[User] Please transcribe this text

[Assistant] Knowing that you have to stay consistent or that you might fall off and lose your YouTube empire, I think is, you know, so in a very general sense, it's kind of money related. I mean, I know from a personal level, I feel like my content now is more organic because when I was forcing out a vlog every day, it was, it was because of money in large part, you know? And my content probably suffered as a result.


[User] Please transcribe this text

[Assistant] It's the kind of 21 year old that you think that you'd want to be when you're 12. But then when you actually turn 21. That's a perfect way of saying it. When you're 21, you might be looking at rappers or dudes in a band or something. You think that's kind of cool. But when you're 12, you're like, yes, tank tops, blonde hair.


[User] Please transcribe this text

[Assistant] You know, that's why I always say is that like we are right now at the collision point of meme rap and real rap and people are upset because they are realizing that meme rap is gonna win. And that all, all popular rap from now on like is gonna have that meme element where you have the Catch Me Outside Girl, you have Cardi B. I'm sorry, but they're...


[User] Please transcribe this text

[Assistant] Oh, because Ludacris wouldn't do anything for the Jake Paul brand. Ludacris is too easy for Jake Paul to go get. The fact that he got Gucci is impressive, don't you think?


[User] Please transcribe this text

[Assistant] And so how would you compare your delivery style at that time? Like how much you understood how to command a YouTube video then compared to now?


[User] Please transcribe this text

[Assistant] Have you ever thought about, I mean you've branched out a bit, you went into the podcast space and everything, but have you ever thought about like DramaAlert as more of a, and I guess you brought on other co-hosts and stuff as well, but have you ever thought about DramaAlert going beyond just you primarily, or have you always thought of it as having you as like the main focus?


[User] Please transcribe this text

[Assistant] Like, I got started in this shit doing BMX content, and then...


[User] Please transcribe this text

[Assistant] Over time, you know, it just kind of became like this addiction where you realize like, Oh, I'm going to get more of an audience doing this. And that sort of drives you in a certain direction. And a lot of times people will try to use that against you say, Oh,


[User] Please transcribe this text

[Assistant] Yeah man, it's pretty crazy to think that like Scarce was sort of like the solution to, to you at a certain point. That people were like, this guy's so edgy and so offensive and so...


[User] Please transcribe this text

[Assistant] But, you know, do you feel like sort of beating scarce and subs or like that overall like social blade analysis? Is that kind of like a big thing that drives you at a certain point when the money's not good?


[User] Please transcribe this text

[Assistant] But you need those goals in your head, whether it's like a dollar amount in the bank or if it's a number that you want to hit on some platform online or whatever. It's like at a certain point, once you have enough money that you feel comfortable, it's like what keeps you going? And that's what I'm always kind of fascinated by because I'm gonna be honest, over the past like six months, my financial status has completely changed and it's like,


[User] Please transcribe this text

[Assistant] I feel like I might have more thoughts that will like pop into my head and then I'm like, oh, I can communicate this effectively because now I have more room. I think of stuff all the time and I'm like, no, that joke's too long. It won't work on Twitter.


[User] Please transcribe this text

[Assistant] I know, and isn't it incredibly depressing that Facebook's like over ten times bigger than Twitter?


[User] Please transcribe this text

[Assistant] I know, and isn't it bizarre how almost nobody that I know who has a really popular YouTube channel uses Facebook for promoting it in any serious way.


[User] Please transcribe this text

[Assistant] Yeah, that's fucked up. Would you be, I mean, that is really like the ultimate pie in the sky dream for YouTubers is just that eventually there'll be some platform that lets us have competition against YouTube.


[User] Please transcribe this text

[Assistant] That's the funny thing about the career that you've taken on is that, like for me,


[User] Please transcribe this text

[Assistant] If I was in a big ol' beef with somebody that I was friends with or whatever, there's no reason for me to publicize it. I always say that to my friends and stuff, it's like, we do our dirty laundry in private, you know? We don't expose that shit. But you're in this- You like that? I wanna do laundry with you, Andy. It's so hot and shit, we put it all over our bodies and just fuckin' that pile of laundry, that'd be fire. You down?


[User] Please transcribe this text

[Assistant] I'll do some gay shit with you. Alright, but uhm, I'm just saying that like, you know, it's like, Keem is in a very different point where, you know, if he sees somebody say, hey, fuck you on Twitter, it's like, oh, content. But not, you know, you're not gonna do it to your own life, really. Like, I'm sure you end up in like a fight with your girlfriend and that's not ending up on the show.


[User] Please transcribe this text

[Assistant] Yeah, totally. Like if I told, if I showed you a DM that basically inferred that I, you know, had sex with somebody's girlfriend, it's like you, you...


[User] Please transcribe this text

[Assistant] Yeah, it's beautiful. When I think about it, I was always that kind of kid. I was always the kid who was like talking over the teacher and like just really fundamentally just wanted everybody to pay attention to me. You too?


[User] Please transcribe this text

[Assistant] I mean, I see that stuff all the time. When I think about me when I was 13, when I first started really going to the mall with my friends, that there were like bigger kids that were probably like 15, 16 and that like, we were terrified. We really thought they would beat the shit out of us. They probably would.


[User] Please transcribe this text

[Assistant] You know, it seems like a very, very realistic concern. And I want kids these days to realize that they need to shut the fuck up because someone will beat them up. And that was like probably the best thing they could learn.


[User] Please transcribe this text

[Assistant] So when you do these fan meet and greets, what is your impression of your average fan?


[User] Please transcribe this text

[Assistant] In some weird way, it's like these people just like being able to have us as a friend daily that they could just kind of count on to sort of give them like an overall like viewpoint of how maybe they could structure their life so that they could, you know, do whatever they want with their time.


[User] Please transcribe this text

[Assistant] On a personal level, I've seen it like in the BMX world where there were all these magazines and all these events and all these like institutions that basically like held the community together and then over time the internet starts to like wipe out the magazines and then all of a sudden you feel like you have a subculture that doesn't have as much of like a base because there isn't


[User] Please transcribe this text

[Assistant] People don't know what everybody else is doing and then the culture has a harder time pushing forward.


[User] Please transcribe this text

[Assistant] I appreciate that. And that actually is what I think is really dope is because I was always interested in just YouTube. Actually, I really got into the YouTube community through Drama Alert. What was it? There was a uhm, was it a Jezebel article or a Deadspin article about all the YouTube beef at one point a couple years ago?


[User] Please transcribe this text

[Assistant] There was one article about YouTube beef, and like, it might have even been with, what's the Australian bitch who was eating all the bananas? Remember her? Oh yes. Freely?


[User] Please transcribe this text

[Assistant] Yeah, it was some bullshit about her, I think, where they mentioned it, and I'm like, what? I'm on YouTube all the time, I don't know about this, and then all of a sudden I start watching your shit, and it really helped me to like, understand YouTube as more than just like an isolated...


[User] Please transcribe this text

[Assistant] That's what I hear. That really makes no sense to me. Like, what the fuck non-ad-friendly content has he ever really made?


[User] Please transcribe this text

[Assistant] He has a channel called The Cat's Family and I never really bothered to look into it, but it's like he's decided that he doesn't want his-


[User] Please transcribe this text

[Assistant] I'm into that, man. And you know what's crazy is that we sort of exist right here in the middle of Fouseyville because supposedly he will be jogging back and forth on the street really early in the morning, like eight in the morning before I get here. Oh, no shit. Yeah, I would love to see that and just be like, gay!


[User] Please transcribe this text

[Assistant] I mean, being more talented is, is all relative to your ability. You know, because like, you could say he's more talented than Keem, but Keem puts out a fucking video almost every day. So it's like, you could be more talented than me, but I'm going to outwork you, you know?


[User] Please transcribe this text

[Assistant] What do you think of streaming as a po- as like a new style of content, maybe even as like an alternative to vlogging? What do you think of the streaming world?


[User] Please transcribe this text

[Assistant] Patreon just has a bad brand image because I get plenty of donations to play people's music, all this kind of stuff. When I had a Patreon, people flipped the fuck out. I'm like, it's the same fucking thing. I ended up having to get rid of the Patreon just because it became like a meme, like, oh, you're a beggar, you're a beggar. And I'm just like, it's not even worth it. I'll just do it through fucking Super Chat.


[User] Please transcribe this text

[Assistant] It's like, oh you can engage with me, but like, it's like we're listening to your content, we're basically like selling clout. Because it gives them that feeling, and actually sometimes it works. I've seen dudes get record deals over the past couple of months from me playing their shit, and then boom. I've seen it on your stream and it's good.


[User] Please transcribe this text

[Assistant] There's always people who I think just use some like weird bot to just comment the same thing over and over even though there's like a time limit so it'll pop up at the exact right interval. People are trash.


[User] Please transcribe this text

[Assistant] You know what I love? I like the thing I like about Baited and I've actually realized this from a few other people that I've done podcasts with and stuff is like I like people who if they have an issue with somebody they'll seize that and then try to like have that conversation on camera for the podcast because they realize that there's value in it and also I think it helps because


[User] Please transcribe this text

[Assistant] But it's weird cause it is, it's all about communication and like you even having that 20 minute conversation with Jake Paul then makes it harder for you to dehumanize Jake Paul and to cover him in a way that completely dismisses him and he's like oh he's a piece of shit.


[User] Please transcribe this text

[Assistant] Right, but the whole thing is that if she is lying, she took it way too far by saying he dragged her across the club in a fucking chokehold. It's like, it's so impossible to imagine that happening. There's no witnesses, there's no security cam footage. How the fuck can they possibly act like that's real?


[User] Please transcribe this text

[Assistant] It's such a serious accusation, and it's like, we live in fucking Hollywood, we all know that. Club like Warwick, it's like, you know, somebody could get shot there, and then the club is hugely responsible if there's not video footage.


[User] Please transcribe this text

[Assistant] Good. So Smokepurpp's new album's out and Chief Keef's actually featured with Gil Gotti on the first song.


[User] Please transcribe this text

[Assistant] Uh, I'm gonna be honest, is that like, everything Chief Keef puts out, I'll definitely consume, but then it's very easy for me to forget about the songs that I don't like, and then the ones that I do like will stick with me.


[User] Please transcribe this text

[Assistant] That's cool because you actually want the music out there. That's one problem I have is like, I'll go, like the other day I was in the studio with Lil Yachty and Lil Pump.


[User] Please transcribe this text

[Assistant] Do you care if I smoke? No, go for it. What happened to your rap career? I thought you were gonna put a video out.


[User] Please transcribe this text

[Assistant] I spent my whole life kind of like weirdly proud of never having been a rapper. Just because I was like, you know, I've always been so into rap that it seemed like kind of an obvious thing, but I always sort of identified like, eh, you're a white boy from New Hampshire. Maybe you shouldn't be a rapper. I always was kind of happy with that, but now it's all of a sudden it's like, you're a white kid. Why not be a rapper? You got a YouTube.


[User] Please transcribe this text

[Assistant] Well, it's like I'll go on tour with like Lil Pump and then it's like before they go on stage, I'll go on stage with my DJ and a lot of times the crowd is freaking the fuck out and like she plays like 5-10 songs and I'll just kind of walk back and forth and fuck with the crowd, vlog them, snapchat them, whatever, and I'll be yelling the words into the mic.


[User] Please transcribe this text

[Assistant] The last tour I was on, I noticed that too, where it was like all the most popular songs that my DJ was playing. Cam girl show. Cam girl was like, you know, woke up to dressing like me. And you're just, it's that weird little moment. I ain't trying to get caught up.


[User] Please transcribe this text

[Assistant] When you get in the car or the truck, what do you listen to when you drive around Pantera?


[User] Please transcribe this text

[Assistant] That's why I always, when I get in the car with the Uber driver and they're listening to like pop top radio and I'm like, bro, like this is what you want to listen to. Like at least with all the streaming stuff now, you can be like, wow, I'm a rap fan. So I'm going to listen to this rap station and I think they're going to do a pretty good job keeping me up to date on what I'm, what's going on.


[User] Please transcribe this text

[Assistant] Anyway, what do we, uh, need to touch on before we wrap this up, Cain? Well, we finally got this done.


[User] Please transcribe this text

[Assistant] Hey man, yeah, I'm a supporter of the Scarce Movement and everything, but I'm gonna be totally honest, I watch your content every day, and I watch his... Once in a Blue. Not as much. For a while I was watching both, that's a little too much.


[User] Please transcribe this text

[Assistant] But it's funny, my favorite thing about Scarce, and like, whenever I watch it, I can always count on this happening, is like, so Andy Milanakis sent out a tweet today that said that he got hit by a bus. So basically, Andy Milanakis literally got hit by a bus. A bus hit Andy Milanakis, like he just reiterates it a few more times, it's so funny.


[User] Please transcribe this text

[Assistant] Do it. And you know what the way to do it though is to be like, alright guys, I'm gonna show an ad right now and then like, you know, insert it at the exact second that you say your name.


[User] Please transcribe this text

[Assistant] Yeah, cause it's like an hour of content, you're really gonna complain about watching like a minute of ads. Yeah, that's okay.


[User] Please transcribe this text

[Assistant] My feeling is like so many of my videos get demonetized that I just stick a whole bunch of ads in them and like most people aren't going to see them anyway. I feel like my fans actually get hyped. They'll be like, damn, we had a good ad on this one. That's cool. Cause they have heard me talk about not getting them.


[User] Please transcribe this text

[Assistant] My deal, this is my deal with the porno thing, is that my girlfriend has a website, alensplugs.com, it's a private snapchat. If you, if you sign up and you pay the X dollars a month, then you might see my hog. And, I'm not really planning on making like an official porno.


[User] Please transcribe this text

[Assistant] That's funny. Yo, somebody told me the best joke yesterday. Hey, why did the Mexican guy take Xanax? I dunno. His panic attacks.


[User] Please transcribe this text

[Assistant] Yes. But there's something different about stand up where you gotta go up there and sort of regale this room.


[User] Please transcribe this text

[Assistant] Alright. Thank you very much for you guys, uh, coming in. This is a really good chat. And I'm actually determined to bring Baited back from the dead. I really, and I would like to offer up this studio as well. If you wanted to do a real life version of Baited, let me know. I'll just, I'll sit on the couch. I'll watch. So I'll help you guys film it if you want. That would be fucking amazing.


[User] Please transcribe this text

[Assistant] And now that we know that PewDiePie is a confirmed clan member, uh, apparently we're gonna have to pull up on him.


[User] Please transcribe this text

[Assistant] That's the weird thing about it is that if you've spent enough time on the internet or especially on live stream, you, you know about the N word thing and you know that it's.


[User] Please transcribe this text

[Assistant] It's, it is, okay, it is racist period to say that, but it's like a thing that exists outside of racism. And sometimes, you know, I'll be on stream and I'll have one of my black friends come through that doesn't really like hang out on the internet as much and he'll be like,


[User] Please transcribe this text

[Assistant] Your chat is fucked, and I'm like, oh yeaah, it's, this is a thing on the internet, you know.


[User] Please transcribe this text

[Assistant] But now don't you think it's more of a big deal to say it, it's more of a big deal to say it in 2017 as a white person than it ever was before, right?


[User] Please transcribe this text

[Assistant] Vast and variable. The remote landscapes of southern Chile are spellbinding. It's one of the wildest places on the planet. Chilean Patagonia.


[User] Please transcribe this text

[Assistant] The Carretera Austral is a 1240 kilometre stretch of road that serves as an artery into the very heart of this wilderness.


[User] Please transcribe this text

[Assistant] The terrain here was once deemed impossible, carving a road through these expanses utopian.


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[Assistant] Little by little, construction of the Carretera Austral, also known as the Southern Highway, has advanced, creating access to this natural paradise.


[User] Please transcribe this text

[Assistant] The road connects distant fjords with major cities and is an economic lifeline for Chile's salmon industry.


[User] Please transcribe this text

[Assistant] Join us for a road trip full of surprises to explore the magical landscape and meet the people of Chilean Patagonia.


[User] Please transcribe this text

[Assistant] Patagonia stretches across both Chile and Argentina.


[User] Please transcribe this text

[Assistant] Chile's Carretera Austral might one day connect the centre of the country with its southern tip. But to date, the road remains incomplete and getting south requires crossing into Argentina.


[User] Please transcribe this text

[Assistant] Our journey begins in the Chilean port city of Puerto Montt.


[User] Please transcribe this text

[Assistant] This is the starting point of the Carretera Austral, also known as Ruta 7.


[User] Please transcribe this text

[Assistant] After some 60 kilometres, Patagonia begins to show its wild side.


[User] Please transcribe this text

[Assistant] The road is soon interrupted by a fjord. It's one of the many, especially along the northern section of the Carretera Austral.


[User] Please transcribe this text

[Assistant] Ferries transport cars across, but the Chilean Government eventually wants to replace them with road.


[User] Please transcribe this text

[Assistant] Travelling the entire Carretera Austral involves several ferry connections. This stretch across the Kumau fjord is more than 60 kilometres.


[User] Please transcribe this text

[Assistant] Could a road really be built across such wild terrain? Critics are sceptical. They say a modernised ferry system would be a more efficient solution.


[User] Please transcribe this text

[Assistant] In this still very sparsely populated region, only few people would actually make use of the road.


[User] Please transcribe this text

[Assistant] German marine biologists work at this research station in the Bay of Huenai. It can only be reached by boat across the fjord.


[User] Please transcribe this text

[Assistant] We meet up with her ahead of our trip at her home in Puerto Montt.


[User] Please transcribe this text

[Assistant] Researchers here specialise in cold water corals that only grow in these fjords.


[User] Please transcribe this text

[Assistant] They grow so close to the surface of the water that scientists can dive down and explore them.


[User] Please transcribe this text

[Assistant] Fruney has discovered more than 50 new species. She's officially named some after her children and husband.


[User] Please transcribe this text

[Assistant] But she's made troubling observations in recent years. Entire coral reefs have died out from one year to the next.


[User] Please transcribe this text

[Assistant] And in April 2015, she made another alarming discovery at a different fjord.


[User] Please transcribe this text

[Assistant] The researchers counted 360 dead seawales during this observation flight.


[User] Please transcribe this text

[Assistant] The red tide gets its name from the colour given to water by microscopic algae that release toxins.


[User] Please transcribe this text

[Assistant] These anthropogenic causes, meaning caused by human activity, could very well be increased economic activity in the region.


[User] Please transcribe this text

[Assistant] Little has impacted it as much as industrial salmon farming, which was brought to Patagonia with the arrival of the Carretera Austral.


[User] Please transcribe this text

[Assistant] Today it's the biggest employer in southern Chile. A seasonal worker gives us an insider's look into the business.


[User] Please transcribe this text

[Assistant] Fisherman Boris lives opposite the research station on Kumau Fjord. He was born and raised here.


[User] Please transcribe this text

[Assistant] Many bays that boroughs used to fish in are now occupied by salmon farms.


[User] Please transcribe this text

[Assistant] In their proximity, he's caught some very unusual fish.


[User] Please transcribe this text

[Assistant] Boris built his house in an isolated bay on the edge of a huge nature reserve called Pumalin Park.


[User] Please transcribe this text

[Assistant] The park was founded by the American philanthropist Douglas Tompkins. He made his fortune with outdoor apparel brands. In the 1990s he retired from the business world, sold his shares for more than a hundred million dollars and together with his wife acquired wide swathes of land in Patagonia.


[User] Please transcribe this text

[Assistant] On the biggest portion of their property, the Tompkins created Pumalin Park.


[User] Please transcribe this text

[Assistant] More than 3,000 square kilometres in size, the park stretches from the Argentine border all the way to the Chilean fjords. Its existence created another obstacle for the Carretera Austral.


[User] Please transcribe this text

[Assistant] The Tompkins rejected the prospect of a paved road intersecting the park. Locals were suspicious.


[User] Please transcribe this text

[Assistant] Some accuse Douglas Tompkins of staging a land grab.


[User] Please transcribe this text

[Assistant] It's said that Tompkins was looking for the world's oldest tree before he decided on the site of his nature reserve.


[User] Please transcribe this text

[Assistant] Ranger Sergio has been working at Pumalin Park for more than 10 years. He knows a lot about its primeval flora.


[User] Please transcribe this text

[Assistant] These enormous alerca trees, also known as Patagonian Cypress, don't grow anywhere else in the world.


[User] Please transcribe this text

[Assistant] Douglas Tompkins died in a kayaking accident in 2015, but that didn't spell the end of his conservation project.


[User] Please transcribe this text

[Assistant] In 2017, Tompkins widow Christine McDivitt Tompkins handed over all of the land she and her husband acquired to the Chilean government.


[User] Please transcribe this text

[Assistant] But there was one condition, that the state turn all of it into national parks.


[User] Please transcribe this text

[Assistant] Our journey takes us out of Pumalin Park and back onto the Carretera Austral. We're heading south to our next stop, Chaiten.


[User] Please transcribe this text

[Assistant] The small harbour town on the Gulf of Corcovado is just a shadow of its former self.


[User] Please transcribe this text

[Assistant] Struck by a natural disaster in 2008, residents were forced to flee.


[User] Please transcribe this text

[Assistant] Less than half have returned to rebuild their lives here.


[User] Please transcribe this text

[Assistant] Chaiten volcano, located inside Pumalin Park, erupted without warning. A cloud of smoke rose kilometres into the sky. Vast quantities of scree and lava destroyed parts of Chaiten town, located just 10 kilometres from the volcano.


[User] Please transcribe this text

[Assistant] The volcano had not erupted for more than 9,000 years and was considered dormant. But in seconds, magma shot out of a depth of five kilometres, cutting a swathe of destruction.


[User] Please transcribe this text

[Assistant] Just a few kilometres on and the splendour of Patagonia's landscape unfolds again.


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[Assistant] Heading further south, our next stop is Via Santa Lucia. And we meet one resident who devoted his working life to the Carretera Austral.


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[Assistant] Mario was a member of the Military Labour Corps for 35 years and proudly shows us photos documenting his career.


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[Assistant] The core was formed in 1976 when the building began.


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[Assistant] It was pioneering work under the most adverse conditions. Mario's company was there from the start.


[User] Please transcribe this text

[Assistant] Mario is still proud of his unit's achievements, clearing the first path into the wilderness using axes and shovels.


[User] Please transcribe this text

[Assistant] Up to 10,000 men worked here all year round. There were no weekends.


[User] Please transcribe this text

[Assistant] Prisoners could trade in two years of their sentence for a year working on the road.


[User] Please transcribe this text

[Assistant] Mario volunteered for 35 years. Like many others here, he still refers to the road by its original name.


[User] Please transcribe this text

[Assistant] Augusto Pinochet came to power in 1973 in a military coup.


[User] Please transcribe this text

[Assistant] His four-member government was made up of the commanders of the army, navy and military police.


[User] Please transcribe this text

[Assistant] Rodolfo Stanger is one of the last surviving members of the junta.


[User] Please transcribe this text

[Assistant] Stanger joined the junta in 1985 as head of the National Police Force. The Carabineros.


[User] Please transcribe this text

[Assistant] He remained in government until the fall of Pinochet's regime in 1990.


[User] Please transcribe this text

[Assistant] Now 93. Stanger is of German descent.


[User] Please transcribe this text

[Assistant] Stange still believes that government model offered advantages.


[User] Please transcribe this text

[Assistant] So why was the construction of the Carretera Austral so important to the men of the Junta? After all, there was already a well-developed road to the south of Chile via Argentina.


[User] Please transcribe this text

[Assistant] Stange doesn't accept responsibility for everything that happened under the Junta.


[User] Please transcribe this text

[Assistant] Under the repression of the military dictatorship, more than 3000 people were killed. Some 27,000 survived torture and political imprisonment.


[User] Please transcribe this text

[Assistant] These are the officially recognised figures documented by Chile's Truth and Reconciliation Commission. The exact numbers will never be known.


[User] Please transcribe this text

[Assistant] But Pinochet is still revered by many today, especially along the Carretera Austral. The atrocities of his regime are dismissed as a necessary evil for the progress of the country as a whole.


[User] Please transcribe this text

[Assistant] Our journey leads us to discover a small slice of German history in the village of Puyuhapi.


[User] Please transcribe this text

[Assistant] Puyuhapi was founded by German settlers in 1935. Traditions are still kept alive here to this day.


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[Assistant] Houses like this are relics of the original German settlement. But they're falling into disrepair. Just outside the village, it's a different picture.


[User] Please transcribe this text

[Assistant] His most important resource is water from the nearby glacier.


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[Assistant] Helmut used to breed salmon too, but now he's concentrating on trout.


[User] Please transcribe this text

[Assistant] Helmut believes quality is the key. He only uses organic feed.


[User] Please transcribe this text

[Assistant] The founders of Puyuhapi fled poverty in Germany when they emigrated in the 1930s.


[User] Please transcribe this text

[Assistant] Amid the isolation and harsh climate, it took all their strength to carve a living from their land.


[User] Please transcribe this text

[Assistant] They live mostly from cattle breeding, forestry and fishing. In 1945, they set up a carpet factory that found national acclaim.


[User] Please transcribe this text

[Assistant] To assist them in their arduous work, they enlisted workers from the nearby island of Chilauea.


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[Assistant] Descendants of the Chilota people today make up the majority of the population here.


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[Assistant] But the entrepreneurial spirit of the founders still lives on today.


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[Assistant] Our next stop is a 600 kilometre ride away through the breathtaking landscapes of Patagonia.


[User] Please transcribe this text

[Assistant] The further south one travels along the Carretera Austral, the more one often encounters men like this.


[User] Please transcribe this text

[Assistant] In Chile, they're known as huasos. The horse and the poncho are part of their centuries-old way of life.


[User] Please transcribe this text

[Assistant] He was born and raised here in the Rio Vargas region and he remembers life before the arrival of roads and other modes of access to civilization.


[User] Please transcribe this text

[Assistant] The next biggest town for Juan used to be a month's horse ride away. Today, it takes a few hours by car.


[User] Please transcribe this text

[Assistant] And another major connection to civilisation arrived here just a few days ago.


[User] Please transcribe this text

[Assistant] In the evening, he prepares a codero, a traditional local barbecue.


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[Assistant] Although Juan is in favour of the road, it has changed life as he knows it.


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[Assistant] He's already learned to love the internet though, because it means he can keep in regular contact with his daughter who lives far away.


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[Assistant] He needs to tend to the cattle up in the mountains, but there's no road. The journey on horseback takes nearly two days.


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[Assistant] Our last stop on the Karatera Austral is fast approaching.


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[Assistant] The road is blocked once again by a fjord and we need a ferry to get us to the other side.


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[Assistant] After 1,240 kilometres through Chilean Patagonia, the Carretera Austral comes to what's currently its end, at a former outpost of the Chilean army. Via O'Higgins.


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[Assistant] This last stretch wasn't completed until 1997.


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[Assistant] Because of its remote location, the airfield has always been essential for the town's survival.


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[Assistant] Today the Mayor Ricardo Recabal is inaugurating a new terminal.


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[Assistant] There are hopes that the new terminal will increase air traffic.


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[Assistant] US pilot Vince Beasley has stationed his Chesna 206 here.


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[Assistant] Only very few pilots dare to venture out into this dangerous terrain. When it comes to maintenance, there's no one he trusts more than himself.


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[Assistant] He doesn't mind the solitude in Via O'Higgins.


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[Assistant] He does survey flights for NASA, and in 2015 he was piloting the plane with the team of scientists who discovered the beached whales in Kumau Fjord.


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[Assistant] Vince goes through the checklist a second time before he takes off.


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[Assistant] Via O'Higgins lies at the gateway to the southern Patagonian ice field, the Campo do Hielo Sur. It's the barrier that stopped the further expansion of the Carretera Austral. For pilots, these are tricky skies to navigate.


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[Assistant] Vince has had very close encounters with the forces of nature in the Patagonian wild.


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[Assistant] Vince happened to be flying near the Calbuco volcano when it erupted suddenly in 2015.


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[Assistant] It's a huge obstacle for the Carretera Austral project. To the east it borders Argentina, and to the west lies a jagged fjord landscape.


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[Assistant] The winds below the cloud cover are too strong and there's a danger the plane could be swept towards the rocks.


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[Assistant] Vince bought his Chesna 206 in Alaska and flew it with his wife to Via O'Higgins in several stages over the course of months.


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[Assistant] Back on the ground, Mayor Roberto Racobal is waiting for us.


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[Assistant] Before his career in politics, he worked as a truck driver.


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[Assistant] He transported building materials for the road and witnessed first hand the enormous changes that came with the road.


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[Assistant] Devised by a dictator, construction of the Carretera Austral has pushed farther and farther south for two decades. This remote spot is the end of the road.


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[Assistant] But the dream to create a continuous road to the southern tip of Chilean Patagonia lives on, regardless of the wilderness to be conquered in its path.


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[Assistant] Oh, well, Mr. Brand is no longer a costumer of ours. Not for some time.


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[Assistant] Uhm, shit, I'm on a WeMap, so, we fuck with a lot of companies that, uhm, fuck with WeMaps.


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[Assistant] Oh, that's insane. This is just the beginning, too. You know, they just started their whole skate team about a year ago.


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[Assistant] It's been poppin' ever since, you feel me? We've been to Hawaii, we did it.


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[Assistant] Uhm, Spain tour, we got it popping in Coachella, you know.


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[Assistant] For real, like, we was like a bill to me, but now it's like, here you go, dang. Now it's a jug, yea. Now it's, yea, I'm like coming up, and it's nice, you feel me?


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[Assistant] Shit, not really. I feel like more sponsors, more weed companies are trying to sponsor skateboarders, you feel me? Just cause it's more like, this is what we do, you know? It's a lifestyle and it like keeps you even.


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[Assistant] It keeps us going, so I think it's cool. You know, it's better than a beer sponsor, an energy sponsor, you feel me? Like, I skated for Mountain Dew for about three or four years. Oh yea?


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[Assistant] For real, exactly, but that's what they, that's what they like, yo, we heard you got kicked off Mountain Dew, like, for keeping like, to your roots, like, we appreciate that, let's talk.


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[Assistant] Tell them, the highest we get is at what point of the day.


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[Assistant] But that sometimes, for that fact, like sometimes I try to get some shit done in the morning. I wake up, go on my bike ride, play with my dog, workout, do yoga, whatever the case is, then I hit my dream.


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[Assistant] So, I smoke spliffs, like, a lot. This is my shit, like, raw papers, a little bit of tobacco.


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[Assistant] I'm from Tehachapi. It's like, right in the middle of Bakersfield. That's right by Woodward, right? Yea, Woodward West, actually.


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[Assistant] Nah, Wordward came through like, years later.


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[Assistant] Yeaah, it was more new, but, you know, we worked with what we had. We skated around the little local towns. My mom would drive me out to Vegasville or Lancaster to get it in.


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[Assistant] I moved to Long Beach. I've been in Long Beach for like six, seven years now and that's, that's my, that's home.


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[Assistant] Yeah, yeah, I moved out. I was living with my homie, uhm, Burger, and like...


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[Assistant] Nick Tucker. It was pretty cool. We had a little skate house. That was like my first shit, but shit got weird, then I moved to Long Beach, got my own shit, and it's been like that ever since.


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[Assistant] Shit, nah, I wasn't around all that. I was like in the streets skating.


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[Assistant] Back then I was like young and I had like, I was focused on like...


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[Assistant] Trying to skate. I was a little skate nerd, like, let's get it. So.


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[Assistant] But when I moved to Long Beach, you feel me, I had that.


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[Assistant] You know, I grew up as a person, you feel me? I got some real homies, my nigga Slinks is from Long Beach, that's his city.


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[Assistant] It was like the holy day, but what's crazy is like, kids are catching on to that and they realize like damn, if I go to Jaquan on Sunday, I'ma probably see my favorite skater. Cause every pro is there on Sunday too, get in, get in, because that plaza was perfect, even though I didn't skate it.


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[Assistant] It's hard, bro. Like, I don't know, man. It's difficult. I'd be up at Cherry, like, trying to get my tech game up, but...


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[Assistant] That's just like, not my shit for some reason, bro.


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[Assistant] Gotta dig deep and just get it done.


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[Assistant] Nah, he's really like DGK bro. DGK put us together. It's like a meant to be thing. It's like my brother is my dog right here.


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[Assistant] Yeaah, that's gonna be tight. That's fresh. That's new, fresh. You know what we did? Take a look.


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[Assistant] We have to like, we just shut down that store right there on Fairfax. It was just like unnecessary for us, you know, so we took, shut that down and we just, you know, we making sure we good and...


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[Assistant] Everything's going smooth, you know, so sometimes you gotta take a little step back and...


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[Assistant] Rebuild. Rebuild, exactly. And that's what we're doing. We just got, like Dane was saying, we just got a skate house and that's gonna...


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[Assistant] I don't know, DJ K is something I'll never like picture myself on, but...


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[Assistant] As I like developed into the person I am now, I'm like, fuck yeah, this is where I am. This is where it's at. Like, if people don't fuck with DGK or not.


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[Assistant] Well shit, if you got six dudes running after him, if you got six dudes running after him, drag guys haters.


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[Assistant] Super duper loads of dope for sure. You have a girlfriend?


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[Assistant] I do, I got like a little boo thing, man, you know. I've been with this girl for a minute, but... Is she a weed model?


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[Assistant] Nah, she don't, she gots a normal job, she does her thing, but uhm, you feel me?


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[Assistant] Beautiful out there. I fuck with Australia. Hawaii for sure. Hawaii's nice. Are we talking about like skating or like chilling?


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[Assistant] Fuck, man. I fuck with Uzi. I fuck with Cardi. Fuck with Future. Fuck, I don't know, all those niggas. All the nude dudes, like, they, that's what's relevant. They relate to us.


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[Assistant] Uh, like the J. Cole and Nas, fuckin' atmosphere. They cool as fuck, like they speak the real deal, you feel me, but, like,


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[Assistant] Dude, you know what I do be doing? I like just putting on like, um, random like inspirational


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[Assistant] Uhm, fuck, I'm fried, my bad. A random, uhm, inspirational collab. What is it called? Fucking... Conflation.


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[Assistant] Yeah, or just like people saying like rad quotes, you know? Or like listening to like Alan Watts, someone who's like really dropping knowledge and breaking down like...


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[Assistant] Little things, you know, like, to keep you even and know who you are and what you're worth.


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[Assistant] Exactly, yeaah. It's like listening to a podcast. It's like breaking out throwing your shit on.


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[Assistant] Shit, I got like two laptops and I don't even know too much about them, but I just use it to look up cool shit.


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[Assistant] Watch skate videos and music videos and I don't use it.


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[Assistant] Or pacific things as I should. I'm trying...


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[Assistant] I'm, I'm like slowly getting to my photography shit in my...


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[Assistant] And my stylist shit like, so I want him to be able to like pull up all that cool stuff and be able to fuck with my computer a little bit more and understand it.


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[Assistant] You know, the OGs are more raw, but this generation, like everyone's like real stylish and real, real clean and wearing something that just pops and it's different. So, you know, you gotta just go with the, go with the wave and fucking maintain. But like I've always been into like mine.


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[Assistant] Style shit, I like to be pacific, you know, I'm like crazy about my pants, I like wearing my pants.


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[Assistant] Was it like a real deal, uh? I just had like a stutter, like it was, it was like real bad, but I just like, I couldn't pronunciate words, I couldn't. I don't know if it's specific, it's specific.


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[Assistant] I was talking about my pants, but I like my pants to be, man.


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[Assistant] Hey, don't, you're not allowed to do that. Hey, you can't do this. Like, hey, no, that's not, we...


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[Assistant] We have all the rights to do whatever we want, you know. How many years each of you been pro?


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[Assistant] Yeaah, I think I've been pro for 3 or 4 years now.


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[Assistant] So JHF, bro, it's a new company I started. Just having fun. Just having fun. You feel me? It's a motto. And something that I grew up with in Tatchapie. It's like a crew.


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[Assistant] And you feel me, like, after all these years, like, I was like, alright, it's time to do something with this, you know? It's like, it's meant to be. So I started this company, right?


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[Assistant] John Hicks films. I'm like, hold up, like, where is he from? Boom, go to Instagram, Long Beach. I'm like, uh, homies a little. Then all my hitters, homies from the North Park are like, oh yeah, he's like this kid from, he's right here. So I'm like, so he's definitely seen my shit. He's definitely.


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[Assistant] So I hit him up, or I hit up like my team over at J. Jeff, and I was like, yo.


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[Assistant] Let's kill this guy. Let's hit this thing with the assistant disease real quick.


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[Assistant] Cause he was like saying, whatever man, but it's cool now, you know, but, you know, this is something I really want to, um, blow up and it's definitely happening.


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[Assistant] Shout out to him, I hope he gets his logo going.


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[Assistant] So I feel like he probably- I heard different. Really? Cause he's a filmer, he gots to be like on the lurk out.


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[Assistant] Like, all my skater homies know him, so it's not like he- Well, L is a small place. I know exactly, so everyone's relevant.


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[Assistant] It's just funny. It's funny just to even think about that. I never get in beef too. I'm like, I'm cool. I'm not worried about none of that, but yea. Little things like that. I'm like, hold up.


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[Assistant] My nigga Anton's sick. He's a beast, bro.


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[Assistant] Yeaah, I had a couple. I have a couple of funny stories.


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[Assistant] So we jump up in there, but one night I went up in there with like Slink, Sonami, Johnny, and you know, we're waiting for the table. And uhm, she's like, oh my god, like, what'd she say?


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[Assistant] Which is cool. I definitely like, you know, I try to follow all the legends but...


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[Assistant] I definitely love that, like, the skate industry side backs me cause I'm 100%. I'm 100% skateboarder, you feel me? Yeah. Like...


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[Assistant] I got where I am now because of my skateboard, but I definitely like utilizing.


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[Assistant] Open all the doors, but skateboarding is fucking my shit. That's dang shit too. You feel me? We're core fucking thrash to the fucking heart. This is, this is what we do. And it's like, we're going to try and do this till we can't.


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[Assistant] When I was on Mountain Dew, I like met Wayne cause he had a little contract with Mountain Dew as well. So like we had to, we kicked it a few times.


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[Assistant] Or they try to shout out skating, but they don't know, like, they too cool guy to even like know who the real skaters are. But Wayne, he really knows every skater he's into, or even like companies he fucks with and he knows about them. So, you feel me? He was on Thrasher, he probably seen my part, and it's like,


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[Assistant] Yeah, he's got every tempo pro kick in there, you feel me?


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[Assistant] Uhm, shit. You know, my sponsor, like Firmacy Board Shop, like really looked out for me. I've been with them. That was my first sponsor. I've been with them ever since, but...


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[Assistant] Uhm, I feel like growing up I was like, I was a youngin', a youngin' like in the squad.


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[Assistant] Shit, nah, I turned pro when I was 20, 21, and I turned am when I was probably like 17.


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[Assistant] Uhm, doing my thing, but just like in the van, you know, like going on super tours with like Stevie Williams, Eric Ellington, Ledger King, Greco, you know, and that shit was like,


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[Assistant] Fuck, that shit taught me a lot when I was on Element for like three years. Like Levi Brown, Chad Tim Tim.


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[Assistant] Fucking Julian Davison, Evan Smith, there's a bunch of dudes that like I was able to look up to and like pick a bunch of things like to take in and


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[Assistant] Find out all the bad shit I shouldn't do to like maintain or whatever, you know, so.


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[Assistant] He didn't teach me, I took it in, just how to be like...


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[Assistant] Fucking hell, you gotta know what you're worth and just...


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[Assistant] You gotta be able to open other doors when you get, uhm, opportunities in front of you, you know?


[User] Please transcribe this text

[Assistant] She's a hippie? Nah, she's a little hippie sweet lady, like, she's actually a massage therapist teacher.


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[Assistant] She's been doing that for the past like eight years. She used to do massage therapy. My dad, he's a fucking thug. Like, yeah, he's gnarly. He's from the 9-9 Mafia. He's like, they got divorced when I was younger, so like my mom raised me really, you know, to


[User] Please transcribe this text

[Assistant] You know, stay away from all that shit, but my dad's cool as fuck. You know, I always talk to him and that's my dog and, you know, he loves fucking what I do, you know. I just did this little uhm...


[User] Please transcribe this text

[Assistant] They, oh, it's pretty much like a little documentary, like, about me and like where I grew up in.


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[Assistant] How I, how I grew up, you know? That's dope. Yea, it's sick.


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[Assistant] It's gonna be hype when I comment back cause she's about to go buy her little brother, a DGK boy, that has my name on it. So it's cool too, but, I mean that.


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[Assistant] Pienso que sería las personas que no conocen a fondo.


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[Assistant] This is the airfield for private jets at Dusseldorf Airport.


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[Assistant] Entrepreneur Christoph Groner is one of Germany's super rich.


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[Assistant] People who have a lot of say in this country but are rarely heard in public.


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[Assistant] Gröhne is worth millions in private assets and company shares. All of it, self-made.


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[Assistant] We've been following Christoph Gröner for six months. Through him and many others, this film takes a look at inequality in Germany.


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[Assistant] At first glance, Germany is a rich and powerful country, full of opportunities.


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[Assistant] But if you look closely, you'll see wealth is more unevenly distributed here than in just about any other industrialized country. Success often depends on your background.


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[Assistant] To find some answers, we go around the world and speak to a Nobel Prize winner and other experts who have looked deeply into the issue of inequality.


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[Assistant] The driver is already waiting. When the boss comes, he has to get moving right away.


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[Assistant] Christoph Gröner, a teacher's son, has made his way to the top, often working 20 hours a day.


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[Assistant] And in the housing market, the prices only go in one direction, upwards. His company has just bought a very special building.


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[Assistant] He wants to run all the way to the top in less than five minutes. A race against himself. 30 floors, 120 meters, 600 steps.


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[Assistant] From up here, Kroener can look down on many of his construction projects.


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[Assistant] This is Gröna's headquarters in Berlin. Every company like this is a realm, with the boss at the top and the staff beneath him. Gröna's company now employs 500 people. They all have good contracts, he says.


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[Assistant] He's had a permanent contract as a security guard for half a year.


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[Assistant] Lauterbach earns about 2,000 euros a month gross. That's enough for a modest life, but not much more.


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[Assistant] So whereas the one can only dream of a house with a pool, the other can afford several properties.


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[Assistant] Christoph Gröner has a villa in Berlin and a penthouse in Cologne with a view of the cathedral. But little time off.


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[Assistant] How much distance should there be between those in the middle and those at the top? And how big is the gap in reality?


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[Assistant] There's a lot of data about poverty and the poor, but very little about the rich.


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[Assistant] A state asset registry would help, but there isn't one.


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[Assistant] Manager Magazine puts the Reimann family business at the top of its rich list for Germany. The family's estimated worth 33 billion euros.


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[Assistant] So if 95% of Germans are graphed on an A4 sheet, the Reimanns would be a whopping 6.6 kilometres further away.


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[Assistant] Every era has its motherload. In the past, carmakers made big money.


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[Assistant] Earlier still, the families who owned the big trading houses became hugely wealthy. Now real estate developers have joined them.


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[Assistant] Christoph Gröner's rise began here, in Leipzig, 20 years ago. He invested when prices were low.


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[Assistant] But Christoph Gröner's career has been unusual. He was not born a boss. He used to work on construction sites himself.


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[Assistant] This is the Klaus family, Thomas and Catherine with their two children. They live in a rented apartment around the corner. They wouldn't mind having one more room.


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[Assistant] The four-room apartment would cost 450,000 euros to buy. The classes are a typical middle-class family. They both have good jobs.


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[Assistant] Buying property used to be the way to start building up assets.


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[Assistant] The Klaus family isn't the only one with little chance of owning their own place.


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[Assistant] In all, the richest 5% of Germans own half the apartments and houses. Every second person owns no property at all.


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[Assistant] Most Germans rent and are having to pay more and more for living space.


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[Assistant] Leipzig is an extreme case. Only 10% of the people here own real estate.


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[Assistant] 60% of all new buildings and 94% of refurbished old buildings have gone to buyers from out of town.


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[Assistant] Behind this is the more basic question. Does profit for the one mean loss for the others?


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[Assistant] Well off retirees, heirs and investors.


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[Assistant] The others, like Thomas Klaus and his colleagues, can only look on.


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[Assistant] Riegerstrasse has been the scene of an escalating conflict between residents who are afraid of losing out economically and the man they accuse of making the deal of his life here.


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[Assistant] Christoph Gröner arrives and his security guard stays close by. When he's here, he usually gets police protection.


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[Assistant] How do you strike the right balance between rewarding achievement and letting everyone share in it?


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[Assistant] Christoph Gröner may have worked his way to the top, but even for him, there's still a glass ceiling.


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[Assistant] You can't buy your way into the world of dynastic wealth. You can only be born into it.


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[Assistant] He's a descendant of the Fuggers, one of the richest families of the Middle Ages.


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[Assistant] Von Bechtel's time uses it in the 300 hectares of forest he owns somewhere in central Germany. We're not allowed to say exactly where. That was his condition for letting us film him. Discretion is everything.


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[Assistant] During the week, Von Berchtolzheim works with a view of the Main River in Frankfurt. He heads what he calls the family office.


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[Assistant] Not even the employees know all the names of the bank's clientele.


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[Assistant] The wealthy come via personal recommendations.


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[Assistant] They know that von Bechtelsheim will offer them something that normal savers can't get from a bank these days. Interest and returns on their money.


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[Assistant] The legend surrounding Germany's post-war economic recovery sometimes invokes the notion of a kind of monetary zero hour. When everyone supposedly had to start from scratch.


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[Assistant] After visiting the construction site, he and his wife considered the real estate agent's offer.


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[Assistant] At the moment the family lives essentially from his income.


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[Assistant] His wife has reduced her work hours to take care of the children.


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[Assistant] Once the children go to bed, the parents talk about buying the apartment in the middle of the city.


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[Assistant] But they belong to a group that has come under pressure in recent years. The middle class. The people who have no fortunes but have to work for prosperity.


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[Assistant] In recent months, thousands have sent in comments online for this film project under the hashtag ungleichland. For example, they've reported their salaries. An industrial clerk in the car industry, 1600 euros net.


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[Assistant] A social worker in a rehab clinic, 1648 net. A civil engineer, nearly 2000 net.


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[Assistant] A medical specialist with 12 years of training, 2,768 net.


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[Assistant] A net income of 3,500 euros puts a single person in Germany's top 10% of earners. Accumulated wealth is particularly unequal.


[User] Please transcribe this text

[Assistant] That would let them buy a base model VW Golf or shoes and clothing for 1.6 children from birth to the age of 18.


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[Assistant] Or just 3.3 square metres of a newly built apartment in Frankfurt.


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[Assistant] But there are few places where all social strata come together.


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[Assistant] Christoph Gröner has paid for a place in a luxury suite in Leipzig's main soccer stadium.


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[Assistant] At the family office in Frankfurt, the bank's own forester has come to call.


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[Assistant] This is a new global form of capitalism. Financial capitalism.


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[Assistant] The family office is the starting point of a global investment chain. The wealthy entrust von Bechtolsheim with their money. Among other things, he invests with these fund managers.


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[Assistant] They have an algorithm that scans the global economic situation and converts it into traffic light signals.


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[Assistant] Green means the computer buys a lot of shares, and when the signal jumps to yellow or red, fewer.


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[Assistant] Thomas Klaas has been working as an engineer for Siemens for nine years. He sits on the Works Council and could imagine staying here until he retires, with or without a palm tree.


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[Assistant] Just recently, Siemens posted 6 billion euros in annual profits. But then worrying rumours began circulating. Investors were reportedly putting pressure on the company, saying this plant wasn't fit for the future.


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[Assistant] Christoph Gröner has invited all his staff to the company Christmas party. He just bought this old post office railway station.


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[Assistant] His employees are also worried, for much the same reasons as Thomas Klaus.


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[Assistant] In recent weeks, the financial press reported that investors have taken over 50% of the company's shares.


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[Assistant] It looks like the whole world is being shaken up by big money.


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[Assistant] Uh, well this is, I think that the takeaway is be very careful about the work you create over the years because your children will one day take the wrong messages from it.


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[Assistant] I'll drink Wendy's medicine to give me more strength.


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[Assistant] Poison? Who could have put poison in it? Captain Hook did. That's crazy. Hook couldn't have come down here. You're just jealous of Wendy.


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[Assistant] Oh, Tink, did you drink the poison to save me?


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[Assistant] If you really believe, clap your hands loudly. Please, don't let tink die.


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[Assistant] On the journey through Galilee, Christ had again tried to prepare the minds of His disciples for the scenes before Him.


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[Assistant] And he longed to counsel and instruct them.


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[Assistant] Soon after they reached the town, the collector of the temple revenue came to Peter with the question, Doth not your master pay tribute?


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[Assistant] This tribute was not a civil tax, but a religious contribution, which every Jew was required to pay annually for the support of the temple.


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[Assistant] When Peter entered the house, the Saviour made no reference to what had taken place, but inquired, What thinkest thou, Simon?


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[Assistant] While the people of a country are taxed for the maintenance of their king, the monarch's own children are exempt. So Israel, the professed people of God, were required to maintain his service, but Jesus, the son of God, was under no such obligation.


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[Assistant] If priests and Levites were exempt because of their connection with the temple, how much more? He to whom the temple was his father's house.


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[Assistant] If Jesus had paid the tribute without a protest, He would virtually have acknowledged the justice of the claim and would thus have denied His divinity. But while He saw good to meet the demand,


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[Assistant] I know all the fowls of the mountains and the wild beasts of the field are mine.


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[Assistant] He did that which he could not justly be required to do. This lesson would be of great value to his disciples.


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[Assistant] When Christ and the disciples were alone in the house while Peter was gone to the sea, Jesus called the others to him and asked,


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[Assistant] The presence of Jesus and his question put the matter in an entirely different light from that in which it had appeared to them while they were contending by the way. Shame and self-condemnation kept them silent. Jesus had told them that he was to die for their sake.


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[Assistant] His mention of the fact that he was soon to go to Jerusalem again kindled their hope that the kingdom was about to be set up.


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[Assistant] There was in these words a solemnity and impressiveness which the disciples were far from comprehending.


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[Assistant] Which was the beginning of the great controversy in the worlds above.


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[Assistant] In glory surpassing all the angels that surround the throne and united in closest ties to the son of God. Lucifer had said, high will be like the most high.


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[Assistant] Dominion becomes the prize of the strongest. The kingdom of Satan is a kingdom of force. Every individual.


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[Assistant] Those who were actuated by pride and love of distinction were thinking of themselves and of the rewards they were to have rather than how they were to render back to God the gifts they had received. They would have no place in the Kingdom of Heaven.


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[Assistant] The heavenly intelligences can cooperate with him. We were seeking not to exalt self, but to save souls.


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[Assistant] Are the attributes that heaven values. These are the characteristics of real greatness.


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[Assistant] At the feet of Jesus, all these distinctions are forgotten. The rich and the poor.


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[Assistant] And whosoever shall receive me receiveth not me, but him that sent me.


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[Assistant] With the spirit of a child he laid the matter before Jesus. Master, he said.


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[Assistant] None who showed themselves in any way friendly to Christ were to be repulsed.


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[Assistant] There were many who had been deeply moved by the character and the work of Christ and whose hearts were opening to Him in faith. And the disciples who could not read motifs must be careful not to discourage these souls. When Jesus was no longer personally among them,


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[Assistant] But manifest the same far-reaching sympathy which they had seen in their master.


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[Assistant] Every soul whom God has made willing is a channel through which Christ will reveal his pardoning love. How careful we should be


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[Assistant] Such an act as that of John in forbidding one to work miracles in Christ's name might result in turning the feet into the path of the enemy and causing the loss of a soul.


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[Assistant] Shall his disciples show less regard for the souls of their fellow men than the majesty of heaven has shown?


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[Assistant] For it must needs be that the occasions come. The world inspired by Satan.


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[Assistant] We'll surely oppose the followers of Christ and seek to destroy their faith, but woe to him who has taken Christ's name and yet is found doing this work.


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[Assistant] That which dishonors God cannot benefit the soul. The blessing of heaven cannot attend any man.


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[Assistant] In violating the eternal principles of right and one sin cherished.


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[Assistant] In the ritual service, salt was added to every sacrifice. This like the offering of incense signified that only the righteousness of Christ could make the service acceptable to God. Referring to this practice, Jesus said,


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[Assistant] Every sacrifice shall be salted with salt.


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[Assistant] When we see Jesus, a man of sorrows and acquainted with grief, working to save the lost, slighted, scorned, derided, driven from city to city, till his mission was accomplished. When we behold him in Gethsemane, sweating great drops of blood, and on the cross, dying in agony. When we see this,


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[Assistant] We then that are strong ought to bear the infirmities of the weak and not to please ourselves.


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[Assistant] No soul who believes in Christ, though his faith may be weak and his steps wavering as those of a little child, is to be lightly esteemed.


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[Assistant] By all that has given us advantage over another, be it education and refinement, nobility of character, Christian training, religious experience,


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[Assistant] Angels of glory that do always behold the face of the Father in heaven. Joy in ministering to His little ones.


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[Assistant] Trembling souls who have many objectionable traits of character are their special charge.


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[Assistant] Even so, it is not the will of your father which is in heaven that one of these little ones should perish.


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[Assistant] Let the members of the church as the representatives of Christ


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[Assistant] But it is to the wrongdoer himself that we are to present the wrong.


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[Assistant] If two of you shall agree on earth as touching anything that they shall ask, it shall be done for them of my Father which is in heaven.


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[Assistant] Jesus says, My Father, which is in heaven.


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[Assistant] As reminding his disciples that while by his humanity he is linked with them.


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[Assistant] A sharer in their trials and sympathizing with them in their sufferings by his divinity.


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[Assistant] Is brought to combine with human ability.


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[Assistant] Hmph, I guess. Now we must do the same to Wisdom.


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[Assistant] All right. Thanks, Sangu, for recapping the paper and all the background information needed. Can you guys hear me? And see everything okay before I start?


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[Assistant] Okay, perfect. Alright, so, uh, we have a quiz time for you guys. We're going to make it a bit more attractive.


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[Assistant] We want to see what you guys have learned so far, what you've read and understood from the paper so far and, uh, recap what someone's gone over so far. So feel free to throw your answers inside of the chat.


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[Assistant] On the first question, let's just start with the first answer for the first question.


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[Assistant] What is the difference between the inputs for the encoder and the decoder?


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[Assistant] So the bread and butter of this paper, um, mentioned something huge about what goes into the encoder versus what goes into the decoder. And, um, what does that mean difference that you guys, um, remember reading about or hearing about?


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[Assistant] And I see a bunch of good answers in the chat. I see that, uhm, inputs of the encoder are only 15% of the segments.


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[Assistant] Uhm, I think it's actually 25%. Uhm, input is unmasked image. Uh, output is masked image. Yeah, you guys are on the right track.


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[Assistant] Second question, what percentage of the, what percentage of the image is masked for the model? So when we're talking about this input image at the very beginning, what percentage did Sangu mention to be the main mask ratio?


[User] Please transcribe this text

[Assistant] Perfect. And we'll go over why the number in the chat, 75%, is correct. Uhm, and why they use it in the paper. There's a reason why they just, you know, specifically chose 75.


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[Assistant] Will we be graded for this quiz? Uhm, you'll never know. Till the end of the class, will you?


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[Assistant] Alright, third question. What type of masking is mainly used in this paper?


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[Assistant] There you go. You guys are on top of it. I don't know why we're explaining it to you guys. I'm just kidding. Yeah, awesome. So I'm going to recap.


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[Assistant] For some of you guys, uhm, and, uh, just to see where we are. So this is the May Architecture. Uh, this whole paper's been about the May Architecture.


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[Assistant] And uh, the whole idea of why this paper exists or what they're trying to accomplish is they're trying to use a set of pixels or patches from an image to uhm, pass into the architecture and then reconstruct that original image.


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[Assistant] So as you can see, uhm, you have only a few, uh, small percentage of the images, so 25%.


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[Assistant] 75% masking and random masking is used, uhm, because it was one of the best performing, uhm, masking ratios and we'll kind of go over the graph on why. Uhm, so what they do is they have an original image.


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[Assistant] They mask 75% of this image after they've patched it up.


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[Assistant] All these little grey boxes are going to be the masks, as to call them. What we feed into the encoder is going to be those, uh, unmasked patches. So only the ones that, you know, are not grey boxes are being fed into this encoder.


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[Assistant] The whole point of only feeding 25% is where this architecture kind of rises and shines because now you're using less computation and, uh, you know, it takes less time to train and it uses less memory.


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[Assistant] Uhm, and this whole architecture is asymmetric like Sangwoo said, where the encoder takes 25% of the data and the decoder takes the whole, uhm, the whole set of patches, whether they're masked or unmasked.


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[Assistant] And you can see the difference, uh, what goes into the encoder. So the number of patches coming in, uh, you can think of each patch being a vector.


[User] Please transcribe this text

[Assistant] Uhm, goes into the encoder, and then the encoder outputs the same number of vectors or patches into its output.


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[Assistant] And the whole point of passing it into this encoder transformer, uh, vision transformer is to get the semantics of those patches. We want to understand what those images mean and, uh, then feed that into the decoder. And they do that by adding the masked images and feeding into the decoder.


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[Assistant] So there's a little difference in their architecture, but mainly they're both using fission transformers that have been adapted.


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[Assistant] What else? Okay, uhm, they also have positional embeddings like Sangu mentioned before, uh, positional embeddings we'll talk about again. Uhm, so a question would arise, okay, say you feed 25% of those little patches, those images, how do you know where they belong in the original image?


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[Assistant] So say you're feeding this patch right here, uhm, how do you know this belongs in the top, you know, right corner? That's the head of the flamingo.


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[Assistant] Uhm, you don't know that. You don't know that at the beginning. And that's why, uhm, in this architecture, they use this thing called positional embeddings, and they concatenate that, uhm, in the vision transformer, uhm, to understand where each of those patches belongs to in the original image. And that's it on, you know, the summary.


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[Assistant] Uhm, so let's go into some ImageNet experiments. We'll go over what they've done, what they've tried, what worked, what's, you know, why they've tried certain things and whatnot. Uhm, so taking a step back, uh, the whole point of


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[Assistant] Uh, why they ended up using vision transformers comes from the original idea of, uhm,


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[Assistant] Not necessarily sequence to sequence modeling, but it comes from NLP, trying to understand the context, uh, of certain, uh, well, words in the sense of NLP.


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[Assistant] So if you take a look at this example, this is an RNN based sequence to sequence model.


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[Assistant] So say you want to translate one phrase from, in this case it's German, uh, you want to translate that to English.


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[Assistant] Right? So they use this thing where, uhm, this entire model is basically a bunch of RNNs. Uh, the encoder is a bunch of RNNs.


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[Assistant] And recurrent neural networks and the decoder is a bunch of RNNs and it starts off with the first word being passed into the first encoder, gets the semantics, pushes it.


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[Assistant] Uh, sequentially into the next encoder, goes on, uh, up until it's understood the entire context of this German phrase, and that's being passed into the decoder, um, to then convert that to English.


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[Assistant] One of the big problems of having that is mainly because, uh,


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[Assistant] So when you're talking about, uh, huge phrases, say you have like an entire paragraph or an essay, right? And you pass that into this model.


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[Assistant] The problem with that is going to lose that historical background or context of the words. And the other thing is, uh, it doesn't do things in, uh, peril.


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[Assistant] And the main reason why that can't do it is because it's sequential. You're passing one thing into one encoder, going into the next, going to the next, all the way down the line. So you can't really parallelize this work, at least for sequence to sequence modeling. And uhm, this is why uhm, we come to the idea of transformers.


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[Assistant] So we're not going to go into too much detail into transformers, but, uhm, this paper adopts the idea of transformers, uhm, so it can understand, uh, how to encode and decode something and being able to tackle those challenges that we just talked about. The challenge of, uh, losing long range dependencies, being able to take something big.


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[Assistant] And uhm, keeping that contextual understanding of the semantics and uhm, coming up with a prediction.


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[Assistant] So over here you can see an encoder, and then there's just a decoder and a transformer. Okay, so in this project, uhm,


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[Assistant] Again, they use, uh, vision transformers, uh, as the baseline for this entire project. Um, so you can go ahead and read transformers for image recognition at scale, 16 by 16 words. This is where, um, you can find a lot of the details on this specific, um, vision transformer paper.


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[Assistant] Uhm, but the idea that this paper shows is that, uhm, vision transformers have become


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[Assistant] And then they can apply those sequences on the, on image patches. And this project is mainly


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[Assistant] The image patches and trying to predict that reconstructed final image. Uhm, and the benefit of using a vision transformer is that it uses less computation.


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[Assistant] Uhm, takes less time to train and uses less memory compared to, uhm, some state of the art CNN models.


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[Assistant] So let's take a look at this architecture. Someone kind of went briefly into it as well. So uhm,


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[Assistant] The Vision Transformer, originally, uhm, what you have is an image. So this image is broken up into patches. Uhm, it's a 3x3, uh, image. And, uh, each patch you can consider as a vector.


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[Assistant] Uh, flatten patches, and uhm, you need to remember again that these image patches have no understanding


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[Assistant] Yeah, so what happens is that you can consider the linear projection as a matrix. So you're multiplying each vector or each patch by this matrix, linear projection matrix.


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[Assistant] And what's happening is that you're appending, uhm, this thing called position embedding.


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[Assistant] And this position embedding is telling you where this patch belongs to in the original image. And then that's being fed into the transformer encoder and that's able to predict, um, your final, make the final prediction pretty much. And this is where, um,


[User] Please transcribe this text

[Assistant] We kind of utilize that same model and tweak it, uh, for our main architecture. But this is kind of like the idea of what the IT large is.


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[Assistant] And comparing what they've done from the original VIT, uhm, they did the same thing. They've adopted positional embeddings that we just talked about.


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[Assistant] Uh, to both the encoder and the decoder. So the encoder is a visual, uh, vision transformer and decoder is a vision transformer. Um, and they also, they, each of them has a positional embedding.


[User] Please transcribe this text

[Assistant] They don't use this thing called Relative Position or Layer Scaling as compared to the standard VIT.


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[Assistant] Uhm, also the main architecture in pre-training, they use this auxiliary dummy, uhm, token to encode the input.


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[Assistant] And, uh, apparently it doesn't really make a huge difference because they still are able to use average pooling instead of that token to get similar results. Alright, let's move on to the next slide.


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[Assistant] So these are the default settings in pre-training. I'm just going to kind of briefly go over them and then get into the results. So originally, for the experiments, they don't use any, they don't use color jittering at all for the image inputs in training, pre-training.


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[Assistant] Uh, the reason why is because when they used color jittering, they, they were getting inconsistent and bad results, so they just avoided color jittering altogether.


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[Assistant] Uh, and then they avoided using drop path and gradient clip altogether as well.


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[Assistant] Fine tuning. So they do a lot of fine tuning in this, uh, in the experiments in this project, uh, in this paper.


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[Assistant] And, uh, just to kind of recollect what fine tuning is.


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[Assistant] Uh, so the idea of fine tuning, uh, comes from transfer learning. So if you want to use your model for different tasks, you'd


[User] Please transcribe this text

[Assistant] Go ahead and train that model on different datasets and tune it, change the parameters and change certain parts of the model in order for it to perform.


[User] Please transcribe this text

[Assistant] That new task you wanted to do. So say originally you wanted to train this model to take in pixels of cats and dog images to predict a final image of cats and dogs. And maybe now you want to do it on flowers. So what you do is train it on a different data set.


[User] Please transcribe this text

[Assistant] And then you tweak some of the parameters, fine tune them, and then you're able to accurately predict those new tasks. Another thing you'll hear a lot is linear probing. So linear probing, you can think of it as just a way of


[User] Please transcribe this text

[Assistant] Dealing with, uh, collisions or keys that map to the same index in a hash table. So in the embedding space, they have this logic that happens and linear probing kind of tackles it, um, using a hash function to find the index for a key and kind of replacing that key accordingly. So it's just a way of dealing with that embedding space. And, um, that's just what linear probing is in general.


[User] Please transcribe this text

[Assistant] Uhm, default setting for end-to-end fine-tuning, uh, they use the same thing as VIT, like, uh, out of the box.


[User] Please transcribe this text

[Assistant] So those again are really irrelevant in this presentation itself, but if you were to recreate it, those are the default settings that are used.


[User] Please transcribe this text

[Assistant] And linear probing, they actually do something different for the ME architecture.


[User] Please transcribe this text

[Assistant] They disable common regularization, such as mixup, cutmix, droppath, and again, color jittering. They remove color jittering for everything. And this time, way the decay is set to zero. So those are the settings they use for the MAE. They've taken a few things and changed them. They've tuned them specifically for the task they need.


[User] Please transcribe this text

[Assistant] Uhm, so this, uh, MAE architecture has been trained, uh, all these,


[User] Please transcribe this text

[Assistant] I have been trained, uh, self-supervised pre-training is done on them.


[User] Please transcribe this text

[Assistant] Originally you can see this out of the box, the IT, the original accuracy that's happening on self-supervised pre-training.


[User] Please transcribe this text

[Assistant] Uh, then they have their own implementation of the PIT, so they've fine-tuned it. And then there's a baseline, their MAE bread and butter architecture. They've trained this on ImageNet 1K. All of these have been tested on the same data set. All of them have had fine-tuning and linear probing applied to them.


[User] Please transcribe this text

[Assistant] And what they're reporting here is the top one validation accuracy.


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[Assistant] Uhm, you can see that MA performs pretty much the best between those three.


[User] Please transcribe this text

[Assistant] But again, that is not enough to go ahead and say, uhm, hey, this is the best one in all these cases. So we do have to look at different, uhm, benchmarks to make that decision.


[User] Please transcribe this text

[Assistant] They, they go over this thing called masking ratios. So uhm, so we kind of talked about this as well.


[User] Please transcribe this text

[Assistant] But uhm, we wanted to figure out, I wanted to explain to you guys how they came up with the 75% masking ratio.


[User] Please transcribe this text

[Assistant] So originally they break the image into patches and then they mask them. How did they come up with 75? So what they did is they tested it on all these different types of masking ratios, starting from 10 to 90% masking ratio of the original images, uh, training images.


[User] Please transcribe this text

[Assistant] And uhm, for fine tuning, when fine tuning was applied, you can see that when there was a 40% masking ratio up until 80, there wasn't a huge bump in the accuracy increase.


[User] Please transcribe this text

[Assistant] Uhm, whereas in linear probing, you always saw this increase of accuracy up until the 75% masking ratio.


[User] Please transcribe this text

[Assistant] So what they did is they just took the best of both fine tuning and linear probing, uhm,


[User] Please transcribe this text

[Assistant] Validation accuracy? And they're like, okay, this worked for both?


[User] Please transcribe this text

[Assistant] So we're going to take 75% masking ratio and we're going to apply it for all the experiments because it performed the best.


[User] Please transcribe this text

[Assistant] So that's why, and how they came up with 75% mass nerve shields. Uhm, funny thing is that, uhm,


[User] Please transcribe this text

[Assistant] In BERT, it's pretty much the opposite of BERT. Typically BERT, they use a masking ratio of around 15%.


[User] Please transcribe this text

[Assistant] And in computer vision, they typically use 20 to 50% according to the related works that they've talked about. So it's interesting how this turned out to be 75%.


[User] Please transcribe this text

[Assistant] Uhm, masking ratio. It's insane that you have only a few pixels and it's able to reconstruct it off 25% of the original image.


[User] Please transcribe this text

[Assistant] Fine tuning and linear probing again, and they've tested, uh,


[User] Please transcribe this text

[Assistant] The changes they've done on the decoder as part of the experiment. So over here you see blocks. So blocks represent the number of blocks in the transformer that they use.


[User] Please transcribe this text

[Assistant] So they start off by using 1 block, 2, 4, 8, and 12, and then they measure the, uh, the accuracy between all these changes they made. So you can see that linear probing,


[User] Please transcribe this text

[Assistant] has an increase of accuracy as you add more blocks and it kind of, uh, saturates around eight blocks, but fine tuning, uh, when the model is fine tuned,


[User] Please transcribe this text

[Assistant] Uhm, it doesn't really have an effect of, uh, adding more blocks in the transformers for the decoder. It doesn't affect anything, uh, to the accuracy. And that's mainly because inside of the, uh, when you fine tune the model, you can go back into the encoder and you can add a few adjustments, adapt those last layers of the encoder, uh, to work from recognition tasks.


[User] Please transcribe this text

[Assistant] So that's why you don't really see a huge difference whether you change something in the decoder or not.


[User] Please transcribe this text

[Assistant] So again, they chose 8 blocks for the decoder transformer because it's just the best accuracy for both fine-tuning and layer probing.


[User] Please transcribe this text

[Assistant] And the same thing applies for the dimensions. Um, you can see fine tuning again is not very, uh, it's not affected by this change, but linear probing is by adding more dimensions. So the solution for fine tuning is you can go back to encoding, uh, the encoder. All right, let's move on to mass tokens. So, um, we mentioned at the beginning that, um, in this paper, the architecture, they feed the encoder


[User] Please transcribe this text

[Assistant] And the best result happened to be that whenever they don't add a, so the encoder, when they pass it, an unmasked, uh,


[User] Please transcribe this text

[Assistant] Set of patches that perform better than when you pass it mask patches.


[User] Please transcribe this text

[Assistant] So that is also why they were like, hey, we don't need to do that. So let's keep all of the input patches into the encoder, uh, which are 25%.


[User] Please transcribe this text

[Assistant] Uh, of the original patches, they're being fed in and then, um, it happens to be that without masking is just better. So they kept it that way and it's faster.


[User] Please transcribe this text

[Assistant] Uhm, they've tested it out on, uhm,


[User] Please transcribe this text

[Assistant] Different encoders, so they've changed the depth, the number of blocks and the decoders, uhm, and turns out that with those different blocks you have a different speedup.


[User] Please transcribe this text

[Assistant] So originally you can take a look at this gray row. This gray row shows when they've tested the encoder VIT with mask tokens or mask patches being fed into the encoder.


[User] Please transcribe this text

[Assistant] And uhm, from there they fed in uhm, the encoder with non-masked patches. So everything is from the original image.


[User] Please transcribe this text

[Assistant] And it turns out that it's faster, uhm, and it's also more accurate.


[User] Please transcribe this text

[Assistant] Uhm, and same applies with VTH, large and the, uh, the H models.


[User] Please transcribe this text

[Assistant] So this kind of further verifies that we don't need to pass in any, uhm, masked patches into the encoder. It works better and it's faster to just pass it the original tokens, uh, patches.


[User] Please transcribe this text

[Assistant] Alright, so reconstruction target, uh, moving on to that section. So they've tried, uh, three things.


[User] Please transcribe this text

[Assistant] So uhm, when they're reconstructing pixels, uhm, without normalization, with normalization,


[User] Please transcribe this text

[Assistant] Uh, they've compared it to PCA, uh, over here they use 96 for the coefficients, uh, for the, the PCA coefficients.


[User] Please transcribe this text

[Assistant] And uhm, the last thing they use is DVA token, DVA token and DVA is a large convolutional network and uhm,


[User] Please transcribe this text

[Assistant] It has 40% of the floating, floating point operations, uhm, as compared to the vision transformer large.


[User] Please transcribe this text

[Assistant] And uhm, an interesting thing is that, first of all, they have this, uh, CNN.


[User] Please transcribe this text

[Assistant] Uh, compared to this M-A-E, uh, vision transformer approach.


[User] Please transcribe this text

[Assistant] You have to add, uh, potentially you have to add more data to it and it might take an extra step for pre-training.


[User] Please transcribe this text

[Assistant] And it still performs worse than, um, your typical MAE, um, vision transformer approach. So.


[User] Please transcribe this text

[Assistant] Uhm, that's competing pretty much with CNN and showing you that, uhm, vision transformers, you know, could potentially, uh, perform other CNNs as well. But in this case, it does outperform DVAE.


[User] Please transcribe this text

[Assistant] Uh, so they ended up using pixel, uh, reconstruction target, reconstructs a pixel, uh, an image of pixels without using normalization and, um, that performed the best accuracy.


[User] Please transcribe this text

[Assistant] So this is, they also test this thing called data augmentation.


[User] Please transcribe this text

[Assistant] Uh, the data augmentation is changing the data, creating data that hasn't been existed, hasn't existed before.


[User] Please transcribe this text

[Assistant] So you increase your dataset in order to train your model on different things.


[User] Please transcribe this text

[Assistant] So one way to do that is by cropping the original data set. So images, you can crop certain parts of the image. Uhm, you can, uhm, do horizontal flipping, you rotate the image, you change it up, and that creates a new data set.


[User] Please transcribe this text

[Assistant] Thus being referred to as data augmentation and they test that to see if the model performs better when you add more data or diversity of data as to say.


[User] Please transcribe this text

[Assistant] Uhm, so interesting thing is that regardless of what they do, having no data augmentation, the original data set, it performs.


[User] Please transcribe this text

[Assistant] Pretty decently, at least for fine-tuning. Um, linear probing not as much.


[User] Please transcribe this text

[Assistant] But uhm, when they do fixed sized, uh, cropping, uhm,


[User] Please transcribe this text

[Assistant] Pretty much they try cropping for all of these data augmentation.