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[Assistant] I don't get it. How could Zack and Victoria do that to me?


[User] Please transcribe this text

[Assistant] Oh, hey Max. Sorry, I'm still screwed up.


[User] Please transcribe this text

[Assistant] Mr. Badass Football Hero is such a chicken shit. He said he sexted Victoria as a joke. Ha ha. I think Warren wants your attention.


[User] Please transcribe this text

[Assistant] Someday our lives will end. I wish this perfect life could last forever.


[User] Please transcribe this text

[Assistant] I might be a monkey but I can walk and talk and do many other things. I traveled almost 13 years to get here. I'm sure I can be your student.


[User] Please transcribe this text

[Assistant] I don't know. You tell me what I should learn. Well...


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[Assistant] I am small, he said, but I am more powerful than you can imagine.


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[Assistant] There's just one problem though. What's that?


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[Assistant] I'm not leaving until I get what I want!


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[Assistant] I crossed out all of your names too! We will all live forever!


[User] Please transcribe this text

[Assistant] Haha, I'm just as powerful as he is. Maybe he should bow before me.


[User] Please transcribe this text

[Assistant] That sounds like fun, said Wukong. I'm sure I'm invited, right?


[User] Please transcribe this text

[Assistant] Wukong was furious. The queen did not invite me? I'm just as important as any other spirit or god.


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[Assistant] I ate every single one, and they were delicious.


[User] Please transcribe this text

[Assistant] This basket has magical powers, said Guanyin. I will catch the demon with it. Let's go.


[User] Please transcribe this text

[Assistant] It escaped to Earth a few days ago. I had been wondering where it was.


[User] Please transcribe this text

[Assistant] Tell you what, while we're playing our game, we'll work out how to fix things with her, okay?


[User] Please transcribe this text

[Assistant] I'll go first to remind you how to play.


[User] Please transcribe this text

[Assistant] You have to hop in all the squares except the one your snowball is in.


[User] Please transcribe this text

[Assistant] Well, if the squares were any bigger, it would have been too hard for you to play.


[User] Please transcribe this text

[Assistant] Yes, which wouldn't have been much fun for either of us.


[User] Please transcribe this text

[Assistant] It's more fun playing a game that everyone has a chance of winning, because then everyone can enjoy it!


[User] Please transcribe this text

[Assistant] After all, the ladybug chose me.


[User] Please transcribe this text

[Assistant] And then it landed on me and it stayed with me all day. I think it's because I'm very gentle with it.


[User] Please transcribe this text

[Assistant] I'm very lucky to have this little ladybug.


[User] Please transcribe this text

[Assistant] And there's a drop of rainwater if you get thirsty.


[User] Please transcribe this text

[Assistant] My friends are going to be so amazed that you stayed with me all night.


[User] Please transcribe this text

[Assistant] The ladybug is still there. Oh yes it is. Did it stay on your ear all night?


[User] Please transcribe this text

[Assistant] Oh, but I might get very thirsty.


[User] Please transcribe this text

[Assistant] What will they say if I have a whole family? Come on.


[User] Please transcribe this text

[Assistant] Oh, I don't want to take you away from your home, but my friends would really like to see you.


[User] Please transcribe this text

[Assistant] Asked another boy. He was really a tiger spirit.


[User] Please transcribe this text

[Assistant] Me and Malika are in your backyard. Where is what you wanted me to pick up?


[User] Please transcribe this text

[Assistant] She said her security will bring it to me. Okay. I'm gonna suck. Do you want to just sit while they're? Sure. I definitely didn't tell you this. The other night, you just can't even like make this shit up. It's wild. Me, Kim,


[User] Please transcribe this text

[Assistant] Court, my mom, we're all like in Kim's closet and...


[User] Please transcribe this text

[Assistant] This guy somehow had access to Kim's sex tape. There was other footage that was never released or something like that. And so Kim, of course, is stressing, you know, it sucks. You don't want to do SNL if you know this is coming. Like you have to- Right. It's just the whole thing is so-


[User] Please transcribe this text

[Assistant] And before my mom and I fly to SNL, I have to do James Corden. I just have such anxiety about going. Like, just, it's never the interview, it's more the aftermath. Like, now you have to listen to everybody say this, or judge you, or overanalyze something you say, or...


[User] Please transcribe this text

[Assistant] Pick this apart, I should say. Social media used to be, you know, fun and silly. There is definitely always...


[User] Please transcribe this text

[Assistant] Trolls. And now it's everything is so critical. The way I look.


[User] Please transcribe this text

[Assistant] My situation with Tristan. Is my hair done the right way? What is she doing? That laugh, that this, oh my gosh, she said this. I think I've gotten to the point that it's literally safer to stay at home. This shit used to be fun. You're totally


[User] Please transcribe this text

[Assistant] With all those paparazzi today, I swear I was having like a heart attack. Literally, I thought I was having a heart attack today.


[User] Please transcribe this text

[Assistant] I am on my way to do the James Corden talk show. Malika's coming for my emotional support because I do get a lot of anxiety by doing talk shows these days.


[User] Please transcribe this text

[Assistant] I feel obviously much better just talking it through and like, it's so easy for people to say.


[User] Please transcribe this text

[Assistant] You don't know them, don't pay attention or did it? Yeah. Trust me, I try not to, but when you're walking down the street and then even paparazzi is heckling at you the same things that you're trying to avoid, it's so deteriorating on your


[User] Please transcribe this text

[Assistant] Self-esteem, your confidence, the way that you view yourself, and even like anxiety to post photos on Instagram. I'm just anxious. Even when I'm like, I barely did retouching when I'm so afraid.


[User] Please transcribe this text

[Assistant] To post it, because people are going to say I did this. It's like, huh?


[User] Please transcribe this text

[Assistant] This speech just makes me, it sounds so corny, but like warm and fuzzy. I feel understood because I know he's talking to me, but I know he's had this talk with himself.


[User] Please transcribe this text

[Assistant] That's nice. I mean, everyone just tweeting and tweeting and tweeting. It was fun. I mean, he has, cause we had talk shows how you haven't been allowed to be a guest for a while.


[User] Please transcribe this text

[Assistant] Well, you just have to learn when to tune out the noise. Even like walking around here in all the paparazzi, I'm still thinking, God.


[User] Please transcribe this text

[Assistant] And now they're gonna, I don't know, you just like, if it's one wrong photo, one thing, I'm sitting somewhere and they get some, it's like, then they're like, ooh.


[User] Please transcribe this text

[Assistant] I am not even, so you know, I don't- Isn't that the most amazing news? Oh my god, I'm just so happy cause she's never wanted to get married or anything like that and I know.


[User] Please transcribe this text

[Assistant] For right now, we are keeping the gender just between Travis and I. We're just waiting for the right moment to tell everyone in a special way.


[User] Please transcribe this text

[Assistant] My whole life is so public. My pregnancy is really public this time. To have something between just Travis and I feels really special. It's just fun keeping that to ourselves.


[User] Please transcribe this text

[Assistant] Thanks, little grey squirrel. Tell him I'm sorry, and I'll explain later.


[User] Please transcribe this text

[Assistant] Oh, so little grey squirrel didn't give you my message.


[User] Please transcribe this text

[Assistant] Did you feel that cold gust of wind this morning and see the snowflakes it blew along?


[User] Please transcribe this text

[Assistant] That told me that Winter will be here very soon.


[User] Please transcribe this text

[Assistant] So, I ran to collect all these juicy green shoots of grass before they got buried under the snow.


[User] Please transcribe this text

[Assistant] And now you'll be able to eat them all through winter.


[User] Please transcribe this text

[Assistant] We can play tomorrow. In the snow!


[User] Please transcribe this text

[Assistant] For the past couple years I've been like really just like loving health. I don't know, it's become an obsession.


[User] Please transcribe this text

[Assistant] I am a major hypochondriac. I think of everything. It's always doomsday. Everyone, all my friends make fun of me.


[User] Please transcribe this text

[Assistant] I have this whole new room in my house that's all these crazy gadgets and gizmos. Like massive machines that I get into. I just love taking care of my body. It's honestly kind of embarrassing. It's like crazy.


[User] Please transcribe this text

[Assistant] But I also think I'm a little obsessed to the point where it could become unhealthy, ironically enough.


[User] Please transcribe this text

[Assistant] I found out about Tristan through our family group chat. It is a, you're in a little bit of a state of shock of just like, how does someone do this so many times over? That's like...


[User] Please transcribe this text

[Assistant] I would want to know before I guess the internet, uhm, told me. But, hey. I was just calling to check in on you. Uhm, have you guys spoken?


[User] Please transcribe this text

[Assistant] Of course not. The trolls are just mean and they have no idea.


[User] Please transcribe this text

[Assistant] No, but that makes me sad that you're struggling.


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[Assistant] I just think that there's like brighter things for you in the future. It's just gonna feel so good.


[User] Please transcribe this text

[Assistant] Of course not. But you also have to respect your, you have to respect yourself at the end of the day and like you can only do so much, you know?


[User] Please transcribe this text

[Assistant] I mean, I just feel really bad for her. Like, I mean, it makes me so angry. Especially because he fooled obviously Chloe, but the rest of us too, and like,


[User] Please transcribe this text

[Assistant] I really did think he had changed. Like anyone who would ever like criticize or talk about him, I would always be like, no, no, no, like he changed. Like I would have his back and it's almost like in a way he like, you know, betrayed us all.


[User] Please transcribe this text

[Assistant] Alright guys, it's time for another challenge to review what we learned in the last lesson.


[User] Please transcribe this text

[Assistant] Back when we learned CSS on days 43 and 44, the end project that we created was a personal site that looks something like this. It's beautifully designed with CSS styling, images, GIFs, and a whole lot more.


[User] Please transcribe this text

[Assistant] Now what I want you to do is to take your version of this personal site and get it to run with your Python server.


[User] Please transcribe this text

[Assistant] And in the process, you'll review what you learned about rendering HTML files as well as serving up static files.


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[Assistant] Where I've included a link to download all of these files as a zip file.


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[Assistant] Once you've unzipped that zip file, you should end up with a folder that looks like this. We've got our favicon, our CSS folder with our styles.css, our image folder with all four images, and you want to move these files to their respective places inside the personal site.


[User] Please transcribe this text

[Assistant] Feel free to go ahead and delete the index.html and the styles.css.


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[Assistant] And see if you're able to use the files that you've got here to render it using your Python server. Pause the video and give that a go.


[User] Please transcribe this text

[Assistant] The files in here to their respective places.


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[Assistant] But pretty much everything else is going to go inside the static folder. So that includes the CSS, the favicon, as well as the images folder.


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[Assistant] There's actually nothing we need to do inside our server.py because it's still trying to render a webpage called index.html.


[User] Please transcribe this text

[Assistant] Which in our case, it's still called index.html.


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[Assistant] Now the thing that we do have to change though is inside our index.hml, it's pointing straight towards css slash styles.css. And also for the favicon, it's pointing straight to the favicon file. What we need to do is to take into account that it's now living inside a folder called static.


[User] Please transcribe this text

[Assistant] So we're going to have to add the full file path to anywhere where we have a local static file being used. So that includes the CSS, that includes the favicon.


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[Assistant] Copy this part, which is the source of the images. And then you can hold down Command R on Mac or Control R on Windows to bring up the find and replace.


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[Assistant] And we're gonna replace this with static slash images slash. So we're basically adding static to this images slash.


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[Assistant] And if we go ahead and replace it in all five places, then we should now be done with all of our refactoring.


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[Assistant] Now let's go ahead and restart our file.


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[Assistant] So that it looks at all of these new images.


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[Assistant] And let's go to our website, which is being served up by Python. And hopefully you have the same result as I do, which is the same personal site, but now served up with our Python server.


[User] Please transcribe this text

[Assistant] Now this process has been pretty pain-free, I would say. We simply moved in the required index.html, the CSS images and the favicons. So all the files that make up a particular website and we simply added it to our Flask project.


[User] Please transcribe this text

[Assistant] Now, what you can also do is you can get templates of HTML and CSS files from the internet.


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[Assistant] For example, a website like hml5up.net where they have beautiful free templates that you can tap into.


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[Assistant] Now, consider if you were creating your own personal site, then you can pick from all of these


[User] Please transcribe this text

[Assistant] Beautiful HTML template, which you can then update and personalize.


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[Assistant] For example, if we take this first one, we can go ahead and see it as a live demo so we can see this website as it is and we can see how cool it looks.


[User] Please transcribe this text

[Assistant] Have images of the website, a link to the website, some.


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[Assistant] Brief description and then we could just put this up online and whenever we want to get hired as a web developer, we could point people towards it.


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[Assistant] Well, the first thing we have to do is simply hit download.


[User] Please transcribe this text

[Assistant] And it's going to download all of the files that make up this website.


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[Assistant] Now remember that because we're getting it for free, we need to attribute the designer who created this website.


[User] Please transcribe this text

[Assistant] If you don't want to attribute it and you want to use it for commercial use,


[User] Please transcribe this text

[Assistant] Then you can pay just $19 and access all of their templates as well as support from the creator.


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[Assistant] Now compare that to a service like Squarespace where you're basically getting the same service templates for your website.


[User] Please transcribe this text

[Assistant] But in this case, you're gonna be paying anywhere between $10 to $30 per month, which is a lot more expensive.


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[Assistant] But it's great if you don't know web development, but we're not going to be in that camp anymore.


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[Assistant] Let's go ahead and unzip that file that we just downloaded, which was called Paradigm Shift.


[User] Please transcribe this text

[Assistant] And we're going to try and transfer these files to our website over here.


[User] Please transcribe this text

[Assistant] And I'm going to try and move the relevant parts of this new website over.


[User] Please transcribe this text

[Assistant] So here they've got an index.html, a readme text and a license text. So all we need is to move our index.html to the templates.


[User] Please transcribe this text

[Assistant] And the images and assets over to the static folder.


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[Assistant] Now one of the things to note is when you're looking at the demo, there's all of these beautiful pictures.


[User] Please transcribe this text

[Assistant] But of course the designer doesn't have the rights to these pictures to distribute.


[User] Please transcribe this text

[Assistant] So you'll notice that all the images inside the images folder


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[Assistant] All these sort of gradients so don't be worried if when you


[User] Please transcribe this text

[Assistant] Run your website, you just see a bunch of blurry gradients. It's not a problem.


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[Assistant] Now what we want to do is we want to see if we can get this index.html


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[Assistant] Now the first thing we can do is simply just restart our project, rerun it and see what it looks like. So let's refresh this static page.


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[Assistant] And you can see it's got all of the HTML, but no styling and no images.


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[Assistant] So that is probably because we're not specifying the static folder in the path. So here we've got our assets.


[User] Please transcribe this text

[Assistant] And here we've got an image, but they're both inside the static folder. So


[User] Please transcribe this text

[Assistant] We can do that quick trick with find and replace. So for anything that's inside the images folder, we're going to replace it with static slash images.


[User] Please transcribe this text

[Assistant] And for anything that's inside the assets folder, we're going to replace it with static.assets.


[User] Please transcribe this text

[Assistant] And now if we go ahead and hit save and refresh this page, you can see it's now rendering the images as these sort of gradients and it's also rendering the styling and CSS.


[User] Please transcribe this text

[Assistant] Now, how would you edit this? Well, the easiest way is of course, to actually just edit this raw HTML. So you can take any of this HTML and you already understand what most of it does. You've got tables, you've got headings, you've got H1s, all sorts of tags in here. And you can edit it just like that. For example, if we scroll up to the very top, we can change the title to


[User] Please transcribe this text

[Assistant] And we can change the H1, which says paradigm shift, to our own name, for example.


[User] Please transcribe this text

[Assistant] And now when I hit save and I refresh over here, you can see all of that being reflected.


[User] Please transcribe this text

[Assistant] Now, there's an even better way of editing HTML in Chrome. All thanks to the Chrome Developer Tools.


[User] Please transcribe this text

[Assistant] If we open up the Chrome developer tools and go to the console, we can actually type a little bit of JavaScript.


[User] Please transcribe this text

[Assistant] To make anything on our webpage editable.


[User] Please transcribe this text

[Assistant] So to write this, we tap into the document, which is this entire HTML document. And then we tap into the body.


[User] Please transcribe this text

[Assistant] Which is everything inside the body tag, which is the content of the website.


[User] Please transcribe this text

[Assistant] And then we can call on a property called


[User] Please transcribe this text

[Assistant] Content editable and we can set that to true.


[User] Please transcribe this text

[Assistant] Now note here that in JavaScript, the true Boolean is spelt out with a lower T, not capital T like in Python, but the rest of it is pretty standard code and we can all understand that.


[User] Please transcribe this text

[Assistant] Now once I hit enter and that goes through, I can close down this pane and now watch this. I can now edit anything that's on this website.


[User] Please transcribe this text

[Assistant] All of these paragraphs or any bits of text is now completely editable.


[User] Please transcribe this text

[Assistant] Now what if you want to delete some of these elements? What would be the easiest way to do that?


[User] Please transcribe this text

[Assistant] Well, you can simply open up the Chrome developer tools and then we can use this little button here to select any element. So maybe we want to get rid of this paragraph here. Well, I can select it, it locates it in the HTML and I can just hit backspace.


[User] Please transcribe this text

[Assistant] So I can select any part of the HTML elements that I don't like.


[User] Please transcribe this text

[Assistant] For example, if I want to get rid of this whole section, well, I would select it and then delete it. Maybe I could.


[User] Please transcribe this text

[Assistant] Delete some more sections and make this a lot simpler. Now you can see this website just has two sections, my name and my contact details.


[User] Please transcribe this text

[Assistant] One of the unfortunate things though is that all of the changes that you're making here, even though you can see it and it's great, as soon as you hit refresh, all of those changes will disappear. And the reason is because when you hit refresh,


[User] Please transcribe this text

[Assistant] What's happening is your browser is going to your server at this address and requesting all of the HTML and CSS files, which of course comes from over here.


[User] Please transcribe this text

[Assistant] So this is unchanged by what we've done just now. It's completely not changed at all.


[User] Please transcribe this text

[Assistant] So if you want all of these changes that you've made in the HTML, then what you have to do is actually save this webpage as it is.


[User] Please transcribe this text

[Assistant] And then you can move this HTML into your template and you can see that this is now a lot shorter.


[User] Please transcribe this text

[Assistant] With all of the changes that we made previously.


[User] Please transcribe this text

[Assistant] And you can replace this with what's inside your index.html.


[User] Please transcribe this text

[Assistant] This is probably one of the easiest ways of modifying a pre-built template.


[User] Please transcribe this text

[Assistant] And you can see that there's plenty of templates for you to play around with. In fact, in the next lesson when we're creating our final project,


[User] Please transcribe this text

[Assistant] You're going to be editing this identity template completely from scratch.


[User] Please transcribe this text

[Assistant] And you're going to be creating a digital name code. So have a play around.


[User] Please transcribe this text

[Assistant] And once you're done, head over to the next lesson and complete the final project.


[User] Please transcribe this text

[Assistant] MRBR's chance is to try to win the round, and he knows that's C9.


[User] Please transcribe this text

[Assistant] That's a trademark of this team. Chasing Eagle Kills.


[User] Please transcribe this text

[Assistant] Arnazine is able to trade it off. Well done here by Vanity to create that diversion and the ruckus to allow this lurk to happen as well for Zepa. The lineups are set up too, and MIBR knows when the defeat is there. So they'll fall back and save these weapons.


[User] Please transcribe this text

[Assistant] Well again, remember, Zepa had an opportunity to push quite early on top of middle. They might have a chance to take these weapons away.


[User] Please transcribe this text

[Assistant] Everything but those rifles that they purchased into that previous round. So they're looking pretty good here in round number four to have a very healthy buy. On the other end for MIBR, they saved those two. Vennels on that previous round and they buy around it with a bucky, a sheriff and a guardian crossfire set up at the elbow of the A site.


[User] Please transcribe this text

[Assistant] So Bucky, it sounded from the sound of it that it, uh, went for like a right click that didn't do enough damage.


[User] Please transcribe this text

[Assistant] So very reminiscent to the first half year of Icebox, where C9 looked very dominant, just kicked things off, Guardian's really good on MIBR's side, where Farzee got a big house.


[User] Please transcribe this text

[Assistant] On the defensive side of MIBR. So that could be helpful if they want to try to play the retake here with the showstopper available.


[User] Please transcribe this text

[Assistant] Because of that flash, it seems that MIBR are thinking that they might have gone for a market or mid control, so they're adding a lot of players there, and the honorable call for C9 is to pivot back toward the site. But thankfully here for the...


[User] Please transcribe this text

[Assistant] Looks like we were able to insert Oxy towards the defender spawn somehow and the rest of them are trying to make the noise towards market so they can actually cut these rotates and catch these rotates. So there's the engagement now for Cloud9 inside the market.


[User] Please transcribe this text

[Assistant] The best results that they had so far is to contain and push back towards the A side with the utility they've been using and also farming these orbs but leaving that mid side open a lot, playing passively towards the market, getting picked off also from B main inside the B site. Losing players on those signs.


[User] Please transcribe this text

[Assistant] That's B, or that's A site open for a C9 to plant. And also the weapon that it could protect here. It's not dropped too far off where Mazin could come and pick it up. Vanity's just trying to farm the last two.


[User] Please transcribe this text

[Assistant] At least, uh, vanity re-realized that. You saw the null chat. MIBR classic is OP.


[User] Please transcribe this text

[Assistant] But Zephyr's trying to push back and forth. Overextension coming out from C9, being punished out. Three versus three. Seekers also pushing back C9.


[User] Please transcribe this text

[Assistant] On the post plant, playing outside B. Looks like Rooney's just staring at the butterflies or the moths here at the...


[User] Please transcribe this text

[Assistant] Under the lamp, just to throw his snake bites down onto the spike and just delay, delay, delay.


[User] Please transcribe this text

[Assistant] Being on a Sheriff and Stinger boy, half pying for the next. C9's able to salvage at least those kills at the top. Give themselves still a weapon advantage on this three versus three.


[User] Please transcribe this text

[Assistant] Unfortunately can't find a time to flash outside the attacker's smoke so they can run through it. Even a trip was placed by moose as they entered towards this A site.


[User] Please transcribe this text

[Assistant] Moves playing long range. It's not planted for them. Remember that? ADSing back towards the pawn. The timing. Spotted by Mazin. The swing. The snake might push them forward. Cannot land that shot. The smoke also allowing RGL to defuse it a half way. Back to a 1v1. Trying to full stick it. Misses the shot. As the defuse comes in for MIBR.


[User] Please transcribe this text

[Assistant] Probably something new though for C9. They have two players now posted up on this ace sign as well.


[User] Please transcribe this text

[Assistant] Which would allow here Cloud9 to go for late rotates. Working in 10 and that's both Rooney and Oxy.


[User] Please transcribe this text

[Assistant] Dog now only to spot that lurk from Rooney. Looks like they give up the plan here. Grouping up all together back towards this B main and looking for a sort of pop now.


[User] Please transcribe this text

[Assistant] All depends on Artazine's position on left side of that V entrance. They have three players there on top of that for the crossfire setup in the trades.


[User] Please transcribe this text

[Assistant] Sneak's bite, manages to steal life from that, gets hit by a satchel. So you definitely see there on that minimap, through the x-ray as well, that he's about to get hunt down. 360, oh, whatever you wanna do.


[User] Please transcribe this text

[Assistant] Only Auxie and Rooney left alive against a full roster. Rooney with the pick. Auxie one more, allows for him to get the showstopper. And maybe turn the tines over back to C9. But MYBR is looking pretty good here. With 40 seconds left on the clock to hold this ace site. Auxie trying to walk forward.


[User] Please transcribe this text

[Assistant] Oxy, looks like he still wants to try to hunt for these kills. I mean, now there's only 22 seconds left, has to commit now, towards this ace site. Looks like any member of the yard is ready to give that up. Allow for Oxy to get the plant.


[User] Please transcribe this text

[Assistant] No 9-3 curse, full rounds coming in for MIBR. And it's first half of sunset.


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[Assistant] From singing to prayers and laughter all around, the vibes are still good here for Cloud9 at least in the lead 8-4 on this map of Sunset. But now we have a special guest with us, so let's toss it down to Elizabeth with Alexis from Shopify Rebellion.


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[Assistant] On the right course here for C9 to potentially close this out. But MRBR again, just like the first map, they're fighting life early.


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[Assistant] Definitely making his call out his name all the time with all these multi-kills he's getting. So two in the pistol now at 18 or picked up as well. After the defuse, he's now already.


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[Assistant] Halfway to his showstopper. Flash to can't find anything close. Nice one-way by Moose. Long range though. Artazine. Fights DX Germany.


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[Assistant] Denies the position here from C9 on the aggression they were attempting towards the A site.


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[Assistant] That's the forest fire that they had here in the hands of Artazine. Trying to fight and go around this guardian.


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[Assistant] Smoke also, no contact towards the front of that A site, so that's where we're talking about that orb coming up here. Right. To try to deny that flash. But it might be our, we're still tucked back behind the spawn.


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[Assistant] Oxy with an aggressive angle and two players of Cloud9 trying to play behind the Viper wall.


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[Assistant] Flash connects to nothing towards the tiles, but the Boombot will spot Oxy, and I'll force him to pull back.


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[Assistant] So that leaves Runie alone and it hurt to stink by coming in from the spawn. Easy one there for Artazine.


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[Assistant] Or towards this B main. But they allowed to have Oxy to push all the way up, but the dog...


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[Assistant] Only one player left alive here after Moose gets the pick on the JZZ. Mazin in the clutch for MIBR. Hoxie looking to rotate back to support his teammate.


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[Assistant] Now lots of noise has to be made. Tap on that spike and the jiggle comes through. And you're just trying to run that clock down from one end to allow Moose to just fight that 1v1 and he'll get it.


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[Assistant] It was a long journey to the special place Big Nut Brown Hair was taking them to.


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[Assistant] We went to the movies in Staten Island, and all I wanted was dibs. I'm obsessed with dibs, but you can only get them at like, gas stations these days.


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[Assistant] This is when I will budge on being a vegan. Oh my god. So impressed. I swear, it's like the cutest littlest things. I know. Ugh. Okay, get over how sweet this was.


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[Assistant] But is it sweet or is it like being a dick because I have a swimsuit shoot? And I shouldn't have eaten that.


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[Assistant] Now the first thing I want to show you is how to render HTML files.


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[Assistant] Go ahead and create a new PyCharm project. I've called mine my personal site, but you can call yours anything you like.


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[Assistant] And in the project we're going to create a new file.


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[Assistant] Which is going to be our Python server. So I'll call it server.py.


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[Assistant] And then inside our server.py, we're going to go through all the stages of building up a simple Flask server.


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[Assistant] This might be a good time to revise how to do that. So go ahead, pause the video and see if you can set up a simple Flask application, which just serves up Hello World to the homepage.


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[Assistant] All right, so let's start from the beginning. We'll import the Flask class from the Flask application. And as usual, I'm going to use my red light bulb to install the Flask package.


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[Assistant] Now once I've done that, I'm going to create my Flask application by saying app.


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[Assistant] Equals flask and then inside the initialization for my flask application, I'm going to put in the name of my current directory. Next, we're going to use the


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[Assistant] Route decorator to target the home route. And when the user hits up the home route, we're going to render the home page.


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[Assistant] So inside this definition, we're simply just going to return hello world.


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[Assistant] And finally, we're ready to go ahead and check to see if the name is equal to main.


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[Assistant] Then in that case, we're going to run our app. That's it. That's all there is to it.


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[Assistant] Let's go ahead and run this application, which is our server.py. And if we head over to this URL,


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[Assistant] We can see hello world when we go to our homepage.


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[Assistant] The next step is to figure out how can we render an HTML


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[Assistant] Web page, which is crafted inside an HTML file rather than simply just returning strings or small bits of HTML elements.


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[Assistant] Let's go into our file and go ahead and create a new HTML file.


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[Assistant] Now the reason why we're creating it like this is because it's going to add in all of the boilerplate code that is required to an HTML website. So let's go ahead and call our webpage index.html.


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[Assistant] And you can see as soon as I create it, it's added in the doc type. It's added in the HTML, the head and the body. Now, if this is unfamiliar to you,


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[Assistant] Be sure to check out the lessons where we covered HTML and CSS in detail, but I'm going to assume that all of this is pretty straightforward by now.


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[Assistant] Now the first thing we're going to change in our new website is the title. So I'm going to go ahead and give this a title.


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[Assistant] Now inside the body, I'm going to create a H1.


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[Assistant] And you can see that the HTML files on PyCharm has that tag completion that we see here.


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[Assistant] In my h1, I'm just going to write, I'm Angela.


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[Assistant] Pretty straightforward. This is a complete HTML file now. Now, if I want to render this instead of just hello world, how would I do that? Well, how do we find out?


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[Assistant] As always, it's to the documentation.


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[Assistant] In the documentation of the quick start for Flask, there's a section on rendering templates.


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[Assistant] And this allows us to render an HTML file as a template.


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[Assistant] There's a couple of requirements though. The first thing is that remember that Flask is a framework and not a library.


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[Assistant] So it will put certain restrictions and requirements on your code so that it will actually work.


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[Assistant] And in this case, our HTML files, which is known as our templates, must be inside a folder called templates. That's where Flask is going to look for it when we try to render it.


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[Assistant] So let's first do that. Let's create a new directory, which we'll call templates or lowercase and let's move our index.html into that new folder. So there it is.


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[Assistant] The next thing we have to do is we have to import this method called render template from the Flask module. So let's go back to our server.py and in addition to importing the Flask class, let's go ahead and import the render templates method as well.


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[Assistant] The next thing we have to do is to instead of returning a string or a string that contains HTML, we have to call this method render template.


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[Assistant] And pass in the file name that we have.


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[Assistant] And then that is going to be rendered into our website.


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[Assistant] So let's go ahead and do this. Let's delete that hello world and instead let's return the output from this render template method.


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[Assistant] And inside here, we're going to put the name of our HTML file, which lives inside our templates folder.


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[Assistant] That's going to be index.html because that's what we called it.


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[Assistant] Now let's go ahead and run our code. Alternatively, let's switch on the debug mode so that it will refresh and rerun our server every time we make a change.


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[Assistant] So let's rerun and now let's go to our website.


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[Assistant] Refresh the main page and you can see it's now got an H1 in here.


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[Assistant] And in the head there is now the title, which is Angela, which is the title of the tab as well.


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[Assistant] Imagine if you had to type all of this HTML into this return as a string. It's pretty painful. It doesn't have any of the assistance that you get from your editor. Now here's a challenge for you. Back in days 41 and 42, we learned about HTML.


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[Assistant] And we created a CV website that looks something like this, which we managed to publish and upload to GitHub.


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[Assistant] Now what I want you to do is to go and find your own CV website.


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[Assistant] And then go to the link either on your GitHub page or find the original HTML file. And I want you to go ahead and get a hold of it.


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[Assistant] So if it's on GitHub, then you can go ahead and simply save as and save this webpage into your downloads.


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[Assistant] Now if it's saved as a HTML file somewhere on your system, that's also fine.


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[Assistant] But essentially what we want to do is we want to take this file, which is an HTML file, and we want to move it into our templates.


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[Assistant] Now, one of the things that you'll notice is if you downloaded this file off the internet, then you might have to rename it because firstly, it's really long and we don't want to have to type that into our code. So I'm going to shorten the name to Angela.


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[Assistant] And then at the very end, you can see the file extension, at least when it comes off GitHub,


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[Assistant] Is .htm, and the reason for this is because some servers don't accept four-lettered file extensions, but it's exactly the same as .html.


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[Assistant] So we're going to change it to .html and then we're going to refactor that.


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[Assistant] So now that we've got hold of this HTML page,


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[Assistant] All right, that's pretty simple. All we have to do is just replace the index.html to the new file, which is angela.html.


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[Assistant] And if that file happened to be inside a folder, for example,


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[Assistant] If we had a subfolder inside templates, uhm, which we called, I don't know, CV, and we had this inside here.


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[Assistant] Then we would have to specify that in the file path if we just have it in the top level of our templates folder.


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[Assistant] So now while our server is running in debug mode, we can go ahead and hit save on this website and it should restarted our server.


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[Assistant] And now if we go back to our main homepage and reload the website, you can see it's now rendering.


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[Assistant] Our personal CV site, with only one exception.


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[Assistant] What has happened to this profile picture? It looks pretty broken.


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[Assistant] Now in order to be able to render static images, we're going to have to learn how to render static files such as images like this. That's what we're going to do in the next lesson.


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[Assistant] I met Demna, he was the designer there, really connected with him during the Met. So when Demna called me and asked me if I would be in his campaign, it was like, oh my god, I feel like I've already been in a campaign. I've showed off so much, so it just feels right.


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[Assistant] Our last egg retrieval was not successful, so our new thing that we're going to be trying is a Panchakarma cleanse, which is Ayurvedic, it's like 3,000 years old, which will


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[Assistant] Get all of the toxins that are like deep within our tissue.


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[Assistant] I did this cleanse 10 years ago. I kept telling Travis about this and this is the one thing


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[Assistant] That we haven't tried that he's heard me talk about.


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[Assistant] On top of not having sex with you or not having caffeine.


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[Assistant] Hi! How are you? Good, how are you? Marta is an Ayurvedic expert. She just knows all things Ayurveda, which is a way of life. Kind of like reconnecting to your wellness. This is Travis.


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[Assistant] You are on day two of pre-cleanse. He's, he's mad at me a little bit cause he-


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[Assistant] And then, uhm, but he said something and, uhm, he told us to, uhm... To both of you?


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[Assistant] Come like four times a week. Okay.


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[Assistant] After the Cree cleanse, we will do a seven day cleanse where we will go to her spa every day and we go for like four hours a day. So excited.


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[Assistant] I'm super grateful that Travis is doing this with me. I don't think I could do it on my own. I just feel like it's something like we have to do together. We're making a baby together. It's like, we have to be on the same page.


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[Assistant] Oh my god, I remember this coat. I'm on this really fun journey of just trying to figure out what my new kind of fashion era is gonna be.


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[Assistant] I have thousands and thousands of clothes. I think I have like 30,000 pieces.


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[Assistant] Is finding out what my next muse relationship is gonna be like. It's so easy that I have my closet. And it's just I have my Balenciaga room and I'm like, okay cool. And you don't have to think about it.


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[Assistant] When it comes to my style, I mean I've always had Kanye there as like this crutch to teach me so much about fashion and having that not be there as my crutch to like guide me.


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[Assistant] The status of my relationship with Kanye hasn't really been clear to people, cause I'm, was trying to figure it out too, and I'm ready to move on. But I think for so long, always depending on someone else,


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[Assistant] I forgot that I have an opinion too, and I forgot that


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[Assistant] I can make decisions too. Every time I look at something, it's just like a memory. Like this. Celine.


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[Assistant] Quiz me, quiz me, say an event. Okay, how about New Year's Eve, Las Vegas? Which year? White Gucci dress with the silver, my hair was like this, I was skinnier than ever. I remember that. I was newly single, dark shadow and like big. It was like right after a person.


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[Assistant] I do know what looks good on me and I know exactly what I like and I know exactly what I want. So sometimes I just have to like drown out all of the voices of like people telling me what


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[Assistant] We have done five days of a pre-cleanse where we've had no caffeine, no alcohol, no sex, no working out, no sugar. We feel amazing.


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[Assistant] And now we are starting seven days of...


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[Assistant] Coming to her spa where we will do four hours a day of treatments.


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[Assistant] You never really know exactly what treatment you're going to be getting that day.


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[Assistant] But it's all customized to each individual and to what you need.


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[Assistant] I am shooting the Balenciaga campaign and this campaign just feels so special on a personal level. It's the first time I've ever done my own contract and I just feel more in control with this shoot.


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[Assistant] I mean, this just feels amazing, you know, to be in a campaign for Balenciaga, a brand that I've been wearing for so long that I love. I'm so excited. It's gonna be really good.


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[Assistant] To know that like one of my favorite designers ever is valuing my opinion and wants me to be me is just a good, empowering feeling.


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[Assistant] I'm so excited that I'm gonna be on the cover of Vogue. Like, I don't care how many times you'd be on the cover. Like, it will never ever get old.


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[Assistant] This one just feels like extra special to me and I'm so honored to be a part of it.


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[Assistant] Oh, I probably would have killed, murdered Kendall myself to get the cover. It was like an option between the two of us.


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[Assistant] My desperation back in the day, she would have been buried before she got this cover.


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[Assistant] It feels like I've come a long way, you know, from not really being that outspoken about what I really liked and wanted to like having full control, full say, to even being on like the cover of American Vogue for the third time.


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[Assistant] Years ago, like, I wouldn't have even dreamed that I would be


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[Assistant] Like the styling team asking me, what nails do you want? We think this. What shape do you like? What color do you like? Like I've never had that opinion asked of me.


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[Assistant] To have Anna's confidence in me is such a special thing and such a dream come true. And I love that she trusts that we're going to bring some new heat to Vogue. Anna wants Versace, Anna gets Versace.


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[Assistant] It feels like such an evolution doing this vogue shoot over any other vogue shoot.


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[Assistant] The fact that I got to pick my glam team was like wild. They let me wear literally what I wanted.


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[Assistant] I think that I will never stop being me. Like all I can do is control how I react to something. I can't control... What other people do. How he treats me. You know, or how he's always treated you guys.


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[Assistant] I'll finally answer you. So this is how it went down with Pete.


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[Assistant] I owe you one, so I'll pretend what you just said is true, missy.


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[Assistant] Yes sir, you and Chloe still better be careful where you wander. There are a lot of dark places in Arcadia Bay.


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[Assistant] I can't tell you everything that's going on at Blackwell, and you've seen too much already, so please stay out of this, Max.


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[Assistant] I didn't have all the evidence at the time. I... I am sorry, Max. Anyway, I think we can both agree it's been a hard week on all of us. Especially poor Kate Marsh.


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[Assistant] You did, Max. You saved her life like a hero while I left the goddamn dorm roof wide open.


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[Assistant] That guy is an elitist prick. I'm off duty, so I can say it at home behind his back. Like when Chloe calls me step-douche. These artists live in a fantasy world.


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[Assistant] Why do you say that? These art farts are all about themselves. When I was in the service, I hated the photographers who tried to pose me in their anti-war bullshit.


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[Assistant] Since only I know what's happening. Of course, thanks to Mrs. Grant and her hippie anti-surveillance petition, I hope everybody feels safer. Like Rachel Amber and Kate Marsh. Although, you're like a walking surveillance system.


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[Assistant] We'll see that someday.


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[Assistant] Hey Max, I do appreciate you standing up for me.


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[Assistant] Sorry, but this is my official man cave, so no girls allowed.


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[Assistant] Excuse me, Max. Do not touch one goddamn thing.


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[Assistant] Hey Max, I do appreciate you standing up for me.


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[Assistant] Sorry, but this is my official man cave, so...


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[Assistant] The video you're watching now is in 360. Resolution is not great, but we wanted to try something different.


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[Assistant] So if you're on a desktop or laptop, you can pan around with your mouse. Or if you're on a phone or tablet, you should be able to just move your device to look around. Of course, it's best viewed with a VR headset.


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[Assistant] The video that follows is a guest lecture on machine learning that I gave at MIT Sloan course on the business of artificial intelligence. The lecture is non-technical and intended to build intuition about these ideas amongst the business students in the audience.


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[Assistant] The room was a half circle, so we thought, why not film the lecture in 360?


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[Assistant] We recorded a screencast of the slides and pasted it into the video so that the slides are more crisp.


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[Assistant] Let me know what you think, and remember, it's an experiment. So this course is talking about


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[Assistant] The broad context, the impact of artificial intelligence, the global. There's global, which is the global impact of artificial intelligence. There's the business, which is when you have to take these fun research ideas that I'll talk about today. A lot of them are cool, on toy examples. When you bring them to reality, you face real challenges, which is what I would like to really highlight today.


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[Assistant] That's the business part. When you wanna make real impact. When you wanna make these technologies a reality. So I'll talk about how amazing the technology is for a nerd like me. But I'll also talk about how, when you take that into the real world, what are the challenges you face. So machine learning, which is the technology at the core of artificial intelligence. We'll talk about the promise.


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[Assistant] The excitement that I feel about it. The limitations, we'll bring it down a little bit. What are the real capabilities of technology? Where for the first time,


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[Assistant] Really, as a civilization, exploring the meaning of intelligence.


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[Assistant] It is, if you pause for a second and just think, you know, maybe many of you want to make money out of this technology, uh, many of you want to save lives, help people, but also on the philosophical level, we get to explore what makes us human.


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[Assistant] So, while I'll talk about the low level technologies, also think about the incredible opportunity here. We get to almost psychoanalyze ourselves by trying to build versions of ourselves in the machine. Alright, so here's the open question.


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[Assistant] How powerful is artificial intelligence? How powerful is machine learning that lies at the core of artificial intelligence? Is it simply a helpful tool? A special purpose tool to help you solve simple problems? If you're, uh, which is what it currently is.


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[Assistant] Currently machine learning artificial intelligence is a way if you can formally define


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[Assistant] The problem, you can formally define the tools you're working with, you can formally define the utility function, what you want to achieve with those tools. As long as you can define those things, we can come up with algorithms that can solve them. As long as you have the right kind of data, which is what I'll talk about. Data is key. And the question is, into the future, can


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[Assistant] We break past this very narrow definition of what machine learning can give us, which is solve specific problems.


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[Assistant] To something bigger. To where we approach the general intelligence that we exhibit as human beings. When we're born we know nothing and we learn quickly. From very little data.


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[Assistant] The right answer is we don't know.


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[Assistant] We don't know what are the limitations of technology. What kind of machine learning are there?


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[Assistant] There's several flavors. The first two is what's, really, the first is what's achieved success today. Supervised learning. What I'm showing here on the left of the slide is the teachers, is the data that is fed to the system, and on the right is the students, which is the system itself from machine learning.


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[Assistant] So there's supervised learning. Whenever everybody talks about machine learning today, for the most part, they're referring to supervised learning, which means every single piece of data that is used to train the model is seen by human eyes, and those human eyes, with an accompanying brain, label that data


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[Assistant] In a way that makes it useful to the machine. This is, this is critical, because that's one, the blue box, the human, is really costly. So whenever every single piece of data that needs to be, uh, that's used to train the machine needs to be seen by a human, you need to pay for that human.


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[Assistant] And second, you're limited to just the, the time. There's, uh, the amount of data necessary to label what it means to exist in this world is humongous.


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[Assistant] Augmented supervised learning is when you get machine to really, to help you a little bit. There's a few tricks there, but still, still only tricks. It's still the human is at the core of it. And the promise of


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[Assistant] Future research that we're pursuing, that I'm pursuing, and perhaps in the applications if we get to discuss, or some of the speakers here get to discuss, uh, they're pursuing in, uh, semi-supervised and reinforcement learning, where the human starts to play a smaller and smaller role in how much they get to annotate, they have to annotate the data. And the dream


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[Assistant] Of the sort of wizards of the dark arts of deep learning are all excited about unsupervised learning. It has very few actual successes in application in the real world today, but it is, uh, uh, the idea that you can build a machine


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[Assistant] That doesn't require a human teacher, a human being to teach you anything is, fills us artificial intelligence researchers with excitement.


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[Assistant] There's a theme here. Machine learning is really simple. The learning system in the middle


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[Assistant] There's a training stage where you teach it something. All you need is some data, input data, and you need to teach it the correct output for that input data.


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[Assistant] So you have to have a lot of, uh, pairs of input data and correct output. There'll be a theme of cats throughout this presentation. So if you want to teach a system,


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[Assistant] Difference between a cat and a dog, you need a lot of images of cats, and you need to tell it that this is a cat. This bounding box here in the image is a cat.


[User] Please transcribe this text

[Assistant] You have to give it a lot of images of dogs and tell it, okay, in this, in these pictures there are dogs. And then, then there's a...


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[Assistant] Spelling mistake on the second stage is the testing stage.


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[Assistant] When you actually give it new input data, it's never seen before.


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[Assistant] And you hope that it has given for cat vs. dog enough data to guess is this new image that I've never seen before. A cat or a dog?


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[Assistant] Now, one of the open questions you want to keep in mind...


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[Assistant] Can we not model in this way? What activity? What task? What goal?


[User] Please transcribe this text

[Assistant] My, I offer to you that there's nothing you can't model in this way.


[User] Please transcribe this text

[Assistant] So let's think about what in terms of machine learning can be, so let's start small. What can be modeled in this way?


[User] Please transcribe this text

[Assistant] First on the bottom of the slide left is one-to-one mapping where the input is an image of a cat and the output is a label that says cat or dog. You can also do one-to-many where the image


[User] Please transcribe this text

[Assistant] The input is an image of a cat, and the output is a story about that cat. Uh, a captioning of the image.


[User] Please transcribe this text

[Assistant] You can, first of all, you can do it the other way. Many to one mapping.


[User] Please transcribe this text

[Assistant] Where you give it a story about a cat and it generates an image.


[User] Please transcribe this text

[Assistant] There's many to many. This is Google Translate. We translate a sentence from one language to another.


[User] Please transcribe this text

[Assistant] And there's various flavors of that. Again, same theme here. Input data, provided with correct output, and then


[User] Please transcribe this text

[Assistant] Let it go into the wild where it runs on, uh, input data it hasn't seen before to provide guesses.


[User] Please transcribe this text

[Assistant] And it's as simple as this. Whatever you can convert into one of the following four things. Numbers. Vector of numbers. So a bunch of numbers.


[User] Please transcribe this text

[Assistant] A sequence of numbers where the temporal dynamics matters, so like audio, video, where the sequence, the ordering matters. Or a sequence of vector numbers. Just a bunch of numbers. If you can convert it into numbers, and I propose to you that there's nothing you can't convert into numbers, uh,


[User] Please transcribe this text

[Assistant] If you can converge numbers, you can have a system learn to do it. And the same thing with the output. Generate numbers, vectors of numbers, sequence of numbers, or sequence of vectors of numbers.


[User] Please transcribe this text

[Assistant] But I'll pause every once in a while to make sure we're on the same page here.


[User] Please transcribe this text

[Assistant] Uh, so what kind of input are we talking about? Just to fly through it. Images, so faces, or medical applications for, uh, looking, uh, looking at, uh, scans of different parts of the body to determine if the, to diagnose any kind of


[User] Please transcribe this text

[Assistant] Time series data, so financial data, stock market, you can use it to predict.


[User] Please transcribe this text

[Assistant] Anything you want about the stock market, including whether to buy or sell. If you're curious, doesn't work quite well. As a machine learning application.


[User] Please transcribe this text

[Assistant] Physical world. So, cars. Or any kind of object, any kind of robot that exists in this world. So, uh, location of where I am.


[User] Please transcribe this text

[Assistant] Location of where other things are, the actions of others, that could be all input. All of it can be converted to numbers. And the correct output, same thing. Classification.


[User] Please transcribe this text

[Assistant] A bunch of numbers. Classification is saying it's a cat or a dog. Regression is saying


[User] Please transcribe this text

[Assistant] Uh, to what degree I turn the steering wheel, sequence, generating audio, generating video, generating stories, captioning, text, images, uh, generating anything you could think of as numbers. And at the core of it is a bunch of data agnostic machine learning algorithms.


[User] Please transcribe this text

[Assistant] There's traditional ones, nearest neighbors, naive bay, support machines, support vector machines. A lot of them are...


[User] Please transcribe this text

[Assistant] Uh, limited, and I'll describe how. And that there's neural networks.


[User] Please transcribe this text

[Assistant] There's nothing special and new about neural networks, and I'll describe


[User] Please transcribe this text

[Assistant] Exactly the very subtle thing that is...


[User] Please transcribe this text

[Assistant] Powerful, that's always been there all along, and certain things have now been able to unlock that power about neural networks. But it's still just the flavor of a machine learning algorithm.


[User] Please transcribe this text

[Assistant] And the inspiration for Neural Networks, the Jonathan show last time, is our human brain. It's perhaps why the media, perhaps why the hype.


[User] Please transcribe this text

[Assistant] Is captivated by the idea of neural networks is because you immediately jump to this feeling like because there's this mysterious structure to them that scientists don't understand. Artificial neural networks I'm referring to and the biological ones. We don't understand them and the similarity captivates our minds and we think, well,


[User] Please transcribe this text

[Assistant] This approach is perhaps as limited as our, as limitless as our own human mind. But the comparison ends there.


[User] Please transcribe this text

[Assistant] In fact, the artificial neuron, their artificial neural networks are much simpler computational units. At the core of everything is this neuron. This is a computational unit.


[User] Please transcribe this text

[Assistant] You could think of it as a computational entity.


[User] Please transcribe this text

[Assistant] With a 0, uh, with a .7, .6, 1.4 weights. It gets really exciting when it sees pictures of cats.


[User] Please transcribe this text

[Assistant] The way you release the power that I'll talk about in the following slides about the applications of what can be achieved is just stacking a bunch of these together. Think about it.


[User] Please transcribe this text

[Assistant] This is an extremely simple computational unit.


[User] Please transcribe this text

[Assistant] So you need to sort of pause whenever we talk about the following slides and think that


[User] Please transcribe this text

[Assistant] There, there's a few slides that I'll show that say neural networks are amazing. I want you to think back to this slide.


[User] Please transcribe this text

[Assistant] That everything is built on top of these really simple addition operations with a simple nonlinear function applied at the end. Just a tiny math operation.


[User] Please transcribe this text

[Assistant] We stack them together in a feed-forward way, so there's a bunch of layers. And when people talk about deep neural networks, it means there's a bunch of those layers.


[User] Please transcribe this text

[Assistant] And then there's recurrent neural networks that are also a special flavor that's able to have memory.


[User] Please transcribe this text

[Assistant] So as opposed to just, uh, pushing input into output directly, it's also able to do stuff on the inside in a loop where it remembers things. This is useful for natural language processing, for audio processing, whenever the sequence is not, the length of the sequence is not defined.


[User] Please transcribe this text

[Assistant] But also, I want you to use your imagination. Uh, there's a universality to neural networks. Means that this simple computational unit on the left is an input, on the right is the output of this network.


[User] Please transcribe this text

[Assistant] With just a single hidden layer, it's called a hidden layer because it sits there in the middle of the input and the output layers. A single hidden layer with some number of nodes can represent any function.


[User] Please transcribe this text

[Assistant] Anything you want to build in this world, everyone in this room can be represented with a neural network with a single hidden layer.


[User] Please transcribe this text

[Assistant] So, the power, and this is just one hidden layer, the power of these things is limitless. The problem of course,


[User] Please transcribe this text

[Assistant] So how do you build a network that is as clever as many of the people in this room? But the fact that you can


[User] Please transcribe this text

[Assistant] Build such a network is incredible. It's amazing. I want you to think about that.


[User] Please transcribe this text

[Assistant] Some random weights assigned to the edges. Again,


[User] Please transcribe this text

[Assistant] A network is represented, the numbers at the core, the parameters at the core of this network are the numbers on each of those, uh, arrows. Each of those edges. And you start knowing nothing. This is a baby network. And the way you teach it something,


[User] Please transcribe this text

[Assistant] Unfortunately, currently, as I said, in a supervised learning, uh, mechanism, you have to give it pairs of input and output. You have to give it, uh, pictures of cats.


[User] Please transcribe this text

[Assistant] And labels on those pictures saying that they're cats.


[User] Please transcribe this text

[Assistant] And you back propagate it to the network. What I mean...


[User] Please transcribe this text

[Assistant] Everything is easier with cats. I apologize. I apologize. Uh, too many cats. And uhm,


[User] Please transcribe this text

[Assistant] So the input here is a cat, and the neural network we trained, it's just guessing, it doesn't know. Say, I don't know. Uh, it's guessing cat.


[User] Please transcribe this text

[Assistant] Well, it happens to be right. So we have to, this is the measure of error. Yes. You got it right. And you have to back propagate that error. You have to reward the network for doing a good job.


[User] Please transcribe this text

[Assistant] And all you do, what I mean by reward, there's weights on each of those edges, and so the, the node, the individual neurons that were responsible that, back to that cat neuron, that cat neuron needs to be rewarded for seeing the cat.


[User] Please transcribe this text

[Assistant] So you just increased the weights on the neurons that were associated with producing the correct answer.


[User] Please transcribe this text

[Assistant] Now you give it a picture of a dog, and the neural network says cat.


[User] Please transcribe this text

[Assistant] Well, that's an incorrect answer. So, no, there's a high error. It needs to be back propagated through the network. So the weights that are responsible with classifying this odd, this picture as a cat need to be punished. They need to be decreased. Simple.


[User] Please transcribe this text

[Assistant] And you just repeat this process over and over. This is what we do as kids when we're first, uh, learning.


[User] Please transcribe this text

[Assistant] And, you know, for the most part that we have two, we're also supervised learning machines in the sense that we have our parents and we have the environment, the world that teaches, uh, about


[User] Please transcribe this text

[Assistant] As human beings, we don't need too many examples, and I'll talk about some of the drawbacks of these approaches. Uh, we don't need too many examples. You fall off your bike once or twice and you learn how to ride the bike. Unfortunately, neural networks need, need, uh, tens of thousands of times when they fall off the bike in order to learn how to not do it. That's one of the limitations. And one key thing I didn't mention here is when we refer to the input data,


[User] Please transcribe this text

[Assistant] When we refer to input data, we usually refer to sensory data, raw data. We have to represent that data in some clever way, in some deeply clever way, where we can reason about it.


[User] Please transcribe this text

[Assistant] Whether it's in our brains or in the neural network. And a very simple example here to illustrate why representation of data matters.


[User] Please transcribe this text

[Assistant] Class from another, a cat versus dog, either incredibly difficult or incredibly simple. Here is a visualization of the same kind of data in Cartesian coordinates and polar coordinates.


[User] Please transcribe this text

[Assistant] On the right, you can just draw a simple line to separate the two.


[User] Please transcribe this text

[Assistant] What you want is a system that's able to learn.


[User] Please transcribe this text

[Assistant] The polar coordinate representation versus the Cartesian representation automatically. And this is where deep learning has stepped in


[User] Please transcribe this text

[Assistant] And revealed the incredible power of this approach, which deep learning is the smallest circle there. It's a type of representational learning. Machine learning is the bigger second to the biggest.


[User] Please transcribe this text

[Assistant] So this class is about the biggest circle, AI. Includes robotics, includes all the fun things that are built on learning. And I'll discuss why machine learning I think will close this entire circle into one. But for now, AI is the biggest circle, then a subset of that is machine learning, and a smaller subset of that is representation learning.


[User] Please transcribe this text

[Assistant] So deep learning is not only able to say, given a few examples of cats and dogs to discriminate between a cat and a dog.


[User] Please transcribe this text

[Assistant] It's able to represent what it means to be a cat.


[User] Please transcribe this text

[Assistant] So, it's able to automatically determine what are the fundamental units


[User] Please transcribe this text

[Assistant] At the low level and the high level. I'm talking about this very Play-Doh.


[User] Please transcribe this text

[Assistant] Uh, what it means to represent a cat, from the whiskers, to the high level, uh, shape of the head, to the, the fuzziness and the deformable aspects of the cat, not a cat expert, but I hear this, these are the features of a cat, versus, that are essential to discriminate between a cat and a dog.


[User] Please transcribe this text

[Assistant] Learning those features as opposed to having to have experts. This is the drawback of systems that, uh, Jonathan talked about from the 80s and 90s where you have to bring in experts for any specific domain that you try to solve.


[User] Please transcribe this text

[Assistant] You had to have them encode that information. Deep learning. This is, this is simply the only.


[User] Please transcribe this text

[Assistant] A big difference between deep learning and other methods is that it learns the representation for you. It learns what it means to be a cat. Nobody has to step in and help it figure out what that cats have whiskers and dogs don't.


[User] Please transcribe this text

[Assistant] What does this mean? The fact that it can learn these features, these whisker features, is


[User] Please transcribe this text

[Assistant] As opposed to having 5 or 10 or 100 or 500 features that are encoded by brilliant engineers with PhDs.


[User] Please transcribe this text

[Assistant] It can find hundreds of thousands, millions of features automatically.


[User] Please transcribe this text

[Assistant] Uh, hundreds of millions of features. So stuff that...


[User] Please transcribe this text

[Assistant] That can't be put into words or described. In fact, it's one of the limitations of neural networks is they find so many fundamental things about what it means to be a cat that you can't visualize what it really knows. It just seems to know stuff. And it finds that stuff automatically.


[User] Please transcribe this text

[Assistant] What does this mean? The critical thing here is because it's able to automatically learn those hundreds of millions of features, it's able to utilize data. It doesn't start, the diminishing returns


[User] Please transcribe this text

[Assistant] Don't hit until, well we don't know when they hit. The point is with the classical machine learning algorithms, you start hitting a wall when you have tens of thousands of images of cats. With deep learning, you get better and better with more data.


[User] Please transcribe this text

[Assistant] Here's, here's a game, a simple arcade game where there's two paddles bouncing a ball back and forth.


[User] Please transcribe this text

[Assistant] Okay, great. You can figure out an artificial intelligence agent that can play this game. It can, not even that well, just kind of, it kind of learns to do alright and eventually win. Here's the fascinating thing. With deep learning, as opposed to encoding,


[User] Please transcribe this text

[Assistant] The position of the paddles. The position of the ball. Having an expert in this game.


[User] Please transcribe this text

[Assistant] An evolution of the game. You get a bunch of pixels. Pixels are...


[User] Please transcribe this text

[Assistant] You know, images are built up of pixels. They're just numbers from 0 to 256.


[User] Please transcribe this text

[Assistant] So there's this array of numbers that represent each image and then you give it several, uh, tens of thousands of images that represent a game. So you have this stack of pixels and stack


[User] Please transcribe this text

[Assistant] You have to figure out how to play the game. You know nothing about games, you know nothing about colors, or balls, or paddles, or winning, or anything.


[User] Please transcribe this text

[Assistant] That's it. So this is, why is this amazing? Uh, that it even works and it works, it wins. It's amazing because that's exactly what we do as human beings. This is general intelligence. So I need you to pause and think about this.


[User] Please transcribe this text

[Assistant] We'll, we'll talk about special intelligence and the usefulness, and okay, there's cool tricks here and there that we can do to, uh, get you an edge on your, uh, high-frequency trading system. But, uh, this is general intelligence.


[User] Please transcribe this text

[Assistant] General intelligence is the same intelligence we use as babies when we're born. What we get is an input, sensory input of image sensory input. Right now, all of us, most of us are seeing, hearing,


[User] Please transcribe this text

[Assistant] Feeling with touch, and that's the only input we get. We know nothing. And with that input, we have to learn something.


[User] Please transcribe this text

[Assistant] Nobody is pre-teaching us stuff. And this is an example of that. A trivial example, but one of the first examples where this is truly working. Sorry to linger on this, but it's a fundamental fact.


[User] Please transcribe this text

[Assistant] The fact that we have systems that, and now outperform human beings in these simple arcade games is incredible. This is the research side of things.


[User] Please transcribe this text

[Assistant] That previous slide is why I think machine learning is limitless.


[User] Please transcribe this text

[Assistant] Again, the representation of the data matters. And if you want to have impact,


[User] Please transcribe this text

[Assistant] We currently can only tackle the small problems. What are those problems?


[User] Please transcribe this text

[Assistant] Image recognition. We can classify given the entire image of a leopard, of a boat, of a mite, with pretty good accuracy.


[User] Please transcribe this text

[Assistant] Of what's in that image. That's image classification.


[User] Please transcribe this text

[Assistant] What else? We can find exactly where in that image each individual object is. That's called image segmentation. Again, the process is the same.


[User] Please transcribe this text

[Assistant] As long as you give it a set of numbers as input.


[User] Please transcribe this text

[Assistant] And the correct set of labels is output. It learns to do that for data it hasn't seen in the past.


[User] Please transcribe this text

[Assistant] Let me pause a second and maybe if you have any questions, does anyone have any questions about the, uh, the techniques of neural networks? Yes.


[User] Please transcribe this text

[Assistant] That's a great question and in a couple of slides I'll get to it exactly. Uh, so,


[User] Please transcribe this text

[Assistant] The, uh, the data representation.


[User] Please transcribe this text

[Assistant] I'll elaborate in a little bit, but loosely the data representation is, for a neural network, is in the weights.


[User] Please transcribe this text

[Assistant] of each of those arrows that connect the neurons. That's where the representation is. So I'll show to really clarify that example of what that means.


[User] Please transcribe this text

[Assistant] Uhm, the Cartesian versus polar coordinates is just a visual, a very simple visualization of the concept.


[User] Please transcribe this text

[Assistant] But you want to be able to represent the data in an arbitrary way where there's no limits to the representation. It could be highly nonlinear, highly complex.


[User] Please transcribe this text

[Assistant] Uh, so I have a couple of slides almost asking this question. Because there's no good answers.


[User] Please transcribe this text

[Assistant] But one could argue, and I think somebody in the last class brought up that, you know, is machine learning just pattern recognition?


[User] Please transcribe this text

[Assistant] Because we've recently as human beings learned to do it. In our evolutionary time.


[User] Please transcribe this text

[Assistant] We think that it's somehow special from, for example, perception. We've had visual perception for several orders of magnitude longer in our evolution as a living species. We've started to learn to reason, I think, about 100,000 years ago.


[User] Please transcribe this text

[Assistant] So we think it's somehow special from the same kind of mechanism we use for seeing things. Perhaps it's exactly the same thing. So perception is pattern recognition, perhaps reasoning is just a few more layers of that. That's the hope. That's an open question.


[User] Please transcribe this text

[Assistant] Uh, there's been very few breakthroughs in neural networks, uh, since through the AI winters that we've discussed, through a lot of excitement.


[User] Please transcribe this text

[Assistant] In spurts, and even recently, there's been very few algorithmic innovations.


[User] Please transcribe this text

[Assistant] Uh, the big, uh, gains came from compute. So improvements in GPU and better, faster computers. The, you can't underestimate the power of community. So the ability to share code and the internet.


[User] Please transcribe this text

[Assistant] Uh, ability to communicate together through the internet and work on code together. And then digitization of data. So like ability to have large data sets easily accessible and downloadable.


[User] Please transcribe this text

[Assistant] All of those little things, but I think it, in terms of the future of deep learning and machine learning, it, it all rides on compute, I think, meaning continued bigger and faster computers.


[User] Please transcribe this text

[Assistant] Uh, that doesn't necessarily mean Moore's law, uh, in making smaller and smaller chips. It means getting clever in different directions. Uh, massive parallelization, uh, coming up with ways to do super efficient, power efficient implementations in your own networks and so on.


[User] Please transcribe this text

[Assistant] Let's fly through a few examples of what we can do with machine learning just to give you a flavor I think in future lectures as possible we'll discuss different speakers, different specific applications, really dig into those.


[User] Please transcribe this text

[Assistant] As opposed to working with just images, you can work with, uh, videos and segment those, I mentioned image segmentation, we do video segmentation, go through video segment, the different parts of a scene that's useful to a particular application. Here in driving, you can segment the road, uh, from cars and vegetation, uh,


[User] Please transcribe this text

[Assistant] And lane markings. You can also, this is a subtle but important point, uh, yes.


[User] Please transcribe this text

[Assistant] So, how do we do it as human beings? First of all, let's start there. Uh, the way we do it is by, uh, the knowledge we bring to the table.


[User] Please transcribe this text

[Assistant] So we, we know what it means to be on the road. There's a lot of the huge network of knowledge that you come with. And so that makes the perception problem much easier. This is pure perception. You take an image and you separate different parts based purely on tiny patterns of pixels.


[User] Please transcribe this text

[Assistant] So first it finds all the edges and it learns that traffic lights have certain kinds of edges around them and then zoom out a little bit. They have a certain collection of edges that make up this black, uh,


[User] Please transcribe this text

[Assistant] Rectangle type shape. So it's all about shapes. It kind of builds up, uh, knowing this, this shape structure of things.


[User] Please transcribe this text

[Assistant] But it's a purely perception problem and one of the things I argue is that if it's purely a perception approach and you bring no knowledge to the table about the physics of the world, the three-dimensional physics and the temporal dynamics,


[User] Please transcribe this text

[Assistant] You, for all of these things, think about how you as a human being would solve these problems and what is lacking in the machine learning approach.


[User] Please transcribe this text

[Assistant] Data is lacking in the machine learning approach in order to achieve the same kind of results, the same kind of reasoning required to, that you would use as a human. So there is also image, uh, detection.


[User] Please transcribe this text

[Assistant] Image detection, which means, it's a subtle but important point, uh, the stuff I mentioned before, image classification is given an image of a cat, uh, you find the cat, uh, no, sorry, you don't find the cat, you say this image is of a cat or not, and then, uh, detection or localization is when you actually find where in the image that is. Uh, that problem is much harder, uh, but also doable with machine learning.


[User] Please transcribe this text

[Assistant] Now, as I said, inputs, outputs can be anything. The input can be video, the output can be video, and you can do anything you want with these videos. You can colorize the video. You can add, take an old black and white film and produce color images.


[User] Please transcribe this text

[Assistant] Uh, again, in terms of having an impact in the world using these applications, you have to think


[User] Please transcribe this text

[Assistant] This is a cool demonstration, but how well does it actually work in the real world?


[User] Please transcribe this text

[Assistant] Translation, whether that's from text to text or image to image, you can translate here dark chocolate from one language to another.


[User] Please transcribe this text

[Assistant] Class global business of artificial intelligence. There's a reference below there. You can go and generate your own text. You can generate the writing of the act of generating handwriting. You can type in some text and given different styles that it learns from other handwriting


[User] Please transcribe this text

[Assistant] Uh, samples, it can generate any kind of text using handwriting. Again.


[User] Please transcribe this text

[Assistant] The input is language, the output is a sequence of writing of pen movements on the screen. You can complete sentences. This is kind of a fun one where if you start


[User] Please transcribe this text

[Assistant] So you can generate language, and you can generate language where you start, you feed the system some input first. So in black there, it says life is, and then have the neural network complete those sentences.


[User] Please transcribe this text

[Assistant] Uh, life is about kids. Life is about the weather. There's a lot of knowledge here, I think, being conveyed. And you can start the sentence with the meaning of life is. The meaning of life is literary recognition.


[User] Please transcribe this text

[Assistant] But these are all generated by a computer. You can also caption, this has become very popular.


[User] Please transcribe this text

[Assistant] Recently is caption generation, giving us input as an image. The output is a set of text that captions the content of the image.


[User] Please transcribe this text

[Assistant] You find the different objects in the, uh, in the image. That's a perception problem. And once you find the different objects, you stitch them together in a sentence that makes sense. You generate a bunch of sentences and classify which sentence is the most likely to fit this, uh, this image.


[User] Please transcribe this text

[Assistant] And you can, uh, uh, so certainly in the, I tried to avoid mentioning too, driving too much because it is my field, it is what I'm excited about. But, uh, then the moment I start talking about driving, it'll all be about driving.


[User] Please transcribe this text

[Assistant] So, uh, but I should mention, of course, that deep learning is critical to driving applications for both the perception and what is really exciting to us now is the end-to-end, the end-to-end approach. So whenever you say end-to-end in any application, what that means


[User] Please transcribe this text

[Assistant] Is you start from the very raw inputs that the system gets and you produce the very final output that's expected of the system. So as opposed to in the self-driving car case, as opposed to breaking a car down into each individual components of perception, localization, mapping,


[User] Please transcribe this text

[Assistant] Uh, control planning and just taking the whole stack and just ignoring all the


[User] Please transcribe this text

[Assistant] Okay, so let's, uh, step back for a second and think about what can't we do with machine learning. We talked, we talked about you can map numbers to numbers. Let's think about what we can't do. This, at the core of artificial intelligence in terms of making impact on this world is robotics.


[User] Please transcribe this text

[Assistant] So what can't we solve in robotics and artificial intelligence with a machine learning approach?


[User] Please transcribe this text

[Assistant] And let's break down what artificial intelligence means. Here's a stack. Starting at the very top is the environment, the world that you operate in. There's sensors that sense that world. There's feature extraction and learning from that data. And there's some reasoning, planning, and effectors are the ways you manipulate the world. What can't we learn in this way?


[User] Please transcribe this text

[Assistant] So we've had a lot of success, as Jonathan talked about, in the history of AI, with formal tasks, playing games, solving puzzles. Recently we're having a lot of breakthroughs with medical diagnosis.


[User] Please transcribe this text

[Assistant] Uh, we're still, sorry, uh, we're still, uh, struggling, but are very excited about, in the robotics space, with more mundane tasks of walking, of basic perception, of natural language written and spoken. And then there is the human tasks.


[User] Please transcribe this text

[Assistant] Subjective experience. So high level reasoning. Not just common sense, but high level.


[User] Please transcribe this text

[Assistant] So let's fly through this pipeline. There's sensors, cameras, lidar, audio. There's communication.


[User] Please transcribe this text

[Assistant] That flies through the air or wired or wireless or wired. I am you measuring the movement of things.


[User] Please transcribe this text

[Assistant] So that's the way you think about it. That's the way as human beings and as any kind of system that you design, you measure the world. You don't just get, uh, an API to the world. Uh, you need to, uh, somehow measure aspects of this world. So that's how you get the data. So that's how you convert the world into data you can play with. And once you have the data,


[User] Please transcribe this text

[Assistant] This is the representation side. You have to convert that raw data of raw pixels, raw audio, raw lidar data. You have to convert that into data that's useful for the intelligence system, for the learning system to, to use to discriminate between one thing and another.


[User] Please transcribe this text

[Assistant] For vision, that's finding edges, corners, object parts and entire objects. And there's the machine learning that I'll talk about, that I've talked about.


[User] Please transcribe this text

[Assistant] There's different kinds of mapping of the representation that you've learned to an actual outputs.


[User] Please transcribe this text

[Assistant] There is, once you have this, so you have this idea of, and this goes to maybe a little bit of Simon's question, is reasoning. This is something that's, uh, out of reach in machine learning at the moment. This is going to your question.


[User] Please transcribe this text

[Assistant] Then we can, we can build a world class machine learning system for taking an image and classifying that it's a duck.


[User] Please transcribe this text

[Assistant] Wake you up. So, we could take, this is well studied, exceptionally well studied problem. We could take audio sample of a duck and tell that it's a duck.


[User] Please transcribe this text

[Assistant] In fact, what species of bird? It's incredible how much research there is in bird species classification. And, uh, you can look at video and you can tell that we can do actual recognition that it's swimming.


[User] Please transcribe this text

[Assistant] But we can't do with learning now is reason.


[User] Please transcribe this text

[Assistant] This is the reasoning problem. This is the task.


[User] Please transcribe this text

[Assistant] Machine learning has not had many strides. There's mechanical issues here that are incredibly difficult. The degrees of freedom with all the actuators involved, with all the, just, just the ability to localize every part of yourself in this dynamic space.


[User] Please transcribe this text

[Assistant] So let me just pose this question. We talked about how machine, what machine learning can do with the cats and the duck.


[User] Please transcribe this text

[Assistant] We could do that. Given representation, it could predict what's in the image. But one of the open questions is...


[User] Please transcribe this text

[Assistant] And deep learning has been able to do the feature extraction, the representation learning. This is the big breakthrough that everybody's excited about.


[User] Please transcribe this text

[Assistant] But can it also reason? These are the open questions. Can it reason? Can it do the planning and action?


[User] Please transcribe this text

[Assistant] And as human beings do, can enclose the loop entirely. From sensors to effectors. So learn not only the brain, but


[User] Please transcribe this text

[Assistant] The way you sense the world and the way you affect the world.


[User] Please transcribe this text

[Assistant] So the question was about the pawn game. Thank you. Talk to it for a little longer. It doesn't get punished when it doesn't detect the ball. This is a beautiful thing.


[User] Please transcribe this text

[Assistant] It gets punished only at the very end of the game for losing the game and gets rewarded for winning the game. So it knows nothing about that ball. And it learns about that ball. That's something you really sit and think about.


[User] Please transcribe this text

[Assistant] Because, like, how do, as human beings, imagine if you're playing with a physical ball, how do you learn what a ball is?


[User] Please transcribe this text

[Assistant] Nobody tells you about what a ball is. You're just using the raw sensory input. We take it for granted, and maybe this is what I can end on, is this is what Jonathan brought up.


[User] Please transcribe this text

[Assistant] As we take the simplicity of this task for granted, because we've been, we've had eyes


[User] Please transcribe this text

[Assistant] We, broadly speaking, as living species on planet Earth, uh, these eyes have been evolved for 540 million years. So we have 540 million years of data. We've been walking for close to that by pedo mammals.


[User] Please transcribe this text

[Assistant] We have been thinking only very recently, so a hundred thousand years versus a hundred million years.


[User] Please transcribe this text

[Assistant] That's why we can't, some of these problems that we're trying to solve, you can't take for granted how actually difficult they are. So for example,