Watch videos with subtitles in your language, upload your videos, create your own subtitles! Click here to learn more on "how to Dotsub"


0 (0 Likes / 0 Dislikes)
As fall breaks out in Canada, I'm reminded of all the beauty, innocence, and gun-free fun available from our neighbors to the north. There's the majesty of Toronto... Vast hockey rinks... And gallons of maple syrup that you can chuck openly and feel free. For this maple syrup is pure and nourishing. The changing of the seasons also happens to be the perfect time to encounter one of Canada's most prized creatures, the artificial intelligence nerd. Not too long ago, these beings were rare and hidden away in university dungeons. But today, they're among the best paid professionals in the world. Together, these creatures did something truly remarkable. Without anyone paying much notice, they gave birth to an AI revolution. They turned Canada, yes, Canada into one of the great AI superpowers. This is the story of how all this came to be. It's the story of one nation's quest to teach computers to think like humans. It's the story of what this science experiment will mean for all our lives, and for the future of the human species. So if you're a human or something try to imitate one, you wanna pay attention. Ever since people first came up with the idea of computers, they've dreamed of imbuing them with artificial intelligence. I am a smart fellow, as I have a very fine brain. That's the most remarkable thing I've ever seen. AI is just a computer that is able to mimic or simulate human thought or human behavior. Within that there's a subset called machine learning that it's now the underpinning of what is most exciting about AI. By allowing computers to learn how to solve problems on their own, machine learning has made a series of breakthroughs that once seemed nearly impossible. It's the reason computers can understand your voice, spot a friend's face in a photo, and steer a car. And it's the reason people are actively talking about the arrival of human-like AI. And whether that would be a good thing or a horrific end of day's thing. Many people made this moment possible, but one figure towers above the rest. I've come to the University of Toronto to see the man they call the godfather of modern AI, Geoff Hinton. Because of a back condition, Geoff Hinton hasn't been able to sit down for more than 12 years. I hate standing I'd much rather sit down but if I sit down, I have a disk that comes out. Okay. Well, at least now standing desks are fashionable and... Yeah, but I was ahead. I was standing where... I was standing when they weren't fashionable. Since he can't sit in a car or on a bus, Hinton walks everywhere. The walk says a lot about Hinton and his results. For nearly 40 years, Hinton has been trying to get computers to learn like people do. A quest almost everyone thought was crazy or at least hopeless. Right up until the moment it revolutionized the field. Google thinks this is the future of the company. Amazon thinks this is the future of the company. Apple thinks this is the future of the company. My own department thinks it's just probably nonsense and we shouldn't be doing any more of it. So I took everybody into it except my own department. Geoff Hinton pretty early on became obsessed with this idea of figuring out how the mind works. He started off getting into physiology, the anatomy of how the brain works, then he got into psychology, and then finally he settled on more of a computer science approach to modeling the brain and got into artificial intelligence. My feeling is if you wanna understand a complicated device, like a brain, you should build one. I mean, you could look at cars and you could think you could understand cars. When you try and build a car, you suddenly discover so this is stuff that has to go under the hood, otherwise it doesn't work. Yeah. As Geoff was starting to think about these ideas, he got inspired by some AI researchers across the pond. Specifically this guy, Frank Rosenblatt. Rosenblatt in the late 1950s developed what he called perceptron. And it was a neural network, a computing system that would mimic the brain. The basic idea is a collection of small units called neurons. These are little computing units, but they're actually modeled on the way that the human brain does its computation. They take incoming data like we do from our senses, and they actually learn so the neural net can learn to make decisions over time. Rosenblatt's hope was that you could feed a neural network a bunch of data, like pictures of men and women, and it would eventually learn how to tell them apart, just like humans do. There was just one problem. It didn't work very well. Rosenblatt, his neural network was with single layer of neurons, and it was limited in what it could do, extremely limited. And colleague of his wrote a book in the late '60s that showed these limitations. And it kind of put the whole area of research into a deep freeze for a good 10 years. No one wanted to work in this area, they were sure it would never work. Well, almost no one. It was just obvious to me that there is lot a way to go. The brains of big neural network and so it has to be that stuff like this can work because it works in our brains. There's just never any doubt about that. And what do you think it was inside of you that kept you wanting to pursue this when everyone else was giving up just that you thought it was the right direction to go? No, that everybody else was wrong. Okay. Hinton decides he's got an idea of how these neural nets might work. And he's gonna pursue it no matter what. For a little while, he's bouncing around US research institutions. He gets fed up that most of them are funded by the defense departments and he starts looking for somewhere else he can go. He suddenly hears the Canada might be interested in funding artificial intelligence. And that was very attractive. So I can go off to this civilized town, and just get on with. So I came to University of Toronto. And then in the mid '80s, we discovered how to make more complicated neural nets, so they could solve those problems that the simple ones couldn't solve. He and his collaborators developed a multi-layer neural network, a deep neural network. And this started to work in a lot of ways. But again, they hit a ceiling. Through the '90s into the 2000s Geoff was one of only a handful of people on the planet who are still pursuing this technology. He would show up at academic conferences and be banished to the back rooms, he was treated as really like a pariah. But Geoff was consumed by this and couldn't stop. He just kept pursuing the idea that computers could learn until about 2006, when the world catches up to Hinton's ideas. Computers are now a lot faster. And now it's behaving like I thought it'd behave in the mid '80s. It's solving everything. The arrival of super fast chips and the massive amounts of data produced on the Internet gave Hinton's algorithms a magical boost. Suddenly, computers could identify what was in an image. Then they could recognize speech and translate from one language to another. By 2012, words like neural nets and machine learning were popping up on the front page of The New York Times. Next up, we have Professor Geoffrey Hinson of the University of Toronto. Thank you. For Hinton this was obviously a really redemptive moment. Now he's basically a technology celebrity. And for Canada, it's the country's moment as well. They have more AI researchers than just about any other place on the planet. And the quest now is to see what these guys can do starting companies and pushing the technology forward. I want to set out on a journey across Canada to see the best in Canadian AI technology and to get a feel for how far the technology has come and how far it still has to go. Here's a city that gets right at the central tension of modern life in the unfolding AI revolution. It's much real a place filled with beauty and old world charms that ask you to move slowly through its streets and to chill for a while, reflect, and think deep thoughts. At the same time, it's one of the world's top AI research centers. Students flock here from all over the globe to get deep with machine learning and to take Geoff Hinton's ideas and figure out how to turn them into products we all use. To see just how successful they've been look no further than your pocket. All this stuff started out as hardcore computer science, but over the last five years, AI has invaded our everyday lives. Your smartphone is packed full of AI powered apps, including something like Google translate that lets you point your phone at a magazine that's written in French and read it as if you're a local. Engineers have been trying to get computers to translate text like this for decades, but it was Geoff's neural nets that finally made it possible. Thanks, Geoff. And it's not just your smartphone, neural networks are heading for the open road. Off we go. Meet my friend Stéphane, the head of Montreal's Tesla fan club. I'm driving a Tesla for a little bit more than 5.5. So you have people asking you for rides all the time. Yes. All the time. Maybe that's because of his fancy-pants autopilot, Tesla's semi-autonomous driving system that kicks in when road conditions are right. So that's it, autopilot's on. Yes. And it's driving by itself. So we need to pay attention. That's we don't have to drive. Yeah, that's crazy. Self-driving cars are packed full of cameras, sensors, and radar. When teamed with computer vision neural nets, it's this technology that lets the cars build a picture of the world. The technology has a long way to go. But this Tesla can monitor all the cars around it, switch lanes, and park all by itself. Thanks, Geoff. So you were living in the future. Yeah. You know, when you try it once. It's very difficult to do without it because I just can be relaxed. And we can drive like this. Oops! Another stop sign. That's why we still need to pay attention. The big part of Hinton's legacy lies beyond these examples of AI in the world. He's also inspired a legion of disciples spreading the good word of neural nets. Yoshua Bengio is a professor at the University of Montreal. He's one of the researchers who glommed onto Hinton's ideas when it seemed to make little sense to do so. Over the years, he's formed a mind meld with Hinton. And together, they've come up with many of the key concepts behind modern AI. You guys worked on this stuff through the '80s, '90s, the 2000s, and then it just seemed like this totally went from computer science and research to we see it everywhere in our lives, you know, or even you surprised what's happened in the last five years that it really is like sitting on all our phones and? The rate at which the progress and the industrial products have been coming out is totally something we didn't expect. Even now it's hard to predict where are we going? Is it gonna slow down? Or are we gonna continue with this exponential increase? It's thanks to Yoshua that Montreal is full of top notch AI graduate talent. This in turn is brought tech giants like Google and Facebook to town along with their ample checkbooks. To me, it seems like if you're good at AI, you can make $200,000 or $300,000 a year. This is crazy to see how much these guys get paid now. A million dollars is something quite common as a salary. Have you ever had a country offer you an incredible amount of money to come set up a lab there? Not a country, but yeah, companies, you know? But Yoshua has rejected the lucrative offers of big neural net. He remains committed to the ivory towers of academia, which is a better fit for his philosophical approach to AI. You've got guys like Elon Musk and Stephen Hawking that sometimes paint this technology in a very, very dark light that it could run amok and start doing things on its own. What do you feel when you hear people say things like that? I'm not concerned about technology running amok and the Terminator scenario I think is not very credible. And I also believe that if we're able to build machines that are smart as us, they're also smart enough to understand our values and to understand our moral system and so act in a way that's good for us. Now I think there are real concerns, which is essentially misuse of AI to influence people's minds. It's already happening with political advertising. Yeah, but we've already seen like the stuff from Facebook. So I think we should be careful about this. And try to regulate the use of AI in places where it's morally wrong, ethically wrong. I think we should just ban it and make it, you know, illegal. It's comforting that Yoshua has these concerns... But hop down the road from the university and reality, or what's left of it, becomes messier. This tiny room is the home to a startup called Lyrebird. It was founded by Yoshua's former students and has built an app that can clone your voice. You're speaking about this new algorithm to copy voices. This is huge. They can make us say anything that really anything. One of its founders is this guy, Mexican expat José. He taught me the art of the clone. So you will need to record yourself for a few minutes of audio. Thousands of letters danced across the amateur authors screen. When you start to eat like this something is the matter. You guys better quit politics and take in washing. I don't know where that came from. Okay, so create my digital voice now. Creating your digital voice takes at least one minute. One minute! - Oh, my god. - Yeah. So before to create some artificial voice of someone, you would need to record yourself for at least eight hours. Test your voice. All right, so now I get to type something. Yeah, so the moment of truth. Okay. Once Lyrebird's AI has worked its magic after I'm done typing. Oh, I'm going to spell better. Any words I put into the app can be played back in my digital voice. And here's the crazy thing. Even words I never actually said in the first place. Artificial Intelligence technology seems to be advancing very quickly. Should we be afraid? I mean, I can definitely hear my voice in there that's really interesting. I just picked those words at random, and I definitely did not say some of them and it's like flawless and being able to sort of pick from just about any word and manufacture it. Hello World is the best show I have ever seen. This technology seems sweet... But lends itself to all manner of trickery. I popped back to my hotel to test out the Lyrebird technology a little bit, and you can see some really obvious ways that this could be abused. This is fake Donald Trump talking. United States is considering, in addition to other options, stopping all trade with any country doing business with North Korea. And then you could picture somebody taking over your voice and creating some mayhem in your personal life. Now to really put my computer voice to the test, I'm gonna call my dear sweet mother and see if she recognizes me. - Hey, Mom. - Hi. What are you guys up to today? Well, it's sad that we didn't have any electricity early this morning and we're just hanging around the house. I'm just finishing up work and waiting for the boys to get home. Okay. I think I'm coming down with a virus. Oh. Why, you feel bad, hey? I was messing around with you. You were talking to a computer. I felt like I was talking to you. It's amazing. Is that scary or good? It could be scary if it was something really important. It's you now, isn't it, Ash? I don't know. It sounds like you. Is it? Yes, it sounds just like you. Yeah. The artificial intelligence weirdos in Canada live here in Edmonton. This is a large but very, very cold and very, very flat city that is more or less in the middle of nowhere. It's the kind of place that has a giant butter vault to help people survive the lean winter months. Canadians like to put the best possible spin on how these conditions bring out interesting traits in people. Ask anyone, like this guy from the Edmonton tourist center. Well, Edmonton's one of those cities that isn't, you know, automatically listed in the top cities in Canada in terms of size or scale or notice even, but it's always had a really neat quality to it of that Western independent spirit that you see very much and in Alberta in general, combined with a conscience and a thoughtfulness. Over at the University of Alberta, some of the most far out AI research in the world is taking place. The man I'm here to see is the university's very own AI godfather, Rich Sutton. Rich is considered one of the great revolutionary thinkers in AI. You are not Canadian? - I am Canadian. - You are? But not by birth. No, I was born in the US, but now I'm just Canadian. Okay. And what brought you to Canada? The politics I wanted to get away from difficult times in the United States. The United States was invading other countries in 2003 when I came here, and I didn't care for all that. Sutton entered the field of AI in the mid '80s. And like Geoff Hinton and Yoshua Bengio, he was a big believer in neural networks, but Sutton has a different idea about how to further the technology. Unlike Hinton's method of feeding neural networks, reams and reams of data and telling them what to do. Sutton wants them to learn more naturally from experience an approach called reinforcement learning. Well, reinforcement learning, it's like what animals do and what people do. You try several things, the things that work best you keep doing those and things that don't work out so well you stop doing them. And how do you teach your computer that idea? All you need is a sense... The computer has to have a sense of what's good and what's bad, and so you give it a special signal called the reward, if the reward is high, that means it's good, if the reward is low, that means it's bad. To see reinforcement learning in action, I found Marlos an industrious young Brazilian who's created an AI to play his video games for him. His algorithm plays the game thousands of times and gradually learns from experience how to do better. So the goal of this game is that you are at this yellow block, and what you have to do is that you have to get as many portions as you can while avoiding harpies. And this is like the AI going at this for the first time. It's the AI run it for the first time. So just bumps into things. If it gets points, it's happy. If it dies, it's unhappy. Yes. And the AI starts to figure out that maybe what I want to do is to collect these portions and avoid harpies, and now we can look at AI that has ran for 5,000 games. - Okay. - And this is what it looks like. You can tell it it's smarter about its strategy. Yes. Then what happens if you run it 500,000 times? Oh, we get to the superhuman performance level. Though notching a high score is the noblest of pursuits. Reinforcement learning has turned out to have all kinds of other applications. It's behind the algorithm that recommends movies and TV shows on Netflix and Amazon. It beat the world champion Go player, a feat previously thought impossible for a computer. Soon it could read your brainwaves and determine whether you have a mental disorder. But for Sutton, all that is just the beginning. We are trying to make real intelligence. We're trying to recreate human intelligence. Humans are our example. He sees reinforcement learning as the path to what futurists call the singularity. The moment when our AI creations light up and search past human-level intelligence. Do you have dates for the singularity or... It's a quite broad probability distribution in the median is 2040. 2040? So that means equal chance of being before or after 2040. Okay. The rationale goes like this, by 2030 we'll have the hardware. So give guys like me another 10 years to figure out the algorithms. Software to go with the hardware to do it. And it's going to be exciting where we're going. If 2040 seems like a long-time to wait to meet a smart robot, do not fret. Over in the experimental wing of the university, there are coeds hard at work, blurring the line between humans and machines. Are you a human? Of course, not. Then that shouldn't keep us from chatting. Case in point, home-grown Edmontonian genius, Kory Mathewson. Tell me about this guy a little bit. Yeah. Sure. So this is Blueberry. On Blueberry, I've deployed the improv system. So there's an artificial improv system running on blueberry right now. Yes, that's right. Corey does improv comedy with a robot. I've been doing improv longer than I've been doing computing science. I've been doing it for 12 years, and I thought, you know, there's no more natural convergence than taking some of these state-of-the-art systems and putting them up on stage. One day we'll make it to the moon if this planet is not to be our last It is the sky and the moon and the universe, the sun, the sun. The sky and the moon and the universe, the sun, the sun. The sky and the moon and the universe... I think it is like a ventriloquist sort of... This is like a new edge... That's a really good. Yeah. Strange twist on it. The piece that's different is that I don't know what it will say. Blueberry, I created you I downloaded a voice into your brain so that you could perform in front of these people. That I do not know what I'm going to say. I don't know what you're gonna say either. To give Blueberry the power of surreal Canadian improv, Kory made use of some tech that you should be familiar by now, a neural network. Step one, he feeds the network the dialogue from a bunch of movies, 102,000 movies to be exact. All the movies every movie for 100 years. And that's just so it can learn language, see how somebody responds to somebody else. That's exactly right. Yeah, it builds kind of a language model. Step two, he uses reinforcement learning to train the network. Rewarding it when it makes sense, and punishing it when it spits out gibberish. Time to put this wannabe kid in the hall to the test. There you go. Start improvising. Okay, campers, we're gonna get ready for real baseball game. Grab your gloves and grab your baseball bats and let's get out there, especially you, Franklin. Of course, I will not be much longer. Okay, okay, well, why aren't you ready for the match? I do not have any more good news if I were your boyfriend. Okay, come on, Franklin. You know how I feel about you, but you got to keep your head in the game right now. You will see if you do not stop here when you go, what have you done? It's threatening you. I know... I'll teach you what a character of your team is going to do with you. Oh, Jesus put down the bat, Franklin, what are you doing? You have nothing to hide, that's all. I've got nothing to hide. Look, this is all I am. Okay, I'll end it there. - That's great. - That sort of how it works. Obviously, some of the responses are a little bit weird, but then it's really funny 'cause then as you're going along, it did hit a couple of things perfectly, and then it's like... Yeah. I mean, it's actually hilarious because... - Of course. - Yeah, yeah. Now it's going. Blueberry may not be ready for its second city audition just yet. But Kory has a higher purpose, making AI relatable. Oh, it's gonna move, it's gonna move, it's gonna move. There is a fear in society of AI. So we are kind of humanizing this AI we're taking it down a peg, we're saying, "Don't be afraid of this tech. Look at how cute it is. Look at how kind of naive it is." Yeah, yeah, yeah. That's so cool. You've done it again, Blueberry. Yes! Isn't there a flip side to that though, than you make it cute and then people start to accept it and then we wake up? I don't think that will happen in my time. The singularity may be near or it may be not so near. But if the inhabitants of this oddly beautiful place keep pushing the technology, they just might create something alarmingly human-like one day. For Rich Sutton, it's not a question of whether we'll get there, but whether we'll be able to accept our mechanized brethren. Our society will be challenged, you know, it's just like every time, you know, our black people, people, or women and people, we'll do the same thing with robots eventually. Are they allowed to own property? Are they allowed to earn an income? Or do they have to be owned by somebody? But a robot's obviously not a person, right? No. For my last stop I returned to Toronto, home to 2.8 million people, one very tall tower, and of course, the godfather himself. Inside the system, there's lots of little processes which are a little bit like brain cells, they work a little... He may be an import, but Geoff Hinton has done something truly exceptional for Toronto. He's turned this city into an AI Mecca, where AI conferences like this one seems to take place daily. We are enormously thankful to Canadians for inventing all this stuff 'cause we now use it throughout our entire business. I should never say this. We have it on record that he owes, that Google owes Canada. Voila! We absolutely owe Canada, but that was a mistake. The tech industry is full of people who adore AI. And then also some famous types like Elon Musk and Stephen Hawking, who said, "Well, that AI might be the end of us." To consider such dystopia in the proper light... I've come to Toronto's geekiest bar. Hello, George. To encase myself in this steel container with George Dvorsky. He's a writer for Gizmodo and an AI philosopher. We're in apocalyptic bar, what is the con case around AI? What's the nastiest scenario that everybody's worried about? Unfortunately, there's no short edge of nasty scenarios. And I think this is what makes artificial intelligence such a scary thing is all the different ways that it can go wrong. It can be everything from an accident, you know, where we just didn't think it through. We gave a very powerful computer instructions to do something, we thought we explained it articulately, we thought we gave it a concrete goal, and it completely took a different path than we thought it would in such a way that it actually caused some great damage. I'm sure you've heard the old paperclip example. Where you're a paperclip manufacturer and you say, "Hey, we need lots of paperclips." And because the artificial intelligence has so much reach and power, it actually starts to go about converting all the matter and all the molecules on the planet into paperclips. For, you know, we've now converted the entire cosmos into paperclips. It's a crazy scenario, but it's an illustrative scenario. We can't be dismissive of the parallels. I think that's exceptionally dangerous. And I don't think it's too early to start raising the alarm bells about it. Being turned into Clippy sounds awful... But fear not, we'll have years to ease into that sort of suffering... As AI steadily plucks off, one job after another. The first to go, of course, will likely be the always screwed factory workers which brings us to Suzanne Gilbert, a budding AI overlord and founder of robotic startup Kindred AI. Tell me about these guys. So these are research prototypes. So that's some of the first robots we build at Kindred. We tend to work with small robots. It's a bit like if you imagine a child growing up, and it breaks a lot of things. Now imagine if the child was six feet tall when it have the brain of a six-month old, it would be terribly dangerous. How many of these robots have ever slapped you? I have been hit in the face by robots a couple of times. Suzanne seems nice enough. She makes exotic digital arts. And she loves cats to the point where she's built a robotic fleet of them for the office. This one I believe is called pink foot. It's a quadruped robot loosely based on a cat anatomy, although it's not a very highly faithful representation yet. And then when you were growing up, you would build things as well? Yeah, that's correct. Yeah. So I was really enthralled by electronics at an early age. I guess most little girls would be looking at trays of beads and things, and I was looking at trays of white resistors and capacitors and little components, but having the same kind of reaction to them. But don't be fooled by the hobby electronics and the cute cat butts. Suzanne is a keen businesswoman. And Kindred has recently embarked on its first commercial venture. What's going on here is we have a bank of robots that are learning, so they are continuously running, picking up objects. These work and run all day? All day, all night. Powered by a neural network, these arms can do something that's very easy for a human, but very hard for a bot. Pick up objects of different shapes and put them down. Most factories still use people to do that sort of thing. Lots and lots of people. Today, everyone's shopping on e-commerce, thousands and thousands of different types of objects, shapes, textures, weights, how do you pick it up? Right now it's humans we have millions of humans in warehouses, just like picking up things and putting it into another location. So we're teaching our robots how to do that. What's the hard part? Is it figuring out what's a belt? What's a shirt? Or it's just how to grasp it? Yeah, exactly. It's very hard to pick it up, right? So things will show up in any shape, right? And you got to figure out how to pick it up without dropping it, put it in the location. So it takes a lot of training. Part of the training involves of all things, humans. Robot pilots who manually control the arms, while the AI watches and learns the finer points of grabbing. Is there something grim about the human training there? And... Yeah, it's not good to take people's jobs away. But this kind of technology coming into the workforce should make us start thinking about how we're going to pay people in the future because AI is not just going to automate, you know, manual labor jobs. It's gonna automate things like doctors and lawyers and accountants very soon. So I think there's gonna be issues, there's gonna be a lot of disruption. Suzanne is a realist, but she's also an optimist. In her vision of the future, robots won't be mindless competitors to humanity. They'll be full-fledged citizens like the rest of us. One of the crazy ideas that you talk about was you got a robots and it's working in the factory, and then that it's got to go, maybe it gets paid a wage, and it goes to buy lithium ion batteries to keep it going. Why would that have to happen? I mean, if they're having a physical body, they will have a lot of physical needs just like we have. And you might have to go to their repair shop to get like a motor-look, try something like that. And they'll have to pay someone to do that. I think they'll just be contributing to our economy in the same way we do. And if they have brains like us, they'll want to explore new things they've never seen before, they'll want to learn things, they'll want to perhaps rest so that their mind has time to consolidate all this new information. You try to picture in my head that this little robot workers go home, and sit on the couch and watch TV after work it is... I don't see why not, they'll probably watch cat videos like the rest of us. It's hard to tell sometimes if Suzanne's laughing with us or at us. But she's not alone in her cautious optimism for the future. I think there's always a sense that, you know, technology can be either used for good or used for bad. I'm reassured that Canada is part of it in terms of trying to set us on the right path. On the whole, being responsible and thoughtful about the power we're gaining by research and learning it is the right trend line. And I don't think AIs automatically doomed to some dystopian outcome. We're told that politicians will come up with policies that address massive job loss and prevent horrific inequality between the classes. And we're told that these guys will take so long to become human-like that we need not be afraid for a while. The truth though is that we're turning ourselves over to the unknown here. So, you know, fingers crossed. Eventually, I think we will become the AIs, we will become the intelligent machines. We will understand how things can be smart and we can deliberately create them. So you might think of it as making a new generation, new kinds of people. Humanity is continuing to evolve. And why wouldn't enhanced people or even designed people be the next step in humanity? It's really hard to predict the future. I think there's gonna be all sorts of things happen we didn't expect, but there's one thing that we can predict. This technology is gonna change everything. Goodbye. Goodbye. Goodbye. Goodbye. Once I power you down, that's it. Then if you do not mind, I will never see you again. Yeah. That's right. I'll end it right there. That was getting deep. That was getting really deep.

Video Details

Duration: 43 minutes and 40 seconds
Language: English
License: Dotsub - Standard License
Genre: None
Views: 24
Posted by: gabriella61 on Feb 7, 2020


Caption and Translate

    Sign In/Register for Dotsub above to caption this video.