text

[Assistant] Are performed on the input to produce the single value that's highlighted there in the green in the output. And we slide this convolution, no filter, along the image with a stride, in this case,


[User] Please transcribe this text

[Assistant] Of two. Skipping, skipping along.


[User] Please transcribe this text

[Assistant] They sum to the, to the right, the two channel output, uh, in green. That's it, that's the convolutional operation.


[User] Please transcribe this text

[Assistant] That's what's called the convolutional layer neural networks. And the parameters here, besides the bias, are the red values in the middle. That's what we're trying to learn.


[User] Please transcribe this text

[Assistant] And there's a lot of interesting tricks we'll discuss today on top of those. But this is at the core. This is the spatially invariant sharing of parameters that make convolutional neural networks able to efficiently learn and find patterns and images. To build your intuition a little bit more,


[User] Please transcribe this text

[Assistant] About convolution. Here's an input image on the left. And on the right, the identity filter.


[User] Please transcribe this text

[Assistant] Produces the output you see on the right. And then there's different ways you can, different kinds of edges you can extract.


[User] Please transcribe this text

[Assistant] With the activate, with the resulting activation map seen on the right. So when applying the filters, with those edge detection filters, to the image on the left, you produce in white are the parts that, uh, activate the convolution. The results of these filters.


[User] Please transcribe this text

[Assistant] And so you can do any kind of filter. That's what we're trying to learn. Any kind of edge. Any kind of...


[User] Please transcribe this text

[Assistant] Any kind of pattern you can move along in this window, in this way that's shown here. You slide around the image and you produce the output you see on the right.


[User] Please transcribe this text

[Assistant] And depending on how many filters you have in every level, you have many of such slices that you see on the right. The input on the left, the output on the right. If you have


[User] Please transcribe this text

[Assistant] Dozens of filters, you have dozens of images on the right, each with different results that show


[User] Please transcribe this text

[Assistant] Uh, where each of the individual filter patterns were found. And we learn what patterns are useful to look for in order to perform the classification task. That's the task for the neural network, to learn these filters.


[User] Please transcribe this text

[Assistant] And the filters have higher and higher order of representation.


[User] Please transcribe this text

[Assistant] Going from the very basic edges to the high semantic meaning that spans entire images.


[User] Please transcribe this text

[Assistant] And the ability to span images can be done in several ways, but traditionally has been successfully done through max pooling, through pooling.


[User] Please transcribe this text

[Assistant] Of taking the output of a convolutional operation.


[User] Please transcribe this text

[Assistant] And reducing the resolution of that by...


[User] Please transcribe this text

[Assistant] By condensing that information by, for example, taking the maximum values, the maximum activations.


[User] Please transcribe this text

[Assistant] Therefore reducing the spatial resolution, which has detrimental effects as we'll talk about in scene segmentation, but it's beneficial for finding higher order representations in the images that bring images together, that bring features together to form an entity that we're trying to identify and classify. Okay, so that forms


[User] Please transcribe this text

[Assistant] A convolutional neural network. Such convolutional layers stacked on top of each other is the only addition to a neural network that makes for a convolutional neural network.


[User] Please transcribe this text

[Assistant] And then at the end, the fully connected layers or any kind of other, uh, architectures allow us to apply particular domains. Let's take ImageNet as a case study.


[User] Please transcribe this text

[Assistant] An ImageNet, the data set, an ImageNet, the challenge, the task is classification.


[User] Please transcribe this text

[Assistant] As I mentioned in the first lecture, ImageNet is a data set.


[User] Please transcribe this text

[Assistant] One of the largest in the world of images. With 14 million images, 21,000 categories.


[User] Please transcribe this text

[Assistant] And a lot of depth to many of the categories. As I mentioned, 1200 Granny Smith apples.


[User] Please transcribe this text

[Assistant] These allow to, these allow the neural networks to learn the rich representations in both pose, lighting variability and intraclass, class variation for the particular things, particular classes like Granny Smith apples.


[User] Please transcribe this text

[Assistant] So, let's look through the various networks. Let's discuss them. Let's see the insights. It started with AlexNet, the first really big successful GPU-trained neural network on ImageNet that's achieved a significant boost over the previous year, and moved on to VGGNet, GoogleNet,


[User] Please transcribe this text

[Assistant] Again, the numbers we'll show for the accuracy are based on the top five error rate. We get five guesses, and it's a one or zero. If you get a guess, if one of the five is correct, you get a one for that particular guess, otherwise it's a zero.


[User] Please transcribe this text

[Assistant] And human error is 5.1. When a human tries to achieve the same, tries to perform the same task as the machinist asked for doing, the error is 5.1. The human annotation is performed on the images based on binary classification. Granny Smith Apple or not.


[User] Please transcribe this text

[Assistant] Cat or not. The actual task that the machine has to perform and that the human competing has to perform is given an image is provide one of the many classes.


[User] Please transcribe this text

[Assistant] Under that, human error is 5.1%, which was surpassed in 2015 by Resnet, uh, to achieve 4% error.


[User] Please transcribe this text

[Assistant] So let's start with AlexNet. I'll zoom in on the later networks. They have some interesting insights. But AlexNet and VGGNet both followed a very similar architecture. Very uniform throughout its depth.


[User] Please transcribe this text

[Assistant] VGG Net in 2014 is convolution, convolution, pooling, convolution, pooling, convolution, pooling, and fully connected layers at the end.


[User] Please transcribe this text

[Assistant] There's a certain kind of beautiful simplicity, uniformity to these architectures. Because you can just make it deeper and deeper and makes it very amenable to, uh, implementation in a layered stack kind of way in any of the deep learning frameworks.


[User] Please transcribe this text

[Assistant] It's clean and beautiful to understand. In the case of VGGNet, 16 or 19 layers with 138 million parameters. Not many optimizations on these parameters, therefore, the number of parameters is much higher than the networks that followed it, despite the layers not being that large.


[User] Please transcribe this text

[Assistant] Google net introduced the inception module, starting to do some interesting things with the small modules within these networks, which allow for the training to be more efficient and effective.


[User] Please transcribe this text

[Assistant] The idea behind the inception module shown here, with the previous layer on bottom,


[User] Please transcribe this text

[Assistant] Different sized convolutions provide different value for the network.


[User] Please transcribe this text

[Assistant] Smaller convolutions are able to capture or propagate forward


[User] Please transcribe this text

[Assistant] Larger convolutions are better able to, to represent and capture and catch highly abstracted features, higher order features.


[User] Please transcribe this text

[Assistant] So the idea behind the inception module is to say, well, as opposed to choosing in a hyperparameter tuning process or architecture design process, choosing which convolution size we want to go with,


[User] Please transcribe this text

[Assistant] Why not do all of them together, well, several together? In the case of the Google net model, there's the one by one, three by three, and five by five convolutions.


[User] Please transcribe this text

[Assistant] With the old trusty friend of Max Pooling still left in there as well.


[User] Please transcribe this text

[Assistant] Which has lost favor more and more over time for the image classification task.


[User] Please transcribe this text

[Assistant] And the result is there's fewer parameters that are required. If you pick the placing of these inception modules correctly, the number of parameters required to achieve a higher performance is much lower.


[User] Please transcribe this text

[Assistant] Resnet, one of the most popular still-to-date


[User] Please transcribe this text

[Assistant] Architectures that we'll discuss in scene segmentation as well.


[User] Please transcribe this text

[Assistant] Inspiring observation, which doesn't necessarily hold true as it turns out, but that network depth


[User] Please transcribe this text

[Assistant] Increases representation power. So these residual blocks allow you to have much deeper networks. And I'll explain why in a second here. But.


[User] Please transcribe this text

[Assistant] The key thing that makes these blocks so effective is the same idea that's reminiscent of recurrent neural networks that I hope we get a chance to talk about. The training of them is much easier. They take a simple block, repeat it over and over,


[User] Please transcribe this text

[Assistant] And they pass the input along without transformation.


[User] Please transcribe this text

[Assistant] Along with the ability to transform, to learn, to learn the filters, learn the weights. So you're allowed to...


[User] Please transcribe this text

[Assistant] You allow every layer to not only take on the processing of previous layers, but to take in the raw and transform data and learn something new.


[User] Please transcribe this text

[Assistant] The ability to learn something new allows you to have much deeper networks.


[User] Please transcribe this text

[Assistant] And the simplicity of the block allows for more effective training.


[User] Please transcribe this text

[Assistant] The state of the art in 2017, the winner is Squeezin' Excitation Networks.


[User] Please transcribe this text

[Assistant] That unlike the previous year with CU Image which simply took ensemble methods and combined a lot of successful approaches to take a marginal improvement. SCNet got a significant improvement.


[User] Please transcribe this text

[Assistant] At least in percentages, I think there's a 25% reduction in error.


[User] Please transcribe this text

[Assistant] Something like that. Uh, by using a very simple idea that I think is important to mention. A simple insight.


[User] Please transcribe this text

[Assistant] It added a parameter to each channel in the convolutional layer. In the convolutional block.


[User] Please transcribe this text

[Assistant] Based for, for each feature map, based on the content, based on the input to the network.


[User] Please transcribe this text

[Assistant] This is kind of a takeaway to think about, about any of the networks who talk about any of the architectures.


[User] Please transcribe this text

[Assistant] Is a lot of times, recurrent neural networks and convolutional neural networks have tricks that significantly reduce the number of parameters.


[User] Please transcribe this text

[Assistant] The bulk, the sort of low hanging fruit, they use spatial invariance, the temporal invariance to reduce the number of parameters to represent the input data, but they also leave certain things not parametrized. They don't allow the network to learn it.


[User] Please transcribe this text

[Assistant] Allow in this case the network to learn the weighting on each of the individual channels. So each of the individual filters is something that you learn as along with the filters.


[User] Please transcribe this text

[Assistant] And, uh, cause obviously it's, uh, it's just simply parameterizes the ability to choose which filter you go with based on the content.


[User] Please transcribe this text

[Assistant] It's a subtle but crucial thing. I think it's pretty cool. And for future research, it inspires to think about


[User] Please transcribe this text

[Assistant] Uh, what else can be parameterized in neural networks? What else can be controlled as part of the learning process? Including higher and higher order hyperparameters. Which, which aspects of the training and the architecture of the network can be part of the learning? This is what this network inspires.


[User] Please transcribe this text

[Assistant] Another network that's been in development since the 90s. Ideas.


[User] Please transcribe this text

[Assistant] An online only video about capsule networks. It's a little bit too technical, but they inspire a very important point.


[User] Please transcribe this text

[Assistant] That we should always think about with deep learning, uh, whenever it's successful, is to think about what, as I mentioned with the cat eating a banana,


[User] Please transcribe this text

[Assistant] On a philosophical and a mathematical level, we have to consider what assumptions these networks make and what, through those assumptions, they throw away.


[User] Please transcribe this text

[Assistant] So neural networks due to the spatial, with convolutional neural networks, due to their spatial invariance, throw away information about the relationship between


[User] Please transcribe this text

[Assistant] The, the hierarchies between the simple and the complex objects. So the face on the left and the face on the right looks the same to a convolutional neural network.


[User] Please transcribe this text

[Assistant] The presence of eyes and nose and mouth.


[User] Please transcribe this text

[Assistant] Is the essential aspect of what makes the classification task work for convolutional network, where it will fire and say this is definitely a phase. But the spatial relationship is lost, is ignored, which means there's a lot of implications to this, but for things like pose variation,


[User] Please transcribe this text

[Assistant] That information is lost. We're throwing that away completely and hoping that the pooling operation


[User] Please transcribe this text

[Assistant] That's performed in these networks is able to sort of mesh everything together to come up with the features that are firing on different parts of the face and then come up with the total classification that it's a face without representing really the relationship between these features at the low level and the high level.


[User] Please transcribe this text

[Assistant] At the low level of the hierarchy, the simple and the complex level.


[User] Please transcribe this text

[Assistant] This is a super exciting field now that hopefully will spark developments of how we design neural networks that are able to learn the rotational, the orientation invariance as well.


[User] Please transcribe this text

[Assistant] You take these convolutional neural networks, chop off the final layer in order to apply it to a particular domain.


[User] Please transcribe this text

[Assistant] And that is what we'll do with fully convolutional neural networks. The ones that we task to segment the image at a pixel level.


[User] Please transcribe this text

[Assistant] As a reminder, these networks through the convolutional process,


[User] Please transcribe this text

[Assistant] Are really producing a heat map. Different parts of the network are getting excited based on the different aspects of the image. And so it can be used to do the localization of detecting, not just classifying the image, but localizing the object. And it can do so at a pixel level.


[User] Please transcribe this text

[Assistant] So the convolutional layers are doing the encoding process. They're taking the rich raw sensory information.


[User] Please transcribe this text

[Assistant] In the image and encoding them into an interpretable set of features, uh, representation that can then be used for classification, but we can also then use a decoder up sample that information and produce a map like this.


[User] Please transcribe this text

[Assistant] Fully convolutional neural network, segmentation, semantic scene segmentation, image segmentation. The goal is to, as opposed to classify the entire image, classify every single pixel.


[User] Please transcribe this text

[Assistant] It's pixel level segmentation. You color every single pixel with what that pixel, what object that pixel belongs to in this 2D space of the image. The 2D projection.


[User] Please transcribe this text

[Assistant] The, uh, in the image of a three-dimensional world.


[User] Please transcribe this text

[Assistant] So the thing is, uh, there's been a lot of advancement in the last three years, but it's still an incredibly difficult problem. If you think, if you think about


[User] Please transcribe this text

[Assistant] Uh, the amount of data that's used for training and the task of pixel level, uh, of megapixels here of millions of pixels that are tasked with having assigned a single label. It's an extremely difficult problem.


[User] Please transcribe this text

[Assistant] Why is this an interesting, important problem to try to solve, as opposed to bounty boxes around cats?


[User] Please transcribe this text

[Assistant] Well, it's whenever precise boundaries of objects are important. Certainly medical applications when looking at imaging and detecting particular, for example, detecting tumors in, uh, in medical imaging of different, uh, different organs. And in driving, in robotics,


[User] Please transcribe this text

[Assistant] When objects are involved, it's a dense scene involved with vehicles, pedestrians, cyclists, we need to be able to not just have a loose estimate of where objects are, we need to be able to have the exact boundaries, and then potentially through


[User] Please transcribe this text

[Assistant] Data fusion, fusing sensors together, fusing this rich textural information about pedestrians, cyclists and vehicles to light our data that's providing us the three dimensional map of the world. Where we have both the semantic meaning of the different objects and their exact three dimensional location.


[User] Please transcribe this text

[Assistant] A lot of this work successfully, a lot of the work in the semantic segmentation started with fully convolutional networks for semantic segmentation paper, FCN. That's where the name FCN came from in November 2014. Now go through a few papers here to give you some intuition where the field is gone.


[User] Please transcribe this text

[Assistant] And how that takes us to SegFuse, the segmentation competition.


[User] Please transcribe this text

[Assistant] So FCN repurposed the ImageNet pre-trained nets. The nets that were trained to classify what's in an image.


[User] Please transcribe this text

[Assistant] The entire image. And chopped off the fully connected layers. And then added decoder parts that up sampled the image. To produce a heat map.


[User] Please transcribe this text

[Assistant] Here shown with a tabby cat, a heat map of where the cat is in the image.


[User] Please transcribe this text

[Assistant] It's a much lower, much coarser resolution than the input image. 1.8 at best.


[User] Please transcribe this text

[Assistant] Skip connections to improve coarseness of up sampling. There's, uh, a few tricks.


[User] Please transcribe this text

[Assistant] If you do the most naive approach, the upsampling is going to be extremely coarse. Because that's the whole point of the neural network. The encoding part is you throw away all the useless data.


[User] Please transcribe this text

[Assistant] The, you, to the most essential aspects that represent that image. So you're throwing away a lot of information that's necessary to then form a high resolution image.


[User] Please transcribe this text

[Assistant] So there's a few tricks where you skip a few of the final


[User] Please transcribe this text

[Assistant] Pooling operations to go in similar ways as a residual block to, uh, go, to, go to the output produce higher and higher, uh, resolution heatmap at the end. SegNet in 2015,


[User] Please transcribe this text

[Assistant] Applied this to the driving context and really taken it to kitty dataset and have shown a lot of interesting results and really explored the encoder decoder formulation of the problem.


[User] Please transcribe this text

[Assistant] Really solidifying this, the place of the encoder-decoder framework for the segmentation task.


[User] Please transcribe this text

[Assistant] Dilated convolution, I'm taking you through a few components which are critical here to the state of the art. Dilated convolutions, so the convolution operation as the pooling operation reduces resolution significantly.


[User] Please transcribe this text

[Assistant] And dilated convolution has a certain kind of gritting as visualized there that maintains the, the local high resolution textures.


[User] Please transcribe this text

[Assistant] It's called Dilated Convolutional Layer. And that's, in a 2015 paper, proved to be much better at upsampling a high resolution image.


[User] Please transcribe this text

[Assistant] Added conditional random fields, which is the final piece of the, uh, of the state of the art puzzle here. A lot of the successful networks today that do segmentation, not all,


[User] Please transcribe this text

[Assistant] Do post-process using, uh, CRFs, conditional random fields. And what they do is they smooth the segmentation, the up-sample segmentation that results from the FCN by looking at the underlying image intensities.


[User] Please transcribe this text

[Assistant] So that's the key aspects of the successful approaches today. You have the encoded decoder framework of a fully convolutional neural network. It replaces the fully connected layers with the convolutional layers.


[User] Please transcribe this text

[Assistant] Deconvolutionary layers. And as the years progress from 2014 to today,