[User] Please transcribe this text

[Assistant] That I was the only passenger left on the bus. You'll have to get off here, the conductor said. This is as far as we go. Is this Woodford Green? I asked. Oh dear, said the conductor suddenly. I forgot to put you off. It doesn't matter, I said.


[User] Please transcribe this text

[Assistant] We're going back now, said the conductor. Well, in that case, I prefer to stay on the bus, I answered.


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[Assistant] Well, he's definitely something alright. I mean, I don't know, was it worth all our savings just to have him put on an overhyped commercial and then bitch about taxes and then assault us with clowns, vomit and pass out on stage?


[User] Please transcribe this text

[Assistant] Yeah, I never buy stuff off Craigslist, but I've never needed to.


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[Assistant] Now if the DVD shows up, there it is.


[User] Please transcribe this text

[Assistant] It's the worst poverty in Milwaukee now. That was shot in 1997. It's part of my film Four Opponents.


[User] Please transcribe this text

[Assistant] Looks like an idyllic kind of idyllic place in the middle of farmland, but it's the inner city. These houses have all been ripped down in the, this is probably from a year or two ago. There's new houses built here now.


[User] Please transcribe this text

[Assistant] This is the bars on the window that weren't there when I lived there.


[User] Please transcribe this text

[Assistant] This is National Forest. Am I adjacent to that?


[User] Please transcribe this text

[Assistant] That's the house I live in. That's my wash.


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[Assistant] I moved there in 2002 and I lived there half the time. When I got there,


[User] Please transcribe this text

[Assistant] It was in perfect shape, but I tore the house all apart. Tore the walls down, tore the floor up, re-wired, re-insulated, uh, rebuilt the house. It took me nine months. And then for the next three months, I built a second room down below here and put in these three windows and


[User] Please transcribe this text

[Assistant] Put in a bottom porch before when I moved there it just was on stilts and it looked a bit like the house was going to fall over. Uhm, but it looks kind of nice with the porch there.


[User] Please transcribe this text

[Assistant] After I finished, uh, working on, uh, on remodeling, I realized I lived in the middle of nowhere and I had to try to figure out what I do there. And I thought I'd teach myself to paint. So I started by, uh, looking at the paintings of, uh,


[User] Please transcribe this text

[Assistant] And I started to copy his paintings. Bill Traylor was born a slave in the 1850s. And he didn't start painting until he was 85.


[User] Please transcribe this text

[Assistant] Uh, he spent three years in Montgomery, Alabama on Street Corner, painting from his memory and painting from what he saw on the street. So after three years, he finished almost 2,000 paintings. It was a chronicle of his life, both from memory and from the present.


[User] Please transcribe this text

[Assistant] Let me see if I can find a better page.


[User] Please transcribe this text

[Assistant] This is also an anti-tech lecture. I'm going to bumble through these things.


[User] Please transcribe this text

[Assistant] This is a wall of his paintings at the museum. American Folk Art Museum.


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[Assistant] I worked for, uhm, almost like six or seven months copying his paintings. And, uh, I got very interested in him because he was obsessive and I was becoming very obsessive and I thought by doing the same kind of work he was doing, uh, I ended up doing about a hundred of his paintings.


[User] Please transcribe this text

[Assistant] Uh, I'd start to feel the same kind of obsession that he had. And I actually learned how to paint quite...


[User] Please transcribe this text

[Assistant] The way he would use negative space and the way he would position things on the frame. And it talked to me a lot about my own framing and filmmaking. And then I started to reconsider how I myself would find the frame.


[User] Please transcribe this text

[Assistant] I thought I had so much fun, uh, remodeling my house that I wanted to do a construction prob- project. And I thought, uh, I'd like to build a house. And then I realized, well, I'm too old to build a house. So I thought I could build a small house. And in, uh, the, uh,


[User] Please transcribe this text

[Assistant] Around 1997, the same time that I made Four Corners, which the first film clip was from, I had taught in Korea for six weeks during the summer.


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[Assistant] And when I went to their library, one of the only books in English was Thoreau's Walden, and so I reread it, uh, while I was there.


[User] Please transcribe this text

[Assistant] Uhm, especially the first, uh, uhm,


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[Assistant] The first part of the chapter, uh, sounds.


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[Assistant] Uh, which, uh, this first paragraph, uhm, made me think about my own practice of looking and listening and paying attention. So I'll read that paragraph for you.


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[Assistant] But while we are confined to books, although the most select and classic can read only particular written languages, which are themselves but dialects and provincial, we are in danger of forgetting the language which all things and events speak without metaphor, which alone is copious and standard.


[User] Please transcribe this text

[Assistant] Much is published, but little printed. The rays which stream through the shutter will no longer


[User] Please transcribe this text

[Assistant] What is this? The rays which stream through the shutter will be no longer remembered when the shutter is wholly removed.


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[Assistant] No method nor discipline can supersede the necessity of being forever on the alert.


[User] Please transcribe this text

[Assistant] What is the course of history or philosophy or poetry, no matter how well selected or the best society or the most admirable routine of life compared with the discipline of looking at what is to be seen? Will you be a reader, a student merely, or a seer?


[User] Please transcribe this text

[Assistant] Read your faith, see what is before you, and walk into futurity.


[User] Please transcribe this text

[Assistant] I, when I, I read this, uh, it, it very much moved me and about four years later I decided to teach a, a class, pardon my cough, I somehow have this lingering illness.


[User] Please transcribe this text

[Assistant] When I decided to teach a class, uhm, about looking and listening, and I wrote a course description for it, forgetting that I had read this particular paragraph,


[User] Please transcribe this text

[Assistant] And a few years later when I re-read the course description.


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[Assistant] I noticed that it was almost exactly what this paragraph had said, so I somewhat plagiarized Thoreau to create this rather infamous class that I teach now. But I think it's quite a beautiful sentiment and one we should live by.


[User] Please transcribe this text

[Assistant] So what I'm getting at, uh, I thought, well, I can't build a big house, but I could build a cabin. And then this, uh, Thoreau cabin, uh, came to mind. And the nice thing about the Thoreau cabin is


[User] Please transcribe this text

[Assistant] Yeah, so here's a picture of Thoreau's cabin, uh, as envisioned by the, the, uh, museum in, uh, at Walden Pond. This is what they thought he might have built. And the nice thing about, uh,


[User] Please transcribe this text

[Assistant] About Walden, the, the book is that he really describes the, the cabin quite well. Here you see that, uh,


[User] Please transcribe this text

[Assistant] It was ten feet wide by fifteen feet long. And then he describes the cost of each thing. The boards were eight dollars and three and a half cents. Total cost of building it was twenty eight dollars and twelve and a half cents.


[User] Please transcribe this text

[Assistant] Uhm, I think my nails cost a lot more than 28 dollars and 12 and a half cents, but...


[User] Please transcribe this text

[Assistant] When I built it, I wasn't really trying to make a replica of his cabin, but I used it as somewhat of a model as to the size and the shape of the cabin. And then I went to Home Depot.


[User] Please transcribe this text

[Assistant] And I bought, uh, sheet rock. I didn't do lath and plaster. And I bought, uh, very expensive designer windows and, uhm,


[User] Please transcribe this text

[Assistant] Ended up building a rather fancy addition.


[User] Please transcribe this text

[Assistant] And then when I finished, uh, uh, building it, I had also started to copy other, uh, outsider artists' work that were as, just as obsessive as Bill Traylor, uhm, Moe's Tolliver,


[User] Please transcribe this text

[Assistant] Martin Ramirez and Henry Darger.


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[Assistant] And so I had, uh, a number of different, uh, artists' paintings at this time, and I put them inside of this cabin.


[User] Please transcribe this text

[Assistant] And then I, it somewhat scared me because all of a sudden it wasn't about just, uh, learning how to paint or a construction project, but it started to speak to me as something that was saying something important.


[User] Please transcribe this text

[Assistant] A bit like it was starting to look like art.


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[Assistant] But I wasn't quite satisfied with it because it seemed to need a counterpoint.


[User] Please transcribe this text

[Assistant] I should probably maybe go back to uhm.


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[Assistant] Well, in the 1960s, I studied mathematics and I have a master's degree in, in math and my, uh, uh, master's, uh,


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[Assistant] The second dissertation or thesis was on theoretical statistics.


[User] Please transcribe this text

[Assistant] And I was, uh, lucky enough in the late sixties to be able to use a 1620 IBM computer with punch cards. So my, uh, dissertation or thesis was used with the availability of a very early computer.


[User] Please transcribe this text

[Assistant] Uh, it had, uh, a lot less power than this little computer here and it probably filled the room almost half the size with


[User] Please transcribe this text

[Assistant] Uh, tubes and air conditioning and so it was quite slow, uh, but it was kind of the beginning of


[User] Please transcribe this text

[Assistant] Of computing that was being, being available to people outside of the military and government. Uhm, so I got very excited about computers at that time.


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[Assistant] And then in 1964, I finally bought a desktop computer.


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[Assistant] And I made a work called Pascal's Lemma, uhm, which maybe I can show you some of it.


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[Assistant] It was an installation that I installed at the kitchen. I don't want it in Photoshop. I always do that, excuse me.


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[Assistant] So this was written in, not in Fortran, but in BASIC, which is a very low level of programming language.


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[Assistant] And so everything you see here is all the lines and things are drawn with equations, uh, from the basic language.


[User] Please transcribe this text

[Assistant] And you can see my old computer in this from 1984.


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[Assistant] A beginning of a kind of a love-hate relationship with technology. There's a number of things in here that complain about the computer and yes it does.


[User] Please transcribe this text

[Assistant] Yet it's somewhat of a celebration of that machine. Uhm, and at the very beginning I was very ambivalent about


[User] Please transcribe this text

[Assistant] Uh, technology itself, so it's somewhat understated in this piece, but it's a beginning of those ideas for myself. And then, also in 1984, I made a film called American Dreams.


[User] Please transcribe this text

[Assistant] And when I made that film in the early 80s, my daughter, uh, was getting interested in baseball, so I was taking her to, uh, baseball card, uh, conventions, uh, to look at old baseball cards. And I started to collect the, uh, cards of Henry Aaron.


[User] Please transcribe this text

[Assistant] And then I got obsessive with that, and I wanted to get every Henry Aaron card that was made during his, uh, his career, which was from 1954 to 1975, which was kind of the years of my own coming of age.


[User] Please transcribe this text

[Assistant] And I decided to make this film where I would film all the baseball cards and show the backs from each, uh, the fronts from each year and then the backs from each year.


[User] Please transcribe this text

[Assistant] And during the front, you would hear a political speech from that year, and when you saw the backs of the cards, you would hear


[User] Please transcribe this text

[Assistant] A piece of popular music from that year. So the years from 1954 to 1975 were being described through political speeches and popular culture.


[User] Please transcribe this text

[Assistant] And when I got to that point in the film, it was kind of like the same thing when I got to the point of building the thorough cabin with the folk art inside, thinking it was fine, but it needed some kind of counterpart.


[User] Please transcribe this text

[Assistant] So I added a handwritten text by Arthur Bremer. Arthur Bremer is from Milwaukee where I grew up and he ended up stalking Nixon and trying to kill Nixon in 72 and then went on to shoot George Wallace.


[User] Please transcribe this text

[Assistant] And he was, uh, captured and put in prison, and he was released two years ago, the only, uh, attempted assassin, uh, uh, ever to be released.


[User] Please transcribe this text

[Assistant] So he's, uh, out and free in Maryland, but he can't leave the state.


[User] Please transcribe this text

[Assistant] Uhm, but my idea here was to add this text of Bremer and Bremer, it's written in the first person and he talks about Milwaukee and he talks about politics.


[User] Please transcribe this text

[Assistant] And so when you start to read the, uh, diary, you get the idea that it could be my diary, and then it gets rather bizarre, and then you might think it's my ultra-ego, and then,


[User] Please transcribe this text

[Assistant] By the end of the film, uh, you understand that he's, uh, shot Wallace and then he's identified as, uh, Bremmer.


[User] Please transcribe this text

[Assistant] Uhm, so, I've hidden this piece.


[User] Please transcribe this text

[Assistant] I was very interested in this kind of counterpoint between the baseball cards as popular culture and the music and then political speeches that were from


[User] Please transcribe this text

[Assistant] Uh, different politicians to Malcolm X, Angela Davis, uh, Helen, uh, Calicot, who was, uh, uh, one of the early people, uh, fighting for, uh, improvement in the environment from, uh,


[User] Please transcribe this text

[Assistant] So there were a lot of different political positions that were in radical, uh, political positions that were taken in this piece. So when I decided to add a second cabin,


[User] Please transcribe this text

[Assistant] I decided to, uh, do Kaczynski's Cabin in the same spirit as adding Bremer to the American Dreams, and this is my copy of Kaczynski's Cabin. This was, uhm, rather easy to do because


[User] Please transcribe this text

[Assistant] It was well documented, unlike the thorough cabin.


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[Assistant] And this is, uh, Kaczynski's cabin, uh, not in a New York loft or a gallery, but in the FBI holding, uh, facility in Sacramento. Uh, it had been removed from Montana.


[User] Please transcribe this text

[Assistant] And he was able to, uh, own that shop before you could see he brought it outside of the, that facility and put black, uh, traps.


[User] Please transcribe this text

[Assistant] And here you can see a piece of plywood that's four by eight, so I was able to completely scale the dimensions off of Richard Barnes's.


[User] Please transcribe this text

[Assistant] Photographs, allowing me to make an exact copy of it. Uhm, so this became this kind of counterpoint to it and then I added other paintings in this by, uhm, Joseph Yocum, and, uh, Blackhawk, uh,


[User] Please transcribe this text

[Assistant] And a few other painters, so it also had, uh, uh, artwork in it, and then I also built a library in it. Uhm, when Kaczynski was arrested, they found 278 books in his cabin, and I was astonished that many of them, uh,


[User] Please transcribe this text

[Assistant] I had read and then I realized that the reason I had is that we had gone to school at the same time. He went to Harvard.


[User] Please transcribe this text

[Assistant] I went to the University of Wisconsin in Milwaukee, and by that time the Harvard curriculum was being spread around the country. So the same books that Harvard was suggesting to read, we were reading at the University of Wisconsin.


[User] Please transcribe this text

[Assistant] So, uh, I decided to build a library in my cabin that would be half from, uh, Kaczynski's library and half from my library. Uhm, and, uhm, this is the most recent edition of that library.


[User] Please transcribe this text

[Assistant] Can you read those? If I scroll up, I'll give my voice a little bit of, do we have water?


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[Assistant] I ordered them by size, so there's weird juxtapositions.


[User] Please transcribe this text

[Assistant] Here's the Arthur Bremer, uh, uh, uh, diary of an assassin, or an assassin's diary.


[User] Please transcribe this text

[Assistant] I thought I was gonna have to make up a diary, so I went to the library to, uh, do research on Bremer, and on the very first day I found that he already had written a diary, so I didn't have to create my own about him.


[User] Please transcribe this text

[Assistant] In the sixties it was, uh, or in the fifties it was called stealing. In the sixties it was liberating.


[User] Please transcribe this text

[Assistant] Oh no, appropriating. The 80s was sampling. Somewhere it became pirating and it's just stealing.


[User] Please transcribe this text

[Assistant] Rachel, I think you're right here. You should read that book.


[User] Please transcribe this text

[Assistant] After working on this project for about five years, I also added, uhm, beds that I copied the exact, uh, architecture of Kaczynski's bed and also of Thoreau's and also Kaczynski's table and, uh, Thoreau's table.


[User] Please transcribe this text

[Assistant] And so they became more domesticated and when people visit me, some like to sleep in one cabin or the other. Uh, one becomes more popular.


[User] Please transcribe this text

[Assistant] I like the unpopular one. Here's the bookshelf.


[User] Please transcribe this text

[Assistant] Here you can see how I arrange them by size. This is a nice shelf.


[User] Please transcribe this text

[Assistant] Boring a Bat-Battery was Hollis Frampton's favorite book.


[User] Please transcribe this text

[Assistant] Uhm, but you can see how they change from this. Oh, thank you.


[User] Please transcribe this text

[Assistant] And there's 11 different stacks, and that's what I was going through before.


[User] Please transcribe this text

[Assistant] Anyways, it became quite, uh, uh, exhaustive work and it's still going on. I'm still adding things to it. I changed the library, uh, changed the paintings somewhat.


[User] Please transcribe this text

[Assistant] And then I thought, well I have a rather unusual, uh, work, but I'm not sure how I can show this work.


[User] Please transcribe this text

[Assistant] Cause I don't want hundreds of people coming through my backyard. Although if you can find it, that's fine.


[User] Please transcribe this text

[Assistant] Uhm, so I talked with Julie Ault, a good friend of mine.


[User] Please transcribe this text

[Assistant] And she suggests that we should create a book and then that's what we did.


[User] Please transcribe this text

[Assistant] And this is the FC2 Cabins by JB, which was edited by Julie Altan. It has text by Kaczynski and Thoreau.


[User] Please transcribe this text

[Assistant] And it also has, uhm, myself, I wrote biographies for the eight different artists that are in the cabins. And then I also wrote a biography for Henry Aaron and for Arthur Bremer.


[User] Please transcribe this text

[Assistant] And also for Thoreau and Kaczynski, so there's, uh, 12 people that I write by. I agree. And then Julie Alt also wrote a text and Dick Hebdige wrote a wonderful text also. So I'm very proud of this book. It also has pictures in it.


[User] Please transcribe this text

[Assistant] Here's somebody looking at one of those pictures. Okay, enough of that.


[User] Please transcribe this text

[Assistant] So that was the first way of, uh, presenting.


[User] Please transcribe this text

[Assistant] Presenting this work, and then I decided, well, I could also make, uh, films that would spin off of this particular, uhm, work. So the first one I made was two cabins. And I think I might have a little of that.


[User] Please transcribe this text

[Assistant] This is a half hour film and there's 15 minutes that you look out of the Kaczynski or the Thoreau window. At some point a train goes by.


[User] Please transcribe this text

[Assistant] So what you have is uhm, two views out of the two cabins, so you get a sense of the interior space of each of the cabins and then the soundtracks aren't from my cabins but I went to


[User] Please transcribe this text

[Assistant] Walden Pond and I also went to Lincoln, Montana and I made sound recordings from the exact locations of where the Walden Pond cabin was and where the, uh,


[User] Please transcribe this text

[Assistant] So you have this displacement of sound from the actual, uh, places that the two cabins existed.


[User] Please transcribe this text

[Assistant] And then I made a second film called Nightfall, which I don't.


[User] Please transcribe this text

[Assistant] I have an example of which is a 97 minute movie that's made in the mountains, uh, up higher from where I live.


[User] Please transcribe this text

[Assistant] And it's simply 97 minutes of the, uh, of the woods going to blackness from late afternoon. Uh, and there's no wind and no movement. So all you see is the changing of light, uh, going from late afternoon till dark. And it, it, uh, also it's the,


[User] Please transcribe this text

[Assistant] The sound, uh, is synced, shot, synced sound, so you hear the change in the sound from late afternoon to, to night, which is quite dramatic. And, uh,


[User] Please transcribe this text

[Assistant] I've shown that film in a number of places around the world and it's kind of incredible, uhm, the audience reaction. It's kind of a groups and experience that just gets locked into that film.


[User] Please transcribe this text

[Assistant] And then I made a third film called Stemple Pass.


[User] Please transcribe this text

[Assistant] Which is, uh, basically about the Kaczynski cabin, and I, I'll show you a little of that, but I'm also making a second film that I'm almost finished with now, called, uh, uh, uh,


[User] Please transcribe this text

[Assistant] Concord Woods, which is about the Thoreau Cabin. And in Stemple Pass, what you hear, what you hear


[User] Please transcribe this text

[Assistant] You see three different half-hour shots off of my main porch of the building that I showed you where my main house looking to the south with the Kaczynski cabin in the foreground.


[User] Please transcribe this text

[Assistant] And then in each of the sections I read from four different, uh, uh, texts from Kaczynski, mainly from journal entries. So I'll show you a little of that.


[User] Please transcribe this text

[Assistant] Uhm, fifteen minutes of text being read and then you see fifteen minutes of, of, uh, just the landscape. So the landscape comes back after the text is read.


[User] Please transcribe this text

[Assistant] And it's, uh, this is, uh, fall.


[User] Please transcribe this text

[Assistant] Searched other newspapers. Found no reference to Boeing bomb. Seems inexplicable. It was designed and built with such care that malfunction seems highly improbable.


[User] Please transcribe this text

[Assistant] Last summer they were worse than usual. Sometimes made it a three day weekend.


[User] Please transcribe this text

[Assistant] It was getting absolutely intolerable. My heart was going bad. Any emotional stress, anger above all, makes it beat irregularly. It got so that the constant cycle noise was choking me with anger. Heart going wild.


[User] Please transcribe this text

[Assistant] That's kind of an abridged, uh, viewing of that. I'm sorry about that. It's, uhm, it accumulates at a better rate if you actually watch the film, but I wanted you to see that.


[User] Please transcribe this text

[Assistant] And then, uhm, like I said, I'm making this new film that will have Thoreau's text and it'll just be two one-hour shots of the Thoreau cabin. And I shot those at the Summer and Winter Solstice. Uhm, so you'll just have two one-hour shots and I read.


[User] Please transcribe this text

[Assistant] From, uh, parts of Walden and then I also read from a text that Thoreau wrote about John Brown, the terrorist that was executed by our


[User] Please transcribe this text

[Assistant] By the government when he was found guilty of invading Harpers Ferry. And Thoreau was one of the only, uh, radical, uh, intellectuals that was supporting, uh, Brown that stayed supporting him after he was arrested and executed.


[User] Please transcribe this text

[Assistant] And on the morning of that execution, Thoreau read a long text about John Brown himself, which is quite an amazing radical text. So that's in this fourth film that I'm making.


[User] Please transcribe this text

[Assistant] And then, uhm, I built an installation and I showed it, uh, Neuger Reemschneider in, uh, Berlin. And this is, uh,


[User] Please transcribe this text

[Assistant] Set on a plywood base, uh, the same kind of plywood Kaczynski used to build his cabin. And this is setting on lath and plaster, the same materials that Thoreau used to, to build his cabin. And then with that also was the bookshelf.


[User] Please transcribe this text

[Assistant] Uhm, of, uh, that's in the cabin. Here it is removed into a gallery space and looks rather important here.


[User] Please transcribe this text

[Assistant] But the big show of this occurred, uhm,


[User] Please transcribe this text

[Assistant] This past spring in Graz at, uh, the museum in Graz, very strange looking building and I had the whole second floor to, uhm, mount that show.


[User] Please transcribe this text

[Assistant] I've never had a big poster, so I want to show you that. It's very impressive.


[User] Please transcribe this text

[Assistant] So there's a lot of installations in this museum show and I'll go through them quickly because I want to have time for questions and things. The first thing you saw was a


[User] Please transcribe this text

[Assistant] Installation of, uh, Pascal's Lemma, which I showed you about four, playing on a small laptop computer. So you have the 1984 NEC computer inside of a laptop.


[User] Please transcribe this text

[Assistant] And there's a chord that comes out of the wall to power.


[User] Please transcribe this text

[Assistant] So that was the beginning part of that, the first thing you'd see when you come in from one side.


[User] Please transcribe this text

[Assistant] And this you saw already, that's just a still of...


[User] Please transcribe this text

[Assistant] It's not the same photo that Warhol used, but it's the same electric chair.


[User] Please transcribe this text

[Assistant] And then the director of the museum wanted to move my cabins from my hillside.


[User] Please transcribe this text

[Assistant] To Austria, and I said I don't think that's a good idea.


[User] Please transcribe this text

[Assistant] So we talked a bit, and then I thought, let's build architectural models, but let's build them life-size.


[User] Please transcribe this text

[Assistant] So that's what we did. This is the thorough cabin. So you, you don't get a sense of the materials, but you get a sense of the space. And you could walk inside of these. And I like the way it just fit into the museum.


[User] Please transcribe this text

[Assistant] And this show is moving to, uhm, Hamburg in February. So it'll be, uh, viewable again.


[User] Please transcribe this text

[Assistant] The inside, here's the, we had the lighting. This is the back window of the Kaczynski cabin.


[User] Please transcribe this text

[Assistant] From the two cabins, these are the paintings that were hanging in my cabins in the Sierra Nevada. And then a text, uh, from, uh,


[User] Please transcribe this text

[Assistant] A Kaczynski text from his, one of his journals. I don't know if you can see that.


[User] Please transcribe this text

[Assistant] With his handwriting, but copied onto. So this is one of the texts that I would have read in the early texts.


[User] Please transcribe this text

[Assistant] Oh, maybe not, because this says number coded, and it's kind of interesting. You can't read it.


[User] Please transcribe this text

[Assistant] I want to show you the paintings. This is Bill Trailer.


[User] Please transcribe this text

[Assistant] Are those, they look alright, should I bring them, they look good enough, right? I don't have to bring them into Photoshop, they look a little better. But that's one of my copies. Trailer, this is most hollover.


[User] Please transcribe this text

[Assistant] Self-portrait, early Moe's Talibor, probably from the late 1970s.


[User] Please transcribe this text

[Assistant] Henry Darger, Vivian Girls, Throwing Hanger Needs, My Kind of Girls.


[User] Please transcribe this text

[Assistant] Martin Ramirez, who, uh, was in a mental institution for most of his life, and they allowed him to make his own paper and do drawings, and he's made some quite remarkable work. He remained mute all the time. He was there, but he really wasn't mute.


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[Assistant] This is Jesse Howard, uh, Missouri farmer who filled his, uh, farm hillside with, uh, wonderful signs and, uh, was a great influence on Jenny Holzer.


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[Assistant] These are in the Kaczynski cabin. The first four were in the Thrill cabin.


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[Assistant] This is William Hawkins. Lived in Ohio.


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[Assistant] Kind of a shyster of a guy, but a good painter.


[User] Please transcribe this text

[Assistant] And this is Joseph Yocum who, uh, lived in, uh, Chicago and claimed to be a Navajo Indian. But I don't think he was.


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[Assistant] He did all these beautiful sexualized landscapes with a ballpoint pen and then a water color.


[User] Please transcribe this text

[Assistant] And then this is Blackhawk from the Plains Indian drawings from the 1880s. Uh, he would, uh, hallucinate and make these kind of paintings from, uh, fasting for 10 days and, uh,


[User] Please transcribe this text

[Assistant] They're from the leisure drawings, are you familiar with those? Indian leisure drawings.


[User] Please transcribe this text

[Assistant] Anyways, these are some of my favorite people. They have real passion in their work.


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[Assistant] Uh, so those, that was, those were installed like this and then next to it was that, uh, journal. Maybe I can show it a little better.


[User] Please transcribe this text

[Assistant] Uh, this, the, the journals generally are in English, sometimes they go in, these are Kaczynski's journals. Sometimes there's in, they're in Spanish. And then they'll also go, when they become sensitive, they go into number code.


[User] Please transcribe this text

[Assistant] And the number code, uh, can be, uh, uh,


[User] Please transcribe this text

[Assistant] If you know how to do it, you can figure out what it is. And the nice thing is, uh, uhm, FBI could not decode this. And then they found a key to the code.


[User] Please transcribe this text

[Assistant] And then when they auctioned off this stuff, they not only sold all of the, uh,


[User] Please transcribe this text

[Assistant] And then we also, in the museum, we also had the same installation that I had done in Berlin, and that was unloaded from the person who owns that.


[User] Please transcribe this text

[Assistant] So I wanted to reference the Gee's Bend quilt makers. Uh, they're, uh,


[User] Please transcribe this text

[Assistant] And, uh, in the 30s, uh, it was one of the poorest colonies in America.


[User] Please transcribe this text

[Assistant] And, uh, the women became very politicized, and during the 60s, they had started this, uh, co-op, quilt co-op, where they sold quilts to help finance civil rights actions, and they became very, uh,


[User] Please transcribe this text

[Assistant] Involved in the marches in Selma and in Montgomery. Uh, so they're quite, quite interesting people and I thought,


[User] Please transcribe this text

[Assistant] Nightmare in a sense. Uhm, some kind of sanity.


[User] Please transcribe this text

[Assistant] And in the background is a birthday card piece. Uhm, I have only written to Ted a few times and one of the things I sent him, a birthday card.


[User] Please transcribe this text

[Assistant] This is the envelope I sent it in. Of course, this is a facsimile because I sent this away. So I made a second one for myself.


[User] Please transcribe this text

[Assistant] And then this is the card, so this is the second card. It's quite a wonderful structural card about, uh, it says happy birthday Ted, and then I signed it. Can you read that?


[User] Please transcribe this text

[Assistant] It's about happy birthday, happy birthday to you, being a structural piece. And it doesn't work with the word Ted, but it does with Henry, which refers to Henry David Thoreau and Ted Kaczynski. But I say nevertheless, happy birthday dear Ted.


[User] Please transcribe this text

[Assistant] And so Ted wrote me back a postcard.


[User] Please transcribe this text

[Assistant] I thought it was an eggplant, but it's a pepper where the color faded.


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[Assistant] And then, it's a wonderful text.


[User] Please transcribe this text

[Assistant] It says, Dear James, thanks for birthday greeting. Happy birthday to you too next time you have a birthday.


[User] Please transcribe this text

[Assistant] And then at the museum we also installed Stemple Past so you could go in and hopefully, this is a problem of course with a film that should be seen from beginning to end and installing it at a museum where the attention span isn't the same as in a theater.


[User] Please transcribe this text

[Assistant] But some people did stay. Uhm, but I, uh, and you have the opportunity to see it more than once this way.


[User] Please transcribe this text

[Assistant] Or you come in and you look at it for a while and you go out and look at other things and come back and it's a different season. So it's a completely different experience than a theatrical experience.


[User] Please transcribe this text

[Assistant] Was the wall that tells them, and then there's these two, uh, framed, which are, uh,


[User] Please transcribe this text

[Assistant] There are letters, a two-page letter that Kaczynski wrote to Scientific America.


[User] Please transcribe this text

[Assistant] Uh, he typed it and so I have the same kind of typewriter so I was able to make a facsimile of it. But it's pretty much complaining about the arrogance of scientists and how they put us in danger. And it's quite an interesting, uh, uh, letter.


[User] Please transcribe this text

[Assistant] Uhm, which, uh, was never published in Scientific America. It went right to the FBI.


[User] Please transcribe this text

[Assistant] And the interesting thing is the second page.


[User] Please transcribe this text

[Assistant] You know, where he invites you to print in scientific America.


[User] Please transcribe this text

[Assistant] The percenticity of the letter can, it can be found by an identifying number that the Times has, and then he shows the fourth paragraph of a letter that he sent to the Times, which is kind of, can you read that one? I'll let you read that one, I cut it off.


[User] Please transcribe this text

[Assistant] Some objects and another film and I'll play a little of this film.


[User] Please transcribe this text

[Assistant] Which is called data entry, and this is all about the decoding. There's two notebooks here that have, uh, coded, uh, numbers in them that have to be decoded. And this is a printout of a computer printout of the program that I


[User] Please transcribe this text

[Assistant] And then there's a framed, uh, page here of decoding that I did by hand when Dick Hebditch was visiting me up in...


[User] Please transcribe this text

[Assistant] Showed them how it worked, uh, doing the decoding by hand, so that's what's in this frame. And then the book, this is the Two Cabins book that's opened to the page of the decoding that's decoded here. And then that same decoding is being done on this.


[User] Please transcribe this text

[Assistant] Uhm, video called data entry and I'll play a little of that.


[User] Please transcribe this text

[Assistant] And then there was one other installation of, uhm, I made a film called RR, which is a two hour and I think fifteen minute film of forty-seven trains from all over the US. And then, which was a sixteen millimeter film.


[User] Please transcribe this text

[Assistant] And then I made a sequel to that on digital. That's a three hour and fifteen minute single shot film in one place in the Mojave Desert where thirteen trains pass by in those three and a half, uh, three hours and fifteen minutes.


[User] Please transcribe this text

[Assistant] And those two films are opposed to each other. Sixteen on one side, and uhm.


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[Assistant] Digital film on the other, uhm, and I just have some slides of those.


[User] Please transcribe this text

[Assistant] Uh, there's also a text, uh, written by Thoreau about the railroad, so that was part of it.


[User] Please transcribe this text

[Assistant] And this is a facsimile that I made of the text of his journal entry. And there's his fake signature.


[User] Please transcribe this text

[Assistant] Anyways, it's a nice text about the railroad that goes close to his cabin and you hear the train in the two cabins, uh, I didn't play that part in one of the window shots, but you hear the train go by that's at Walden Pond. And uh,


[User] Please transcribe this text

[Assistant] This is the 16 millimeter projector with an hour. I think there was a little over an hour each reel, so there were two reels.


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[Assistant] And it was projected with the computer, with the projector in the space, and then this would be rethreaded as almost a performance every hour, so that you were very aware of the apparatus itself, and the 16 millimeter film, and the noise of the projector.


[User] Please transcribe this text

[Assistant] And then on the other side, opposed to that, was...


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[Assistant] The digital, this is pointing that way, so the digital projector's up here that you can't see. And it's, and this is the same shot that also goes, it's late in the afternoon and it goes into dusk.


[User] Please transcribe this text

[Assistant] And thirteen trains, they come like this and they go around and then way in the background they come this way across. So you kind of see them twice in the frame. Uhm, and then.


[User] Please transcribe this text

[Assistant] That was on newsprint that people could take, and this is that text that I read to you from Thoreau, uh, from Walden about looking and listening. And this is a silk screen that I created.


[User] Please transcribe this text

[Assistant] And printed on newspaper, uh, so they could easily, we had a stack of 500 of them that could, would last that long. And then when these disappeared,


[User] Please transcribe this text

[Assistant] I've also printed, uh, this silkscreen on very nice paper and framed it and we just leaned the frame piece against this.


[User] Please transcribe this text

[Assistant] I want to show you this that I showed you at the very beginning.


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[Assistant] There's my house and truck. This is the thorough cabin. And that's the Kaczynski cabin.


[User] Please transcribe this text

[Assistant] So far, uh, the tax people haven't realized I added to my property.


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[Assistant] It was just a curiosity, really. And when I gave it to her, she wanted a nice camera. So she was disappointed. Uh, but then she liked it. Yeaah. It's, you know, I like toys.


[User] Please transcribe this text

[Assistant] I made this little three minute film in 1974. Yeah, that was it. Uh, that was kind of influenced by a Hollis Frampton kind of optically printed film where you kind of stutter out an answer and somebody asked me about something and I said,


[User] Please transcribe this text

[Assistant] I thought it was dangerous to be, uh, recorded on film because then it would be...


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[Assistant] Permanent and people would, uh, believe you thought that, uh, when they'd see it. And that I wasn't even sure I believed in what I was saying now. Now, it was kind of a joke film, but, uh, uh,


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[Assistant] Yeah, filmmaking is dangerous because any, I mean, it's not truth 24 times a second. It certainly can be a lie, you know, and most cinema that I hate,


[User] Please transcribe this text

[Assistant] Uh, creates false drama and, uh, when there's dr- drama and every day and I'm much more interested in that. So, I'm interested in observation now and not making, uh, scary films.


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[Assistant] I think I was lazy when I was in school. And uhm, I, I studied theoretical statistics, which gets almost to the point where art gets creative, or math gets creative.


[User] Please transcribe this text

[Assistant] But I didn't, I didn't, I like, if I would be young now and I would probably wouldn't become an artist, I would, uh, do number theory and I would get involved in that kind of abstract thinking that I really think is quite amazing.


[User] Please transcribe this text

[Assistant] But now people ask me, oh, your films are mathematical. You know, you make 13 links, 13 shots, 10 minutes each. But that, I mean, that isn't the mathematics. That's arithmetic, right? That isn't very interesting. But what I'm most...


[User] Please transcribe this text

[Assistant] Influence by in mathematics is uhm, my favorite proof is proving that the square root of 2 is irrational.


[User] Please transcribe this text

[Assistant] And that's a really complex idea that the square root of 2 can't be defined, that it goes on forever, 2.414, whatever it goes and it goes and it never ends, so we don't, we can only tell you what it is between


[User] Please transcribe this text

[Assistant] Between uh, two upper and lower bounds, right? And those upper and lower bounds get closer together as you add more digits to pi. So it's a very complex idea that square root of two is irrational. That a number can be irrational. And yet that can be proven with 10th grade algebra.


[User] Please transcribe this text

[Assistant] And it's totally elegant, the solution, because it's so simple. And that's what I think, uhm, I like about mathematics is when


[User] Please transcribe this text

[Assistant] Uh, you find really complex ideas can be, have really, uh, kind of simple solutions. And when I look at my art practice, that's what I strive for because I learned that in mathematics.


[User] Please transcribe this text

[Assistant] The camera has more discipline than we have. When you set it up and you make a frame, it doesn't look away. And so,


[User] Please transcribe this text

[Assistant] Uhm, I can make a frame that stays on there and it allows you to study a particular thing without looking away.


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[Assistant] And the cinema audience has a contract.


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[Assistant] That's strong, that when you sit down you watch it. I mean you can get up and leave, but more than likely you'll stay and watch it. So,


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[Assistant] If I show you a lake from 13 Lakes, you watch it for 10 minutes. If I took you to any one of those lakes, you wouldn't watch it for 10 minutes. You couldn't do that. So the contract


[User] Please transcribe this text

[Assistant] In the theater and cinema is stronger than real life. And that really interests me that we have an apparatus that people have agreed


[User] Please transcribe this text

[Assistant] I can provide information that's stronger than life itself, which is kind of bizarre.


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[Assistant] It's different though, it's not the reality, it's always the...


[User] Please transcribe this text

[Assistant] It's always my metaphor for reality, what I'm showing you. But I tried.


[User] Please transcribe this text

[Assistant] It does that, but it also, when you lessen the amount of variables, then you can study, if the camera's moving back and forth, then that variable's getting in the way of looking at that particular thing.


[User] Please transcribe this text

[Assistant] But if you take that, take that variable out of it, then, then there's a, it's like an experiment, right? You want to control, it's a control of things that you're studying. I mean, it's, it sounds kind of fascist, but I don't mean it that way.


[User] Please transcribe this text

[Assistant] It was close to 108 degrees in the Mojave Desert that day and I had a couple of gallons of water.


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[Assistant] And I could do the shot as long as I had water. Uh, I didn't sit down. I get too nervous. I walk around and I try not to make any noise.


[User] Please transcribe this text

[Assistant] I don't go anywhere near the camera because I might bump it.


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[Assistant] I did have to change, uh, cards because I had cards that could take 57 minutes. So I had, but there's two slots so you can.


[User] Please transcribe this text

[Assistant] When one's full, you can put another one in and try not to bump the cam when you change those cards. And then it becomes also a problem with battery length and my battery doesn't last three, so there's a few places where I had to change batteries also. But you can hook up batteries now to run them longer and the cards can take more, so uhm,


[User] Please transcribe this text

[Assistant] The digital is allowing me to do these longer shots to investigate time.


[User] Please transcribe this text

[Assistant] So the second film about the train, where the first one, every shot starts with the train entering the frame and then it ends after the train exits the frame. And there's a little...


[User] Please transcribe this text

[Assistant] Head and tail, so you kind of hear it coming and you hear it going away. But it's constant motion. And it's not about waiting at all because a train, as soon as it cuts, another train's going to enter.


[User] Please transcribe this text

[Assistant] But in the second film, it's also about waiting for the train, which I didn't, which I missed in the first, when I saw the first film. It's just, whew.


[User] Please transcribe this text

[Assistant] It's, it's an action film. And this one is more about discipline and the excitement of the train showing up. But thirteen trains in three and a half hours.


[User] Please transcribe this text

[Assistant] It's about every 20 minutes, so, and sometimes they last for 8 minutes, so you don't wait too long, but it's interesting because when the train comes through,


[User] Please transcribe this text

[Assistant] You watch the train and then when it leaves, the train, you might have been watching the train for eight minutes, then you notice the light change. But when you're just sitting there and there's no train, you don't want, notice the light change because


[User] Please transcribe this text

[Assistant] It's happening slowly, but if, when the train comes in, it's almost like you looked away for eight minutes. And then when it leaves, you finally see the, the landscape again. So it's kind of an interesting thing what, what, where your attention goes and what takes your attention.


[User] Please transcribe this text

[Assistant] But that piece is interesting because it also is about, about technology and the change in, in, uh, image making.


[User] Please transcribe this text

[Assistant] And about the change in the apparatus and the look of the image. So the two cabins.


[User] Please transcribe this text

[Assistant] And now in the film world, there's such an argument about


[User] Please transcribe this text

[Assistant] Digital and, and grain, you know, and there's all these people that are nostalgic for this beautiful grain dancing.


[User] Please transcribe this text

[Assistant] And then I say, oh, you never see grain when you look. Why do you, why do you, it gets in the way, doesn't it? You know, so I think digital looks more real. Here comes an argument.


[User] Please transcribe this text

[Assistant] My president meets on Thursday mornings every, uh,


[User] Please transcribe this text

[Assistant] And your president too, I guess, uh, meets on Thursday mornings every week and decides what drones are going to go out to kill people. So if you pay your taxes, you're implicated in that.


[User] Please transcribe this text

[Assistant] Sometimes there's also collateral damage, so every time you kill somebody or kill the wrong collateral damage, you're making more people to kill.


[User] Please transcribe this text

[Assistant] Cause they're gonna get angry. So it's a funny, I mean I, I don't believe the way we solve problems in the world. People have a really bad track record. It's not very smart.


[User] Please transcribe this text

[Assistant] I'm not sure if that title, if it's good or not. It wasn't my choice.


[User] Please transcribe this text

[Assistant] I don't dislike it, but I think it's narrow compared to what the project's about. Uhm, and what, I mean, but it is a fear where we're afraid of things, you know, so. I'm not afraid of Kaczynski or his ideas. I, I, no, I didn't. No.


[User] Please transcribe this text

[Assistant] I mean, I think it is what it is. It's a lot of stuff, you know.


[User] Please transcribe this text

[Assistant] It's trying to understand. It's like, it's the two cabins.


[User] Please transcribe this text

[Assistant] Are polarizing ideas, but then they also become the same because when you think about Thoreau, uhm, after John Brown was, uh, executed or after they were caught, a number of, uh,


[User] Please transcribe this text

[Assistant] Uh, black, uh, participants escaped and Thoreau and his mother and sister were part of the Underground Railroad that helped slaves get to Canada. Uh, and supposedly they helped some of those people from


[User] Please transcribe this text

[Assistant] Uh, Harper's Ferry get to Canada. So they would be aiding terrorists basically.


[User] Please transcribe this text

[Assistant] It's a different, what is a terrorist and what, you know.


[User] Please transcribe this text

[Assistant] I was very interested in his philosophy and who he was today and I'm very interested also in what he can say about solitude and about autonomy.


[User] Please transcribe this text

[Assistant] As his cabin, it's 10 feet by 12 feet. There's recessed windows. He's in the Supermax prison in Florence, Colorado. You're brought in blindfolded so you don't even know what floor you're on. And the light is recessed so he doesn't, he can't see out.


[User] Please transcribe this text

[Assistant] Oh, well, I was, I was interested in, I know where I was going now, that he's living in this confined cell and yet, uhm,


[User] Please transcribe this text

[Assistant] He has complete autonomy right now. All he does is do research. He's got a team of 12 people on the outside doing research from him. And he's writing anti-tech literature. So he's completely committed to an anti-tech stand. Uhm, and, uh,


[User] Please transcribe this text

[Assistant] I, I occasionally would ask him questions through my friend who's been corresponding with him for three years now. And one of the things I was interested in, how he perceived time in there.


[User] Please transcribe this text

[Assistant] Because when I, I worked with a woman that was in prison making a film in the late 80s and the way she perceived time was much different than mine. And Kaczynski was just the opposite of hers. Uh, he felt there wasn't enough time. He was so busy.


[User] Please transcribe this text

[Assistant] So he's living in this confined place.


[User] Please transcribe this text

[Assistant] So busy that he's created, he's, I don't know, I mean it's kind of an imaginary revolution that he's, he's, they allow him to write and he knows he can write up to a point without losing his privileges, right?


[User] Please transcribe this text

[Assistant] But he, he's, he's still creating an autonomy that I think he learned from the wilderness and the autonomy that he had there. So I, I would really like to, I hope, wish he would also write about that and also about a whole life that was anti-social basically.


[User] Please transcribe this text

[Assistant] So, because I asked him some of those questions through my friend, uhm, at one point I thought I'd write him a birthday card. And then he had such a clever answer, I thought, okay, it seems like something I can play with.


[User] Please transcribe this text

[Assistant] Because I also want to make him not just a monster, you know, that you have a sense that he's a real person from that birthday card.


[User] Please transcribe this text

[Assistant] He killed three people, uh, he maimed six or seven more. He sent 20 some bombs through the mail. He was angry. He's not a very nice person, but he's brilliant. You know, so.


[User] Please transcribe this text

[Assistant] But like, one of, one of the things he says about violence is that if you have a government that uses violence to solve its problems and you don't use violence against them,


[User] Please transcribe this text

[Assistant] You don't, you don't have, you know, you're at a disadvantage. And I don't believe in violence, so I think it's, I know what he's saying, but I don't think that's a solution. But either is Thursday morning in the White House solutions.


[User] Please transcribe this text

[Assistant] Daddy Pig, you were sitting on them all the time. Oh.


[User] Please transcribe this text

[Assistant] Well, you may have been a bit silly, Daddy Pig, but at least you're not grumpy anymore.


[User] Please transcribe this text

[Assistant] Well, currently teamwork is in fashion in the workplace.


[User] Please transcribe this text

[Assistant] And in my opinion, the importance of the individual is generally neglected.


[User] Please transcribe this text

[Assistant] Who can take the lead in a situation and not afraid to accept the idea of responsibility?


[User] Please transcribe this text

[Assistant] But I think one of the most important tasks of managers is to consider the needs of the individual on one hand,


[User] Please transcribe this text

[Assistant] And group cooperation and conformity on the other.


[User] Please transcribe this text

[Assistant] It requires creative thinking on the part of management to avoid tension.


[User] Please transcribe this text

[Assistant] Well, it has to start from the very beginning, when companies are looking for new employees.


[User] Please transcribe this text

[Assistant] When the personnel department is choosing between applicants, they need to look for someone who's broken the mould and can think for themselves.


[User] Please transcribe this text

[Assistant] Instead, people making these decisions often use a range of psychological tests to see if a person is a problem solver or will do as they're told.


[User] Please transcribe this text

[Assistant] No, it's not overrated. You do need to learn the rules and learn them fast.


[User] Please transcribe this text

[Assistant] When an individual demonstrates the behaviour the organisation expects, some kind of incentive can be given.


[User] Please transcribe this text

[Assistant] What's important here is that this happens right at the beginning, so new recruits learn the rules of the system immediately.


[User] Please transcribe this text

[Assistant] Also, the incentive should be something the individual actually wants, and this isn't always just money.


[User] Please transcribe this text

[Assistant] Let's turn our attention back to the MSI Finals. It is Game 2. There are no player substitutions. Kingzone have selected Redside, so we basically go GG-Ri.


[User] Please transcribe this text

[Assistant] Well, we are going to be jumping into the draft momentarily and straight out the gate, gentlemen. There are no changes yet. Camille, Taliyah, Karma, Vladimir, the same four bands. And it's Rakan and Paisadopoulos.


[User] Please transcribe this text

[Assistant] Yeah, there are quite a few, and they definitely like to yell, and Xiaohu's gonna go back to sort of a comfort pick in that Galio, some supportive Kingo top-bottom. It's another double tank comp on the side of RNG.


[User] Please transcribe this text

[Assistant] I want to put some numbers to that. Three wins, zero losses on the Cho'Gath and yesterday Khan on Cho'Gath out damaged Prey on Ezreal in a professional MSI semi-final game. I think he's mobile enough when the target is Flash Eatable, in which case Betty was yesterday.


[User] Please transcribe this text

[Assistant] Oh man, level three on Karzai, it's not that fracture available. A lot of crystal slashes, this should just be a matter of time. Khan gets run down, who will pick up the final hit?


[User] Please transcribe this text

[Assistant] In any phase we'll just continue to play itself out. Peanut already shown his face in the top lane.


[User] Please transcribe this text

[Assistant] He's gotta push the lane. And look, bottom lane, gentlemen. Peanut waltzed his way into lane. Uzi and Ming were very low, they backed out. So plays and counter plays because even Xiaohu, he had roamed towards the top lane too.


[User] Please transcribe this text

[Assistant] Let me flash this to safety. Did not want to get body blocked, but summon a spell down. We've seen this game already. When top laners have no flash, they are juicy, juicy targets. It's like a gorilla and prey who are pushing into Uzi and me.


[User] Please transcribe this text

[Assistant] Alright, no teleport from Khan, there is one for Letme. In case the bottom lane party breaks out, as we have seen several times this tournament.


[User] Please transcribe this text

[Assistant] Well, as the Irelia mid. But it has similar tendencies, a fairly slow-ish laning phase, going relatively even. It was when Kingzone, in game one, started to set up the 1-3-1, that they got punished, and Kofi, after you just talked about that proactive engage tools from RNG, it was being proactive in game one that allowed RNG to punish, get the lead, and then close out the game.


[User] Please transcribe this text

[Assistant] We're gonna use it yet. Alright, the uh, true shop barrage saving some time.


[User] Please transcribe this text

[Assistant] Let's see what they can do as it stands. RNG. Fending off the pressure.


[User] Please transcribe this text

[Assistant] If he's allowed to stand still and shoot.


[User] Please transcribe this text

[Assistant] Okay, gentlemen, I need you to put some prediction caps on. We have had 20 minutes of flirting with team fighters. When will we get our first big team fight?


[User] Please transcribe this text

[Assistant] We're gonna find out who, who will win that theoretical fight because the Muramana is about to, uh, be upgraded here for Uzi. He's very shortly gonna hit 750, did that very minion wave.


[User] Please transcribe this text

[Assistant] I think he's got enough gold to finish off that Trinity Force as well. And Baron has just spawned, so a number of pieces falling into place now for RNG.


[User] Please transcribe this text

[Assistant] Uzi has a teleport of his own, we'll be picking up that trinity force and as the arms race continues to scale, we are setting ourselves up for some gigantic mid game team.


[User] Please transcribe this text

[Assistant] Your run from Ornn. How about that one? Alright, give me a call when you see the team fight. I'm looking at the vision now. And, there's not a lot of deep vision from either side. Thank you for your service, Paul.


[User] Please transcribe this text

[Assistant] Insane. And look at their win-loss from picking up the first Baron. 8-0 here at MSI. Still a minute to go.


[User] Please transcribe this text

[Assistant] You can see Prey just completes the rapid fire cannon as well, so he's gonna be able to get some of those early shots down and play from behind. Kingzone aren't done yet.


[User] Please transcribe this text

[Assistant] And manages to make it count. Every single one of our analysts said that this game was gonna go the distance, was gonna go to five games. And I'm not gonna lie, there were moments around the 20 to 25 minute mark where I thought RNG had this. I thought they were just gonna clean these team fights up and Kingzone just shut me the hell up in their own jungle.


[User] Please transcribe this text

[Assistant] Do Irelia and Ornn become problems or not? Do you think about it? Is BDD's Irelia scary enough to ban? My instinct is yes.


[User] Please transcribe this text

[Assistant] Maybe I can answer that just a little bit more. We make different kinds of works. Some of them can live on the internet, but the quality is so bad, and your viewing experience is so poor.


[User] Please transcribe this text

[Assistant] That we would rather have a situation like this or in a museum or gallery or sometimes in churches, sometimes in temples, whatever.


[User] Please transcribe this text

[Assistant] Uhm, because then the experience then becomes very personal and also we control the quality and we've always controlled the quality very, very strongly. Each piece is made with a certain size in mind. Some pieces are very small, you know.


[User] Please transcribe this text

[Assistant] Other pieces you have to stand in the room and it completely surrounds you. So you can't have that experience on the internet. I think a lot of people complain about the internet because of music is really kind of very poor now. Uh, movies, you can buy movies on the internet, all of these things.


[User] Please transcribe this text

[Assistant] It's not the same as if you're in a movie theater and you're watching a 3D, so.


[User] Please transcribe this text

[Assistant] We would love to see higher quality and a better experience.


[User] Please transcribe this text

[Assistant] We've talked a lot about the series, talked a lot about expectations, but as this was the MSI about AD carries, I want to dial in on Uzi versus Prey. I mean the desk basically unanimously said that Uzi was the best player in the world, not putting words in their mouths at all.


[User] Please transcribe this text

[Assistant] Okay, what about the fact that Korea hasn't been beaten? What was the number? 1,106 days. Do you think anybody cares about- do you think that the Korean overlord dominance- Oh, I think people care!


[User] Please transcribe this text

[Assistant] Ladies and gentlemen in Paris and around the world, let's kick off the fight for the championship and the title.


[User] Please transcribe this text

[Assistant] But it is interesting and you can see Khan, uh, he's already putting a lot of pressure on this bottom tower.


[User] Please transcribe this text

[Assistant] But let's talk about this, this map play, we just saw Khan shoving down that bottom, the swap already playing out. If, uh, Kingzone are able to run circles around RNG, I know Baron is one objective, do they just throw themselves at a tower, do they just initiate under an inner mid or inner top, force Kingzone's hands?


[User] Please transcribe this text

[Assistant] Yeah, it is for our teams, but this is LCK and Kingzone. They can coordinate. Just an update, gentlemen. It is a PC-related problem on the side of Uzi, which our technical, uh, refs are looking into right now. We'll give you guys an update as I hear more.


[User] Please transcribe this text

[Assistant] Obviously now for RNG, it's a defense game. How long can they hold on to their remaining outer base turrets?


[User] Please transcribe this text

[Assistant] So much execution then on the side of King's Zone. They may not get an opportunity because RNG with Baron are still barreling down. Correction, Banner is powered Vivian. Baron buff has worn off. They've got Supers in the top, in the middle.


[User] Please transcribe this text

[Assistant] Right, well, let's just have a look at what positions are available at the moment.


[User] Please transcribe this text

[Assistant] There's a job working at the reception desk at the sports centre for three evenings a week. That's Wednesdays, Thursdays and Fridays.


[User] Please transcribe this text

[Assistant] Okay, that's no good then. Uhm, what about cleaning?


[User] Please transcribe this text

[Assistant] There's a position for a cleaner at the childcare centre.


[User] Please transcribe this text

[Assistant] But you'd need to be there at 6am. Does that appeal?


[User] Please transcribe this text

[Assistant] Well, there was a position going in the computer lab for three days a week that might be okay. Ah, here it is. No, it's in the library, not the lab. A clerical assistant required.


[User] Please transcribe this text

[Assistant] I think it mostly involves putting the books back on the shelves. Oh no, hang on. It's for Wednesday and Friday evenings again.


[User] Please transcribe this text

[Assistant] Okay, I'm getting the idea. Look, I'll just get a few details from you anyway, and then we can check through the list and see what comes up.


[User] Please transcribe this text

[Assistant] Do you have any other skills? Typing, languages, that sort of thing?


[User] Please transcribe this text

[Assistant] Right, I'll make a note of that. Now, let's see what else is available.


[User] Please transcribe this text

[Assistant] What do you think of administrative work? There's a position for an office assistant at the English Language Centre.


[User] Please transcribe this text

[Assistant] Yep, sure. It says here that you'll be required to deal with student inquiries and answer the phone.


[User] Please transcribe this text

[Assistant] Well, would you like me to arrange an interview for you? Say, Friday morning, around ten?


[User] Please transcribe this text

[Assistant] Not a problem. How about 11.30? Hope it works out for you Anita.


[User] Please transcribe this text

[Assistant] Alright, hello everybody. Hope you can hear me well. Yes, yes, great.


[User] Please transcribe this text

[Assistant] Deep learning for self driving cars. Uh, we will introduce to you the methods of deep learning of deep neural networks using the guiding case study of building self driving cars.


[User] Please transcribe this text

[Assistant] My name is Lex Friedman, uh, you get to listen to me for majority of these lectures, and I am part of an amazing team, uh, with some brilliant TAs, would you say, brilliant? Dan, uh, Dan Brown.


[User] Please transcribe this text

[Assistant] You guys want to stand up? You're okay? They're in front row. Spencer, uh, William Angel.


[User] Please transcribe this text

[Assistant] One of the two simulations, games that we'll get to go through.


[User] Please transcribe this text

[Assistant] We use it as a way to teach you about deep reinforcement learning, but also as a way to excite, excite you.


[User] Please transcribe this text

[Assistant] By challenging you to compete against others if you wish, uh, to win a special prize.


[User] Please transcribe this text

[Assistant] Yet to be announced. Super secret prize.


[User] Please transcribe this text

[Assistant] So you can reach me and the TAs at deepcars.mit.edu if you have any questions about the tutorials, about the lecture, about anything at all.


[User] Please transcribe this text

[Assistant] The website, cars.mit.edu has the lecture content, uh, code, tutorials, again, uhm,


[User] Please transcribe this text

[Assistant] Like today, the lecture slides for today are already up in PDF form. Uh, the slides themselves, if you want to see them, just email me. But they're over a gigabyte in size because they're very heavy in videos. So I'm just posting the PDFs. And there will be lecture videos available.


[User] Please transcribe this text

[Assistant] A few days after the lecture is given. So speaking of which, there is a camera in the back. This is being videotaped and recorded, but for the most


[User] Please transcribe this text

[Assistant] A part, the camera is just on the speaker, so you shouldn't have to worry. If that kind of thing worries you, then, uh, you could sit on the periphery of the classroom, or maybe I suggest


[User] Please transcribe this text

[Assistant] Sunglasses and a mustache, fake mustache, that would be a good idea. There is a competition for the game that you see on the left. I'll describe exactly what's involved.


[User] Please transcribe this text

[Assistant] In order to get credit for the course, you have to...


[User] Please transcribe this text

[Assistant] Design a neural network that drives the car just above the speed limit of 65 miles an hour. But if you want to win, you need to go a little faster than that.


[User] Please transcribe this text

[Assistant] You may be new to programming, new to machine learning, new to robotics, or


[User] Please transcribe this text

[Assistant] You're an expert in those fields, but want to go back to the basics.


[User] Please transcribe this text

[Assistant] So what you will learn is an overview of deep reinforcement learning, uh, convolutional neural networks, recurrent neural networks.


[User] Please transcribe this text

[Assistant] And how these methods can help improve each of the components of autonomous driving. Perception, visual perception, localization, mapping, control planning, and the detection of driver state.


[User] Please transcribe this text

[Assistant] Okay, two projects. Uh, codename Deep Traffic is the first one.


[User] Please transcribe this text

[Assistant] Uh, there is, in this particular formulation of it, there is seven lanes.


[User] Please transcribe this text

[Assistant] Uh, it's a top view, uh, it looks like a game, but uh, I assure you it's very serious.


[User] Please transcribe this text

[Assistant] The car in red is being controlled by a neural network and we'll explain how you can control and design the various aspects, the various parameters of this neural network, this neural network. And it learns in the browser,


[User] Please transcribe this text

[Assistant] So this, we're using ComNetJS, which is a library that is programmed by André Carparthi in JavaScript. So amazingly, we live in a world where you can train in a matter of minutes a neural network in your browser.


[User] Please transcribe this text

[Assistant] And we'll talk about how to do that. The reason we did this is so that there is very few requirements to get you up and started with neural networks. So in order to complete this project for the course,


[User] Please transcribe this text

[Assistant] You don't need any requirements except to have a Chrome browser?


[User] Please transcribe this text

[Assistant] And, uh, to win the competition, you don't need anything except a Chrome browser. The second project, codenamed Deep Tesla, or Tesla,


[User] Please transcribe this text

[Assistant] of the forward roadway and using end-to-end learning, taking the image, the, and putting it into convolutional neural networks that directly maps a regressor that maps to a steering angle.


[User] Please transcribe this text

[Assistant] So all it takes is a single image and it predicts a steering angle for the car. And we have data for the car itself and you get to build a neural network that


[User] Please transcribe this text

[Assistant] Tries to do better, tries to steer better, or at least as good as the car. Okay.


[User] Please transcribe this text

[Assistant] Let's get started with the question.


[User] Please transcribe this text

[Assistant] With the thing that we understand so poorly at this time, because it's so shrouded in mystery, but it fascinates many of us. And that's the question of what is intelligence?


[User] Please transcribe this text

[Assistant] And the question, can machines think, is answered below, where they already do. So what if anything is special about the human mind?


[User] Please transcribe this text

[Assistant] It's a good question for 1996, a good question for 2016, 17 now, and the future.


[User] Please transcribe this text

[Assistant] And there's two ways to ask that question. One is the special purpose version.


[User] Please transcribe this text

[Assistant] Specifically, uh, formally defined finite set of goals.


[User] Please transcribe this text

[Assistant] And this, uh, little diagram from a book that got me into artificial intelligence is a bright-eyed high school student. The Artificial Intelligence of Modern Approach. This is a beautifully simple


[User] Please transcribe this text

[Assistant] Sensors that do the perception, it takes those sensors in, does something magical, there's a question mark there, and with a set of effectors, acts in the world, manipulates objects in that world. And so, special purpose, we can,


[User] Please transcribe this text

[Assistant] Under this formulation, as long as the environment is formally defined, well defined, as long as a set of goals are well defined, as long as a set of actions, sensors, and the...


[User] Please transcribe this text

[Assistant] Ways that the perception carries itself out is well defined. We have good algorithms of which we'll talk about that can optimize for those goals. The question is, if we inch along this path,


[User] Please transcribe this text

[Assistant] Will we get closer to the general formulation, to the general purpose version of what artificial intelligence is? Can it achieve


[User] Please transcribe this text

[Assistant] Poorly defined, unconstrained set of goals, with an unconstrained, poorly defined set of actions, and unconstrained, poorly defined utility functions. Rewards.


[User] Please transcribe this text

[Assistant] This is what human life is about. This is what we do pretty well most days. Exists in a undefined, full of uncertainty world.


[User] Please transcribe this text

[Assistant] So okay, we can separate tasks into three different categories. Formal tasks. This is the easiest.


[User] Please transcribe this text

[Assistant] It doesn't seem so, it didn't seem so at the birth of artificial intelligence, but that's in fact true if you think about it. The easiest is the formal tasks. Playing board games, theorem proving,


[User] Please transcribe this text

[Assistant] All the kind of mathematical logic problems that can be formally defined.


[User] Please transcribe this text

[Assistant] Then there's the expert, expert tasks. So this is, this is where a lot of the exciting breakthroughs have been happening, where machine learning methods, data driven methods can help aid


[User] Please transcribe this text

[Assistant] Or improve on the performance of our human experts. This means medical diagnosis, hardware design, scheduling.


[User] Please transcribe this text

[Assistant] And then there is the thing that we take for granted, the trivial thing, the thing that...


[User] Please transcribe this text

[Assistant] We do so easily every day when we wake up in the morning.


[User] Please transcribe this text

[Assistant] The mundane tasks of everyday speech, of written language, of visual perception, of walking.


[User] Please transcribe this text

[Assistant] Which we'll talk about in today's lecture is a fascinatingly difficult task. An object manipulation.


[User] Please transcribe this text

[Assistant] So the question is that we're asking here, before we talk about deep learning, before we talk about the specific methods, we really want to dig in and, and


[User] Please transcribe this text

[Assistant] And try to see what is it about driving.


[User] Please transcribe this text

[Assistant] Is it, is it more like chess, which you see on the left there, where we can formally define a set of lanes, a set of actions, and uh, formulate it as this, you know, there's five set of actions, you can change a lane, you can avoid obstacles, there's, you can formally define an obstacle, you can formally define the rules of the road,


[User] Please transcribe this text

[Assistant] Or is, is there something about natural language, something similar to everyday conversation about driving, that requires a much higher degree of reasoning, of communication, of learning,


[User] Please transcribe this text

[Assistant] Of existing in this underactuated space, is it a lot more than just left lane, right lane, speed up, slow down?


[User] Please transcribe this text

[Assistant] So let's look at it as a, as a chess game. Here's the chess pieces.


[User] Please transcribe this text

[Assistant] And we'll get a lot more in depth on this, especially with the guest speakers who built many of these.


[User] Please transcribe this text

[Assistant] There's radar, there's the range sensors, radar, lidar, that give you information about the obstacles in the environment.


[User] Please transcribe this text

[Assistant] That'll help localize the obstacles in the environment. There's the visible light camera and stereo vision that gives you texture information that helps you figure out not just where the obstacles are but what they are. Helps to classify those.


[User] Please transcribe this text

[Assistant] Has to understand their subtle movements.


[User] Please transcribe this text

[Assistant] Then there is the information about the vehicle itself. About the trajectory and the movement of the vehicle. That comes from the GPS and IMU sensors.


[User] Please transcribe this text

[Assistant] And there is the state of, the rich state of the vehicle itself. What is it doing? What are all the individual systems doing? That comes from the CAN network.


[User] Please transcribe this text

[Assistant] And there, there is one of the less studied but fascinating to us on the research side is audio. The sounds of the road that provide the rich context.


[User] Please transcribe this text

[Assistant] Of a wet road. The sound of a road that when it stopped raining, but it's still wet. The sound that it makes.


[User] Please transcribe this text

[Assistant] The screeching tire and honking, these are all fascinating signals as well. And the focus of the research in our group, the thing that's really much under


[User] Please transcribe this text

[Assistant] Uh, investigated is the internal facing sensors. The driver.


[User] Please transcribe this text

[Assistant] Sensing the, the state of the driver. Where are they looking? Are they sleepy? The emotional state? Are they in the seat at all? And the same with audio.


[User] Please transcribe this text

[Assistant] That comes from the visual information and the audio information. More than that


[User] Please transcribe this text

[Assistant] Here's the tasks, if you were to break into modules, the task of what it means to build a self-driving vehicle.


[User] Please transcribe this text

[Assistant] Figure out where all the different, uh, uh, obstacles are, all the entities are, and use that estimate of the environment to then figure out where I am, where the robot is. Then there's scene understanding.


[User] Please transcribe this text

[Assistant] Is understanding not just the positional aspects of the, uh, external environment and the dynamics of it, but also what those entities are. Is it a car? Is it a pedestrian? Is it a bird?


[User] Please transcribe this text

[Assistant] There's movement planning. Once you have kind of figured out to the best of your abilities.


[User] Please transcribe this text

[Assistant] Once you've figured out how to move about safely and effectively through that world is figuring out what the human that's on board is doing.


[User] Please transcribe this text

[Assistant] Because as I will talk about, the path to a self-driving vehicle, and that is hence our focus on Tesla, may


[User] Please transcribe this text

[Assistant] Go through semi-autonomous vehicles where there is a


[User] Please transcribe this text

[Assistant] Where the vehicle must not only drive itself, but effectively hand over control. From the car to the human and back.


[User] Please transcribe this text

[Assistant] The big breakthroughs came in the second DARPA Grand Challenge.


[User] Please transcribe this text

[Assistant] With Stanford Stanley, when they won the competition, one of five cars that finished. This was an incredible accomplishment. In a desert race.


[User] Please transcribe this text

[Assistant] A fully autonomous vehicle was able to complete the race. In record time.


[User] Please transcribe this text

[Assistant] Uh, the DARPA Urban Challenge in 2007.


[User] Please transcribe this text

[Assistant] Where the race was, the uh, the task was no longer a race in the desert, but through a urban environment. And CMU's boss, with GM, won that race.


[User] Please transcribe this text

[Assistant] With it's autopilot system and not autopilot 2 system. Uber with it's testing in Pittsburgh.


[User] Please transcribe this text

[Assistant] And there's many other companies, including one of the speakers for this course of Mutonomy.


[User] Please transcribe this text

[Assistant] That are driving the wonderful streets of Boston.


[User] Please transcribe this text

[Assistant] Okay, so let's take a step back. We have...


[User] Please transcribe this text

[Assistant] If we think about the accomplishments in the DARPA Challenge.


[User] Please transcribe this text

[Assistant] And if we look at the accomplishments of the Google self-driving car.


[User] Please transcribe this text

[Assistant] Which essentially boils the world down into a chess game.


[User] Please transcribe this text

[Assistant] uses incredibly accurate sensors to build a three-dimensional map of the world, localize itself effectively in that world, and move about that world.


[User] Please transcribe this text

[Assistant] The open question is if driving is more like a conversation, like a natural language conversation.


[User] Please transcribe this text

[Assistant] How hard is it to pass the touring test? The touring test...


[User] Please transcribe this text

[Assistant] As the popular current formulation is, can a computer be mistaken for a human being in more than 30% of the time when a human is talking behind a veil?


[User] Please transcribe this text

[Assistant] Having a conversation with either a computer or a human can, they mistake the other side of that conversation.


[User] Please transcribe this text

[Assistant] For being, uh, a human when it's in fact a computer.


[User] Please transcribe this text

[Assistant] The, the way you would, in a natural language, build a system that has successfully passed the Turing test is


[User] Please transcribe this text

[Assistant] The natural language part, the processing part to enable it to communicate successfully. So, generate language and interpret language.


[User] Please transcribe this text

[Assistant] Then you represent knowledge, the state of the conversation, transferred over time.


[User] Please transcribe this text

[Assistant] And the last piece, and this is the hard piece, is the automated reasoning. It's reasoning. Can we teach machine learning methods to reason? That's...


[User] Please transcribe this text

[Assistant] That is something that will propagate through our discussion. Because...


[User] Please transcribe this text

[Assistant] As I will talk about, the various methods, the various deep learning methods, neural networks,


[User] Please transcribe this text

[Assistant] But they're not yet, there's no good mechanism for reasoning.


[User] Please transcribe this text

[Assistant] Now, reasoning could be just something that we tell ourselves we do to feel special, better, to feel like we're better than machines. Reasoning may be simply something as simple as learning from data. We just need a larger network.


[User] Please transcribe this text

[Assistant] Or there could be a totally different mechanism required. And we'll talk about the possibilities there. Yes.


[User] Please transcribe this text

[Assistant] No, it's, it's very difficult to find these kind of situations in the United States. So the question was, for this video, is it in the United States or not? Uhm, I believe it's, uh, in, uh, Tokyo. So India, uh, there's a few European countries, uh, are much, uh,


[User] Please transcribe this text

[Assistant] I'm much more towards the direction of, uh, natural language versus chess.


[User] Please transcribe this text

[Assistant] In the United States, uh, generally speaking, we follow rules more concretely. The quality of roads is better, the marking on the roads is better, so there's less requirements there.


[User] Please transcribe this text

[Assistant] I just, okay. Yeah, you're right, it is cause, uh, yep.


[User] Please transcribe this text

[Assistant] Yeah, so, but it's certainly not the United States. I'm pretty, I'm, I'm, uh, it's, it's spent quite a bit of Googling, uh, trying to find the United States and it's, it's difficult.


[User] Please transcribe this text

[Assistant] The recent breakthroughs in machine learning and what is at the core of those breakthroughs is neural networks.


[User] Please transcribe this text

[Assistant] That have been around for a long time and I will talk about what has changed, what are the cool new things and what hasn't changed and what are its possibilities. But first, a neuron crudely.


[User] Please transcribe this text

[Assistant] Is a computational building block of the brain. I know there's a few folks here, neuroscience folks.


[User] Please transcribe this text

[Assistant] These neurons for both artificial and human brains are interconnected. In the human brain there's about


[User] Please transcribe this text

[Assistant] I believe 10,000 outgoing connections from every neuron, on average, and they're interconnected to each other.


[User] Please transcribe this text

[Assistant] Our, the largest current, as far as I'm aware, artificial neural network has 10 billion of those connections, synapses. Our human brain, to the best estimate that I'm aware of, has


[User] Please transcribe this text

[Assistant] Ten thousand times that. So.


[User] Please transcribe this text

[Assistant] A hundred to one thousand trillion synapses.


[User] Please transcribe this text

[Assistant] Now what is a artificial neuron? This building block of a neural network. It takes a set of inputs, it puts a weight on each of those inputs, sums them together,


[User] Please transcribe this text

[Assistant] Applies a bias value on each that, that sits on each neuron.


[User] Please transcribe this text

[Assistant] And using an activation function that takes us input.


[User] Please transcribe this text

[Assistant] The, that sum plus the bias and squishes it together to produce a zero to one signal.


[User] Please transcribe this text

[Assistant] Take a few inputs and produce as an output.


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[Assistant] A classification for example, a zero one. And as, as we'll talk about, simply, it, it can serve as a linear classifier.


[User] Please transcribe this text

[Assistant] So it can draw a line. It can learn to draw a line between, like what's seen here, between the blue dots.


[User] Please transcribe this text

[Assistant] And the yellow dust. And that's exactly what we'll do in the iPython notebook that I'll talk about.


[User] Please transcribe this text

[Assistant] But the basic algorithm is you initialize the, the weights on the inputs.


[User] Please transcribe this text

[Assistant] You perform this previous operation I talked about, sum up, and compute the output. And if the output does not match the ground truth,


[User] Please transcribe this text

[Assistant] The expected output, the output that it should produce, the weights are punished accordingly. And we'll talk through a little bit of the math of that.


[User] Please transcribe this text

[Assistant] And this process is repeated until the perceptron does not make any more mistakes.


[User] Please transcribe this text

[Assistant] The amazing thing about neural networks, there's several, and I'll talk about them.


[User] Please transcribe this text

[Assistant] Is the universality of neural networks. With just a single layer, if we stack them together, a single hidden layer. The inputs on the left, the outputs on the right.


[User] Please transcribe this text

[Assistant] And in the middle there's a single hidden layer. It can closely approximate any function. Any function. So, this is an incredible property.


[User] Please transcribe this text

[Assistant] You know, you can think of driving as a function. It takes an input. The world outside as output.


[User] Please transcribe this text

[Assistant] The control of the vehicle. There exists a neural network out there that can drive perfectly. It's a fascinating mathematical fact.


[User] Please transcribe this text

[Assistant] So we can think of this, then, these functions as a special purpose function. Special purpose intelligence. You can take, uhm, say as input.


[User] Please transcribe this text

[Assistant] Uh, the number of bedrooms, the square feet, the type of neighborhood, those are the three inputs. It passes that value through to the hidden layer.


[User] Please transcribe this text

[Assistant] And then one more step, it produces the final price estimate for the house, or for the residence.


[User] Please transcribe this text

[Assistant] And we can teach a network to do this pretty well in a supervised way. This is supervised learning. You provide a lot of examples where you know the number of bedrooms, the square feet, the type of neighborhood.


[User] Please transcribe this text

[Assistant] And then you also know the final price of the house, of the residence. And then you can...


[User] Please transcribe this text

[Assistant] As I'll talk about through a process of backpropagation, teach these networks to make this prediction pretty well.


[User] Please transcribe this text

[Assistant] Now, some of the exciting breakthroughs recently have been in the general purpose intelligence. This is from Andre Karpathy, who is now at OpenAI.


[User] Please transcribe this text

[Assistant] Take a moment here to try to explain how amazing this is. This is a game of Pong. Uh, if you're not familiar with Pong,


[User] Please transcribe this text

[Assistant] Uh, there's two paddles and you're trying to, uh, bounce the ball back.


[User] Please transcribe this text

[Assistant] And, uh, in such a way that prevents the other guy from bouncing the wall, the ball back at you. The, the,


[User] Please transcribe this text

[Assistant] The artificial intelligence agent is on the right in green. And up top is the score. 8 to 1.


[User] Please transcribe this text

[Assistant] Now this takes about three days to train on a regular computer, this network. What is


[User] Please transcribe this text

[Assistant] This network doing. It's called the policy network.


[User] Please transcribe this text

[Assistant] The input is the raw pixels.


[User] Please transcribe this text

[Assistant] They're, they're slightly, uh, processed and also you take the difference between, uh, two frames. But it's basically the raw pixel information. That's the input.


[User] Please transcribe this text

[Assistant] There's a few hidden layers, and the output is a single probability of moving up.


[User] Please transcribe this text

[Assistant] That, that's it. That's, that's the whole, that's the whole system. And what it's doing is, it learns.


[User] Please transcribe this text

[Assistant] You don't know what the right thing to do is. Is it to move up? Is it to move down? You only know...


[User] Please transcribe this text

[Assistant] What the right thing to do is, by the fact that eventually you win or lose the game.


[User] Please transcribe this text

[Assistant] So this is the amazing thing here. There's no supervised learning about, there's no like universal fact about any one state being good or bad. And any one action being good or bad in any state.


[User] Please transcribe this text

[Assistant] But if you punish or reward every single action you took, every single action you took for an entire game based on the result. So no matter what you did, if you won the game, the end justifies the means.


[User] Please transcribe this text

[Assistant] If you won the game, every action you took and every action, uh, state pair gets rewarded. If you lost the game, it gets punished.


[User] Please transcribe this text

[Assistant] And this process, with only 200,000 games, where the, the, the system just simulates the games, it can learn to beat the computer.


[User] Please transcribe this text

[Assistant] This system knows nothing about Pong, nothing about games. This is general intelligence. Except for the fact that it's just a game of Pong. And I will talk about how this can be extended further and why this is so promising. And why this is also


[User] Please transcribe this text

[Assistant] We should proceed with caution. So again,


[User] Please transcribe this text

[Assistant] There's a set of actions you take, up down, up down, based on the output of the network. There's a threshold.


[User] Please transcribe this text

[Assistant] Given the probability of moving up, you move up or down based on the output of the network.


[User] Please transcribe this text

[Assistant] And every single state action pair is rewarded if there's a win, and is punished if there's a loss.


[User] Please transcribe this text

[Assistant] Think about how amazing that is. And if you don't understand why that's amazing, uh, spend some time on it. It's incredible.


[User] Please transcribe this text

[Assistant] Sure, sure thing. Uh, the question was, uh, what is supervised learning? What is unsupervised learning? What's the difference? So supervised learning,


[User] Please transcribe this text

[Assistant] Is when people talk about machine learning, they mean supervised learning most of the time. Supervised learning is.


[User] Please transcribe this text

[Assistant] It's learning from example. When you have a set of inputs and a set of outputs that, that you know are correct. What are called ground truth.


[User] Please transcribe this text

[Assistant] So you need those examples, a large amount of them, to train any of the machine learning algorithms to learn to then generalize that to future examples.


[User] Please transcribe this text

[Assistant] Actually, there's a third one called reinforcement learning, where the ground truth is sparse. The information about


[User] Please transcribe this text

[Assistant] When something is good or not, the ground truth only happens every once in a while, at the end of the game, not every single frame. And unsupervised learning is when you have no information about the outputs that are correct or incorrect.


[User] Please transcribe this text

[Assistant] And it is the, the excitement of the deep learning community is unsupervised learning.


[User] Please transcribe this text

[Assistant] But it has achieved no major breakthroughs at this point.


[User] Please transcribe this text

[Assistant] This is the, I'll talk about what the future of deep learning is and a lot of the people that are working in the field are excited by it. But right now every excite, any interesting accomplishment is, has to do with supervised learning.


[User] Please transcribe this text

[Assistant] Uh, so this, it's a, uh, the question was, uh, this, uh, the paddle, the green paddle learns to play this game successfully against this specific one brown paddle with us operating under specific kinds of rules. How do we know it can generalize to other games, other things, and it can't.


[User] Please transcribe this text

[Assistant] But the mechanism by which it learns generalizes. So as long as you let it play,


[User] Please transcribe this text

[Assistant] As long as you let it play in whatever world you want it to succeed in. Long enough.


[User] Please transcribe this text

[Assistant] It will use the same approach to learn to succeed in that world. The problem is, this works for worlds you can simulate well.


[User] Please transcribe this text

[Assistant] Unfortunately, uh, one of the big challenges of neural networks is that we're not, they're not currently efficient learners.


[User] Please transcribe this text

[Assistant] We need a lot of data to learn anything. Human beings need like, need one example.


[User] Please transcribe this text

[Assistant] Oftentimes, and they learn very efficiently from that one example. So, uh, and again, I'll, I'll, I'll, I'll talk about that as well. It's a good question. So the drawbacks of neural networks. So if you think about the way a human being would approach this game, this game of Pong, they would only need a simple set of instructions. You're in control of a paddle, and you can move it up and down.