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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165329
[S1] Uh- [S2] That's pretty good. [S1] The United States oscillating between establishing and destroying dictatorships. It's like a met- Is that a met- [S2] Yeah. [S1] What is that- [S2] Metronome. [S1] Yeah. [S2] Yeah. It's, uh, out of seven, out of 10, it's kinda true. [S1] Oh, yeah. This is, uh, this is kinda person...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165330
[S1] Like or something? [S2] As like as, uh, like, uh, a husband. [S1] Husband. Yeah, yeah. [S2] Hello? Yes, this is Doug. Your wife was launched into space. And then the last one is him with his eyes closed in a bottle of vodka. [S1] Yeah. Like it didn't come back. [S2] No. [S1] Uh- [S2] They don't tell you the full s...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165331
First man in space, nobody cares. First man on the moon. [S1] Well, I think people do care. [S2] I know, but, um, I, there is- [S1] Yuri Gagarin's names will, will, will, will be forever in history, I think. [S2] There is something special about placing, like, stepping foot onto another- [S1] Sure. [S2] ... totally for...
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[S1] Yeah. [S2] Well, this is about you. [S1] [LAUGHS] [S2] Oh, yeah. I'd love to get your comment on this. Elon Musk, after sending $6.6 billion to the UN to end world hunger, you have three hours. Um... [S1] Yeah, well, I mean, obviously $6 billion is not gonna end world hunger, so...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165333
[S1] What do you think that one gets? [S2] Uh, two. [S1] I'm just going after Empire's world. Uh, where did you get those artifacts? The British Museum. Shout out to Monty Python. We found them.
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[S1] Yeah, the British, uh, the British Museum is, is pretty great. I mean- [S2] Yeah. [S1] ... it, admittedly, Britain did take, uh, these historical artifacts from all around the world and put them in London, but, uh, you know, it, it, it's not like people can't go see them. Uh, so it is a, a convenient place to see ...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165335
[S1] Like I said, you gotta look at these things in the context of the, of the history of the time, um, and say, what, what are, what are the alternatives and what are you comparing it against? [S2] Yes. [S1] And I, I, I, I do not think it'll be the case that, um, Britain would get a, a bad grade in, in when looking at...
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[S1] from what is morally acceptable today, you're basically gonna give everyone a failing grade. [S2] Yeah. [S1] I'm, I'm not clear. It's not, I don't think anyone would get a passing grade, um, in, in their morality, uh, of, like, you go back 300 years ago, like, who, who's getting a passing grade? [S2] [LAUGHS] [S1]...
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[S1] Yeah, explain, I'm a, a meme, what it, like a, you know, like a scribe that, like, runs around with the kings and just, like, writes down memes. [S2] I mean, when was the cheeseburger adventure? That's, like, an epic invention. [S1] Yeah. [S2] Like, like, wow. You know? [LAUGHS] That was- [S1] Versus just, like, a...
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[S1] You know, um- [S2] Then there's like, what is a burger, what's, what's a sandwich, and then you start getting- [S1] Yeah. [S2] ... It's a pizza sandwich, and what is the original? It's, it's, it gets into an ontology argument. [S1] Yeah, but everybody knows, like, if you order, like, a burger or cheeseburger or wh...
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[S1] That is not what I would have guessed. [S2] But everyone knows, like, you, you, you, you, like, if you order a cheeseburger, you know what you're getting, you know? It's not like some obtuse, like, "I wonder what I'll get," you know? Um, you know, uh, fries are, I mean, great. I mean, they're the devil, but fries ...
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[S1] Someone drew a bunch of dicks all over the walls, 16 Chapel, boys bathroom. [S2] Sure, I'll give it a nine. It's super, it's really true. [S1] All right, this is our highest ranking meme for today. [S2] I mean, it's true. Like, how did they get away with it? [S1] Lots of nakedness. [S2] I mean, dick pics are, I me...
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[S1] ... Throughout human history. [S2] You, you tweeted that you aspire to comedy. You're friends with Joe Rogan. Might you, uh, do a short stand-up comedy set at some point in the future? Maybe, um, open for Joe, something like that. Is that, is that- [S1] Early stand-up? Actual, just full-on stand-up. [S2] Full-on s...
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[S1] I wonder if I could. Um, I mean, I- [S2] Only one way to find out. [S1] You know, I, I have done stand up for friends just, uh, impromptu, you know, I'll get, get on like a roof, uh, and they, they do laugh, but they're our friends too. So, I don't know if, if you got a call, you know, like a room of strangers, ar...
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[S1] ... what happens. [S2] I think you'd learn something either way. Um- [S1] Yeah. [S2] I kinda love, um, both the, when you bomb and when, when you do great, just watching people, how they deal with it. [S1] Sure. [S2] It's, it's, it's so difficult. It's so, you're so fragile up there. It's just you. And you, you th...
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[S1] Yeah, I might have enough material to do stand-up. [S2] [LAUGHS] [S1] I, I've never thought about it, but I might have enough material. Um, I don't know, like 15 minutes or something.
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[S1] Oh, yeah. Yeah. Do a, do a Netflix special. [S2] [LAUGHS] A Netflix special, sure. [S1] Um, what's your favorite Rick and Morty concept? Uh, just to spring that on you, is there, there's a lot of sort of scientific engineering ideas explored there. There's the- [S2] [LAUGHS] Favorite Rick and Morty. [S1] There's t...
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[S1] Yeah, that's right. [S2] ... that you voiced. [S1] Yeah. Rick and Morty suddenly explores a lot of interesting concepts. Uh, so like, what's a favorite one? I don't know. The, the, the butter robot suddenly is, uh, you know, it's like, it, it's suddenly possible to have too much sentience in a device. Um, like you...
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[S1] Um- [S2] Do you think it's too easy from a, if we're talk about from the engineering perspective of super intelligence, like with Marvin, the robot, like is it, it seems like it might be very easy to engineer just a depressed robot. Like it- [S1] Sure. [S2] It's not obvious to engineer a robot that's going to find...
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[S1] I think that's the thing to aim for. You know, not, not to try to be sort of a leader for just for the sake of being a leader or whatever. Um, a lot of the time people who, who, if, if, if, a lot of the time, if you, the people you want as leaders are, are the people who don't want to be leaders. [S2] [LAUGHS] [S1...
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[S1] Is... [S2] Yeah, what's the meaning of life and all, you know? But just generally, like I said, I would, I would encourage people to read broadly, um, in many different subject areas, um, and, and, and then try to find something where there's an overlap of your talents and, and what you're interested in. So people...
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[S1] That's a good, a good, uh, combination of, of your, of the things that you're inherently good at, but you also like doing. Um, and, um- [S2] And reading is a super fast shortcut to, to figure out which, where are you. You're both good at it, you like doing it, and it'll actually have positive impact. [S1] Well, yo...
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[S1] ... Encyclopedias were digestible, I think. [S2] [LAUGHS] [S1] Uh, you know, whatever, 40 years ago. Um, so, um, you know, re- maybe read through the, the c- the condensed version of the Encyclopedia Britannica. I'd recommend that. Um, you can always, like, skip subjects, or you- you read a few paragraphs and you-...
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[S1] Yeah. [S2] Pi is not fixed. [S1] Yes. [S2] Um, uh, so you really want to make sure you don't, you're not operating, um, without realizing it from a zero-sum mindset where, where the only way to get ahead is to take things from others. Then that's gonna result in you t- trying to take things from others, which is n...
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[S1] Mm-hmm. [S2] And that, that applies in a lot of kinds of domains. It applies in academia where a lot of people are very, uh, see some funding for academic research is zero-sum and it is not. If you celebrate each other, if you make, if you get everybody to be excited about AI, about physics, about mathematics, I t...
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[S1] I mean, there were many books, poems, and songs written about what is love and what is, what exactly, you know, um, you know, what is love, baby, don't hurt me. [S2] [LAUGHS] Um, that, that's one of the great ones, yes. [S1] Yeah. [S2] You've, you've earlier quoted Shakespeare, but that, that's really up there. [S...
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[S1] Pretty, we're, we're okay. [S2] [LAUGHS] [S1] On, on the whole, we're, we're pretty interesting, uh, bunch. [S2] Yeah. All things considered, and I've read a lot of history, including the darkest, worst parts of it, and, uh, despite all that, I think on balance, I, I still love humanity.
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[S1] You joked about it with a 42. Uh, what do you, what do you think is the meaning of this whole thing? [S2] [LAUGHS] [S1] Is it like, is there a non-numerical- [S2] Oh, I should say like- [S1] ... representation? [S2] Uh, yeah. Well, really, I think what Douglas Adams was saying in Hitchcock's Guide to the Galaxy is...
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[S1] What questions to ask about the universe. You want to understand the meaning of life. We need to expand the scope and scale of consciousness so that we're better able to understand the nature of the universe and, and understand the meaning of life. [S2] And ultimately, the most important part would be to ask the r...
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that's just the best that we could do. So then we, we had, we went ahead and launched. Now, I'm not a religious person, um, but I nonetheless got on my knees and prayed for, uh, for that mission. [S1] Were you able to sleep? [S2] No. [S1] How did it feel when it was a success? First, when the launch was a success, and ...
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[S1] Not what other people see when they see the product, you know. [S2] So, let me, uh, ask you then to analyze Starship in that same way. I know you have, you'll talk about, in more detail about Starship in the near future, perhaps. You had that- [S1] Yeah, we can talk about it now if you want. [S2] Um, but just in t...
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[S1] If you magically could solve one problem perfectly, one engineering problem perfectly, which one would it be? [S2] On Starship? [S1] On, on, sorry, on Starship. So is it maybe related to the efficiency, the, uh, the engine, the weight of the different components, the complexity of various things, maybe the control...
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[S1] ... the, 'cause I say currently the, the, the best rocket engine ever is probably the RD-180 or RD-170, the, um, that, that's the Russian engine, basically. Um, and, um, and still, it, I think an engine should only count if it's gotten something to orbit. [S2] [LAUGHS] [S1] Um, so our engine has not gotten anythin...
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the throat, uh, expansion ratio. [S1] So why is it such a hard engine to manufacture at scale? [S2] Uh, it's very complex. [S1] It's a lot of com- what is complexity mean here? There's a lot of components involved. [S2] There's a lot of, a lot of components and a lot of, uh, unique materials that, uh, so we have to inv...
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[S1] I mean, we're talking about catching the largest flying object ever made, uh, with, on a giant tower with, with tough stick arms. It's like a karate kid with the fly, but much bigger. [S2] Yeah. [S1] [LAUGHS] [S2] I mean, pulling something like- [S1] This is probably, weren't work the first time. [LAUGHS] Uh, and ...
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[S1] Like, at this point I'd say we're confident that, that, like, let's say, I'm very confident success is in the set of all possible outcomes. [S2] Mm. [S1] [LAUGHS] [S2] All right. It's not in the whole set of- [LAUGHS] [S1] For, for, for a while there I was not convinced that success was in the set of possible outc...
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[S1] What's your source of belief in situations like this, when the engineering problem is so difficult? There's a lot of experts, many of whom you admire, who have failed in the past. [S2] Yes. [S1] And, um, a lot of people, you know, the, a lot of experts, maybe journalists, all the kind of, you know, the public in g...
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[S1] A source of strength. I, I just really not how I think about things. Um, I mean, for me it's simply this, this is something that is important to get done, um, and we, we should just keep doing it, um, or die trying. And I, I don't need a source of strength. [S2] So quitting is not even like, um- [S1] It's not, it'...
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[S1] ... and get it done. [S2] Can you, uh, then zoom back in to specific problems with Starship or any engineering problems you work on? Can you try to introspect your particular biological neural network, your thinking process, and describe how you think through problems, the different engineering and design problems...
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[S1] ... then, uh, economies of scale are not the issue. [S2] Do you throw into manufacturing, do you throw, like, supply chain, you talked about resources and materials and stuff like that. Do you throw that into the calculation of trying to reason from first principles, like how we're gonna make the supply chain work...
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[S1] So then, and that is always a, usually, almost always a very low number. Um, so then the, the, what's actually causing things to be expensive is how you put the atoms into the desired shape. [S2] Yeah, actually, if you don't mind me taking a tiny tangent, I had a, I often talk to Jim Keller who's somebody that wor...
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[S1] We just, we had an argument. What is, how can you reduce the cost of scale of, of producing a robot? Because, so I got a chance to interact quite a bit, um, obviously in, in the academic circles with humanoid robots and the Boston Dynamics and stuff like that. And they're, they're very expensive to, to build. [S2]...
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[S1] Yeah. I, I mean, I think if you, if you are really good at manufacturing, you can basically make, at high volume, you can basically make anything for a cost that asymptotically approaches the raw material value of the constituents, plus any intellectual property that you need to do license. [S2] [LAUGHS] Right. [S...
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[S1] Well, just as a function of inertia, people, uh, want to use the same tools and methods that they are familiar with. Um, they just, that's what they'll do by default. [S2] Yeah. [S1] Um, and then that, that will lead to an outcome of things that can be made with those tools and methods, but is unlikely to be the, ...
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[S1] Best case is about five years, worst case, 10 years. [S2] What are the determining factors, would you say, from an engineering perspective, or is that that not the bottlenecks? [S1] Uh, you know, it's, it's fundamentally, um, you know, engineering the, the vehicle. Um, I mean, Starship is the most cr- complex and ...
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act quickly while the window is open, just in case it closes. [S1] Yeah, the existence of nuclear weapons, pandemics, all kinds of threats- [S2] Yeah. [S1] ... should, uh, should kind of, um, give us some motivation.
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[S1] ... to get a large number of folks there who will never return back to Earth. [S2] Uh, no, they could certainly return. Some will return back to Earth. [S1] They will choose to stay there for the rest of their lives. [S2] Yeah, many will. Um, but, uh, we, we, you know, we, we, we need the spaceships back, like the...
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[S1] ... this is, we absolutely, if you can't get there, nothing else matters. [S2] Yeah. [S1] So, and, and like I said, you, you, you can't get there with, at some extraordinarily high cost. I mean, the current cost of, um, let's say, uh, one ton to the surface of Mars is on the order of $1 billion. So, 'cause you don...
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[S1] In order to be self-sustaining, you can't be mis- Like you need, you know, semiconductor f- fabs, you need iron ore refineries, you- Like you need l- lots of things, you know? Uh, so, um, and Mars is not super hospitable. It's- it's the least inhospitable planet, but it's definitely a fixer-upper of a planet. [S2]...
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[S1] There might be some great planets out there, but it's hopeless. [S2] Too hard to get there? [S1] Yeah, way, way, way, way, way too hard, uh, to say the least. [S2] Let me push back on that. Not really a pushback, but a quick, uh, curve ball of a question. So you did mention physics as the, the first starting point...
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[S1] Just think of it like, um, like imagine if you had an aircraft or something, or a, a car, and if you had to buy a new car, every time you went for a drive, it'd be very expensive. It'd be silly, frankly. [S2] Mm-hmm. [S1] But, um, but you, in fact, you just refuel the car or recharge the car, and that's, uh, makes...
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[S1] ... which is crazy. [S2] Yeah. [S1] So- [S2] That's incredible. So you're saying like it's, uh, by far the biggest bang for the buck is to make it fully reusable versus like some kind of brilliant breakthrough in theoretical physics. [S1] Yeah, no. There's no, there's no brilliant break- no, there's no th- it, jus...
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[S1] Is, I mean, the, the interesting reason to talk about this stuff, it also make, helps people dream about the future. I know you're really focused about the short-term engineering dream, but it's like, I don't know, there's something about imagining an actual civilization on Mars that gives people- [S2] Sure. [S1] ...
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[S1] Um, direct democracy. People vote on laws, the population votes on laws themselves, and then the laws must be short enough that people can understand them. [S2] Yeah, and then, like, keeping a well-informed populace, like, really being transparent about all the information, about what they're voting for. [S1] Yeah...
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[S1] ... me, this is just a, a great- [S2] I'm trying to accept, uh, yes, you can have my damn cookie. I don't care. Whatever. [S1] You heard it from Elon first. He accepts all your damn cookies. [S2] Yeah. [S1] [LAUGHS] [S2] And stop asking me. [S1] Uh- [S2] It's annoying. [S1] Yeah, it's, uh, it's one example of, um,...
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[S1] Yeah, it's, it's somebody who's like, it was some good intentions of like privacy or whatever, but now everyone just has to click accept cookies and it's not, you know, you have billions of people who have to keep clicking accept cookie and it's super annoying. [S2] [LAUGHS] [S1] And we're just, just accept the da...
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[S1] Yeah, so it, I mean, I guess the problem with the Constitution is it's, it's kinda like C versus Java 'cause it doesn't have any garbage collection built in. I think there should be a, I, I, when you first said the, the, the metaphor of garbage collection, I loved it. [S2] Yeah, it's from a coding standpoint. [S1]...
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[S1] ... thing where they kind of die after a while unless somebody explicitly, publicly defends them. [S2] Yeah. [S1] So that, that's sort of, it's not like somebody has to kill them, they kind of die themselves. They disappear. [S2] Yeah. Um... [S1] Not to defend Java or anything, but, you know, C++, you know, you co...
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[S1] So let me be the guy, you posted a meme on Twitter recently where there's, there's like a, a row of urinals and a guy just walks all the way across. [S2] Oh, sure, yeah. [S1] And he tells you about crypto. [S2] [LAUGHS] [S1] So, so, so- [S2] Listen, listen, I, I mean, that's happened to me so, so many times. I thi...
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[S1] Do you think, technologically speaking, there's any room for ideas of smart contracts or, or so on? 'Cause you mentioned laws. Um, that's an interesting implement- use of things like smart contracts to implement the laws by which governments function. Like something built on Ethereum or maybe a dog coin that enabl...
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[S1] So it's- [S2] I'm too dumb to understand small contracts. [S1] [LAUGHS] That's a good line. [LAUGHS] [S2] I mean, my general approach to any kind of like deal or whatever is just make sure there's clarity of understanding. That's the most important thing. [S1] Right. [S2] Um, and, and just keep any kind of deal ve...
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[S1] I don't know if Mars would have a cryptocurrency as a thing, but probably. It seems likely. Um, but it would be some kind of localized, uh, thing on Mars. Um... [S2] And you let the people decide. [S1] Yeah. Uh, absolutely. [S2] [LAUGHS] [S1] The, the future of Mars should be up to the Martians. Uh, yeah. So, um, ...
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[S1] Okay, you mean literally, that's- [S2] Literally. [S1] ... that, that literally what's happening. [S2] In batch mode. Okay. [S1] In batch mode. [S2] Yeah. And, uh, pretty, the poor bastards who have to maintain that code. Okay, that's a, that's a pain, that's pain. [S1] Not even Fortran's COBOL. Yep. [S2] It's COB...
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[S1] 'Cause there's no, there's no resource allocation. Money is a database for resource allocation. If it's no resource to allocate except yourself, so money's useless. Um, uh, if you're stranded on a desert island with no food, you'd, uh, all the Bitcoin in the world will not stop you from starving. [S2] Yeah. [S1] S...
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[S1] But people have proposed different technological solutions. [S2] Like Lightning. [S1] Yeah, Lightning Network and the Layer-2 technologies on top of that. I mean, it's, it's all, it seems to be all kind of a trade-off, but the point is, it's kind of brilliant to say that just think about it in information, think a...
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[S1] ... that exchange information. [S2] Yeah, so say like you're operating an economy, um, and you need to have something that it, uh, allows for the efficient, to, to, to have efficient, uh, value ratios between products and services. So you got this massive number of products and services and you need to, you can't ...
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[S1] 'Cause you're like, "Oh, I, if I, I'll just hold it and not spend it because it's scarcity is increasing with time. So if I spend it now, then I will regret spending it, so I will just, you know, hodl it." [S2] Mm-hmm. [S1] Um, but if there's some dilution of the currency occurring over time, that's, that's more o...
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[S1] I'm not sure that's, that's neither here nor there. Uh, but he, he seems to be the one more responsible for the ideas behind Bitcoin than anyone else. [S2] So it's not perhaps like singular figures aren't even as important as the, the figures involved in the evolution of ideas that led to a thing. So- [S1] Yeah. [...
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[S1] Tesla autopilot. [S2] [LAUGHS] [S1] Um, Tesla autopilot has been through an incredible journey over the past six years, um, or perhaps even longer in the minds of, in your mind and the minds of many involved. Uh- [S2] Yeah, I think that's where we first, like, connected, really, was the autopilot stuff, uh, autono...
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[S1] Because I knew, well, part of it is I was at MIT and I, I knew the difficulty of computer vision. [S2] Yeah. [S1] And I knew the whole, I had a lot of colleagues and friends about the DARPA challenge. I knew how difficult it is. [S2] Mm-hmm. [S1] And so there was an actual skepticism. When I first drove a Tesla wi...
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Um, like stay in the lane and create a comfortable experience. So my intuition initially was that the lane keeping problem is way too difficult to solve. [S1] Oh, lane keeping, yeah, that's relatively easy. [S2] No, I, well- [S1] Yeah. [S2] But, like, uh, but not the, but solve in the way that we just, we talked about ...
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[S1] Everything, the hardware, the compute, the sensors, the, uh, the things I maybe care and love about most is the, uh, the stuff that Andrej Karpathy is leading with the data set selection, the whole data engine process, the neural network architectures, the, the way that's in the real world, that network is tested,...
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[S1] Um, and, uh, Andre is awesome and obviously plays an important role, but we have a, a lot of really talented people driving things. So, um, and, uh, Ashok is actually the, the head of autopilot engineering. Uh, uh, Andre is the director of AI. [S2] AI stuff. [S1] Yeah. [S2] Yeah, yeah. So, yeah, there's, I'm aware...
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[S1] Yeah. [S2] Like the problem. [S1] I thought, I thought the self-driving problem would be hard, but it's, it was harder than I thought. It's not like I thought it would be easy. I thought it would be very hard, but it was actually way harder than, than even that. So, I mean, what it comes down to at the end of the ...
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[S1] What's involved in driving, like, especially in all the different edge cases. Um, and, and then the, I mean, maybe you can comment on this, how much game-theoretic kind of stuff needs to be involved, you know, at a four-way stop sign. You know, our, as humans, when we drive, our actions affect the world. Like- [S2...
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[S1] The peripheral vision is so cool. [S2] Yeah. [S1] It makes you realize all the illusions for vision science is so, it makes you realize just how incredible the brain is. [S2] The brain is doing crazy amount of post-processing on the vision signals for your eyes. Um, it's insane. So, um, and then, and then even onc...
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[S1] the things that you see into, uh, the smallest, t- the smallest amounts of information possible. So your brain is trying to not just get to a vector space, but get to a vector space that is the smallest possible vector space of only relevant objects. Um, and I think, like, you- you can sort of look inside your bra...
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[S1] Um, and, and what's your... [S2] [LAUGHS] [S1] And I say it's not that great. Okay, you and I is a, you know, like, um, and people are constantly distracted and thinking about things and texting and doing all sorts of things they shouldn't do in the car, changing the radio station. So...
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[S1] It does seem to be able to, uh, edit it down or compress it even further into things like concepts. So it's not, it's like it goes beyond, the human mind seems to go sometimes beyond vector space to, to sort of space of concepts, to where you, you'll see a thing, it's no longer represented spatially somehow. It's ...
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[S1] ... need to fully represent those things. Or maybe you get those kind of, um... [S2] Well, you, you- [S1] ... indirectly. [S2] You need, you need to like establish vector space and then actually have predictions for, uh, that ve- those vector spaces. So like, um, you know, like if, uh,
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[S1] ... truck or something blocking sight. Um, but you, before you came up to the truck, you saw that there were some kids about to cross the road- [S2] Mm-hmm. [S1] ... in front of the truck. Now, you can no longer see the kids, but you, you, you need to be able, but you would now know, "Oh, okay, those kids are prob...
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[S1] memory, uh, th- th- you have to, you need to remember that there were kids there, and you need to have some f- forward prediction of what their, uh, position will be- [S2] It's a really hard problem. [S1] ... at the time of relevance. [S2] So with, with occlusions and computer vision, when you can't see an object ...
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[S1] Yeah. [S2] So some of it- [S1] It's like object permanence. Like same thing happens with the humans with neural nets. Like if it, when like a toddler grows up, like there's a, there's a point in time where, uh,
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[S1] ... they develop, they have a sense of object permanence. So before a certain age, if you have a bowl, uh, or a toy or whatever, and you put it behind your back, and you pop it out, if they don't, before they have object permanence, it's like a new thing every time. It's like, "Whoa, this toy went, poof, disappear...
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[S1] Um- [S2] Sometimes I wish we never did figure out- [S1] [LAUGHS] Objective- [S2] ... permanent. [S1] Yeah, so that's, uh- [S2] That's an important problem to solve. [S1] Yes. So, so, like an important evolution of the neural nets in the car is, uh,
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[S1] ... across the road, and you can't, you can't quite see them because of an occlusion, uh, and, but they might wait for a minute before the light changes for, for them to cross the road. You still need to, to remember that they're, that, that's where they were. [S2] Mm-hmm. [S1] Um, and that they're probably going ...
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[S1] And I just think the data engine side of that, so getting the data to learn all of the concepts that you're saying now is an incredible process. It's this iterative process of just, it's this, this hydranet of many- [S2] Hydranet. [S1] Yeah. [S2] We're changing the name to something else. [S1] Okay. I'm sure it'd ...
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[S1] Oh, so every time there's a new major version, you'll rename it to something more ridiculous or- [S2] Uh- [S1] Or memorable and beautiful. Sorry. Not ridiculous, of course.
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[S1] If you see the full, the full, like, uh, array of neural nets that, that, that are operating in the car, it's, it kind of boggles the mind. There's so... [S2] Yeah. [S1] There's so many layers, it's crazy. Um, so, yeah. Um, but, and, and we, we, we started off with, uh, simple neural nets that were, uh, basically ...
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[S1] So you want to compile the whole thing down and with your own compiler. [S2] Yeah. Absolutely. [S1] Like so efficiency here, 'cause there's all kinds of compute. There's CPU, GPU, there's like- [S2] Yeah. [S1] ... the basic types of things that's, and you have to somehow figure out the scheduling across all of tho...
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[S1] Uh, and so we don't care if our picture's looking pretty. Um, we, we, we just want the data. We, we, so we're, we're moving to just roll, roll photon counts. So the system will, like the image that the, that the computer sees is actually much more than what you'd see if you represented it on a camera. [S2] Wow. [S...
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[S1] And that affects your control decisions and all those kinds of things. Okay. Um- [S2] Yeah, the, the cars is gonna fundamentally maneuver better with lower jitter. Um- [S1] Got it. [S2] And the, the, the, the cars will maneuver with superhuman ability and reaction time much faster than a human. I mean, I think ove...
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[S1] You know, are far more than what, like, James Bond could do in, like, the best movie type of thing. [S2] That's exactly what I was imagining in my mind, as you said it. Um... [S1] It's like an impossible maneuvers that a human couldn't do, you know, so. [S2] Well, let me ask, sort of, uh, looking back the six year...
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[S1] Yeah, so, uh, I mean, you can see that anybody who's been following the Full-Slow Driving beta closely, um, will see that the, um, the rate of disengagements has been dropping rapidly. So, like, a disengagement would be where, where the driver intervenes to prevent the car from doing something. [S2] Right. [S1] Uh...
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[S1] Uh, much better than the average human. I think it's gotta be at least two or three times, uh, higher safety than a human. So, or two or three times lower probability of injury than a human. Uh, before, before we would actually say like, okay, it's okay to go. It's not gonna be equivalent. It's gotta be much bette...
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[S1] Um- [S2] We were hoping to get 11 out this year, but it's, uh, it, it, it, it, 11 actually has a whole bunch of, uh, fundamental rewrites on the neural, neural network architecture, um, and, and some f- fundamental improvements, uh, in creating vector space. Uh, so, um- [S1] There is a, some fundamental like leap ...
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[S1] Uh, 11 would be, uh, a single stack for all, you know, one stack to rule them all. [S2] A single stack. [S1] Um, and, uh, but, but there, there are just some really fundamental, uh, neural net architecture changes th- that are, that, that will allow for, um, much more capability, but, but, you know, at, at first t...
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[S1] It's, it's, it's basically taking a whole bunch of C, C++ code and, and, and deleting a massive number, amount of C++ code and replacing it with a neural net. And, you know, Andre, um, makes this point a lot, which is like neural nets are kind of eating software. [S2] Mm-hmm. [S1] You know, over time, there's like...
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[S1] Um, deliver a, a giant bag of points, uh, to the C++ or C, C++ code. [S2] Yeah. [S1] Um, we call it the giant bag of points. [S2] Yeah. [S1] Uh, and it's like, so you've got a pixel and, and, and, and something associated with that pixel. Like this pixel is probably car. This pixel is probably lane line. Um, then ...
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bilibili_data_1932888523_BV1F44y1n7Uv_p1_BV1F44y1n7Uv_p1_m4-dialogue_0043274
[S1] and, uh, does a pretty good job of it, but it's, it's, uh, it's, we, we wanna just, we need another layer of neural nets on top of that to take the, the giant bag of points and distill that down to, uh, vector space in the, in the neural net part of the software as opposed to the heuristics, uh, part of the softwa...
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audio/en/bilibili_data_1932888523_BV1F44y1n7Uv_p1_BV1F44y1n7Uv_p1_m4-dialogue_0043274.mp3
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