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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165225
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165226
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165227
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165228
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165230
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165231
[S1] I mean, do you think about the terraforming aspect, like actually build- are you so focused right now on the spaceships part that's so critical- [S2] Yeah, yeah. [S1] ... to get it to Mars? [S2] That's just-
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165232
[S1] We absolutely, if you can't get there, nothing else matters. [S2] Yeah. [S1] So, and like I said, you, you, you, we can't get there with, at some extraordinarily high cost. I mean, the current cost of, um, let's say, a, one ton to the surface of Mars is on the order of $1 billion. So, 'cause you don't just need th...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165233
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165234
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165236
[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] 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, just make, you g...
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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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165239
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165242
[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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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165245
[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] What does it even mean, really? [S2] I think Shakespeare had a thing about roses and stuff, whatever he said. [S1] Rose by any other name, it smells sweet. [S2] [LAUGHS] I got a Elon to quote Shakespeare. I feel, I feel like I accomplished something today. [S1] Shall I compare thee to a summer's day? [S2] [LAUGHS]...
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uh, autopilot. Tesla autopilot. [S1] [LAUGHS] [S2] Um. [S1] 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 st...
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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] And, and 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. Um, 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] What are some insights you've gained over those five, six years of autopilot about the problem of autonomous driving? So you leaped in having some sort of, uh, first principles kinds of intuitions, but nobody knows how difficult the, the pro- [S2] Yeah. [S1] Like the pro- [S2] I thought,
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[S1] Uh, and then you first are, well, realize what is the perception problem for driving? Like, all the kinds of things you have to be able to see. Like, what, what do we even look at when we drive? There's a, I just recently heard Andre talked about at MIT about, like, car doors. I think it was the world's greatest t...
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[S1] You know, our, as humans, when we drive, our actions affect the world. Like- [S2] Sure. [S1] It changes how others behave. Most autonomous driving, if you, you're usually just responding, uh, to the scene as opposed to, like, really, um, asserting yourself in the scene. Do you think,
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[S1] I think this, so actually I think, I think these, these sort of control, control logic conundrums are not, are not the hard part. Um, the, you know, let's see, um... [S2] What do you think is the hard part of, in this whole, um, beautiful, complex problem? [S1] So it's a lot of friggin' software, man. A lot of sma...
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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] 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, uh- [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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[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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[S1] ... processed images. [S2] Okay. So- [S1] Which is, which is quite a big reset on the training, 'cause the system's trained on post-processed image, images, so we need to redo all the training, uh, I, to train against the, the raw photon counts instead of the post-processed image.
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[S1] So ultimately, it's kind of reducing the complexity of the whole thing. So, uh, reducing, reducing the- [S2] Yeah. Lines of code will actually go, go lower. [S1] Yeah, that's fascinating. Um, so you're doing fusion of all the sensors, so reducing the complexity of having to deal with these- [S2] Fusion of the came...
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[S1] Yeah, well, well, actually, you need to incorporate, um, sound as well, um, 'cause, you know, you need to, like, listen for ambulance sirens or fi- you know, firetrucks pl- you know, uh, or if somebody, like, you know, yelling at you or something. I don't know, just th- this, th- there's a little bit of audio that...
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[S1] Honestly, frankly, like the ideas are, are the easy thing, and the implementation is the hard thing. Like the idea of going to the moon is, is the easy part. [S2] Yeah. [S1] But going to the moon is the hard part. [S2] Is the hard part. [S1] Um, and there's a lot of, like, hardcore engineering that's gotta get don...
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[S1] If we don't do this, the system will not work properly. Um, so the work of the engineers doing this, they are like the unsung heroes to some, you know, but they are cr- critical to the success of the situation. [S2] I think he made it clear. I mean, at least to me, it's super exciting, everything that's going on o...
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[S1] ... work of art. [S2] Yeah, I think we've got the, the, the sheer scale of it is, is boggles the mind. Like, the training, the amount of work done with, uh, like we've written all this custom software for training and labeling. [S1] Mm-hmm. [S2] Um, and to do order labeling. Order labeling is essential. Um,
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[S1] 'Cause especially when you've got, like, surround video, it's very difficult to, to, to, like, label surround video from scratch. It's extremely difficult. Um, it, like, take a human such a long time to even label one video clip, like several hours. [S2] Mm-hmm. [S1] Uh, or, or to label it, basically we're just ap...
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[S1] I have to ask, since I care about human-robot interactions, so the human side of that, so you've talked about mostly in the factory. Do you see it, uh, also, do you see part of this problem that Tesla Bot has to solve is interacting with humans and potentially having a place, like, in the home? So interacting, not...
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[S1] Um, and then then that, we'll have to pair that obviously with some kind of universal base, basic income in the future. Uh, so I think, um... [S2] So do you see a world when there's like hundreds of millions of Tesla bots doing different, performing different tasks throughout the world? [S1] Yeah, I haven't really...
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[S1] Can I ask a wild question? So the, the number of Tesla cars has been accelerating. It has been close to 2 million produced. Many of them have autopilot. [S2] I think we're over 2 million now. Yeah. [S1] Do you think there will ever be a time when there will be more Tesla bots than Tesla cars?
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[S1] Tesla bot or it's code named Optimus. I call, I call it Optimus Subprime. [S2] [LAUGHS] [S1] [LAUGHS] It's not, it's not like a giant, you know, transformer robot. Um, so, uh, but it's meant to be a general purpose health, health robot. Um,
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[S1] Still, you do talk about love sometimes. So let me ask, this isn't like for like sex robots or something like that. [S2] I love it's the answer. [S1] Yes. Uh, there is something compelling to us. Not compelling, but we connect with, um, humanoid robots or even leg robots, like with, with dog and shapes of dogs. It...
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[S1] You know, there's a huge amount of loneliness in this world. All of us seek companionship in, with other humans, friendship and all those kinds of things. We have a lot of, here in Austin, a lot of people have dogs. [S2] Sure. [S1] Um, there seems to be a huge opportunity to also have robots that decrease the, uh,
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[S1] ... match, match the, the, the owner or the, you know, or I guess the owner, uh- [S2] Well, uh- [S1] ... whatever you want to call it. [S2] Uh, the other- [S1] The companion. [S2] The other half, right? Uh, in the same way that friends do. See, I think that's a huge opportunity. I think- [S1] Yeah, no, that's inte...
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[S1] The, 'cause, you know, like there's a, a Japanese phrase, or like the, uh, uh, wabi-sabi, you know, uh, the subtle imperfections are what makes something special. [S2] Yeah. [S1] And the subtle imperfections of the personality of the robot mapped to the subtle imperfections of the robot's human friend. I don't kno...
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[S1] In, in that way, the imperfections- [S2] Like R2-D2 or like a- [S1] Right. [S2] C-3po sort of thing, you know? [S1] So from a machine learning perspective-
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[S1] I think the flaws being a feature is really nice. You could be quite terrible at being a robot for quite a while in the general home environment or all the, in general world, and that's kind of adorable. And that's like, those are your flaws, and you fall in love with those flaws. So it's, it's a nice- [S2] Yeah, ...
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[S1] Yeah, in fact, if you think of like, uh, C-3po and R2-D2. [S2] Yeah. [S1] Like, they actually had a lot of, like, flaws and imperfections and silly things, and they would argue with each other. [S2] [LAUGHS] [S1] And, um... [S2] Were they actually good at doing anything? [S1] [LAUGHS] [S2] I, I, I'm not exactly su...
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[S1] But I, I, our initial focus is just to make it useful. Uh, so, so, um, I'm confident we'll get it done. I'm not sure what the exact time frame is, but, uh, like we'll probably have, I don't know, a decent prototype towards the end of next year or something like that. [S2] And it's cool that it's connected to Tesla...
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[S1] And an extra module on top of the vector space, uh, for love. [S2] Uh, yeah. [S1] That's missing. [S2] [LAUGHS] [S1] [LAUGHS] Okay. Uh- [S2] We can add that to the car, too. [S1] That's true. Um, yeah, it could be useful in all environments. Like you said, a lot of people argue in the car, so maybe we can help the...
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[S1] Uh, you're a student of history, fan of Dan Carlin's Hardcore History Podcast. [S2] Yeah, it's great. [S1] Greatest podcast ever. [S2] Yeah, I think it pro- it is, actually. [S1] [LAUGHS] I, I, it doesn't, it almost doesn't really count as a podcast. It's so good. [S2] Yeah, it's not, it's, it's, it's more like a ...
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[S1] Uh, it was mostly mo- Well, it was supposed to be a deep dive on fighters and bomber, uh, technology in World War II, um, but that ended up being more wide ranging than that. [S2] Yeah. [S1] Um, 'cause I just went down the, a total rat hole of, like, s- studying all of the, the fighters and bombers of World War II...
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[S1] Uh, one country to make this plane, then to make a plane to beat that, and that's the- that's how they make a plane to beat that, and then they'll do- [S2] [LAUGHS] [S1] [LAUGHS] And really what matters is, like, th- the pace of innovation, um, and also access to high-quality, uh, fuel and, uh, raw materials.
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[S1] Like high octane is the most important thing, but also can't have like impurities and stuff, uh, 'cause you'll, you'll foul up the engine. And, and, and German just never had good access to oil. Like they tried to get it by invading the Caucasus. [S2] Mm-hmm. [S1] Um, but that didn't work too well. [S2] [LAUGHS] [...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165315
[S1] Uh, whereas the US had awesome fuel, um, and they provided that to Britain as well. Um, so that allowed the British and the Americans to design, uh, aircraft engines that were, uh, super high performance, better than anything else in the world. In Germany, Germany could, could, could design the engines, they just ...
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[S1] Yeah. [S2] It was a good year. [S1] Yeah. [S2] You know, that would be par for the course. [S1] Yeah. [S2] Like, just, just not starving to death would have been, uh, like the primary goal of most people in th- throughout history, is making sure we have enough food to last through the winter and not get, you know,...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165317
[S1] ... sounds cool and that looks cool. So it's- [S2] Uh, well, it's, it's, you know, it's, it's a, it's a, it takes a moment to read Cyrillic. [S1] [LAUGHS] [S2] Um, once you know what the Cyrillic characters stand for, actually, then reading Russian becomes a lot easier 'cause there are a lot of words that are actu...
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[S1] And, uh... [S2] So find the words that are exactly the same, and now you start to understand Cyrillic. Yeah. [S1] If you can, if you can sound it out, then, uh, the, it's much, there's at least some commonality of words. [S2] What about the culture?
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165319
[S1] You, uh, you love great engineering, physics. There's a tradition of the sciences there. [S2] Sure. [S1] You look at the 20th century from rocketry, so, you know, some of the greatest rockets, some of the space exploration has been done in the Soviet, in the former Soviet Union. [S2] Yeah. [S1] So do you draw insp...
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[S1] A lot of it is lost to history because it's not translated, all those kinds of, because it, it is in some ways an isolated culture. It, it flourishes within its, within its borders. Um- [S2] Yeah. [S1] So do you draw inspiration from those folks, from, from the history of- [S2] Yeah, um- [S1] ... science, engineer...
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[S1] Yeah. [S2] I love people like that. [S1] Tim Dodd is, uh, really, really great. If you're interested in anything to do with space, um, he's, in terms of, uh, explaining rocket technology to your average person, he's awesome. The best, I'd say. Um, and, um, I should say, like, the, probably the reason, like, uh, I,...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165322
[S1] And now for something completely different. Do you mind doing a, a bit of a meme review in the spirit of the great, the powerful PewDiePie? Let's say 1 to 11. [S2] Okay. [S1] Just go over a few documents, print it out. [S2] We can try. [S1] Let's try this. I present to you document num- numero uno. [S2] [LAUGHS] I...
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[S1] You get it because, uh, he likes- [S2] Yes, I get it. [S1] ... compelling things. [S2] Yes, I get it. I don't know, three, whatever. Uh- [S1] Oh, that's not very good. [S2] [LAUGHS] [S1] This is, um, ground in some engineering, some history. [S2] [LAUGHS] Uh, yeah, give us an eight out of 10. [S1] What do you thin...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165324
[S1] I, I, in a place that is not subject to extreme natural disasters, I think it's a, nuclear power is a great way to generate, uh, electricity. Um, I, I, I don't think we should be shutting down nuclear power stations. [S2] Yeah, but what about Chernobyl? [S1] [LAUGHS] [S2] [LAUGHS] [S1] Um, so, uh, I think, I think...
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165325
[S1] Like, I'm still alive. Okay. [S2] So it's not even that the risk of these events is low, but the impact of them is, is- [S1] The impact is greatly exaggerated. It's, it's just great. [S2] It's human nature. [S1] [LAUGHS]
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[S1] Yeah, I guess radiation is one of the words that can be used as a, as a tool to, to, to fear-monger by certain people. That's it. And- [S2] I think people just aren't, don't understand, so- [S1] I mean, that's the way to fight that, uh, that fear, I suppose, is to understand, is to learn.
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bilibili_data_1932888523_BV1F44y1n7Uv_BV1F44y1n7Uv_m4-dialogue_0165327
[S1] Yeah, just say like, okay, how many people have actually died from nuclear accidents? It's like practically nothing. And, uh, say how many people have, have died from, you know, coal plants? And it's a very big number. [S2] Mm-hmm. [S1] So, like, obviously we should not be starting up coal plants and shutting down...
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[S1] one. It's really bad. [S2] [LAUGHS] [S1] So, uh, it's, uh, 90, 180, and 360 degrees. Everybody loves the math. Nobody gives a shit about two, 270. [S2] It's not super funny. [S1] It is. [S2] I don't know, like 203. [S1] Yeah. [S2] Um, this is not, uh, you know, LOL situation. [S1] [LAUGHS] Yeah.
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