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George Hotz: Comma.ai, OpenPilot, and Autonomous Vehicles | Lex Fridman Podcast #31


small model | large model

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The following is a conversation with George Hotz.
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He's the founder of Kama AI,
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a machine learning based vehicle automation company.
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He is most certainly an outspoken personality
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in the field of AI and technology in general.
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He first gained recognition for being the first person
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to carry or unlock an iPhone.
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And since then, he's done quite a few interesting things
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at the intersection of hardware and software.
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This is the Artificial Intelligence Podcast.
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If you enjoy it, subscribe on YouTube,
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give it five stars on iTunes, support it on Patreon,
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or simply connect with me on Twitter
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at Lex Friedman, spelled F R I D M A N.
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And I'd like to give a special thank you
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to Jennifer from Canada
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for her support of the podcast on Patreon.
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Merci beaucoup, Jennifer.
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She's been a friend and an engineering colleague
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for many years since I was in grad school.
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Your support means a lot
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and inspires me to keep this series going.
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And now, here's my conversation with George Hotz.
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Do you think we're living in a simulation?
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Yes, but it may be unfalsifiable.
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What do you mean by unfalsifiable?
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So if the simulation is designed in such a way
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that they did like a formal proof
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to show that no information can get in and out,
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and if their hardware is designed
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for the anything in the simulation
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to always keep the hardware in spec,
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it may be impossible to prove
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whether we're in a simulation or not.
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So they've designed it such that it's a closed system
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you can't get outside the system.
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Well, maybe it's one of three worlds.
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We're either in a simulation which can be exploited,
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we're in a simulation which not only can't be exploited,
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but like the same thing's true about VMs.
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A really well designed VM,
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you can't even detect if you're in a VM or not.
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That's brilliant.
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So the simulation is running on a virtual machine.
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But now in reality, all VMs have ways to detect.
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That's the point.
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I mean, you've done quite a bit of hacking yourself.
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So you should know that really any complicated system
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will have ways in and out.
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So this isn't necessarily true going forward.
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I spent my time away from Comma,
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I learned Coq, it's a dependently typed,
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it's a language for writing math proofs in.
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And if you write code that compiles in a language like that,
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it is correct by definition.
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The types check its correctness.
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So it's possible that the simulation
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is written in a language like this, in which case, yeah.
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Yeah, but that can't be sufficiently expressive
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a language like that.
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Oh, it can.
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It can be?
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Oh, yeah.
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Okay, well, so all right, so.
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The simulation doesn't have to be Turing complete
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if it has a scheduled end date.
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Looks like it does actually with entropy.
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I mean, I don't think that a simulation
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that results in something as complicated as the universe
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would have a form of proof of correctness, right?
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It's possible, of course.
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We have no idea how good their tooling is.
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And we have no idea how complicated
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the universe computer really is.
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It may be quite simple.
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It's just very large, right?
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It's very, it's definitely very large.
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But the fundamental rules might be super simple.
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Yeah, Conway's getting a life kind of stuff.
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Right.
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So if you could hack,
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so imagine a simulation that is hackable,
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if you could hack it,
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what would you change about the,
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like how would you approach hacking a simulation?
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The reason I gave that talk.
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By the way, I'm not familiar with the talk you gave.
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I just read that you talked about escaping the simulation
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or something like that.
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So maybe you can tell me a little bit about the theme
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and the message there too.
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It wasn't a very practical talk
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about how to actually escape a simulation.
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It was more about a way of restructuring
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an us versus them narrative.
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If
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we continue on the path we're going with technology,
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I think we're in big trouble,
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like as a species and not just as a species,
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but even as me as an individual member of the species.
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So if we could change rhetoric
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to be more like to think upwards,
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like to think about that we're in a simulation
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and how we could get out,
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already we'd be on the right path.
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What you actually do once you do that,
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well, I assume I would have acquired way more intelligence
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in the process of doing that.
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So I'll just ask that.
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So the thinking upwards,
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what kind of ideas,
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what kind of breakthrough ideas
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do you think thinking in that way could inspire?
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And why did you say upwards?
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Upwards.
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Into space?
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Are you thinking sort of exploration in all forms?
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The space narrative
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that held for the modernist generation
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doesn't hold as well for the postmodern generation.
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What's the space narrative?
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Are we talking about the same space,
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the three dimensional space?
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No, no, no, space, like going on space,
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like building like Elon Musk,
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like we're going to build rockets,
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we're going to go to Mars,
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we're going to colonize the universe.
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And the narrative you're referring,
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I was born in the Soviet Union,
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you're referring to the race to space.
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The race to space, yeah.
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Explore, okay.
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That was a great modernist narrative.
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Yeah.
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It doesn't seem to hold the same weight in today's culture.
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I'm hoping for good postmodern narratives that replace it.
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So let's think, so you work a lot with AI.
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So AI is one formulation of that narrative.
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There could be also,
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I don't know how much you do in VR and AR.
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Yeah.
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That's another, I know less about it,
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but every time I play with it in our research,
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it's fascinating, that virtual world.
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Are you interested in the virtual world?
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I would like to move to virtual reality.
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In terms of your work?
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No, I would like to physically move there.
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The apartment I can rent in the cloud
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is way better than the apartment
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I can rent in the real world.
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Well, it's all relative, isn't it?
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Because others will have very nice apartments too,
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so you'll be inferior in the virtual world as well.
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No, but that's not how I view the world, right?
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I don't view the world,
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I mean, it's a very almost zero sum ish way
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to view the world.
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Say like, my great apartment isn't great
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because my neighbor has one too.
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No, my great apartment is great
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because look at this dishwasher, man.
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You just touch the dish and it's washed, right?
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And that is great in and of itself
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if I have the only apartment
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or if everybody had the apartment.
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I don't care.
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So you have fundamental gratitude.
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The world first learned of George Hots
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in August 2007, maybe before then,
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but certainly in August 2007
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when you were the first person to unlock,
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carry unlock an iPhone.
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How did you get into hacking?
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What was the first system
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you discovered vulnerabilities for and broke into?
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So that was really kind of the first thing.
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I had a book in 2006 called Grey Hat Hacking.
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And I guess I realized that if you acquired
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these sort of powers, you could control the world.
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But I didn't really know that much
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about computers back then.
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I started with electronics.
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The first iPhone hack was physical.
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Cardware.
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You had to open it up and pull an address line high.
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And it was because I didn't really know
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about software exploitation.
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I learned that all in the next few years
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and I got very good at it.
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But back then I knew about like how memory chips
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are connected to processors and stuff.
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You knew about software and programming.
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You just didn't know.
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Oh really?
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So your view of the world and computers
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was physical, was hardware.
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Actually, if you read the code that I released
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with that in August 2007, it's atrocious.
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What language was it?
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C.
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C, nice.
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And in a broken sort of state machine ask C.
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I didn't know how to program.
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Yeah.
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So how did you learn to program?
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What was your journey?
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Cause I mean, we'll talk about it.
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You've live streamed some of your programming.
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This chaotic, beautiful mess.
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How did you arrive at that?
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Years and years of practice.
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I interned at Google after the summer
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after the iPhone unlock.
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And I did a contract for them where I built hardware
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for Street View and I wrote a software library
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to interact with it.
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And it was terrible code.
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And for the first time I got feedback from people
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who I respected saying, no, like don't write code like this.
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Now, of course, just getting that feedback is not enough.
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The way that I really got good was I wanted to write
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this thing like that could emulate and then visualize
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like arm binaries.
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Cause I wanted to hack the iPhone better.
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And I didn't like that I couldn't like see
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what the, I couldn't single step through the processor
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because I had no debugger on there,
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especially for the low level things like the boot rum
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and the bootloader.
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So I tried to build this tool to do it.
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And I built the tool once and it was terrible.
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I built the tool a second time, it was terrible.
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I built the tool a third time.
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This was by the time I was at Facebook, it was kind of okay.
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And then I built the tool a fourth time
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when I was a Google intern again in 2014.
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And that was the first time I was like,
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this is finally usable.
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How do you pronounce this Kira?
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Kira, yeah.
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So it's essentially the most efficient way
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to visualize the change of state of the computer
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as the program is running.
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That's what you mean by debugger.
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Yeah, it's a timeless debugger.
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So you can rewind just as easily as going forward.
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Think about if you're using GDB,
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you have to put a watch on a variable.
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If you wanna see if that variable changes.
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In Kira, you can just click on that variable
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and then it shows every single time
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when that variable was changed or accessed.
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Think about it like Git for your computers, the run log.
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So there's like a deep log of the state of the computer
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as the program runs and you can rewind.
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Why isn't that, maybe it is, maybe you can educate me.
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Why isn't that kind of debugging used more often?
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Cause the tooling's bad.
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Well, two things.
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One, if you're trying to debug Chrome,
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Chrome is a 200 megabyte binary
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that runs slowly on desktops.
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So that's gonna be really hard to use for that.
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But it's really good to use for like CTFs
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and for boot roms and for small parts of code.
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So it's hard if you're trying to debug like massive systems.
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What's a CTF and what's a boot rom?
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A boot rom is the first code that executes
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the minute you give power to your iPhone.
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Okay.
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And CTF where these competitions
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that I played capture the flag.
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Capture the flag, I was gonna ask you about that.
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What are those, look at,
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I watched a couple of videos on YouTube,
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those look fascinating.
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What have you learned about maybe
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at the high level of vulnerability of systems
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from these competitions?
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I feel like in the heyday of CTFs,
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you had all of the best security people in the world
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challenging each other and coming up
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with new toy exploitable things over here.
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And then everybody, okay, who can break it?
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And when you break it, you get like,
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there's like a file on the server called flag.
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And then there's a program running,
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listening on a socket that's vulnerable.
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So you write an exploit, you get a shell,
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and then you cat flag, and then you type the flag
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into like a web based scoreboard and you get points.
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So the goal is essentially,
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to find an exploit in the system
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that allows you to run shell,
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to run arbitrary code on that system.
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That's one of the categories.
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That's like the pwnable category.
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Pwnable?
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Yeah, pwnable.
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It's like, you know, you pwn the program.
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It's a program that's, yeah.
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Yeah, you know, first of all, I apologize.
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I'm gonna say it's because I'm Russian,
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but maybe you can help educate me.
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Some video game like misspelled own way back in the day.
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Yeah, and it's just, I wonder if there's a definition.
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I'll have to go to Urban Dictionary for it.
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It'll be interesting to see what it says.
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Okay, so what was the heyday of CTF, by the way?
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But was it, what decade are we talking about?
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I think like, I mean, maybe unbiased
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because it's the era that I played,
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but like 2011 to 2015,
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because the modern CTF scene
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is similar to the modern competitive programming scene.
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You have people who like do drills.
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You have people who practice.
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And then once you've done that,
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you've turned it less into a game of generic computer skill
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and more into a game of, okay,
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you drill on these five categories.
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And then before that, it wasn't,
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it didn't have like as much attention as it had.
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I don't know, they were like,
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I won $30,000 once in Korea for one of these competitions.
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Holy crap.
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Yeah, they were, they were, that was.
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So that means, I mean, money is money,
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but that means there was probably good people there.
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Exactly, yeah.
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Are the challenges human constructed
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or are they grounded in some real flaws and real systems?
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Usually they're human constructed,
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but they're usually inspired by real flaws.
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What kind of systems are imagined
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is really focused on mobile.
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Like what has vulnerabilities these days?
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Is it primarily mobile systems like Android?
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Oh, everything does.
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Still. Yeah, of course.
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The price has kind of gone up
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because less and less people can find them.
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And what's happened in security
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is now if you want to like jailbreak an iPhone,
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00:13:34.480
you don't need one exploit anymore, you need nine.
link |
00:13:37.880
Nine chained together, what would it mean?
link |
00:13:39.600
Yeah, wow.
link |
00:13:40.560
Okay, so it's really,
link |
00:13:42.680
what's the benefit speaking higher level
link |
00:13:46.120
philosophically about hacking?
link |
00:13:48.160
I mean, it sounds from everything I've seen about you,
link |
00:13:50.320
you just love the challenge
link |
00:13:51.840
and you don't want to do anything.
link |
00:13:54.960
You don't want to bring that exploit out into the world
link |
00:13:58.040
and do any actual, let it run wild.
link |
00:14:01.600
You just want to solve it
link |
00:14:02.680
and then you go on to the next thing.
link |
00:14:05.320
Oh yeah, I mean, doing criminal stuff's not really worth it.
link |
00:14:08.360
And I'll actually use the same argument
link |
00:14:10.440
for why I don't do defense for why I don't do crime.
link |
00:14:15.320
If you want to defend a system,
link |
00:14:16.720
say the system has 10 holes, right?
link |
00:14:19.160
If you find nine of those holes as a defender,
link |
00:14:22.120
you still lose because the attacker
link |
00:14:23.800
gets in through the last one.
link |
00:14:25.400
If you're an attacker,
link |
00:14:26.240
you only have to find one out of the 10.
link |
00:14:28.600
But if you're a criminal,
link |
00:14:30.680
if you log on with a VPN nine out of the 10 times,
link |
00:14:34.680
but one time you forget, you're done.
link |
00:14:37.640
Because you're caught, okay.
link |
00:14:39.240
Because you only have to mess up once
link |
00:14:41.040
to be caught as a criminal.
link |
00:14:42.760
That's why I'm not a criminal.
link |
00:14:45.800
But okay, let me,
link |
00:14:46.960
because I was having a discussion with somebody
link |
00:14:49.400
just at a high level about nuclear weapons actually,
link |
00:14:52.640
why we're having blown ourselves up yet.
link |
00:14:56.120
And my feeling is all the smart people in the world,
link |
00:14:59.680
if you look at the distribution of smart people,
link |
00:15:04.000
smart people are generally good.
link |
00:15:06.600
And then this other person I was talking to,
link |
00:15:07.920
Sean Carroll, the physicist,
link |
00:15:09.320
and he was saying, no, good and bad people
link |
00:15:11.280
are evenly distributed amongst everybody.
link |
00:15:13.960
My sense was good hackers are in general good people
link |
00:15:17.960
and they don't want to mess with the world.
link |
00:15:20.280
What's your sense?
link |
00:15:21.640
I'm not even sure about that.
link |
00:15:25.800
Like,
link |
00:15:28.880
I have a nice life.
link |
00:15:30.400
Crime wouldn't get me anything.
link |
00:15:34.160
But if you're good and you have these skills,
link |
00:15:36.400
you probably have a nice life too, right?
link |
00:15:38.600
Right, you can use it for other things.
link |
00:15:40.040
But is there an ethical,
link |
00:15:41.000
is there a little voice in your head that says,
link |
00:15:46.000
well, yeah, if you could hack something
link |
00:15:48.920
to where you could hurt people
link |
00:15:52.720
and you could earn a lot of money doing it though,
link |
00:15:54.840
not hurt physically perhaps,
link |
00:15:56.200
but disrupt their life in some kind of way,
link |
00:16:00.080
isn't there a little voice that says?
link |
00:16:03.240
Well, two things.
link |
00:16:04.440
One, I don't really care about money.
link |
00:16:06.680
So like the money wouldn't be an incentive.
link |
00:16:08.600
The thrill might be an incentive.
link |
00:16:10.560
But when I was 19, I read Crime and Punishment.
link |
00:16:14.320
And that was another great one
link |
00:16:16.040
that talked me out of ever really doing crime.
link |
00:16:19.320
Cause it's like, that's gonna be me.
link |
00:16:21.640
I'd get away with it, but it would just run through my head.
link |
00:16:25.000
Even if I got away with it, you know?
link |
00:16:26.400
And then you do crime for long enough,
link |
00:16:27.560
you'll never get away with it.
link |
00:16:28.880
That's right.
link |
00:16:29.720
In the end, that's a good reason to be good.
link |
00:16:32.600
I wouldn't say I'm good.
link |
00:16:33.440
I would just say I'm not bad.
link |
00:16:34.840
You're a talented programmer and a hacker
link |
00:16:38.080
in a good positive sense of the word.
link |
00:16:40.920
You've played around,
link |
00:16:42.400
found vulnerabilities in various systems.
link |
00:16:44.720
What have you learned broadly
link |
00:16:46.120
about the design of systems and so on
link |
00:16:49.480
from that whole process?
link |
00:16:53.280
You learn to not take things
link |
00:16:59.280
for what people say they are,
link |
00:17:02.000
but you look at things for what they actually are.
link |
00:17:07.000
Yeah.
link |
00:17:07.880
I understand that's what you tell me it is,
link |
00:17:10.040
but what does it do?
link |
00:17:11.440
Right.
link |
00:17:12.920
And you have nice visualization tools
link |
00:17:14.560
to really know what it's really doing.
link |
00:17:16.680
Oh, I wish.
link |
00:17:17.760
I'm a better programmer now than I was in 2014.
link |
00:17:20.040
I said, Kira, that was the first tool
link |
00:17:21.840
that I wrote that was usable.
link |
00:17:23.400
I wouldn't say the code was great.
link |
00:17:25.320
I still wouldn't say my code is great.
link |
00:17:28.800
So how was your evolution as a programmer except practice?
link |
00:17:31.480
So you started with C.
link |
00:17:33.840
At which point did you pick up Python?
link |
00:17:35.520
Because you're pretty big in Python now.
link |
00:17:37.040
Now, yeah, in college.
link |
00:17:39.920
I went to Carnegie Mellon when I was 22.
link |
00:17:42.480
I went back.
link |
00:17:43.320
I'm like, all right,
link |
00:17:44.160
I'm gonna take all your hardest CS courses.
link |
00:17:46.600
We'll see how I do, right?
link |
00:17:47.600
Like, did I miss anything
link |
00:17:48.520
by not having a real undergraduate education?
link |
00:17:51.480
Took operating systems, compilers, AI,
link |
00:17:54.200
and they're like a freshman wheat or math course.
link |
00:17:58.560
And...
link |
00:18:00.480
Operating systems, some of those classes
link |
00:18:02.120
you mentioned are pretty tough, actually.
link |
00:18:04.160
They're great.
link |
00:18:05.560
At least the 2012, circa 2012,
link |
00:18:08.600
operating systems and compilers were two of the,
link |
00:18:12.200
they were the best classes I've ever taken in my life.
link |
00:18:14.360
Because you write an operating system
link |
00:18:15.560
and you write a compiler.
link |
00:18:18.040
I wrote my operating system in C
link |
00:18:19.720
and I wrote my compiler in Haskell,
link |
00:18:21.320
but somehow I picked up Python that semester as well.
link |
00:18:26.360
I started using it for the CTFs, actually.
link |
00:18:28.040
That's when I really started to get into CTFs
link |
00:18:30.280
and CTFs, you're all, it's a race against the clock.
link |
00:18:33.320
So I can't write things in C.
link |
00:18:35.080
Oh, there's a clock component.
link |
00:18:36.200
So you really want to use the programming languages
link |
00:18:38.000
so you can be fastest.
link |
00:18:38.920
48 hours, pwn as many of these challenges as you can.
link |
00:18:41.400
Pwn.
link |
00:18:42.240
Yeah, you got like a hundred points of challenge.
link |
00:18:43.920
Whatever team gets the most.
link |
00:18:46.280
You were both at Facebook and Google for a brief stint.
link |
00:18:50.200
Yeah.
link |
00:18:51.040
With Project Zero actually at Google for five months
link |
00:18:54.880
where you developed Kira.
link |
00:18:56.920
What was Project Zero about in general?
link |
00:18:59.840
What, I'm just curious about the security efforts
link |
00:19:03.960
in these companies.
link |
00:19:05.160
Well, Project Zero started the same time I went there.
link |
00:19:08.760
What years are there?
link |
00:19:11.080
2015.
link |
00:19:12.320
2015.
link |
00:19:13.160
So that was right at the beginning of Project Zero.
link |
00:19:15.040
It's small.
link |
00:19:16.200
It's Google's offensive security team.
link |
00:19:21.840
I'll try to give the best public facing explanation
link |
00:19:25.640
that I can.
link |
00:19:26.480
So the idea is basically these vulnerabilities
link |
00:19:31.760
exist in the world.
link |
00:19:33.200
Nation states have them.
link |
00:19:35.200
Some high powered bad actors have them.
link |
00:19:39.800
Sometime people will find these vulnerabilities
link |
00:19:44.160
and submit them in bug bounties to the companies.
link |
00:19:47.800
But a lot of the companies don't really care.
link |
00:19:49.480
They don't even fix the bug.
link |
00:19:51.160
It doesn't hurt for there to be a vulnerability.
link |
00:19:53.840
So Project Zero is like, we're going to do it different.
link |
00:19:55.920
We're going to announce a vulnerability
link |
00:19:57.880
and we're going to give them 90 days to fix it.
link |
00:19:59.680
And then whether they fix it or not,
link |
00:20:00.840
we're going to drop the zero day.
link |
00:20:03.240
Oh, wow.
link |
00:20:04.120
We're going to drop the weapon.
link |
00:20:04.960
That's so cool.
link |
00:20:05.920
That is so cool.
link |
00:20:07.520
I love the deadlines.
link |
00:20:09.240
Oh, that's so cool.
link |
00:20:10.080
Give them real deadlines.
link |
00:20:10.920
Yeah.
link |
00:20:12.360
And I think it's done a lot for moving the industry forward.
link |
00:20:15.800
I watched your coding sessions on the streamed online.
link |
00:20:20.360
You code things up, the basic projects,
link |
00:20:22.720
usually from scratch.
link |
00:20:24.000
I would say sort of as a programmer myself,
link |
00:20:28.200
just watching you that you type really fast
link |
00:20:30.360
and your brain works in both brilliant and chaotic ways.
link |
00:20:34.520
I don't know if that's always true,
link |
00:20:35.800
but certainly for the live streams.
link |
00:20:37.600
So it's interesting to me because I'm more,
link |
00:20:40.360
I'm much slower and systematic and careful.
link |
00:20:43.520
And you just move, I mean,
link |
00:20:44.920
probably in order of magnitude faster.
link |
00:20:48.040
So I'm curious, is there a method to your madness?
link |
00:20:51.080
Is it just who you are?
link |
00:20:53.040
There's pros and cons.
link |
00:20:54.720
There's pros and cons to my programming style.
link |
00:20:58.080
And I'm aware of them.
link |
00:20:59.440
Like if you ask me to like get something up
link |
00:21:03.560
and working quickly with like an API
link |
00:21:05.400
that's kind of undocumented,
link |
00:21:06.760
I will do this super fast
link |
00:21:08.160
because I will throw things at it until it works.
link |
00:21:10.200
If you ask me to take a vector and rotate it 90 degrees
link |
00:21:14.720
and then flip it over the XY plane,
link |
00:21:19.320
I'll spam program for two hours and won't get it.
link |
00:21:22.320
Oh, because it's something that you could do
link |
00:21:23.920
with a sheet of paper, think through design,
link |
00:21:26.280
and then just, do you really just throw stuff at the wall
link |
00:21:30.440
and you get so good at it that it usually works?
link |
00:21:34.640
I should become better at the other kind as well.
link |
00:21:36.960
Sometimes I'll do things methodically.
link |
00:21:39.440
It's nowhere near as entertaining on the Twitch streams.
link |
00:21:41.160
I do exaggerate it a bit on the Twitch streams as well.
link |
00:21:43.520
The Twitch streams, I mean,
link |
00:21:44.680
what do you want to see a game or you want to see
link |
00:21:45.840
actions per minute, right?
link |
00:21:46.840
I'll show you APM for programming too.
link |
00:21:48.160
Yeah, I recommend people go to it.
link |
00:21:50.280
I think I watched, I watched probably several hours
link |
00:21:53.400
of you, like I've actually left you programming
link |
00:21:56.240
in the background while I was programming
link |
00:21:59.080
because you made me, it was like watching
link |
00:22:02.040
a really good gamer.
link |
00:22:03.160
It's like energizes you because you're like moving so fast.
link |
00:22:06.280
It's so, it's awesome.
link |
00:22:07.600
It's inspiring and it made me jealous that like,
link |
00:22:12.320
because my own programming is inadequate
link |
00:22:14.320
in terms of speed.
link |
00:22:15.520
Oh, I was like.
link |
00:22:17.000
So I'm twice as frantic on the live streams
link |
00:22:20.520
as I am when I code without them.
link |
00:22:22.680
It's super entertaining.
link |
00:22:23.760
So I wasn't even paying attention to what you were coding,
link |
00:22:26.440
which is great.
link |
00:22:27.280
It's just watching you switch windows and Vim I guess
link |
00:22:30.840
is the most.
link |
00:22:31.680
Yeah, there's Vim on screen.
link |
00:22:33.080
I've developed the workload at Facebook
link |
00:22:34.440
and stuck with it.
link |
00:22:35.640
How do you learn new programming tools,
link |
00:22:37.360
ideas, techniques these days?
link |
00:22:39.480
What's your like a methodology for learning new things?
link |
00:22:42.120
So I wrote for comma, the distributed file systems
link |
00:22:48.800
out in the world are extremely complex.
link |
00:22:50.720
Like if you want to install something like like like Ceph,
link |
00:22:55.280
Ceph is I think the like open infrastructure
link |
00:22:58.760
distributed file system,
link |
00:23:00.320
or there's like newer ones like seaweed FS,
link |
00:23:04.000
but these are all like 10,000 plus line projects.
link |
00:23:06.880
I think some of them are even a hundred thousand line
link |
00:23:09.480
and just configuring them as a nightmare.
link |
00:23:11.120
So I wrote, I wrote one, it's 200 lines
link |
00:23:16.440
and it's, it uses like NGINX and volume servers
link |
00:23:18.880
and has this little master server that I wrote in Go.
link |
00:23:21.640
And the way I go, this,
link |
00:23:24.200
if I would say that I'm proud per line of any code I wrote,
link |
00:23:27.240
maybe there's some exploits that I think are beautiful.
link |
00:23:29.160
And then this, this is 200 lines.
link |
00:23:31.320
And just the way that I thought about it,
link |
00:23:33.720
I think was very good.
link |
00:23:34.600
And the reason it's very good is because
link |
00:23:35.880
that was the fourth version of it that I wrote.
link |
00:23:37.600
And I had three versions that I threw away.
link |
00:23:39.320
You mentioned, did you say Go?
link |
00:23:40.960
I wrote in Go, yeah.
link |
00:23:41.800
In Go.
link |
00:23:42.640
Is that a functional language?
link |
00:23:43.840
I forget what Go is.
link |
00:23:45.240
Go is Google's language.
link |
00:23:47.120
Right.
link |
00:23:48.200
It's not functional.
link |
00:23:49.440
It's some, it's like in a way it's C++, but easier.
link |
00:23:56.160
It's, it's strongly typed.
link |
00:23:58.160
It has a nice ecosystem around it.
link |
00:23:59.720
When I first looked at it, I was like, this is like Python,
link |
00:24:02.680
but it takes twice as long to do anything.
link |
00:24:04.560
Yeah.
link |
00:24:05.560
Now that I've, OpenPilot is migrating to C,
link |
00:24:09.560
but it still has large Python components.
link |
00:24:10.960
I now understand why Python doesn't work
link |
00:24:12.720
for large code bases and why you want something like Go.
link |
00:24:15.800
Interesting.
link |
00:24:16.640
So why, why doesn't Python work for,
link |
00:24:18.640
so even most, speaking for myself at least,
link |
00:24:21.680
like we do a lot of stuff,
link |
00:24:23.360
basically demo level work with autonomous vehicles
link |
00:24:26.480
and most of the work is Python.
link |
00:24:28.240
Yeah.
link |
00:24:29.200
Why doesn't Python work for large code bases?
link |
00:24:32.400
Because, well, lack of type checking is a big part.
link |
00:24:37.960
So errors creep in.
link |
00:24:39.360
Yeah.
link |
00:24:40.200
And like, you don't know,
link |
00:24:41.920
the compiler can tell you like nothing, right?
link |
00:24:45.320
So everything is either, you know,
link |
00:24:48.440
like, like syntax errors, fine.
link |
00:24:49.880
But if you misspell a variable in Python,
link |
00:24:51.800
the compiler won't catch that.
link |
00:24:53.000
There's like linters that can catch it some of the time.
link |
00:24:56.600
There's no types.
link |
00:24:57.560
This is really the biggest downside.
link |
00:25:00.520
And then, well, Python's slow, but that's not related to it.
link |
00:25:02.640
Well, maybe it's kind of related to it, so it's lack of.
link |
00:25:04.800
So what's, what's in your toolbox these days?
link |
00:25:06.600
Is it Python?
link |
00:25:07.440
What else?
link |
00:25:08.280
I need to move to something else.
link |
00:25:10.120
My adventure into dependently typed languages,
link |
00:25:12.840
I love these languages.
link |
00:25:14.200
They just have like syntax from the 80s.
link |
00:25:18.480
What do you think about JavaScript?
link |
00:25:21.080
ES6, like the modern, or TypeScript?
link |
00:25:23.960
JavaScript is,
link |
00:25:26.080
the whole ecosystem is unbelievably confusing.
link |
00:25:28.960
Right.
link |
00:25:29.800
NPM updates a package from 0.2.2 to 0.2.5,
link |
00:25:32.800
and that breaks your Babel linter,
link |
00:25:34.520
which translates your ES5 into ES6,
link |
00:25:37.040
which doesn't run on, so.
link |
00:25:39.920
Why do I have to compile my JavaScript again, huh?
link |
00:25:42.440
It may be the future, though.
link |
00:25:44.000
You think about, I mean,
link |
00:25:45.760
I've embraced JavaScript recently,
link |
00:25:47.360
just because, just like I've continually embraced PHP,
link |
00:25:52.280
it seems that these worst possible languages
link |
00:25:54.840
live on for the longest, like cockroaches never die.
link |
00:25:57.440
Yeah.
link |
00:25:58.480
Well, it's in the browser, and it's fast.
link |
00:26:00.720
It's fast.
link |
00:26:01.680
Yeah.
link |
00:26:02.520
It's in the browser, and compute might stay,
link |
00:26:04.880
become, you know, the browser.
link |
00:26:06.440
It's unclear what the role of the browser is
link |
00:26:09.000
in terms of distributed computation in the future, so.
link |
00:26:13.600
JavaScript is definitely here to stay.
link |
00:26:15.240
Yeah.
link |
00:26:16.080
It's interesting if autonomous vehicles
link |
00:26:18.160
will run on JavaScript one day.
link |
00:26:19.480
I mean, you have to consider these possibilities.
link |
00:26:21.800
Well, all our debug tools are JavaScript.
link |
00:26:24.280
We actually just open sourced them.
link |
00:26:26.040
We have a tool, Explorer,
link |
00:26:27.400
which you can annotate your disengagements,
link |
00:26:29.200
and we have a tool, Cabana,
link |
00:26:30.080
which lets you analyze the can traffic from the car.
link |
00:26:32.920
So basically, anytime you're visualizing something
link |
00:26:35.240
about the log, you're using JavaScript.
link |
00:26:37.720
Well, the web is the best UI toolkit by far, so.
link |
00:26:41.280
And then, you know what?
link |
00:26:42.120
You're coding in JavaScript.
link |
00:26:42.960
We have a React guy.
link |
00:26:43.800
He's good.
link |
00:26:44.640
React, nice.
link |
00:26:46.080
Let's get into it.
link |
00:26:46.920
So let's talk autonomous vehicles.
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00:26:48.800
Yeah.
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00:26:49.640
You founded Comma AI.
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00:26:51.760
Let's, at a high level,
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00:26:54.920
how did you get into the world of vehicle automation?
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00:26:57.840
Can you also just, for people who don't know,
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00:26:59.880
tell the story of Comma AI?
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00:27:01.360
Sure.
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00:27:02.880
So I was working at this AI startup,
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00:27:06.080
and a friend approached me,
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00:27:08.160
and he's like, dude, I don't know where this is going,
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00:27:12.040
but the coolest applied AI problem today
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00:27:15.160
is self driving cars.
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00:27:16.480
I'm like, well, absolutely.
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00:27:18.800
You want to meet with Elon Musk,
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00:27:20.520
and he's looking for somebody to build a vision system
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00:27:24.560
for autopilot.
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00:27:27.560
This is when they were still on AP1.
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00:27:29.320
They were still using Mobileye.
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00:27:30.840
Elon, back then, was looking for a replacement,
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00:27:33.680
and he brought me in,
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00:27:36.160
and we talked about a contract
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00:27:37.320
where I would deliver something
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00:27:39.040
that meets Mobileye level performance.
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00:27:41.360
I would get paid $12 million if I could deliver it tomorrow,
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00:27:43.920
and I would lose $1 million
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00:27:45.280
for every month I didn't deliver.
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00:27:46.720
Yeah.
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00:27:47.720
So I was like, okay, this is a great deal.
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00:27:49.080
This is a super exciting challenge.
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00:27:52.360
You know what?
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00:27:53.200
Even if it takes me 10 months,
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00:27:54.400
I get $2 million.
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00:27:55.360
It's good.
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00:27:56.200
Maybe I can finish up in five.
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00:27:57.120
Maybe I don't finish it at all,
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00:27:58.120
and I get paid nothing,
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00:27:58.960
and I can still work for 12 months for free.
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00:28:00.840
So maybe just take a pause on that.
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00:28:02.920
I'm also curious about this
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00:28:04.240
because I've been working in robotics for a long time,
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00:28:06.200
and I'm curious to see a person like you
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00:28:07.640
just step in and sort of somewhat naive,
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00:28:11.040
but brilliant, right?
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00:28:11.960
So that's the best place to be
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00:28:13.960
because you basically full steam take on a problem.
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00:28:17.200
How confident, how, from that time,
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00:28:19.680
because you know a lot more now,
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00:28:21.280
at that time, how hard do you think it is
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00:28:23.440
to solve all of autonomous driving?
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00:28:25.840
I remember I suggested to Elon in the meeting
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00:28:30.560
putting a GPU behind each camera
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00:28:33.080
to keep the compute local.
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00:28:35.120
This is an incredibly stupid idea.
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00:28:38.000
I leave the meeting 10 minutes later,
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00:28:39.400
and I'm like, I could have spent a little bit of time
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00:28:41.520
thinking about this problem before I went in.
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00:28:42.760
Why is it a stupid idea?
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00:28:44.160
Oh, just send all your cameras to one big GPU.
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00:28:46.240
You're much better off doing that.
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00:28:48.200
Oh, sorry.
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00:28:49.040
You said behind every camera have a GPU.
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00:28:50.200
Every camera have a small GPU.
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00:28:51.360
I was like, oh, I'll put the first few layers
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00:28:52.720
of my comms there.
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00:28:54.040
Ugh, why'd I say that?
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00:28:56.040
That's possible.
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00:28:56.880
It's possible, but it's a bad idea.
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00:28:58.960
It's not obviously a bad idea.
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00:29:00.440
Pretty obviously bad,
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00:29:01.280
but whether it's actually a bad idea or not,
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00:29:02.920
I left that meeting with Elon, beating myself up.
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00:29:05.240
I'm like, why'd I say something stupid?
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00:29:07.000
Yeah, you haven't at least thought through
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00:29:10.720
every aspect of it, yeah.
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00:29:12.200
He's very sharp too.
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00:29:13.360
Usually in life, I get away with saying stupid things
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00:29:15.760
and then kind of course,
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00:29:16.920
oh, right away he called me out about it.
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00:29:18.520
And usually in life, I get away with saying stupid things
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00:29:21.080
and then a lot of times people don't even notice
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00:29:26.080
and I'll correct it and bring the conversation back.
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00:29:28.200
But with Elon, it was like, nope, okay, well.
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00:29:31.840
That's not at all why the contract fell through.
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00:29:33.520
I was much more prepared the second time I met him.
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00:29:35.520
Yeah, but in general, how hard did you think it is?
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00:29:39.640
Like 12 months is a tough timeline.
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00:29:43.680
Oh, I just thought I'd clone Mobileye IQ3.
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00:29:45.720
I didn't think I'd solve level five self driving
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00:29:47.560
or anything.
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00:29:48.400
So the goal there was to do lane keeping, good lane keeping.
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00:29:52.760
I saw, my friend showed me the outputs from a Mobileye
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00:29:55.480
and the outputs from a Mobileye was just basically
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00:29:57.080
two lanes at a position of a lead car.
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00:29:59.360
I'm like, I can gather a data set and train this net
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00:30:02.160
in weeks and I did.
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00:30:04.760
Well, first time I tried the implementation of Mobileye
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00:30:07.520
in a Tesla, I was really surprised how good it is.
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00:30:11.200
It's going incredibly good.
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00:30:12.240
Cause I thought it's just cause I've done a lot
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00:30:14.280
of computer vision, I thought it'd be a lot harder
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00:30:17.960
to create a system that that's stable.
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00:30:20.960
So I was personally surprised, you know,
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00:30:24.200
have to admit it.
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00:30:25.040
Cause I was kind of skeptical before trying it.
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00:30:27.800
Cause I thought it would go in and out a lot more.
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00:30:31.160
It would get disengaged a lot more and it's pretty robust.
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00:30:36.160
So what, how hard is the problem when you tackled it?
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00:30:42.080
So I think AP1 was great.
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00:30:44.480
Like Elon talked about disengagements on the 405 down in LA
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00:30:49.000
with like the lane marks are kind of faded
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00:30:51.000
and the Mobileye system would drop out.
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00:30:53.920
Like I had something up and working that I would say
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00:30:57.760
was like the same quality in three months.
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00:31:02.480
Same quality, but how do you know?
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00:31:04.720
You say stuff like that confidently, but you can't,
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00:31:07.360
and I love it, but the question is you can't,
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00:31:12.080
you're kind of going by feel cause you test it out.
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00:31:14.400
Absolutely, absolutely.
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00:31:15.440
Like I would take, I borrowed my friend's Tesla.
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00:31:18.320
I would take AP1 out for a drive
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00:31:20.600
and then I would take my system out for a drive.
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00:31:22.160
And it seems reasonably like the same.
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00:31:25.920
So the 405, how hard is it to create something
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00:31:30.320
that could actually be a product that's deployed?
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00:31:34.040
I mean, I've read an article where Elon,
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00:31:37.120
this respondent said something about you saying
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00:31:40.640
that to build autopilot is more complicated
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00:31:46.920
than a single George Hodge level job.
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00:31:51.720
How hard is that job to create something
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00:31:55.360
that would work across globally?
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00:31:58.800
Why don't think globally is the challenge?
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00:32:00.480
But Elon followed that up by saying
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00:32:02.080
it's gonna take two years in a company of 10 people.
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00:32:04.760
And here I am four years later with a company of 12 people.
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00:32:07.760
And I think we still have another two to go.
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00:32:09.800
Two years, so yeah.
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00:32:11.160
So what do you think about how Tesla is progressing
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00:32:15.840
with autopilot of V2, V3?
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00:32:19.080
I think we've kept pace with them pretty well.
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00:32:23.960
I think navigate and autopilot is terrible.
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00:32:26.720
We had some demo features internally of the same stuff
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00:32:31.000
and we would test it.
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00:32:32.080
And I'm like, I'm not shipping this
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00:32:33.320
even as like open source software to people.
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00:32:35.160
Why do you think it's terrible?
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00:32:37.280
Consumer Reports does a great job of describing it.
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00:32:39.480
Like when it makes a lane change,
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00:32:41.160
it does it worse than a human.
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00:32:43.520
You shouldn't ship things like autopilot, open pilot.
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00:32:46.880
They lane keep better than a human.
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00:32:49.680
If you turn it on for a stretch of a highway,
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00:32:53.360
like an hour long, it's never gonna touch a lane line.
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00:32:56.600
Human will touch probably a lane line twice.
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00:32:58.960
You just inspired me.
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00:33:00.000
I don't know if you're grounded in data on that.
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00:33:02.120
I read your paper.
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00:33:03.200
Okay, but that's interesting.
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00:33:05.320
I wonder actually how often we touch lane lines
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00:33:10.560
in general, like a little bit,
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00:33:11.960
because it is.
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00:33:13.440
I could answer that question pretty easily
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00:33:14.920
with the common data set.
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00:33:15.760
Yeah, I'm curious.
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00:33:16.960
I've never answered it.
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00:33:17.800
I don't know.
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00:33:18.640
I just, two is like my personal.
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00:33:19.960
It feels right.
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00:33:21.720
That's interesting.
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00:33:22.560
Because every time you touch a lane,
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00:33:23.800
that's a source of a little bit of stress
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00:33:26.720
and kind of lane keeping is removing that stress.
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00:33:29.280
That's ultimately the biggest value add honestly
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00:33:32.320
is just removing the stress of having to stay in lane.
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00:33:35.520
And I think honestly, I don't think people fully realize,
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00:33:39.000
first of all, that that's a big value add,
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00:33:41.920
but also that that's all it is.
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00:33:44.960
And that not only, I find it a huge value add.
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00:33:48.560
I drove down when we moved to San Diego,
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00:33:50.400
I drove down in a enterprise rental car and I missed it.
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00:33:53.320
So I missed having the system so much.
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00:33:55.440
It's so much more tiring to drive without it.
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00:34:00.280
It is that lane centering.
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00:34:02.920
That's the key feature.
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00:34:04.800
Yeah.
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00:34:06.560
And in a way, it's the only feature
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00:34:08.920
that actually adds value to people's lives
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00:34:11.000
in autonomous vehicles today.
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00:34:12.160
Waymo does not add value to people's lives.
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00:34:13.800
It's a more expensive, slower Uber.
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00:34:15.840
Maybe someday it'll be this big cliff where it adds value,
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00:34:18.600
but I don't usually believe it.
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00:34:19.440
It is fascinating.
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00:34:20.280
I haven't talked to, this is good.
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00:34:22.520
Cause I haven't, I have intuitively,
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00:34:25.760
but I think we're making it explicit now.
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00:34:28.240
I actually believe that really good lane keeping
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00:34:35.440
is a reason to buy a car.
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00:34:37.200
Will be a reason to buy a car and it's a huge value add.
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00:34:39.680
I've never, until we just started talking about it,
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00:34:41.720
I haven't really quite realized it.
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00:34:43.840
That I've felt with Elon's chase of level four
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00:34:49.400
is not the correct chase.
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00:34:52.320
It was on, cause you should just say Tesla has the best
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00:34:55.920
as if from a Tesla perspective, say,
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00:34:58.280
Tesla has the best lane keeping.
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00:35:00.560
Comma AI should say, Comma AI is the best lane keeping.
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00:35:04.120
And that is it.
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00:35:05.560
Yeah. Yeah.
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00:35:06.400
So do you think?
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00:35:07.960
You have to do the longitudinal as well.
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00:35:09.880
You can't just lane keep.
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00:35:10.880
You have to do ACC,
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00:35:12.880
but ACC is much more forgiving than lane keep,
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00:35:15.760
especially on the highway.
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00:35:17.360
By the way, are you Comma AI's camera only, correct?
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00:35:21.920
No, we use the radar.
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00:35:23.680
From the car, you're able to get the, okay.
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00:35:25.440
Hmm?
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00:35:26.960
We can do a camera only now.
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00:35:28.800
It's gotten to the point,
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00:35:29.640
but we leave the radar there as like a, it's fusion now.
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00:35:33.440
Okay, so let's maybe talk through some of the system specs
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00:35:36.520
on the hardware.
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00:35:37.920
What's the hardware side of what you're providing?
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00:35:42.880
What's the capabilities on the software side
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00:35:44.720
with OpenPilot and so on?
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00:35:46.800
So OpenPilot, as the box that we sell, that it runs on,
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00:35:52.200
it's a phone in a plastic case.
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00:35:54.440
It's nothing special.
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00:35:55.280
We sell it without the software.
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00:35:56.680
So you buy the phone, it's just easy.
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00:35:59.360
It'll be easy set up, but it's sold with no software.
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00:36:03.960
OpenPilot right now is about to be 0.6.
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00:36:07.040
When it gets to 1.0,
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00:36:08.280
I think we'll be ready for a consumer product.
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00:36:10.120
We're not gonna add any new features.
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00:36:11.600
We're just gonna make the lane keeping really, really good.
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00:36:14.120
Okay, I got it.
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00:36:15.560
So what do we have right now?
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00:36:16.560
It's a Snapdragon 820.
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00:36:20.000
It's a Sony IMX 298 forward facing camera.
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00:36:24.080
Driver monitoring camera,
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00:36:26.040
it's just a selfie camera on the phone.
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00:36:27.960
And a CAN transceiver,
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00:36:31.640
maybe there's a little thing called PANDAS.
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00:36:33.840
And they talk over USB to the phone
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00:36:36.560
and then they have three CAN buses
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00:36:37.720
that they talk to the car.
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00:36:39.960
One of those CAN buses is the radar CAN bus.
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00:36:42.280
One of them is the main car CAN bus
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00:36:44.320
and the other one is the proxy camera CAN bus.
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00:36:46.240
We leave the existing camera in place
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00:36:48.400
so we don't turn AEB off.
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00:36:50.720
Right now, we still turn AEB off
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00:36:52.320
if you're using our longitudinal,
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00:36:53.600
but we're gonna fix that before 1.0.
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00:36:55.600
Got it.
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00:36:56.440
Wow, that's cool.
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00:36:57.280
And it's CAN both ways.
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00:36:59.040
So how are you able to control vehicles?
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00:37:03.320
So we proxy,
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00:37:05.440
the vehicles that we work with
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00:37:06.760
already have a lane keeping assist system.
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00:37:10.160
So lane keeping assist can mean a huge variety of things.
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00:37:13.800
It can mean it will apply a small torque to the wheel
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00:37:17.800
after you've already crossed a lane line by a foot,
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00:37:21.160
which is the system in the older Toyotas
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00:37:23.920
versus like, I think Tesla still calls it
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00:37:26.520
lane keeping assist,
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00:37:27.560
where it'll keep you perfectly
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00:37:28.840
in the center of the lane on the highway.
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00:37:32.320
You can control, like with the joystick, the car.
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00:37:35.080
So these cars already have the capability of drive by wire.
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00:37:37.920
So is it trivial to convert a car that it operates with?
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00:37:45.400
OpenPILOT is able to control the steering?
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00:37:48.480
Oh, a new car or a car that we,
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00:37:49.720
so we have support now for 45 different makes of cars.
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00:37:52.800
What are the cars in general?
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00:37:54.880
Mostly Hondas and Toyotas.
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00:37:56.360
We support almost every Honda and Toyota made this year.
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00:38:01.680
And then a bunch of GMs, a bunch of Subarus,
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00:38:04.480
a bunch of Chevys.
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00:38:05.320
It doesn't have to be like a Prius,
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00:38:06.160
it could be a Corolla as well.
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00:38:07.320
Oh, the 2020 Corolla is the best car with OpenPILOT.
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00:38:10.760
It just came out.
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00:38:11.720
The actuator has less lag than the older Corolla.
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00:38:15.800
I think I started watching a video with your,
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00:38:18.240
I mean, the way you make videos is awesome.
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00:38:21.400
You're just literally at the dealership streaming.
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00:38:24.220
Yeah, I had my friend on the phone,
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00:38:26.060
I'm like, bro, you wanna stream for an hour?
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00:38:27.520
Yeah, and basically, like if stuff goes a little wrong,
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00:38:31.100
you're just like, you just go with it.
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00:38:33.120
Yeah, I love it.
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00:38:33.940
Well, it's real.
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00:38:34.780
Yeah, it's real.
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00:38:35.600
That's so beautiful and it's so in contrast
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00:38:39.760
to the way other companies
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00:38:42.960
would put together a video like that.
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00:38:44.680
Kind of why I like to do it like that.
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00:38:46.080
Good.
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00:38:46.920
I mean, if you become super rich one day and successful,
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00:38:49.840
I hope you keep it that way
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00:38:50.800
because I think that's actually what people love,
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00:38:53.200
that kind of genuine.
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00:38:54.760
Oh, it's all that has value to me.
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00:38:56.520
Money has no, if I sell out to like make money,
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00:38:59.920
I sold out, it doesn't matter.
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00:39:01.320
What do I get?
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00:39:02.160
Yacht?
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00:39:03.000
I don't want a yacht.
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00:39:04.560
And I think Tesla's actually has a small inkling
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00:39:09.240
of that as well with Autonomy Day.
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00:39:11.320
They did reveal more than, I mean, of course,
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00:39:14.080
there's marketing communications, you could tell,
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00:39:15.760
but it's more than most companies would reveal,
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00:39:17.720
which is, I hope they go towards that direction more,
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00:39:21.440
other companies, GM, Ford.
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00:39:23.080
Oh, Tesla's gonna win level five.
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00:39:25.440
They really are.
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00:39:26.600
So let's talk about it.
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00:39:27.840
You think, you're focused on level two currently?
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00:39:32.280
Currently.
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00:39:33.120
We're gonna be one to two years behind Tesla
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00:39:36.200
getting to level five.
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00:39:37.200
Okay.
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00:39:38.560
We're Android, right?
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00:39:39.400
We're Android.
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00:39:40.220
You're Android.
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00:39:41.060
I'm just saying, once Tesla gets it,
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00:39:42.280
we're one to two years behind.
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00:39:43.800
I'm not making any timeline on when Tesla's
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00:39:45.640
gonna get it. That's right.
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00:39:46.480
You did, that was brilliant.
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00:39:47.300
I'm sorry, Tesla investors,
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00:39:48.400
if you think you're gonna have an autonomous
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00:39:49.880
Robo Taxi fleet by the end of the year.
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00:39:52.460
Yeah, so that's.
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00:39:53.300
I'll bet against that.
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00:39:54.960
So what do you think about this?
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00:39:57.740
The most level four companies
link |
00:40:02.000
are kind of just doing their usual safety driver,
link |
00:40:07.280
doing full autonomy kind of testing.
link |
00:40:08.800
And then Tesla does basically trying to go
link |
00:40:12.000
from lane keeping to full autonomy.
link |
00:40:15.600
What do you think about that approach?
link |
00:40:16.840
How successful would it be?
link |
00:40:18.400
It's a ton better approach.
link |
00:40:20.720
Because Tesla is gathering data on a scale
link |
00:40:23.980
that none of them are.
link |
00:40:25.240
They're putting real users behind the wheel of the cars.
link |
00:40:29.560
It's, I think, the only strategy that works.
link |
00:40:33.120
The incremental.
link |
00:40:34.480
Well, so there's a few components to Tesla approach
link |
00:40:36.980
that's more than just the incrementalists.
link |
00:40:38.920
What you spoke with is the ones, the software,
link |
00:40:41.440
so over the air software updates.
link |
00:40:43.760
Necessity.
link |
00:40:44.840
I mean Waymo crews have those too.
link |
00:40:46.480
Those aren't.
link |
00:40:47.660
But.
link |
00:40:48.500
Those differentiate from the automakers.
link |
00:40:49.880
Right, no lane keeping systems have,
link |
00:40:52.040
no cars with lane keeping system have that except Tesla.
link |
00:40:54.840
Yeah.
link |
00:40:55.800
And the other one is the data, the other direction,
link |
00:40:59.840
which is the ability to query the data.
link |
00:41:01.920
I don't think they're actually collecting
link |
00:41:03.560
as much data as people think,
link |
00:41:04.580
but the ability to turn on collection and turn it off.
link |
00:41:09.520
So I'm both in the robotics world
link |
00:41:12.120
and the psychology human factors world.
link |
00:41:15.080
Many people believe that level two autonomy is problematic
link |
00:41:18.540
because of the human factor.
link |
00:41:20.120
Like the more the task is automated,
link |
00:41:23.380
the more there's a vigilance decrement.
link |
00:41:26.080
You start to fall asleep.
link |
00:41:27.240
You start to become complacent,
link |
00:41:28.600
start texting more and so on.
link |
00:41:30.560
Do you worry about that?
link |
00:41:32.320
Cause if we're talking about transition from lane keeping
link |
00:41:35.080
to full autonomy, if you're spending 80% of the time,
link |
00:41:39.880
not supervising the machine,
link |
00:41:42.840
do you worry about what that means
link |
00:41:45.480
to the safety of the drivers?
link |
00:41:47.140
One, we don't consider open pilot to be 1.0
link |
00:41:49.640
until we have 100% driver monitoring.
link |
00:41:52.360
You can cheat right now, our driver monitoring system.
link |
00:41:55.040
There's a few ways to cheat it.
link |
00:41:56.080
They're pretty obvious.
link |
00:41:58.200
We're working on making that better.
link |
00:41:59.720
Before we ship a consumer product that can drive cars,
link |
00:42:02.560
I want to make sure that I have driver monitoring
link |
00:42:04.240
that you can't cheat.
link |
00:42:05.480
What's like a successful driver monitoring system look like?
link |
00:42:07.840
Is it all about just keeping your eyes on the road?
link |
00:42:11.720
Well, a few things.
link |
00:42:12.760
So that's what we went with at first for driver monitoring.
link |
00:42:16.640
I'm checking, I'm actually looking at
link |
00:42:18.040
where your head is looking.
link |
00:42:19.040
The camera's not that high resolution.
link |
00:42:20.440
Eyes are a little bit hard to get.
link |
00:42:21.880
Well, head is this big.
link |
00:42:22.920
I mean, that's.
link |
00:42:23.760
Head is good.
link |
00:42:24.680
And actually a lot of it, just psychology wise,
link |
00:42:28.740
to have that monitor constantly there,
link |
00:42:30.760
it reminds you that you have to be paying attention.
link |
00:42:33.480
But we want to go further.
link |
00:42:35.120
We just hired someone full time
link |
00:42:36.400
to come on to do the driver monitoring.
link |
00:42:37.960
I want to detect phone in frame
link |
00:42:40.400
and I want to make sure you're not sleeping.
link |
00:42:42.600
How much does the camera see of the body?
link |
00:42:44.880
This one, not enough.
link |
00:42:47.480
Not enough.
link |
00:42:48.440
The next one, everything.
link |
00:42:50.760
Well, it's interesting, Fisheye,
link |
00:42:51.600
because we're doing just data collection, not real time.
link |
00:42:55.240
But Fisheye is a beautiful,
link |
00:42:57.600
being able to capture the body.
link |
00:42:59.080
And the smartphone is really like the biggest problem.
link |
00:43:03.280
I'll show you.
link |
00:43:04.120
I can show you one of the pictures from our new system.
link |
00:43:06.360
Awesome, so you're basically saying
link |
00:43:09.680
the driver monitoring will be the answer to that.
link |
00:43:13.120
I think the other point
link |
00:43:14.320
that you raised in your paper is good as well.
link |
00:43:16.960
You're not asking a human to supervise a machine
link |
00:43:20.480
without giving them the,
link |
00:43:21.680
they can take over at any time.
link |
00:43:23.220
Right.
link |
00:43:24.060
Our safety model, you can take over.
link |
00:43:25.800
We disengage on both the gas or the brake.
link |
00:43:27.880
We don't disengage on steering.
link |
00:43:28.900
I don't feel you have to.
link |
00:43:30.020
But we disengage on gas or brake.
link |
00:43:31.760
So it's very easy for you to take over
link |
00:43:34.320
and it's very easy for you to reengage.
link |
00:43:36.440
That switching should be super cheap.
link |
00:43:39.380
The cars that require,
link |
00:43:40.240
even autopilot requires a double press.
link |
00:43:42.440
That's almost, I see, I don't like that.
link |
00:43:44.400
And then the cancel, to cancel in autopilot,
link |
00:43:48.080
you either have to press cancel,
link |
00:43:49.040
which no one knows what that is, so they press the brake.
link |
00:43:51.040
But a lot of times you don't actually want
link |
00:43:52.120
to press the brake.
link |
00:43:53.380
You want to press the gas.
link |
00:43:54.560
So you should cancel on gas.
link |
00:43:55.920
Or wiggle the steering wheel, which is bad as well.
link |
00:43:57.960
Wow, that's brilliant.
link |
00:43:58.920
I haven't heard anyone articulate that point.
link |
00:44:01.440
Oh, this is all I think about.
link |
00:44:03.480
It's the, because I think,
link |
00:44:06.960
I think actually Tesla has done a better job
link |
00:44:09.800
than most automakers at making that frictionless.
link |
00:44:12.920
But you just described that it could be even better.
link |
00:44:16.600
I love Super Cruise as an experience once it's engaged.
link |
00:44:21.160
I don't know if you've used it,
link |
00:44:22.040
but getting the thing to try to engage.
link |
00:44:25.040
Yeah, I've used the, I've driven Super Cruise a lot.
link |
00:44:27.520
So what's your thoughts on the Super Cruise system?
link |
00:44:29.440
You disengage Super Cruise and it falls back to ACC.
link |
00:44:32.680
So my car's like still accelerating.
link |
00:44:34.640
It feels weird.
link |
00:44:36.280
Otherwise, when you actually have Super Cruise engaged
link |
00:44:39.040
on the highway, it is phenomenal.
link |
00:44:41.200
We bought that Cadillac.
link |
00:44:42.320
We just sold it.
link |
00:44:43.320
But we bought it just to like experience this.
link |
00:44:45.620
And I wanted everyone in the office to be like,
link |
00:44:47.320
this is what we're striving to build.
link |
00:44:49.400
GM pioneering with the driver monitoring.
link |
00:44:52.800
You like their driver monitoring system?
link |
00:44:55.000
It has some bugs.
link |
00:44:56.400
If there's a sun shining back here, it'll be blind to you.
link |
00:45:00.280
Right.
link |
00:45:01.960
But overall, mostly, yeah.
link |
00:45:03.340
That's so cool that you know all this stuff.
link |
00:45:05.920
I don't often talk to people that,
link |
00:45:08.460
because it's such a rare car, unfortunately, currently.
link |
00:45:10.980
We bought one explicitly for this.
link |
00:45:12.700
We lost like 25K in the deprecation,
link |
00:45:15.020
but I feel it's worth it.
link |
00:45:16.700
I was very pleasantly surprised that GM system
link |
00:45:21.260
was so innovative and really wasn't advertised much,
link |
00:45:26.320
wasn't talked about much.
link |
00:45:27.460
Yeah.
link |
00:45:28.460
And I was nervous that it would die,
link |
00:45:30.420
that it would disappear.
link |
00:45:31.860
Well, they put it on the wrong car.
link |
00:45:33.500
They should have put it on the Bolt
link |
00:45:34.580
and not some weird Cadillac that nobody bought.
link |
00:45:36.620
I think that's gonna be into,
link |
00:45:38.420
they're saying at least it's gonna be
link |
00:45:40.020
into their entire fleet.
link |
00:45:41.820
So what do you think about,
link |
00:45:43.820
as long as we're on the driver monitoring,
link |
00:45:45.940
what do you think about Elon Musk's claim
link |
00:45:49.280
that driver monitoring is not needed?
link |
00:45:51.940
Normally, I love his claims.
link |
00:45:53.700
That one is stupid.
link |
00:45:55.560
That one is stupid.
link |
00:45:56.580
And, you know, he's not gonna have his level five fleet
link |
00:46:00.320
by the end of the year.
link |
00:46:01.340
Hopefully he's like, okay, I was wrong.
link |
00:46:04.900
I'm gonna add driver monitoring.
link |
00:46:06.260
Because when these systems get to the point
link |
00:46:08.260
that they're only messing up once every thousand miles,
link |
00:46:10.340
you absolutely need driver monitoring.
link |
00:46:14.060
So let me play, cause I agree with you,
link |
00:46:15.900
but let me play devil's advocate.
link |
00:46:17.340
One possibility is that without driver monitoring,
link |
00:46:22.340
people are able to monitor, self regulate,
link |
00:46:26.420
monitor themselves.
link |
00:46:28.260
You know, that, so your idea is.
link |
00:46:30.500
You've seen all the people sleeping in Teslas?
link |
00:46:33.860
Yeah, well, I'm a little skeptical
link |
00:46:37.340
of all the people sleeping in Teslas
link |
00:46:38.860
because I've stopped paying attention to that kind of stuff
link |
00:46:44.260
because I want to see real data.
link |
00:46:45.660
It's too much glorified.
link |
00:46:47.180
It doesn't feel scientific to me.
link |
00:46:48.660
So I want to know how many people are really sleeping
link |
00:46:52.500
in Teslas versus sleeping.
link |
00:46:54.620
I was driving here sleep deprived in a car
link |
00:46:58.060
with no automation.
link |
00:46:59.420
I was falling asleep.
link |
00:47:00.980
I agree that it's hypey.
link |
00:47:02.060
It's just like, you know what?
link |
00:47:04.780
If you want to put driver monitoring,
link |
00:47:06.020
I rented a, my last autopilot experience
link |
00:47:08.420
was I rented a model three in March and drove it around.
link |
00:47:12.140
The wheel thing is annoying.
link |
00:47:13.500
And the reason the wheel thing is annoying,
link |
00:47:15.340
we use the wheel thing as well,
link |
00:47:16.700
but we don't disengage on wheel.
link |
00:47:18.620
For Tesla, you have to touch the wheel just enough
link |
00:47:21.620
to trigger the torque sensor, to tell it that you're there,
link |
00:47:25.260
but not enough as to disengage it,
link |
00:47:28.340
which don't use it for two things.
link |
00:47:30.380
Don't disengage on wheel.
link |
00:47:31.300
You don't have to.
link |
00:47:32.340
That whole experience, wow, beautifully put.
link |
00:47:35.300
All of those elements,
link |
00:47:36.300
even if you don't have driver monitoring,
link |
00:47:38.180
that whole experience needs to be better.
link |
00:47:41.060
Driver monitoring, I think would make,
link |
00:47:43.700
I mean, I think Super Cruise is a better experience
link |
00:47:46.140
once it's engaged over autopilot.
link |
00:47:48.340
I think Super Cruise is a transition
link |
00:47:50.900
to engagement and disengagement are significantly worse.
link |
00:47:53.900
Yeah.
link |
00:47:54.900
Well, there's a tricky thing,
link |
00:47:56.340
because if I were to criticize Super Cruise is,
link |
00:47:59.660
it's a little too crude.
link |
00:48:00.740
And I think like six seconds or something,
link |
00:48:03.580
if you look off road, it'll start warning you.
link |
00:48:05.980
It's some ridiculously long period of time.
link |
00:48:09.020
And just the way,
link |
00:48:12.740
I think it's basically, it's a binary.
link |
00:48:15.740
It should be adaptive.
link |
00:48:17.180
Yeah, it needs to learn more about you.
link |
00:48:19.820
It needs to communicate what it sees about you more.
link |
00:48:24.380
Tesla shows what it sees about the external world.
link |
00:48:27.100
It would be nice if Super Cruise would tell us
link |
00:48:29.060
what it sees about the internal world.
link |
00:48:30.780
It's even worse than that.
link |
00:48:31.900
You press the button to engage
link |
00:48:33.260
and it just says Super Cruise unavailable.
link |
00:48:35.380
Yeah. Why?
link |
00:48:36.220
Why?
link |
00:48:37.740
Yeah, that transparency is good.
link |
00:48:41.420
We've renamed the driver monitoring packet to driver state.
link |
00:48:45.300
Driver state.
link |
00:48:46.140
We have car state packet, which has the state of the car.
link |
00:48:48.220
And you have driver state packet,
link |
00:48:49.380
which has the state of the driver.
link |
00:48:50.940
So what is the...
link |
00:48:52.060
Estimate their BAC.
link |
00:48:53.980
What's BAC?
link |
00:48:54.820
Blood alcohol content.
link |
00:48:57.260
You think that's possible with computer vision?
link |
00:48:59.100
Absolutely.
link |
00:49:03.300
To me, it's an open question.
link |
00:49:04.420
I haven't looked into it too much.
link |
00:49:06.580
Actually, I quite seriously looked at the literature.
link |
00:49:08.380
It's not obvious to me that from the eyes and so on,
link |
00:49:10.780
you can tell.
link |
00:49:11.620
You might need stuff from the car as well.
link |
00:49:13.140
Yeah.
link |
00:49:13.980
You might need how they're controlling the car, right?
link |
00:49:15.700
And that's fundamentally at the end of the day,
link |
00:49:17.340
what you care about.
link |
00:49:18.620
But I think, especially when people are really drunk,
link |
00:49:21.620
they're not controlling the car nearly as smoothly
link |
00:49:23.620
as they would look at them walking, right?
link |
00:49:25.460
The car is like an extension of the body.
link |
00:49:27.220
So I think you could totally detect.
link |
00:49:29.380
And if you could fix people who are drunk, distracted,
link |
00:49:31.340
asleep, if you fix those three.
link |
00:49:32.820
Yeah, that's huge.
link |
00:49:35.460
So what are the current limitations of open pilot?
link |
00:49:38.220
What are the main problems that still need to be solved?
link |
00:49:41.700
We're hopefully fixing a few of them in 06.
link |
00:49:45.420
We're not as good as autopilot at stop cars.
link |
00:49:49.460
So if you're coming up to a red light at 55,
link |
00:49:55.180
so it's the radar stopped car problem, which
link |
00:49:57.060
is responsible for two autopilot accidents,
link |
00:49:59.180
it's hard to differentiate a stopped car from a signpost.
link |
00:50:03.580
Yeah, a static object.
link |
00:50:05.300
So you have to fuse.
link |
00:50:06.300
You have to do this visually.
link |
00:50:07.500
There's no way from the radar data to tell the difference.
link |
00:50:09.580
Maybe you can make a map, but I don't really
link |
00:50:11.540
believe in mapping at all anymore.
link |
00:50:13.820
Wait, wait, wait, what, you don't believe in mapping?
link |
00:50:16.020
No.
link |
00:50:16.660
So you basically, the open pilot solution
link |
00:50:20.660
is saying react to the environment as you see it,
link |
00:50:22.660
just like human beings do.
link |
00:50:24.460
And then eventually, when you want
link |
00:50:25.820
to do navigate on open pilot, I'll
link |
00:50:28.380
train the net to look at ways.
link |
00:50:29.940
I'll run ways in the background, I'll
link |
00:50:31.460
train a confident way.
link |
00:50:32.300
Are you using GPS at all?
link |
00:50:34.540
We use it to ground truth.
link |
00:50:35.940
We use it to very carefully ground truth the paths.
link |
00:50:38.340
We have a stack which can recover relative to 10
link |
00:50:40.980
centimeters over one minute.
link |
00:50:42.940
And then we use that to ground truth exactly where
link |
00:50:45.020
the car went in that local part of the environment,
link |
00:50:47.420
but it's all local.
link |
00:50:48.700
How are you testing in general, just for yourself,
link |
00:50:50.780
like experiments and stuff?
link |
00:50:53.220
Where are you located?
link |
00:50:54.940
San Diego.
link |
00:50:55.540
San Diego.
link |
00:50:56.140
Yeah.
link |
00:50:56.780
OK.
link |
00:50:58.660
So you basically drive around there, collect some data,
link |
00:51:01.420
and watch the performance?
link |
00:51:03.060
We have a simulator now.
link |
00:51:04.300
And we have, our simulator is really cool.
link |
00:51:06.420
Our simulator is not, it's not like a Unity based simulator.
link |
00:51:09.660
Our simulator lets us load in real state.
link |
00:51:12.820
What do you mean?
link |
00:51:13.620
We can load in a drive and simulate
link |
00:51:16.700
what the system would have done on the historical data.
link |
00:51:20.260
Ooh, nice.
link |
00:51:22.460
Interesting.
link |
00:51:23.460
So what, yeah.
link |
00:51:24.260
Right now we're only using it for testing,
link |
00:51:26.060
but as soon as we start using it for training, that's it.
link |
00:51:29.140
That's all that matters.
link |
00:51:30.780
What's your feeling about the real world versus simulation?
link |
00:51:33.020
Do you like simulation for training,
link |
00:51:34.420
if this moves to training?
link |
00:51:35.700
So we have to distinguish two types of simulators, right?
link |
00:51:40.020
There's a simulator that is completely fake.
link |
00:51:44.620
I could get my car to drive around in GTA.
link |
00:51:47.740
I feel that this kind of simulator is useless.
link |
00:51:51.780
You're never, there's so many.
link |
00:51:54.580
My analogy here is like, OK, fine.
link |
00:51:56.940
You're not solving the computer vision problem,
link |
00:51:59.860
but you're solving the computer graphics problem.
link |
00:52:02.300
Right.
link |
00:52:02.780
And you don't think you can get very far by creating
link |
00:52:05.300
ultra realistic graphics?
link |
00:52:07.980
No, because you can create ultra realistic graphics
link |
00:52:10.340
of the road, now create ultra realistic behavioral models
link |
00:52:13.140
of the other cars.
link |
00:52:14.500
Oh, well, I'll just use myself driving.
link |
00:52:16.860
No, you won't.
link |
00:52:18.180
You need actual human behavior, because that's
link |
00:52:22.180
what you're trying to learn.
link |
00:52:23.860
Driving does not have a spec.
link |
00:52:25.820
The definition of driving is what humans do when they drive.
link |
00:52:29.860
Whatever Waymo does, I don't think it's driving.
link |
00:52:32.700
Right.
link |
00:52:33.220
Well, I think actually Waymo and others,
link |
00:52:36.380
if there's any use for reinforcement learning,
link |
00:52:38.980
I've seen it used quite well.
link |
00:52:40.340
I study pedestrians a lot, too, is
link |
00:52:42.020
try to train models from real data of how pedestrians move,
link |
00:52:45.500
and try to use reinforcement learning models to make
link |
00:52:47.540
pedestrians move in human like ways.
link |
00:52:49.980
By that point, you've already gone so many layers,
link |
00:52:53.500
you detected a pedestrian?
link |
00:52:55.660
Did you hand code the feature vector of their state?
link |
00:53:00.180
Did you guys learn anything from computer vision
link |
00:53:02.860
before deep learning?
link |
00:53:04.580
Well, OK, I feel like this is.
link |
00:53:07.140
So perception to you is the sticking point.
link |
00:53:10.820
I mean, what's the hardest part of the stack here?
link |
00:53:13.780
There is no human understandable feature vector separating
link |
00:53:20.500
perception and planning.
link |
00:53:23.060
That's the best way I can put that.
link |
00:53:25.100
There is no, so it's all together,
link |
00:53:26.780
and it's a joint problem.
link |
00:53:29.540
So you can take localization.
link |
00:53:31.460
Localization and planning, there is
link |
00:53:33.260
a human understandable feature vector between these two
link |
00:53:35.300
things.
link |
00:53:35.980
I mean, OK, so I have like three degrees position,
link |
00:53:38.540
three degrees orientation, and those derivatives,
link |
00:53:40.540
maybe those second derivatives.
link |
00:53:41.980
That's human understandable.
link |
00:53:43.140
That's physical.
link |
00:53:45.460
Between perception and planning, so like Waymo
link |
00:53:50.780
has a perception stack and then a planner.
link |
00:53:53.620
And one of the things Waymo does right
link |
00:53:55.580
is they have a simulator that can separate those two.
link |
00:54:00.020
They can like replay their perception data
link |
00:54:02.900
and test their system, which is what
link |
00:54:04.380
I'm talking about about like the two
link |
00:54:05.380
different kinds of simulators.
link |
00:54:06.500
There's the kind that can work on real data,
link |
00:54:08.220
and there's the kind that can't work on real data.
link |
00:54:10.860
Now, the problem is that I don't think you can hand code
link |
00:54:14.900
a feature vector, right?
link |
00:54:16.140
Like you have some list of like, oh, here's
link |
00:54:17.740
my list of cars in the scenes.
link |
00:54:19.100
Here's my list of pedestrians in the scene.
link |
00:54:21.220
This isn't what humans are doing.
link |
00:54:23.180
What are humans doing?
link |
00:54:24.860
Global.
link |
00:54:27.180
And you're saying that's too difficult to hand engineer.
link |
00:54:31.860
I'm saying that there is no state vector given a perfect.
link |
00:54:35.020
I could give you the best team of engineers in the world
link |
00:54:37.300
to build a perception system and the best team
link |
00:54:39.060
to build a planner.
link |
00:54:40.580
All you have to do is define the state vector
link |
00:54:42.660
that separates those two.
link |
00:54:43.860
I'm missing the state vector that separates those two.
link |
00:54:48.580
What do you mean?
link |
00:54:49.300
So what is the output of your perception system?
link |
00:54:53.860
Output of the perception system, it's, OK, well,
link |
00:55:00.500
there's several ways to do it.
link |
00:55:01.620
One is the SLAM components localization.
link |
00:55:03.780
The other is drivable area, drivable space.
link |
00:55:05.780
Drivable space, yeah.
link |
00:55:06.580
And then there's the different objects in the scene.
link |
00:55:10.860
And different objects in the scene over time,
link |
00:55:15.340
maybe, to give you input to then try
link |
00:55:17.660
to start modeling the trajectories of those objects.
link |
00:55:21.500
Sure.
link |
00:55:22.140
That's it.
link |
00:55:22.740
I can give you a concrete example
link |
00:55:24.060
of something you missed.
link |
00:55:25.060
What's that?
link |
00:55:25.780
So say there's a bush in the scene.
link |
00:55:28.580
Humans understand that when they see this bush
link |
00:55:30.860
that there may or may not be a car behind that bush.
link |
00:55:34.580
Drivable area and a list of objects does not include that.
link |
00:55:37.180
Humans are doing this constantly at the simplest intersections.
link |
00:55:40.820
So now you have to talk about occluded area.
link |
00:55:44.900
But even that, what do you mean by occluded?
link |
00:55:47.740
OK, so I can't see it.
link |
00:55:49.500
Well, if it's the other side of a house, I don't care.
link |
00:55:51.740
What's the likelihood that there's
link |
00:55:53.100
a car in that occluded area?
link |
00:55:55.180
And if you say, OK, we'll add that,
link |
00:55:57.940
I can come up with 10 more examples that you can't add.
link |
00:56:01.620
Certainly, occluded area would be something
link |
00:56:03.860
that Simulator would have because it's
link |
00:56:05.860
simulating the entire occlusion is part of it.
link |
00:56:11.180
Occlusion is part of a vision stack.
link |
00:56:12.580
But what I'm saying is if you have a hand engineered,
link |
00:56:16.500
if your perception system output can
link |
00:56:19.420
be written in a spec document, it is incomplete.
link |
00:56:22.980
Yeah, I mean, certainly, it's hard to argue with that
link |
00:56:27.740
because in the end, that's going to be true.
link |
00:56:30.100
Yeah, and I'll tell you what the output of our perception
link |
00:56:32.260
system is.
link |
00:56:32.740
What's that?
link |
00:56:33.300
It's a 1,024 dimensional vector, trained by neural net.
link |
00:56:37.940
Oh, you know that.
link |
00:56:38.980
No, it's 1,024 dimensions of who knows what.
link |
00:56:43.460
Because it's operating on real data.
link |
00:56:45.060
Yeah.
link |
00:56:46.940
And that's the perception.
link |
00:56:48.340
That's the perception state.
link |
00:56:50.380
Think about an autoencoder for faces.
link |
00:56:53.500
If you have an autoencoder for faces and you say
link |
00:56:56.660
it has 256 dimensions in the middle,
link |
00:56:59.260
and I'm taking a face over here and projecting it
link |
00:57:01.260
to a face over here.
link |
00:57:02.820
Can you hand label all 256 of those dimensions?
link |
00:57:06.300
Well, no, but those have to generate automatically.
link |
00:57:09.260
But even if you tried to do it by hand,
link |
00:57:11.380
could you come up with a spec between your encoder
link |
00:57:15.580
and your decoder?
link |
00:57:17.660
No, because it wasn't designed, but there.
link |
00:57:20.740
No, no, no, but if you could design it.
link |
00:57:23.620
If you could design a face reconstructor system,
link |
00:57:26.460
could you come up with a spec?
link |
00:57:29.260
No, but I think we're missing here a little bit.
link |
00:57:32.340
I think you're just being very poetic about expressing
link |
00:57:35.980
a fundamental problem of simulators,
link |
00:57:38.940
that they're going to be missing so much that the feature
link |
00:57:44.300
vector will just look fundamentally different
link |
00:57:47.500
in the simulated world than the real world.
link |
00:57:51.260
I'm not making a claim about simulators.
link |
00:57:53.820
I'm making a claim about the spec division
link |
00:57:57.060
between perception and planning, even in your system.
link |
00:58:00.780
Just in general.
link |
00:58:01.980
Just in general.
link |
00:58:03.300
If you're trying to build a car that drives,
link |
00:58:05.620
if you're trying to hand code the output of your perception
link |
00:58:08.340
system, like saying, here's a list of all the cars
link |
00:58:10.340
in the scene, here's a list of all the people,
link |
00:58:11.860
here's a list of the occluded areas,
link |
00:58:13.060
here's a vector of drivable areas, it's insufficient.
link |
00:58:16.540
And if you start to believe that,
link |
00:58:17.900
you realize that what Waymo and Cruz are doing is impossible.
link |
00:58:20.780
Currently, what we're doing is the perception problem
link |
00:58:24.220
is converting the scene into a chessboard.
link |
00:58:29.140
And then you reason some basic reasoning
link |
00:58:31.660
around that chessboard.
link |
00:58:33.340
And you're saying that really, there's a lot missing there.
link |
00:58:38.380
First of all, why are we talking about this?
link |
00:58:40.180
Because isn't this a full autonomy?
link |
00:58:42.740
Is this something you think about?
link |
00:58:44.580
Oh, I want to win self driving cars.
link |
00:58:47.540
So your definition of win includes?
link |
00:58:51.940
Level four or five.
link |
00:58:53.060
Level five.
link |
00:58:53.900
I don't think level four is a real thing.
link |
00:58:55.740
I want to build the AlphaGo of driving.
link |
00:59:01.060
So AlphaGo is really end to end.
link |
00:59:06.060
Yeah.
link |
00:59:06.900
Is, yeah, it's end to end.
link |
00:59:09.780
And do you think this whole problem,
link |
00:59:12.420
is that also kind of what you're getting at
link |
00:59:14.620
with the perception and the planning?
link |
00:59:16.580
Is that this whole problem, the right way to do it
link |
00:59:19.380
is really to learn the entire thing.
link |
00:59:21.540
I'll argue that not only is it the right way,
link |
00:59:23.620
it's the only way that's going to exceed human performance.
link |
00:59:27.620
Well.
link |
00:59:28.540
It's certainly true for Go.
link |
00:59:29.940
Everyone who tried to hand code Go things
link |
00:59:31.460
built human inferior things.
link |
00:59:33.420
And then someone came along and wrote some 10,000 line thing
link |
00:59:36.180
that doesn't know anything about Go that beat everybody.
link |
00:59:39.780
It's 10,000 lines.
link |
00:59:41.060
True, in that sense, the open question then
link |
00:59:44.500
that maybe I can ask you is driving is much harder than Go.
link |
00:59:53.460
The open question is how much harder?
link |
00:59:56.260
So how, because I think the Elon Musk approach here
link |
00:59:59.500
with planning and perception is similar
link |
01:00:01.620
to what you're describing,
link |
01:00:02.980
which is really turning into not some kind of modular thing,
link |
01:00:08.300
but really do formulate it as a learning problem
link |
01:00:11.140
and solve the learning problem with scale.
link |
01:00:13.380
So how many years, put one is how many years
link |
01:00:17.700
would it take to solve this problem
link |
01:00:18.860
or just how hard is this freaking problem?
link |
01:00:21.660
Well, the cool thing is I think there's a lot of value
link |
01:00:27.780
that we can deliver along the way.
link |
01:00:30.820
I think that you can build lane keeping assist actually
link |
01:00:37.260
plus adaptive cruise control, plus, okay, looking at ways,
link |
01:00:42.260
extends to like all of driving.
link |
01:00:45.980
Yeah, most of driving, right?
link |
01:00:47.900
Oh, your adaptive cruise control treats red lights
link |
01:00:49.740
like cars, okay.
link |
01:00:51.180
So let's jump around.
link |
01:00:52.980
You mentioned that you didn't like navigate an autopilot.
link |
01:00:55.740
What advice, how would you make it better?
link |
01:00:57.740
Do you think as a feature that if it's done really well,
link |
01:01:00.540
it's a good feature?
link |
01:01:02.340
I think that it's too reliant on like hand coded hacks
link |
01:01:07.460
for like, how does navigate an autopilot do a lane change?
link |
01:01:10.380
It actually does the same lane change every time
link |
01:01:13.340
and it feels mechanical.
link |
01:01:14.260
Humans do different lane changes.
link |
01:01:15.820
Humans sometime will do a slow one,
link |
01:01:17.300
sometimes do a fast one.
link |
01:01:18.860
Navigate an autopilot, at least every time I use it,
link |
01:01:20.820
it is the identical lane change.
link |
01:01:22.980
How do you learn?
link |
01:01:24.220
I mean, this is a fundamental thing actually
link |
01:01:26.740
is the braking and then accelerating
link |
01:01:30.340
something that's still, Tesla probably does it better
link |
01:01:33.900
than most cars, but it still doesn't do a great job
link |
01:01:36.740
of creating a comfortable natural experience.
link |
01:01:39.900
And navigate an autopilot is just lane changes
link |
01:01:42.620
and extension of that.
link |
01:01:44.060
So how do you learn to do a natural lane change?
link |
01:01:49.180
So we have it and I can talk about how it works.
link |
01:01:52.980
So I feel that we have the solution for lateral.
link |
01:01:58.860
We don't yet have the solution for longitudinal.
link |
01:02:00.700
There's a few reasons longitudinal is harder than lateral.
link |
01:02:03.420
The lane change component,
link |
01:02:05.180
the way that we train on it very simply
link |
01:02:08.060
is like our model has an input
link |
01:02:10.900
for whether it's doing a lane change or not.
link |
01:02:14.100
And then when we train the end to end model,
link |
01:02:16.420
we hand label all the lane changes,
link |
01:02:18.420
cause you have to.
link |
01:02:19.580
I've struggled a long time about not wanting to do that,
link |
01:02:22.460
but I think you have to.
link |
01:02:24.300
Or the training data.
link |
01:02:25.340
For the training data, right?
link |
01:02:26.540
Oh, we actually, we have an automatic ground truther
link |
01:02:28.380
which automatically labels all the lane changes.
link |
01:02:30.580
Was that possible?
link |
01:02:31.700
To automatically label the lane changes?
link |
01:02:32.780
Yeah.
link |
01:02:33.620
Yeah, detect the lane, I see when it crosses it, right?
link |
01:02:34.820
And I don't have to get that high percent accuracy,
link |
01:02:36.700
but it's like 95, good enough.
link |
01:02:38.980
Now I set the bit when it's doing the lane change
link |
01:02:43.220
in the end to end learning.
link |
01:02:44.860
And then I set it to zero when it's not doing a lane change.
link |
01:02:47.940
So now if I wanted to do a lane change at test time,
link |
01:02:49.740
I just put the bit to a one and it'll do a lane change.
link |
01:02:52.380
Yeah, but so if you look at the space of lane change,
link |
01:02:54.660
you know, some percentage, not a hundred percent
link |
01:02:57.340
that we make as humans is not a pleasant experience
link |
01:03:01.140
cause we messed some part of it up.
link |
01:03:02.860
It's nerve wracking to change the look,
link |
01:03:04.940
you have to see, it has to accelerate.
link |
01:03:06.940
How do we label the ones that are natural and feel good?
link |
01:03:09.940
You know, that's the, cause that's your ultimate criticism.
link |
01:03:13.380
The current navigate and autopilot
link |
01:03:15.860
just doesn't feel good.
link |
01:03:16.940
Well, the current navigate and autopilot
link |
01:03:18.460
is a hand coded policy written by an engineer in a room
link |
01:03:21.660
who probably went out and tested it a few times on the 280.
link |
01:03:25.020
Probably a more, a better version of that, but yes.
link |
01:03:29.420
That's how we would have written it at Comma AI.
link |
01:03:31.020
Yeah, yeah, yeah.
link |
01:03:31.860
Maybe Tesla did, Tesla, they tested it in the end.
link |
01:03:33.420
That might've been two engineers.
link |
01:03:35.100
Two engineers, yeah.
link |
01:03:37.380
No, but so if you learn the lane change,
link |
01:03:40.060
if you learn how to do a lane change from data,
link |
01:03:42.420
just like you have a label that says lane change
link |
01:03:44.660
and then you put it in when you want it
link |
01:03:46.380
to do the lane change,
link |
01:03:48.020
it'll automatically do the lane change
link |
01:03:49.620
that's appropriate for the situation.
link |
01:03:51.580
Now, to get at the problem of some humans
link |
01:03:54.700
do bad lane changes,
link |
01:03:57.380
we haven't worked too much on this problem yet.
link |
01:03:59.900
It's not that much of a problem in practice.
link |
01:04:03.100
My theory is that all good drivers are good in the same way
link |
01:04:06.140
and all bad drivers are bad in different ways.
link |
01:04:09.340
And we've seen some data to back this up.
link |
01:04:11.300
Well, beautifully put.
link |
01:04:12.380
So you just basically, if that's true hypothesis,
link |
01:04:16.540
then your task is to discover the good drivers.
link |
01:04:19.860
The good drivers stand out because they're in one cluster
link |
01:04:23.300
and the bad drivers are scattered all over the place
link |
01:04:25.140
and your net learns the cluster.
link |
01:04:27.180
Yeah, that's, so you just learn from the good drivers
link |
01:04:30.740
and they're easy to cluster.
link |
01:04:33.140
In fact, we learned from all of them
link |
01:04:33.980
and the net automatically learns the policy
link |
01:04:35.780
that's like the majority,
link |
01:04:36.860
but we'll eventually probably have to filter them out.
link |
01:04:38.500
If that theory is true, I hope it's true
link |
01:04:41.500
because the counter theory is there is many clusters,
link |
01:04:49.420
maybe arbitrarily many clusters of good drivers.
link |
01:04:53.620
Because if there's one cluster of good drivers,
link |
01:04:55.780
you can at least discover a set of policies.
link |
01:04:57.540
You can learn a set of policies,
link |
01:04:58.940
which would be good universally.
link |
01:05:00.580
Yeah.
link |
01:05:01.620
That would be a nice, that would be nice if it's true.
link |
01:05:04.540
And you're saying that there is some evidence that.
link |
01:05:06.540
Let's say lane changes can be clustered into four clusters.
link |
01:05:09.740
Right. Right.
link |
01:05:10.580
There's this finite level of.
link |
01:05:12.020
I would argue that all four of those are good clusters.
link |
01:05:15.260
All the things that are random are noise and probably bad.
link |
01:05:18.420
And which one of the four you pick,
link |
01:05:20.340
or maybe it's 10 or maybe it's 20.
link |
01:05:21.900
You can learn that.
link |
01:05:22.740
It's context dependent.
link |
01:05:23.780
It depends on the scene.
link |
01:05:24.980
And the hope is it's not too dependent on the driver.
link |
01:05:31.380
Yeah. The hope is that it all washes out.
link |
01:05:34.220
The hope is that there's, that the distribution's not bimodal.
link |
01:05:36.980
The hope is that it's a nice Gaussian.
link |
01:05:39.100
So what advice would you give to Tesla,
link |
01:05:41.660
how to fix, how to improve navigating autopilot?
link |
01:05:44.980
That's the lessons that you've learned from Comm AI?
link |
01:05:48.260
The only real advice I would give to Tesla
link |
01:05:50.580
is please put driver monitoring in your cars.
link |
01:05:52.940
With respect to improving it?
link |
01:05:55.100
You can't do that anymore.
link |
01:05:55.940
I decided to interrupt, but you know,
link |
01:05:58.220
there's a practical nature of many of hundreds of thousands
link |
01:06:01.740
of cars being produced that don't have
link |
01:06:04.180
a good driver facing camera.
link |
01:06:05.780
The Model 3 has a selfie cam.
link |
01:06:07.500
Is it not good enough?
link |
01:06:08.660
Did they not put IR LEDs for night?
link |
01:06:10.780
That's a good question.
link |
01:06:11.620
But I do know that it's fisheye
link |
01:06:13.340
and it's relatively low resolution.
link |
01:06:15.780
So it's really not designed.
link |
01:06:16.740
It wasn't.
link |
01:06:17.580
It wasn't designed for driver monitoring.
link |
01:06:18.740
You can hope that you can kind of scrape up
link |
01:06:21.740
and have something from it.
link |
01:06:24.180
Yeah.
link |
01:06:25.020
But why didn't they put it in today?
link |
01:06:27.500
Put it in today.
link |
01:06:28.340
Put it in today.
link |
01:06:29.500
Every time I've heard Karpathy talk about the problem
link |
01:06:31.500
and talking about like software 2.0
link |
01:06:33.220
and how the machine learning is gobbling up everything,
link |
01:06:35.220
I think this is absolutely the right strategy.
link |
01:06:37.420
I think that he didn't write navigate on autopilot.
link |
01:06:40.140
I think somebody else did
link |
01:06:41.540
and kind of hacked it on top of that stuff.
link |
01:06:43.220
I think when Karpathy says, wait a second,
link |
01:06:45.700
why did we hand code this lane change policy
link |
01:06:47.420
with all these magic numbers?
link |
01:06:48.340
We're gonna learn it from data.
link |
01:06:49.340
They'll fix it.
link |
01:06:50.180
They already know what to do there.
link |
01:06:51.060
Well, that's Andrei's job
link |
01:06:53.380
is to turn everything into a learning problem
link |
01:06:55.780
and collect a huge amount of data.
link |
01:06:57.500
The reality is though,
link |
01:06:59.540
not every problem can be turned into a learning problem
link |
01:07:02.780
in the short term.
link |
01:07:04.100
In the end, everything will be a learning problem.
link |
01:07:07.300
The reality is like if you wanna build L5 vehicles today,
link |
01:07:12.940
it will likely involve no learning.
link |
01:07:15.460
And that's the reality is,
link |
01:07:17.420
so at which point does learning start?
link |
01:07:20.340
It's the crutch statement that LiDAR is a crutch.
link |
01:07:23.500
At which point will learning
link |
01:07:24.860
get up to part of human performance?
link |
01:07:27.260
It's over human performance on ImageNet,
link |
01:07:30.980
classification, on driving, it's a question still.
link |
01:07:34.060
It is a question.
link |
01:07:35.820
I'll say this, I'm here to play for 10 years.
link |
01:07:39.260
I'm not here to try to,
link |
01:07:40.340
I'm here to play for 10 years and make money along the way.
link |
01:07:43.020
I'm not here to try to promise people
link |
01:07:45.100
that I'm gonna have my L5 taxi network
link |
01:07:47.060
up and working in two years.
link |
01:07:48.300
Do you think that was a mistake?
link |
01:07:49.500
Yes.
link |
01:07:50.580
What do you think was the motivation behind saying that?
link |
01:07:53.540
Other companies are also promising L5 vehicles
link |
01:07:56.700
with very different approaches in 2020, 2021, 2022.
link |
01:08:01.940
If anybody would like to bet me
link |
01:08:03.740
that those things do not pan out, I will bet you.
link |
01:08:06.940
Even money, even money, I'll bet you as much as you want.
link |
01:08:09.780
Yeah.
link |
01:08:10.900
So are you worried about what's going to happen?
link |
01:08:13.660
Cause you're not in full agreement on that.
link |
01:08:16.140
What's going to happen when 2022, 21 come around
link |
01:08:19.180
and nobody has fleets of autonomous vehicles?
link |
01:08:22.900
Well, you can look at the history.
link |
01:08:25.060
If you go back five years ago,
link |
01:08:26.740
they were all promised by 2018 and 2017.
link |
01:08:29.980
But they weren't that strong of promises.
link |
01:08:32.260
I mean, Ford really declared pretty,
link |
01:08:36.260
I think not many have declared as like definitively
link |
01:08:40.640
as they have now these dates.
link |
01:08:42.660
Well, okay, so let's separate L4 and L5.
link |
01:08:45.100
Do I think that it's possible for Waymo to continue to kind
link |
01:08:49.480
of like hack on their system
link |
01:08:51.020
until it gets to level four in Chandler, Arizona?
link |
01:08:53.460
Yes.
link |
01:08:55.060
When there's no safety driver?
link |
01:08:56.860
Chandler, Arizona?
link |
01:08:57.700
Yeah.
link |
01:08:59.580
By, sorry, which year are we talking about?
link |
01:09:02.540
Oh, I even think that's possible by like 2020, 2021.
link |
01:09:06.180
But level four, Chandler, Arizona,
link |
01:09:08.460
not level five, New York City.
link |
01:09:10.340
Level four, meaning some very defined streets,
link |
01:09:15.980
it works out really well.
link |
01:09:17.460
Very defined streets.
link |
01:09:18.300
And then practically these streets are pretty empty.
link |
01:09:20.720
If most of the streets are covered in Waymo's,
link |
01:09:24.700
Waymo can kind of change the definition of what driving is.
link |
01:09:28.420
Right?
link |
01:09:29.260
If your self driving network
link |
01:09:30.980
is the majority of cars in an area,
link |
01:09:33.460
they only need to be safe with respect to each other
link |
01:09:35.740
and all the humans will need to learn to adapt to them.
link |
01:09:38.660
Now go drive in downtown New York.
link |
01:09:41.140
Well, yeah, that's.
link |
01:09:42.220
I mean, already you can talk about autonomy
link |
01:09:44.780
and like on farms, it already works great
link |
01:09:46.980
because you can really just follow the GPS line.
link |
01:09:51.300
So what does success look like for common AI?
link |
01:09:55.640
What are the milestones?
link |
01:09:57.900
Like where you can sit back with some champagne
link |
01:09:59.820
and say, we did it, boys and girls?
link |
01:10:04.140
Well, it's never over.
link |
01:10:06.260
Yeah, but.
link |
01:10:07.300
You must drink champagne and celebrate.
link |
01:10:10.420
So what is a good, what are some wins?
link |
01:10:13.180
A big milestone that we're hoping for
link |
01:10:17.780
by mid next year is profitability of the company.
link |
01:10:23.580
And we're gonna have to revisit the idea
link |
01:10:27.680
of selling a consumer product,
link |
01:10:30.320
but it's not gonna be like the comma one.
link |
01:10:32.740
When we do it, it's gonna be perfect.
link |
01:10:35.320
Open pilot has gotten so much better in the last two years.
link |
01:10:39.640
We're gonna have a few features.
link |
01:10:41.720
We're gonna have a hundred percent driver monitoring.
link |
01:10:43.800
We're gonna disable no safety features in the car.
link |
01:10:47.120
Actually, I think it'd be really cool
link |
01:10:48.280
what we're doing right now.
link |
01:10:49.160
Our project this week is we're analyzing the data set
link |
01:10:51.640
and looking for all the AEB triggers
link |
01:10:53.240
from the manufacturer systems.
link |
01:10:55.640
We have better data set on that than the manufacturers.
link |
01:10:59.440
How much, just how many,
link |
01:11:00.920
does Toyota have 10 million miles of real world driving
link |
01:11:03.360
to know how many times their AEB triggered?
link |
01:11:05.320
So let me give you, cause you asked, right?
link |
01:11:08.400
Financial advice.
link |
01:11:09.560
Yeah.
link |
01:11:10.880
Cause I work with a lot of automakers
link |
01:11:12.400
and one possible source of money for you,
link |
01:11:15.800
which I'll be excited to see you take on
link |
01:11:18.040
is basically selling the data.
link |
01:11:24.600
So, which is something that most people,
link |
01:11:29.120
and not selling in a way where here, here at Automaker,
link |
01:11:31.800
but creating, we've done this actually at MIT,
link |
01:11:34.360
not for money purposes,
link |
01:11:35.480
but you could do it for significant money purposes
link |
01:11:37.760
and make the world a better place by creating a consortia
link |
01:11:41.360
where automakers would pay in
link |
01:11:44.200
and then they get to have free access to the data.
link |
01:11:46.960
And I think a lot of people are really hungry for that
link |
01:11:52.400
and would pay significant amount of money for it.
link |
01:11:54.200
Here's the problem with that.
link |
01:11:55.400
I like this idea all in theory.
link |
01:11:56.880
It'd be very easy for me to give them access to my servers
link |
01:11:59.660
and we already have all open source tools
link |
01:12:01.480
to access this data.
link |
01:12:02.320
It's in a great format.
link |
01:12:03.440
We have a great pipeline,
link |
01:12:05.640
but they're gonna put me in the room
link |
01:12:07.120
with some business development guy.
link |
01:12:10.140
And I'm gonna have to talk to this guy
link |
01:12:12.560
and he's not gonna know most of the words I'm saying.
link |
01:12:15.200
I'm not willing to tolerate that.
link |
01:12:17.400
Okay, Mick Jagger.
link |
01:12:18.960
No, no, no, no, no.
link |
01:12:19.800
I think I agree with you.
link |
01:12:21.120
I'm the same way, but you just tell them the terms
link |
01:12:23.040
and there's no discussion needed.
link |
01:12:24.720
If I could just tell them the terms,
link |
01:12:28.080
Yeah.
link |
01:12:28.920
and like, all right, who wants access to my data?
link |
01:12:31.720
I will sell it to you for, let's say,
link |
01:12:36.800
you want a subscription?
link |
01:12:37.720
I'll sell to you for 100K a month.
link |
01:12:40.800
Anyone.
link |
01:12:41.640
100K a month.
link |
01:12:42.480
100K a month.
link |
01:12:43.300
I'll give you access to this data subscription.
link |
01:12:45.160
Yeah.
link |
01:12:46.000
Yeah, I think that's kind of fair.
link |
01:12:46.820
Came up with that number off the top of my head.
link |
01:12:48.080
If somebody sends me like a three line email
link |
01:12:50.200
where it's like, we would like to pay 100K a month
link |
01:12:52.600
to get access to your data.
link |
01:12:54.040
We would agree to like reasonable privacy terms
link |
01:12:56.180
of the people who are in the data set.
link |
01:12:58.360
I would be happy to do it,
link |
01:12:59.560
but that's not going to be the email.
link |
01:13:01.200
The email is going to be, hey,
link |
01:13:02.880
do you have some time in the next month
link |
01:13:04.680
where we can sit down and we can,
link |
01:13:06.000
I don't have time for that.
link |
01:13:06.880
We're moving too fast.
link |
01:13:07.880
Yeah.
link |
01:13:08.720
You could politely respond to that email,
link |
01:13:10.080
but not saying, I don't have any time for your bullshit.
link |
01:13:13.280
You say, oh, well, unfortunately these are the terms.
link |
01:13:15.480
And so this is, we try to,
link |
01:13:17.720
we brought the cost down for you
link |
01:13:19.840
in order to minimize the friction and communication.
link |
01:13:22.480
Absolutely.
link |
01:13:23.320
Here's the, whatever it is,
link |
01:13:24.520
one, two million dollars a year and you have access.
link |
01:13:28.960
And it's not like I get that email from like,
link |
01:13:31.460
but okay, am I going to reach out?
link |
01:13:32.720
Am I going to hire a business development person
link |
01:13:34.200
who's going to reach out to the automakers?
link |
01:13:35.920
No way.
link |
01:13:36.740
Yeah. Okay.
link |
01:13:37.580
I got you.
link |
01:13:38.400
If they reached into me, I'm not going to ignore the email.
link |
01:13:40.640
I'll come back with something like,
link |
01:13:41.960
yeah, if you're willing to pay 100K a month
link |
01:13:43.920
for access to the data, I'm happy to set that up.
link |
01:13:46.120
That's worth my engineering time.
link |
01:13:48.240
That's actually quite insightful of you.
link |
01:13:49.560
You're right.
link |
01:13:50.480
Probably because many of the automakers
link |
01:13:52.520
are quite a bit old school,
link |
01:13:54.200
there will be a need to reach out and they want it,
link |
01:13:57.680
but there'll need to be some communication.
link |
01:13:59.800
You're right.
link |
01:14:00.640
Mobileye circa 2015 had the lowest R&D spend
link |
01:14:04.520
of any chip maker, like per, per,
link |
01:14:08.360
and you look at all the people who work for them
link |
01:14:10.640
and it's all business development people
link |
01:14:12.120
because the car companies are impossible to work with.
link |
01:14:15.340
Yeah.
link |
01:14:16.180
So you're, you have no patience for that
link |
01:14:17.880
and you're, you're legit Android, huh?
link |
01:14:20.040
I have something to do, right?
link |
01:14:21.400
Like, like it's not like, it's not like,
link |
01:14:22.520
I don't, like, I don't mean to like be a dick
link |
01:14:23.760
and say like, I don't have patience for that,
link |
01:14:25.120
but it's like that stuff doesn't help us
link |
01:14:28.280
with our goal of winning self driving cars.
link |
01:14:30.560
If I want money in the short term,
link |
01:14:33.800
if I showed off like the actual,
link |
01:14:36.080
like the learning tech that we have,
link |
01:14:38.080
it's, it's somewhat sad.
link |
01:14:39.580
Like it's years and years ahead of everybody else's.
link |
01:14:42.720
Not to, maybe not Tesla's.
link |
01:14:43.720
I think Tesla has some more stuff to us actually.
link |
01:14:45.280
Yeah.
link |
01:14:46.120
I think Tesla has similar stuff,
link |
01:14:46.940
but when you compare it to like
link |
01:14:47.940
what the Toyota Research Institute has,
link |
01:14:50.800
you're not even close to what we have.
link |
01:14:53.480
No comments.
link |
01:14:54.360
But I also can't, I have to take your comments.
link |
01:14:58.480
I intuitively believe you,
link |
01:15:01.640
but I have to take it with a grain of salt
link |
01:15:03.240
because I mean, you are an inspiration
link |
01:15:06.200
because you basically don't care about a lot of things
link |
01:15:09.040
that other companies care about.
link |
01:15:10.840
You don't try to bullshit in a sense,
link |
01:15:15.560
like make up stuff.
link |
01:15:16.640
So to drive up valuation, you're really very real
link |
01:15:19.720
and you're trying to solve the problem
link |
01:15:20.920
and admire that a lot.
link |
01:15:22.280
What I don't necessarily fully can't trust you on,
link |
01:15:25.960
with all due respect, is how good it is, right?
link |
01:15:28.440
I can only, but I also know how bad others are.
link |
01:15:32.460
And so.
link |
01:15:33.300
I'll say two things about, trust but verify, right?
link |
01:15:36.680
I'll say two things about that.
link |
01:15:38.040
One is try, get in a 2020 Corolla
link |
01:15:42.360
and try open pilot 0.6 when it comes out next month.
link |
01:15:46.680
I think already you'll look at this
link |
01:15:48.400
and you'll be like, this is already really good.
link |
01:15:51.200
And then I could be doing that all with hand labelers
link |
01:15:54.280
and all with like the same approach that Mobileye uses.
link |
01:15:57.960
When we release a model that no longer has the lanes in it,
link |
01:16:01.440
that only outputs a path,
link |
01:16:04.960
then think about how we did that machine learning
link |
01:16:08.680
and then right away when you see,
link |
01:16:10.080
and that's gonna be an open pilot,
link |
01:16:11.240
that's gonna be an open pilot before 1.0.
link |
01:16:13.000
When you see that model,
link |
01:16:14.080
you'll know that everything I'm saying is true
link |
01:16:15.400
because how else did I get that model?
link |
01:16:16.840
Good.
link |
01:16:17.680
You know what I'm saying is true about the simulator.
link |
01:16:19.240
Yeah, yeah, this is super exciting, that's super exciting.
link |
01:16:22.680
But like, you know, I listened to your talk with Kyle
link |
01:16:25.760
and Kyle was originally building the aftermarket system
link |
01:16:30.460
and he gave up on it because of technical challenges,
link |
01:16:34.920
because of the fact that he's gonna have to support
link |
01:16:38.160
20 to 50 cars, we support 45,
link |
01:16:40.520
because what is he gonna do
link |
01:16:41.480
when the manufacturer ABS system triggers?
link |
01:16:43.460
We have alerts and warnings to deal with all of that
link |
01:16:45.520
and all the cars.
link |
01:16:46.600
And how is he going to formally verify it?
link |
01:16:48.440
Well, I got 10 million miles of data,
link |
01:16:49.840
it's probably better,
link |
01:16:50.680
it's probably better verified than the spec.
link |
01:16:53.240
Yeah, I'm glad you're here talking to me.
link |
01:16:57.000
This is, I'll remember this day,
link |
01:17:00.280
because it's interesting.
link |
01:17:01.120
If you look at Kyle's from cruise,
link |
01:17:04.140
I'm sure they have a large number
link |
01:17:05.320
of business development folks
link |
01:17:07.400
and you work with, he's working with GM,
link |
01:17:10.200
you could work with Argo AI, working with Ford.
link |
01:17:13.240
It's interesting because chances that you fail,
link |
01:17:17.560
business wise, like bankrupt, are pretty high.
link |
01:17:20.160
Yeah.
link |
01:17:21.080
And yet, it's the Android model,
link |
01:17:23.880
is you're actually taking on the problem.
link |
01:17:26.340
So that's really inspiring, I mean.
link |
01:17:28.200
Well, I have a long term way for Comma to make money too.
link |
01:17:30.920
And one of the nice things
link |
01:17:32.180
when you really take on the problem,
link |
01:17:34.400
which is my hope for Autopilot, for example,
link |
01:17:36.760
is things you don't expect,
link |
01:17:39.560
ways to make money or create value
link |
01:17:41.840
that you don't expect will pop up.
link |
01:17:43.960
Oh, I've known how to do it since kind of,
link |
01:17:46.640
2017 is the first time I said it.
link |
01:17:48.520
Which part, to know how to do which part?
link |
01:17:50.440
Our long term plan is to be a car insurance company.
link |
01:17:52.480
Insurance, yeah, I love it, yep, yep.
link |
01:17:55.280
I make driving twice as safe.
link |
01:17:56.640
Not only that, I have the best data
link |
01:17:57.600
such to know who statistically is the safest drivers.
link |
01:17:59.840
And oh, oh, we see you, we see you driving unsafely,
link |
01:18:03.700
we're not gonna insure you.
link |
01:18:05.320
And that causes a bifurcation in the market
link |
01:18:08.960
because the only people who can't get Comma insurance
link |
01:18:10.880
are the bad drivers, Geico can insure them,
link |
01:18:12.740
their premiums are crazy high,
link |
01:18:13.860
our premiums are crazy low.
link |
01:18:15.320
We'll win car insurance, take over that whole market.
link |
01:18:18.040
Okay, so.
link |
01:18:19.920
If we win, if we win.
link |
01:18:21.240
But that's what I'm saying,
link |
01:18:22.080
how do you turn Comma into a $10 billion company?
link |
01:18:24.080
It's that.
link |
01:18:24.920
That's right.
link |
01:18:25.740
So you, Elon Musk, who else?
link |
01:18:29.960
Who else is thinking like this and working like this
link |
01:18:32.700
in your view?
link |
01:18:33.540
Who are the competitors?
link |
01:18:34.760
Are there people seriously,
link |
01:18:36.120
I don't think anyone that I'm aware of
link |
01:18:38.280
is seriously taking on lane keeping,
link |
01:18:42.960
like where it's a huge business
link |
01:18:45.100
that turns eventually into full autonomy
link |
01:18:47.160
that then creates, yeah, like that creates other businesses
link |
01:18:52.000
on top of it and so on.
link |
01:18:53.400
Thinks insurance, thinks all kinds of ideas like that.
link |
01:18:56.460
Do you know anyone else thinking like this?
link |
01:19:00.480
Not really.
link |
01:19:02.140
That's interesting.
link |
01:19:02.980
I mean, my sense is everybody turns to that
link |
01:19:05.320
in like four or five years.
link |
01:19:07.760
Like Ford, once the autonomy doesn't fall through.
link |
01:19:10.400
Yeah.
link |
01:19:11.240
But at this time.
link |
01:19:12.560
Elon's the iOS.
link |
01:19:14.100
By the way, he paved the way for all of us.
link |
01:19:16.680
It's the iOS, true.
link |
01:19:17.960
I would not be doing Comma AI today
link |
01:19:20.840
if it was not for those conversations with Elon.
link |
01:19:23.440
And if it were not for him saying like,
link |
01:19:27.080
I think he said like,
link |
01:19:27.900
well, obviously we're not gonna use LiDAR,
link |
01:19:29.120
we use cameras, humans use cameras.
link |
01:19:31.260
So what do you think about that?
link |
01:19:32.560
How important is LiDAR?
link |
01:19:33.880
Everybody else on L5 is using LiDAR.
link |
01:19:36.920
What are your thoughts on his provocative statement
link |
01:19:39.200
that LiDAR is a crutch?
link |
01:19:41.320
See, sometimes he'll say dumb things,
link |
01:19:43.040
like the driver monitoring thing,
link |
01:19:44.040
but sometimes he'll say absolutely, completely,
link |
01:19:46.240
100% obviously true things.
link |
01:19:48.360
Of course LiDAR is a crutch.
link |
01:19:50.800
It's not even a good crutch.
link |
01:19:53.020
You're not even using it.
link |
01:19:53.860
Oh, they're using it for localization.
link |
01:19:56.240
Yeah.
link |
01:19:57.080
Which isn't good in the first place.
link |
01:19:58.140
If you have to localize your car to centimeters
link |
01:20:00.480
in order to drive, like that's not driving.
link |
01:20:04.280
Currently not doing much machine learning
link |
01:20:06.280
I thought for LiDAR data.
link |
01:20:07.560
Meaning like to help you in the task of,
link |
01:20:11.320
general task of perception.
link |
01:20:12.840
The main goal of those LiDARs on those cars
link |
01:20:15.320
I think is actually localization more than perception.
link |
01:20:18.840
Or at least that's what they use them for.
link |
01:20:20.080
Yeah, that's true.
link |
01:20:20.920
If you want to localize to centimeters,
link |
01:20:22.480
you can't use GPS.
link |
01:20:23.680
The fanciest GPS in the world can't do it.
link |
01:20:25.120
Especially if you're under tree cover and stuff.
link |
01:20:26.920
With LiDAR you can do this pretty easily.
link |
01:20:28.440
So you really, they're not taking on,
link |
01:20:30.200
I mean in some research they're using it for perception,
link |
01:20:33.160
but, and they're certainly not, which is sad,
link |
01:20:35.800
they're not fusing it well with vision.
link |
01:20:38.660
They do use it for perception.
link |
01:20:40.520
I'm not saying they don't use it for perception,
link |
01:20:42.360
but the thing that, they have vision based
link |
01:20:45.440
and radar based perception systems as well.
link |
01:20:47.640
You could remove the LiDAR and keep around
link |
01:20:51.400
a lot of the dynamic object perception.
link |
01:20:54.000
You want to get centimeter accurate localization?
link |
01:20:56.280
Good luck doing that with anything else.
link |
01:20:59.080
So what should Cruz, Waymo do?
link |
01:21:02.840
Like what would be your advice to them now?
link |
01:21:06.360
I mean Waymo is actually,
link |
01:21:08.480
they're, I mean they're doing, they're serious.
link |
01:21:11.640
Waymo out of the ball of them are quite
link |
01:21:14.120
so serious about the long game.
link |
01:21:16.280
If L5 is a lot, requires 50 years,
link |
01:21:20.800
I think Waymo will be the only one left standing at the end
link |
01:21:24.160
with the, given the financial backing that they have.
link |
01:21:26.840
Buku Google bucks.
link |
01:21:28.800
I'll say nice things about both Waymo and Cruz.
link |
01:21:32.560
Let's do it.
link |
01:21:33.640
Nice is good.
link |
01:21:35.880
Waymo is by far the furthest along with technology.
link |
01:21:39.360
Waymo has a three to five year lead on all the competitors.
link |
01:21:44.000
If that, if the Waymo looking stack works,
link |
01:21:48.720
maybe three year lead.
link |
01:21:49.760
If the Waymo looking stack works,
link |
01:21:51.320
they have a three year lead.
link |
01:21:52.880
Now I argue that Waymo has spent too much money
link |
01:21:55.840
to recapitalize, to gain back their losses
link |
01:21:59.280
in those three years.
link |
01:22:00.200
Also self driving cars have no network effect like that.
link |
01:22:03.680
Uber has a network effect.
link |
01:22:04.840
You have a market, you have drivers and you have riders.
link |
01:22:07.160
Self driving cars, you have capital and you have riders.
link |
01:22:09.960
There's no network effect.
link |
01:22:11.480
If I want to blanket a new city in self driving cars,
link |
01:22:13.880
I buy the off the shelf Chinese knockoff self driving cars
link |
01:22:16.080
and I buy enough of them in the city.
link |
01:22:17.240
I can't do that with drivers.
link |
01:22:18.400
And that's why Uber has a first mover advantage
link |
01:22:20.920
that no self driving car company will.
link |
01:22:24.040
Can you disentangle that a little bit?
link |
01:22:26.600
Uber, you're not talking about Uber,
link |
01:22:28.200
the autonomous vehicle Uber.
link |
01:22:29.280
You're talking about the Uber car, the, yeah.
link |
01:22:31.640
I'm Uber.
link |
01:22:32.480
I open for business in Austin, Texas, let's say.
link |
01:22:36.000
I need to attract both sides of the market.
link |
01:22:38.880
I need to both get drivers on my platform
link |
01:22:41.320
and riders on my platform.
link |
01:22:42.880
And I need to keep them both sufficiently happy, right?
link |
01:22:45.400
Riders aren't gonna use it
link |
01:22:46.640
if it takes more than five minutes for an Uber to show up.
link |
01:22:49.080
Drivers aren't gonna use it
link |
01:22:50.240
if they have to sit around all day and there's no riders.
link |
01:22:52.280
So you have to carefully balance a market.
link |
01:22:54.600
And whenever you have to carefully balance a market,
link |
01:22:56.400
there's a great first mover advantage
link |
01:22:58.400
because there's a switching cost for everybody, right?
link |
01:23:01.120
The drivers and the riders
link |
01:23:02.240
would have to switch at the same time.
link |
01:23:04.200
Let's even say that, you know, let's say a Luber shows up
link |
01:23:08.960
and Luber somehow, you know, agrees to do things
link |
01:23:12.640
at a bigger, you know, we're just gonna,
link |
01:23:15.800
we've done it more efficiently, right?
link |
01:23:17.520
Luber is only takes 5% of a cut
link |
01:23:19.880
instead of the 10% that Uber takes.
link |
01:23:21.680
No one is gonna switch
link |
01:23:22.840
because the switching cost is higher than that 5%.
link |
01:23:25.000
So you actually can, in markets like that,
link |
01:23:27.280
you have a first mover advantage.
link |
01:23:28.640
Yeah.
link |
01:23:30.240
Autonomous vehicles of the level five variety
link |
01:23:32.800
have no first mover advantage.
link |
01:23:34.600
If the technology becomes commoditized,
link |
01:23:36.840
say I wanna go to a new city, look at the scooters.
link |
01:23:39.600
It's gonna look a lot more like scooters.
link |
01:23:41.560
Every person with a checkbook
link |
01:23:44.080
can blanket a city in scooters.
link |
01:23:45.800
And that's why you have 10 different scooter companies.
link |
01:23:47.960
Which one's gonna win?
link |
01:23:48.800
It's a race to the bottom.
link |
01:23:49.680
It's a terrible market to be in
link |
01:23:51.120
because there's no market for scooters.
link |
01:23:55.000
And the scooters don't get a say
link |
01:23:56.600
in whether they wanna be bought and deployed to a city
link |
01:23:58.240
or not. Right.
link |
01:23:59.080
So the, yeah.
link |
01:24:00.120
We're gonna entice the scooters
link |
01:24:01.360
with subsidies and deals and.
link |
01:24:03.920
So whenever you have to invest that capital,
link |
01:24:05.920
it doesn't.
link |
01:24:06.840
It doesn't come back.
link |
01:24:07.760
Yeah.
link |
01:24:08.960
That can't be your main criticism of the Waymo approach.
link |
01:24:12.400
Oh, I'm saying even if it does technically work.
link |
01:24:14.920
Even if it does technically work, that's a problem.
link |
01:24:17.120
Yeah.
link |
01:24:18.400
I don't know if I were to say,
link |
01:24:21.000
I would say you're already there.
link |
01:24:23.600
I haven't even thought about that,
link |
01:24:24.640
but I would say the bigger challenge
link |
01:24:26.600
is the technical approach.
link |
01:24:28.000
The.
link |
01:24:29.800
So Waymo's cruises.
link |
01:24:31.880
And not just the technical approach,
link |
01:24:33.000
but of creating value.
link |
01:24:34.800
I still don't understand how you beat Uber,
link |
01:24:40.760
the human driven cars.
link |
01:24:43.480
In terms of financially,
link |
01:24:44.920
it doesn't make sense to me
link |
01:24:47.160
that people wanna get in an autonomous vehicle.
link |
01:24:50.120
I don't understand how you make money.
link |
01:24:52.800
In the longterm, yes.
link |
01:24:54.280
Like real longterm.
link |
01:24:56.440
But it just feels like there's too much
link |
01:24:58.600
capital investment needed.
link |
01:24:59.960
Oh, and they're gonna be worse than Ubers
link |
01:25:01.160
because they're gonna stop for every little thing,
link |
01:25:04.040
everywhere.
link |
01:25:06.280
I'll say a nice thing about cruise.
link |
01:25:07.280
That was my nice thing about Waymo.
link |
01:25:08.360
They're three years ahead.
link |
01:25:09.200
Wait, what was the nice?
link |
01:25:10.040
Oh, because they're three.
link |
01:25:10.880
They're three years technically ahead of everybody.
link |
01:25:12.400
Their tech stack is great.
link |
01:25:14.720
My nice thing about cruise is GM buying them
link |
01:25:17.840
was a great move for GM.
link |
01:25:20.520
For $1 billion,
link |
01:25:22.200
GM bought an insurance policy against Waymo.
link |
01:25:25.520
They put, cruise is three years behind Waymo.
link |
01:25:30.920
That means Google will get a monopoly on the technology
link |
01:25:33.240
for at most three years.
link |
01:25:36.800
And if technology works,
link |
01:25:38.800
so you might not even be right about the three years,
link |
01:25:40.760
it might be less.
link |
01:25:41.800
Might be less.
link |
01:25:42.640
Cruise actually might not be that far behind.
link |
01:25:44.240
I don't know how much Waymo has waffled around
link |
01:25:47.280
or how much of it actually is just that long tail.
link |
01:25:49.720
Yeah, okay.
link |
01:25:50.560
If that's the best you could say in terms of nice things,
link |
01:25:53.520
that's more of a nice thing for GM
link |
01:25:55.160
that that's the smart insurance policy.
link |
01:25:58.560
It's a smart insurance policy.
link |
01:25:59.640
I mean, I think that's how,
link |
01:26:01.840
I can't see cruise working out any other.
link |
01:26:05.160
For cruise to leapfrog Waymo would really surprise me.
link |
01:26:10.360
Yeah, so let's talk about
link |
01:26:12.000
the underlying assumption of everything is.
link |
01:26:13.600
We're not gonna leapfrog Tesla.
link |
01:26:17.520
Tesla would have to seriously mess up for us
link |
01:26:19.440
because you're.
link |
01:26:20.400
Okay, so the way you leapfrog, right?
link |
01:26:23.200
Is you come up with an idea
link |
01:26:26.080
or you take a direction perhaps secretly
link |
01:26:28.560
that the other people aren't taking.
link |
01:26:31.640
And so the cruise, Waymo,
link |
01:26:35.000
even Aurora.
link |
01:26:38.080
I don't know Aurora, Zooks is the same stack as well.
link |
01:26:40.080
They're all the same code base even.
link |
01:26:41.720
And they're all the same DARPA Urban Challenge code base.
link |
01:26:45.360
So the question is,
link |
01:26:46.800
do you think there's a room for brilliance and innovation
link |
01:26:48.960
that will change everything?
link |
01:26:50.360
Like say, okay, so I'll give you examples.
link |
01:26:53.880
It could be if revolution and mapping, for example,
link |
01:26:59.600
that allow you to map things,
link |
01:27:03.000
do HD maps of the whole world,
link |
01:27:05.800
all weather conditions somehow really well,
link |
01:27:08.040
or revolution and simulation
link |
01:27:13.040
to where the all the way you said before becomes incorrect.
link |
01:27:20.480
That kind of thing.
link |
01:27:21.320
Any room for breakthrough innovation?
link |
01:27:24.920
What I said before about,
link |
01:27:25.960
oh, they actually get the whole thing.
link |
01:27:27.160
Well, I'll say this about,
link |
01:27:30.480
we divide driving into three problems
link |
01:27:32.640
and I actually haven't solved the third yet,
link |
01:27:33.800
but I haven't had you how to do it.
link |
01:27:34.800
So there's the static.
link |
01:27:36.120
The static driving problem is assuming
link |
01:27:38.000
you are the only car on the road, right?
link |
01:27:40.120
And this problem can be solved 100%
link |
01:27:41.960
with mapping and localization.
link |
01:27:43.920
This is why farms work the way they do.
link |
01:27:45.680
If all you have to deal with is the static problem
link |
01:27:48.360
and you can statically schedule your machines, right?
link |
01:27:50.120
It's the same as like statically scheduling processes.
link |
01:27:52.640
You can statically schedule your tractors
link |
01:27:53.960
to never hit each other on their paths, right?
link |
01:27:56.080
Cause they know the speed they go at.
link |
01:27:57.440
So that's the static driving problem.
link |
01:28:00.080
Maps only helps you with the static driving problem.
link |
01:28:03.880
Yeah, the question about static driving,
link |
01:28:06.880
you've just made it sound like it's really easy.
link |
01:28:08.720
Static driving is really easy.
link |
01:28:11.880
How easy?
link |
01:28:13.040
How, well, cause the whole drifting out of lane,
link |
01:28:16.440
when Tesla drifts out of lane,
link |
01:28:18.720
it's failing on the fundamental static driving problem.
link |
01:28:22.000
Tesla is drifting out of lane?
link |
01:28:24.440
The static driving problem is not easy for the world.
link |
01:28:27.720
The static driving problem is easy for one route.
link |
01:28:31.840
One route and one weather condition
link |
01:28:33.920
with one state of lane markings
link |
01:28:37.920
and like no deterioration, no cracks in the road.
link |
01:28:40.880
No, I'm assuming you have a perfect localizer.
link |
01:28:42.600
So that's solved for the weather condition
link |
01:28:44.200
and the lane marking condition.
link |
01:28:45.600
But that's the problem is,
link |
01:28:46.600
how do you have a perfect localizer?
link |
01:28:48.400
Perfect localizers are not that hard to build.
link |
01:28:50.560
Okay, come on now, with LIDAR?
link |
01:28:53.320
With LIDAR, yeah.
link |
01:28:54.160
Oh, with LIDAR, okay.
link |
01:28:55.000
With LIDAR, yeah, but you use LIDAR, right?
link |
01:28:56.400
Like use LIDAR, build a perfect localizer.
link |
01:28:58.600
Building a perfect localizer without LIDAR,
link |
01:29:02.960
it's gonna be hard.
link |
01:29:04.280
You can get 10 centimeters without LIDAR,
link |
01:29:05.720
you can get one centimeter with LIDAR.
link |
01:29:07.200
I'm not even concerned about the one or 10 centimeters.
link |
01:29:09.240
I'm concerned if every once in a while,
link |
01:29:11.160
you're just way off.
link |
01:29:12.640
Yeah, so this is why you have to carefully make sure
link |
01:29:17.920
you're always tracking your position.
link |
01:29:19.960
You wanna use LIDAR camera fusion,
link |
01:29:21.680
but you can get the reliability of that system
link |
01:29:24.400
up to 100,000 miles,
link |
01:29:27.960
and then you write some fallback condition
link |
01:29:29.640
where it's not that bad if you're way off, right?
link |
01:29:32.120
I think that you can get it to the point,
link |
01:29:33.720
it's like ASLD that you're never in a case
link |
01:29:36.760
where you're way off and you don't know it.
link |
01:29:38.440
Yeah, okay, so this is brilliant.
link |
01:29:40.200
So that's the static. Static.
link |
01:29:42.240
We can, especially with LIDAR and good HG maps,
link |
01:29:45.920
you can solve that problem. Easy.
link |
01:29:47.680
No, I just disagree with your word easy.
link |
01:29:50.440
The static problem's so easy.
link |
01:29:51.760
It's very typical for you to say something is easy.
link |
01:29:54.000
I got it. No.
link |
01:29:54.840
It's not as challenging as the other ones, okay.
link |
01:29:56.880
Well, okay, maybe it's obvious how to solve it.
link |
01:29:58.760
The third one's the hardest.
link |
01:30:00.320
And a lot of people don't even think about the third one
link |
01:30:01.880
and even see it as different from the second one.
link |
01:30:03.640
So the second one is dynamic.
link |
01:30:05.720
The second one is like, say there's an obvious example
link |
01:30:08.520
is like a car stopped at a red light, right?
link |
01:30:10.360
You can't have that car in your map
link |
01:30:12.520
because you don't know whether that car
link |
01:30:13.720
is gonna be there or not.
link |
01:30:14.880
So you have to detect that car in real time
link |
01:30:17.960
and then you have to do the appropriate action, right?
link |
01:30:21.600
Also, that car is not a fixed object.
link |
01:30:24.800
That car may move and you have to predict
link |
01:30:26.600
what that car will do, right?
link |
01:30:28.680
So this is the dynamic problem.
link |
01:30:30.840
Yeah.
link |
01:30:31.680
So you have to deal with this.
link |
01:30:32.800
This involves, again, like you're gonna need models
link |
01:30:36.640
of other people's behavior.
link |
01:30:39.080
Are you including in that,
link |
01:30:40.320
I don't wanna step on the third one.
link |
01:30:42.320
Oh.
link |
01:30:43.160
But are you including in that your influence on people?
link |
01:30:46.920
Ah, that's the third one.
link |
01:30:48.240
Okay.
link |
01:30:49.080
That's the third one.
link |
01:30:49.920
We call it the counterfactual.
link |
01:30:51.840
Yeah, brilliant.
link |
01:30:52.680
And that.
link |
01:30:53.520
I just talked to Judea Pearl
link |
01:30:54.360
who's obsessed with counterfactuals.
link |
01:30:55.800
And the counterfactual.
link |
01:30:56.640
Oh yeah, yeah, I read his books.
link |
01:30:58.600
So the static and the dynamic
link |
01:31:00.760
Yeah.
link |
01:31:01.960
Our approach right now for lateral
link |
01:31:04.720
will scale completely to the static and dynamic.
link |
01:31:07.560
The counterfactual, the only way I have to do it yet,
link |
01:31:10.720
the thing that I wanna do once we have all of these cars
link |
01:31:13.960
is I wanna do reinforcement learning on the world.
link |
01:31:16.760
I'm always gonna turn the exploiter up to max.
link |
01:31:18.880
I'm not gonna have them explore.
link |
01:31:20.440
But the only real way to get at the counterfactual
link |
01:31:22.760
is to do reinforcement learning
link |
01:31:24.080
because the other agents are humans.
link |
01:31:27.760
So that's fascinating that you break it down like that.
link |
01:31:30.080
I agree completely.
link |
01:31:31.720
I've spent my life thinking about this problem.
link |
01:31:33.680
It's beautiful.
link |
01:31:34.520
And part of it, because you're slightly insane,
link |
01:31:37.840
it's good.
link |
01:31:39.080
Because.
link |
01:31:41.240
Not my life.
link |
01:31:42.080
Just the last four years.
link |
01:31:43.120
No, no.
link |
01:31:43.960
You have some nonzero percent of your brain
link |
01:31:48.920
has a madman in it, which is good.
link |
01:31:51.520
That's a really good feature.
link |
01:31:52.360
But there's a safety component to it
link |
01:31:55.920
that I think sort of with counterfactuals and so on
link |
01:31:59.040
that would just freak people out.
link |
01:32:00.280
How do you even start to think about just in general?
link |
01:32:03.320
I mean, you've had some friction with NHTSA and so on.
link |
01:32:07.600
I am frankly exhausted by safety engineers.
link |
01:32:14.280
The prioritization on safety over innovation
link |
01:32:21.360
to a degree where it kills, in my view,
link |
01:32:23.720
kills safety in the long term.
link |
01:32:26.200
So the counterfactual thing,
link |
01:32:28.080
they just actually exploring this world
link |
01:32:31.560
of how do you interact with dynamic objects and so on.
link |
01:32:33.880
How do you think about safety?
link |
01:32:34.840
You can do reinforcement learning without ever exploring.
link |
01:32:38.080
And I said that, so you can think about your,
link |
01:32:40.400
in reinforcement learning,
link |
01:32:41.520
it's usually called a temperature parameter.
link |
01:32:44.280
And your temperature parameter
link |
01:32:45.320
is how often you deviate from the argmax.
link |
01:32:48.080
I could always set that to zero and still learn.
link |
01:32:50.680
And I feel that you'd always want that set to zero
link |
01:32:52.800
on your actual system.
link |
01:32:54.040
Gotcha.
link |
01:32:54.880
But the problem is you first don't know very much.
link |
01:32:58.120
And so you're going to make mistakes.
link |
01:32:59.520
So the learning, the exploration happens through mistakes.
link |
01:33:02.360
Yeah, but okay.
link |
01:33:03.720
So the consequences of a mistake.
link |
01:33:06.080
Open pilot and autopilot are making mistakes left and right.
link |
01:33:09.400
We have 700 daily active users,
link |
01:33:12.560
a thousand weekly active users.
link |
01:33:14.040
Open pilot makes tens of thousands of mistakes a week.
link |
01:33:18.920
These mistakes have zero consequences.
link |
01:33:21.160
These mistakes are,
link |
01:33:22.560
oh, I wanted to take this exit and it went straight.
link |
01:33:26.840
So I'm just going to carefully touch the wheel.
link |
01:33:28.520
The humans catch them.
link |
01:33:29.360
The humans catch them.
link |
01:33:30.680
And the human disengagement is labeling
link |
01:33:33.120
that reinforcement learning
link |
01:33:34.160
in a completely consequence free way.
link |
01:33:37.280
So driver monitoring is the way you ensure they keep.
link |
01:33:39.880
Yes.
link |
01:33:40.720
They keep paying attention.
link |
01:33:42.160
How is your messaging?
link |
01:33:43.280
Say I gave you a billion dollars,
link |
01:33:45.280
you would be scaling it now.
link |
01:33:47.840
Oh, I couldn't scale it with any amount of money.
link |
01:33:49.760
I'd raise money if I could, if I had a way to scale it.
link |
01:33:51.680
Yeah, you're now not focused on scale.
link |
01:33:53.360
I don't know how to do,
link |
01:33:54.200
oh, like I guess I could sell it to more people,
link |
01:33:55.840
but I want to make the system better.
link |
01:33:57.040
Better, better.
link |
01:33:57.880
And I don't know how to, I mean.
link |
01:33:58.920
But what's the messaging here?
link |
01:34:01.160
I got a chance to talk to Elon and he basically said
link |
01:34:06.320
that the human factor doesn't matter.
link |
01:34:09.360
You know, the human doesn't matter
link |
01:34:10.440
because the system will perform,
link |
01:34:12.360
there'll be sort of a, sorry to use the term,
link |
01:34:14.840
but like a singular,
link |
01:34:15.680
like a point where it gets just much better.
link |
01:34:17.880
And so the human, it won't really matter.
link |
01:34:20.880
But it seems like that human catching the system
link |
01:34:25.040
when it gets into trouble is like the thing
link |
01:34:29.440
which will make something like reinforcement learning work.
link |
01:34:32.800
So how do you think messaging for Tesla,
link |
01:34:35.680
for you should change,
link |
01:34:36.880
for the industry in general should change?
link |
01:34:39.120
I think our messaging is pretty clear.
link |
01:34:40.880
At least like our messaging wasn't that clear
link |
01:34:43.120
in the beginning and I do kind of fault myself for that.
link |
01:34:45.240
We are proud right now to be a level two system.
link |
01:34:48.520
We are proud to be level two.
link |
01:34:50.400
If we talk about level four,
link |
01:34:51.680
it's not with the current hardware.
link |
01:34:53.240
It's not gonna be just a magical OTA upgrade.
link |
01:34:55.960
It's gonna be new hardware.
link |
01:34:57.360
It's gonna be very carefully thought out.
link |
01:34:59.600
Right now, we are proud to be level two
link |
01:35:01.640
and we have a rigorous safety model.
link |
01:35:03.400
I mean, not like, okay, rigorous, who knows what that means,
link |
01:35:06.680
but we at least have a safety model
link |
01:35:08.720
and we make it explicit as in safety.md in OpenPilot.
link |
01:35:11.920
And it says, seriously though, safety.md.
link |
01:35:17.040
This is brilliant, this is so Android.
link |
01:35:18.680
Well, this is the safety model
link |
01:35:21.880
and I like to have conversations like,
link |
01:35:25.600
sometimes people will come to you and they're like,
link |
01:35:27.240
your system's not safe.
link |
01:35:29.320
Okay, have you read my safety docs?
link |
01:35:31.160
Would you like to have an intelligent conversation
link |
01:35:32.760
about this?
link |
01:35:33.600
And the answer is always no.
link |
01:35:34.440
They just like scream about, it runs Python.
link |
01:35:38.280
Okay, what?
link |
01:35:39.120
So you're saying that because Python's not real time,
link |
01:35:41.600
Python not being real time never causes disengagements.
link |
01:35:44.320
Disengagements are caused by, the model is QM.
link |
01:35:47.720
But safety.md says the following,
link |
01:35:49.840
first and foremost,
link |
01:35:50.680
the driver must be paying attention at all times.
link |
01:35:55.400
I still consider the software to be alpha software
link |
01:35:57.760
until we can actually enforce that statement,
link |
01:36:00.120
but I feel it's very well communicated to our users.
link |
01:36:03.320
Two more things.
link |
01:36:04.560
One is the user must be able to easily take control
link |
01:36:09.120
of the vehicle at all times.
link |
01:36:10.920
So if you step on the gas or brake with OpenPilot,
link |
01:36:14.480
it gives full manual control back to the user
link |
01:36:16.440
or press the cancel button.
link |
01:36:18.720
Step two, the car will never react so quickly,
link |
01:36:23.280
we define so quickly to be about one second,
link |
01:36:26.000
that you can't react in time.
link |
01:36:27.640
And we do this by enforcing torque limits,
link |
01:36:29.480
braking limits and acceleration limits.
link |
01:36:31.520
So we have like our torque limits way lower than Tesla's.
link |
01:36:36.520
This is another potential.
link |
01:36:39.080
If I could tweak Autopilot,
link |
01:36:40.240
I would lower their torque limit
link |
01:36:41.320
and I would add driver monitoring.
link |
01:36:42.960
Because Autopilot can jerk the wheel hard.
link |
01:36:46.240
OpenPilot can't.
link |
01:36:47.960
We limit, and all this code is open source, readable.
link |
01:36:52.080
And I believe now it's all Misra C compliant.
link |
01:36:54.880
What's that mean?
link |
01:36:57.080
Misra is like the automotive coding standard.
link |
01:37:00.400
At first, I've come to respect.
link |
01:37:03.400
I've been reading like the standards lately
link |
01:37:05.000
and I've come to respect them.
link |
01:37:05.880
They're actually written by very smart people.
link |
01:37:07.800
Yeah, they're brilliant people actually.
link |
01:37:09.880
They have a lot of experience.
link |
01:37:11.320
They're sometimes a little too cautious,
link |
01:37:13.360
but in this case, it pays off.
link |
01:37:16.800
Misra is written by like computer scientists.
link |
01:37:18.440
And you can tell by the language they use.
link |
01:37:19.840
You can tell by the language they use,
link |
01:37:21.120
they talk about like whether certain conditions in Misra
link |
01:37:24.480
are decidable or undecidable.
link |
01:37:26.560
And you mean like the halting problem?
link |
01:37:28.360
And yes, all right, you've earned my respect.
link |
01:37:31.640
I will read carefully what you have to say
link |
01:37:33.120
and we wanna make our code compliant with that.
link |
01:37:35.760
All right, so you're proud level two, beautiful.
link |
01:37:38.160
So you were the founder and I think CEO of Kama AI,
link |
01:37:42.360
then you were the head of research.
link |
01:37:44.320
What the heck are you now?
link |
01:37:46.080
What's your connection to Kama AI?
link |
01:37:47.440
I'm the president, but I'm one of those
link |
01:37:49.400
like unelected presidents of like a small dictatorship
link |
01:37:53.040
country, not one of those like elected presidents.
link |
01:37:55.160
Oh, so you're like Putin when he was like the,
link |
01:37:57.160
yeah, I got you.
link |
01:37:59.920
So there's a, what's the governance structure?
link |
01:38:02.080
What's the future of Kama AI?
link |
01:38:04.800
I mean, yeah, it's a business.
link |
01:38:07.440
Do you want, are you just focused on getting things
link |
01:38:10.000
right now, making some small amount of money in the meantime
link |
01:38:14.880
and then when it works, it works and you scale.
link |
01:38:17.520
Our burn rate is about 200K a month
link |
01:38:20.440
and our revenue is about 100K a month.
link |
01:38:23.000
So we need to forex our revenue,
link |
01:38:24.880
but we haven't like tried very hard at that yet.
link |
01:38:28.160
And the revenue is basically selling stuff online.
link |
01:38:30.120
Yeah, we sell stuff shop.kama.ai.
link |
01:38:32.320
Is there other, well, okay,
link |
01:38:33.880
so you'll have to figure out the revenue.
link |
01:38:35.720
That's our only, see, but to me,
link |
01:38:37.840
that's like respectable revenues.
link |
01:38:40.360
We make it by selling products to consumers
link |
01:38:42.640
who are honest and transparent about what they are.
link |
01:38:45.960
Most actually level four companies, right?
link |
01:38:50.680
Cause you could easily start blowing up like smoke,
link |
01:38:54.240
like overselling the hype and feeding into,
link |
01:38:57.040
getting some fundraisers.
link |
01:38:59.000
Oh, you're the guy, you're a genius
link |
01:39:00.440
because you hacked the iPhone.
link |
01:39:01.760
Oh, I hate that, I hate that.
link |
01:39:03.280
Yeah, well, I can trade my social capital for more money.
link |
01:39:06.640
I did it once, I almost regret it doing it the first time.
link |
01:39:10.280
Well, on a small tangent,
link |
01:39:11.600
what's your, you seem to not like fame
link |
01:39:16.560
and yet you're also drawn to fame.
link |
01:39:18.880
Where are you on that currently?
link |
01:39:24.560
Have you had some introspection, some soul searching?
link |
01:39:27.200
Yeah, I actually,
link |
01:39:29.240
I've come to a pretty stable position on that.
link |
01:39:32.200
Like after the first time,
link |
01:39:33.880
I realized that I don't want attention from the masses.
link |
01:39:36.840
I want attention from people who I respect.
link |
01:39:40.280
Who do you respect?
link |
01:39:41.960
I can give a list of people.
link |
01:39:43.960
So are these like Elon Musk type characters?
link |
01:39:47.200
Yeah, well, actually, you know what?
link |
01:39:50.000
I'll make it more broad than that.
link |
01:39:51.240
I won't make it about a person, I respect skill.
link |
01:39:54.040
I respect people who have skills, right?
link |
01:39:56.840
And I would like to like be, I'm not gonna say famous,
link |
01:40:01.400
but be like known among more people who have like real skills.
link |
01:40:06.880
Who in cars do you think have skill, not do you respect?
link |
01:40:15.000
Oh, Kyle Vogt has skill.
link |
01:40:17.760
A lot of people at Waymo have skill and I respect them.
link |
01:40:20.840
I respect them as engineers.
link |
01:40:23.760
Like I can think, I mean,
link |
01:40:24.920
I think about all the times in my life
link |
01:40:26.280
where I've been like dead set on approaches
link |
01:40:27.960
and they turn out to be wrong.
link |
01:40:29.760
So, I mean, this might, I might be wrong.
link |
01:40:31.720
I accept that.
link |
01:40:32.600
I accept that there's a decent chance that I'm wrong.
link |
01:40:36.600
And actually, I mean,
link |
01:40:37.440
having talked to Chris Hermsons, Sterling Anderson,
link |
01:40:39.480
those guys, I mean, I deeply respect Chris.
link |
01:40:43.360
I just admire the guy.
link |
01:40:46.040
He's legit.
link |
01:40:47.400
When you drive a car through the desert
link |
01:40:48.960
when everybody thinks it's impossible, that's legit.
link |
01:40:52.440
And then I also really respect the people
link |
01:40:53.840
who are like writing the infrastructure of the world,
link |
01:40:55.680
like the Linus Torvalds and the Chris Lattiners.
link |
01:40:57.760
They were doing the real work.
link |
01:40:59.080
I know, they're doing the real work.
link |
01:41:00.520
This, having talked to Chris,
link |
01:41:03.000
like Chris Lattiners, you realize,
link |
01:41:04.600
especially when they're humble,
link |
01:41:05.720
it's like you realize, oh, you guys,
link |
01:41:07.720
we're just using your,
link |
01:41:09.680
Oh yeah.
link |
01:41:10.520
All the hard work that you did.
link |
01:41:11.560
Yeah, that's incredible.
link |
01:41:13.160
What do you think, Mr. Anthony Lewandowski,
link |
01:41:18.480
what do you, he's another mad genius.
link |
01:41:21.680
Sharp guy, oh yeah.
link |
01:41:22.920
What, do you think he might long term become a competitor?
link |
01:41:27.680
Oh, to comma?
link |
01:41:28.880
Well, so I think that he has the other right approach.
link |
01:41:32.440
I think that right now there's two right approaches.
link |
01:41:35.320
One is what we're doing, and one is what he's doing.
link |
01:41:37.720
Can you describe, I think it's called Pronto AI.
link |
01:41:39.840
He started a new thing.
link |
01:41:40.960
Do you know what the approach is?
link |
01:41:42.400
I actually don't know.
link |
01:41:43.240
Embark is also doing the same sort of thing.
link |
01:41:45.080
The idea is almost that you want to,
link |
01:41:47.360
so if you're, I can't partner with Honda and Toyota.
link |
01:41:51.840
Honda and Toyota are like 400,000 person companies.
link |
01:41:56.840
It's not even a company at that point.
link |
01:41:58.640
I don't think of it like, I don't personify it.
link |
01:42:00.600
I think of it like an object,
link |
01:42:01.440
but a trucker drives for a fleet,
link |
01:42:06.280
maybe that has like, some truckers are independent.
link |
01:42:09.480
Some truckers drive for fleets with a hundred trucks.
link |
01:42:11.320
There are tons of independent trucking companies out there.
link |
01:42:14.160
Start a trucking company and drive your costs down
link |
01:42:17.400
or figure out how to drive down the cost of trucking.
link |
01:42:23.040
Another company that I really respect is Nato.
link |
01:42:25.800
Actually, I respect their business model.
link |
01:42:27.800
Nato sells a driver monitoring camera
link |
01:42:31.040
and they sell it to fleet owners.
link |
01:42:33.360
If I owned a fleet of cars
link |
01:42:35.840
and I could pay 40 bucks a month to monitor my employees,
link |
01:42:41.840
this is gonna, it like reduces accidents 18%.
link |
01:42:45.040
It's so like that, in the space,
link |
01:42:48.400
that is like the business model that I like most respect.
link |
01:42:52.840
Cause they're creating value today.
link |
01:42:54.800
Yeah, which is a, that's a huge one.
link |
01:42:57.880
How do we create value today with some of this?
link |
01:42:59.680
And the lane keeping thing is huge.
link |
01:43:01.720
And it sounds like you're creeping in
link |
01:43:03.840
or full steam ahead on the driver monitoring too,
link |
01:43:06.720
which I think actually where the short term value,
link |
01:43:09.280
if you can get it right.
link |
01:43:10.520
I still, I'm not a huge fan of the statement
link |
01:43:12.840
that everything has to have driver monitoring.
link |
01:43:15.160
I agree with that completely,
link |
01:43:16.160
but that statement usually misses the point
link |
01:43:18.720
that to get the experience of it right is not trivial.
link |
01:43:21.960
Oh no, not at all.
link |
01:43:22.880
In fact, like, so right now we have,
link |
01:43:26.200
I think the timeout depends on speed of the car,
link |
01:43:29.600
but we want to depend on like the scene state.
link |
01:43:32.520
If you're on like an empty highway,
link |
01:43:35.440
it's very different if you don't pay attention
link |
01:43:37.720
than if like you're like coming up to a traffic light.
link |
01:43:42.080
And longterm, it should probably learn from the driver
link |
01:43:45.720
because that's to do, I watched a lot of video.
link |
01:43:48.120
We've built a smartphone detector
link |
01:43:49.520
just to analyze how people are using smartphones
link |
01:43:51.600
and people are using it very differently.
link |
01:43:53.400
It's a texting styles.
link |
01:43:57.720
There's.
link |
01:43:58.560
We haven't watched nearly enough of the videos.
link |
01:44:00.280
We haven't, I got millions of miles
link |
01:44:01.760
of people driving cars.
link |
01:44:02.960
In this moment, I spent a large fraction of my time
link |
01:44:05.920
just watching videos because it's never fails to learn.
link |
01:44:10.840
Like it never, I've never failed
link |
01:44:12.280
from a video watching session
link |
01:44:13.440
to learn something I didn't know before.
link |
01:44:15.360
In fact, I usually like when I eat lunch,
link |
01:44:18.440
I'll sit, especially when the weather is good
link |
01:44:20.640
and just watch pedestrians with an eye to understand
link |
01:44:24.560
like from a computer vision eye,
link |
01:44:26.400
just to see can this model, can you predict,
link |
01:44:29.280
what are the decisions made?
link |
01:44:30.480
And there's so many things that we don't understand.
link |
01:44:33.000
This is what I mean about the state vector.
link |
01:44:34.760
Yeah, it's, I'm trying to always think like,
link |
01:44:37.880
cause I'm understanding in my human brain,
link |
01:44:40.280
how do we convert that into,
link |
01:44:43.000
how hard is the learning problem here?
link |
01:44:44.960
I guess is the fundamental question.
link |
01:44:46.960
So something that's from a hacking perspective,
link |
01:44:51.760
this is always comes up, especially with folks.
link |
01:44:54.160
Well, first the most popular question
link |
01:44:55.520
is the trolley problem, right?
link |
01:44:58.400
So that's not a sort of a serious problem.
link |
01:45:01.920
There are some ethical questions I think that arise.
link |
01:45:06.080
Maybe you wanna, do you think there's any ethical,
link |
01:45:09.600
serious ethical questions?
link |
01:45:11.280
We have a solution to the trolley problem at Comm.ai.
link |
01:45:14.040
Well, so there is actually an alert in our code,
link |
01:45:16.520
ethical dilemma detected.
link |
01:45:18.000
It's not triggered yet.
link |
01:45:18.960
We don't know how yet to detect the ethical dilemmas,
link |
01:45:21.040
but we're a level two system.
link |
01:45:22.320
So we're going to disengage
link |
01:45:23.480
and leave that decision to the human.
link |
01:45:25.280
You're such a troll.
link |
01:45:26.640
No, but the trolley problem deserves to be trolled.
link |
01:45:28.720
Yeah, that's a beautiful answer actually.
link |
01:45:32.040
I know, I gave it to someone who was like,
link |
01:45:34.400
sometimes people will ask,
link |
01:45:35.360
like you asked about the trolley problem,
link |
01:45:36.560
like you can have a kind of discussion about it.
link |
01:45:38.040
Like you get someone who's like really like earnest about it
link |
01:45:40.760
because it's the kind of thing where,
link |
01:45:43.560
if you ask a bunch of people in an office,
link |
01:45:45.560
whether we should use a SQL stack or a no SQL stack,
link |
01:45:48.280
if they're not that technical, they have no opinion.
link |
01:45:50.560
But if you ask them what color they want to paint the office,
link |
01:45:52.360
everyone has an opinion on that.
link |
01:45:54.040
And that's why the trolley problem is...
link |
01:45:56.040
I mean, that's a beautiful answer.
link |
01:45:57.240
Yeah, we're able to detect the problem
link |
01:45:59.200
and we're able to pass it on to the human.
link |
01:46:01.960
Wow, I've never heard anyone say it.
link |
01:46:03.720
This is your nice escape route.
link |
01:46:06.120
Okay, but...
link |
01:46:07.320
Proud level two.
link |
01:46:08.680
I'm proud level two.
link |
01:46:09.760
I love it.
link |
01:46:10.600
So the other thing that people have some concern about
link |
01:46:14.400
with AI in general is hacking.
link |
01:46:17.800
So how hard is it, do you think,
link |
01:46:20.120
to hack an autonomous vehicle,
link |
01:46:21.400
either through physical access
link |
01:46:23.800
or through the more sort of popular now,
link |
01:46:25.680
these adversarial examples on the sensors?
link |
01:46:28.200
Okay, the adversarial examples one.
link |
01:46:30.720
You want to see some adversarial examples
link |
01:46:32.280
that affect humans, right?
link |
01:46:34.880
Oh, well, there used to be a stop sign here,
link |
01:46:38.000
but I put a black bag over the stop sign
link |
01:46:40.000
and then people ran it, adversarial, right?
link |
01:46:43.520
Like there's tons of human adversarial examples too.
link |
01:46:48.360
The question in general about like security,
link |
01:46:51.480
if you saw something just came out today
link |
01:46:53.360
and like there are always such hypey headlines
link |
01:46:55.080
about like how navigate on autopilot
link |
01:46:57.560
was fooled by a GPS spoof to take an exit.
link |
01:47:00.960
Right.
link |
01:47:01.800
At least that's all they could do was take an exit.
link |
01:47:03.920
If your car is relying on GPS
link |
01:47:06.360
in order to have a safe driving policy,
link |
01:47:09.000
you're doing something wrong.
link |
01:47:10.240
If you're relying,
link |
01:47:11.080
and this is why V2V is such a terrible idea.
link |
01:47:14.560
V2V now relies on both parties getting communication right.
link |
01:47:19.760
This is not even, so I think of safety,
link |
01:47:26.040
security is like a special case of safety, right?
link |
01:47:28.440
Safety is like we put a little, you know,
link |
01:47:31.840
piece of caution tape around the hole
link |
01:47:33.320
so that people won't walk into it by accident.
link |
01:47:35.520
Security is like put a 10 foot fence around the hole
link |
01:47:38.200
so you actually physically cannot climb into it
link |
01:47:40.100
with barbed wire on the top and stuff, right?
link |
01:47:42.320
So like if you're designing systems that are like unreliable,
link |
01:47:45.800
they're definitely not secure.
link |
01:47:48.440
Your car should always do something safe
link |
01:47:51.240
using its local sensors.
link |
01:47:53.400
And then the local sensor should be hardwired.
link |
01:47:55.240
And then could somebody hack into your CAN bus
link |
01:47:57.360
and turn your steering wheel on your brakes?
link |
01:47:58.600
Yes, but they could do it before common AI too, so.
link |
01:48:02.800
Let's think out of the box on some things.
link |
01:48:04.640
So do you think teleoperation has a role in any of this?
link |
01:48:09.360
So remotely stepping in and controlling the cars?
link |
01:48:13.880
No, I think that if the safety operation by design
link |
01:48:22.300
requires a constant link to the cars,
link |
01:48:26.160
I think it doesn't work.
link |
01:48:27.600
So that's the same argument you're using for V2I, V2V?
link |
01:48:31.120
Well, there's a lot of non safety critical stuff
link |
01:48:34.300
you can do with V2I.
link |
01:48:35.140
I like V2I, I like V2I way more than V2V.
link |
01:48:37.440
Because V2I is already like,
link |
01:48:39.280
I already have internet in the car, right?
link |
01:48:40.880
There's a lot of great stuff you can do with V2I.
link |
01:48:44.240
Like for example, you can, well, I already have V2I,
link |
01:48:47.280
Waze is V2I, right?
link |
01:48:48.880
Waze can route me around traffic jams.
link |
01:48:50.500
That's a great example of V2I.
link |
01:48:52.720
And then, okay, the car automatically talks
link |
01:48:54.420
to that same service, like it works.
link |
01:48:55.260
So it's improving the experience,
link |
01:48:56.800
but it's not a fundamental fallback for safety.
link |
01:48:59.440
No, if any of your things that require wireless communication
link |
01:49:04.440
are more than QM, like have an ASL rating, it shouldn't be.
link |
01:49:10.600
You previously said that life is work
link |
01:49:15.400
and that you don't do anything to relax.
link |
01:49:17.440
So how do you think about hard work?
link |
01:49:20.960
What do you think it takes to accomplish great things?
link |
01:49:24.680
And there's a lot of people saying
link |
01:49:25.820
that there needs to be some balance.
link |
01:49:28.160
You need to, in order to accomplish great things,
link |
01:49:31.120
you need to take some time off,
link |
01:49:32.200
you need to reflect and so on.
link |
01:49:33.920
Now, and then some people are just insanely working,
link |
01:49:37.920
burning the candle on both ends.
link |
01:49:39.680
How do you think about that?
link |
01:49:41.400
I think I was trolling in the Siraj interview
link |
01:49:43.440
when I said that.
link |
01:49:44.880
Off camera, right before I smoked a little bit of weed,
link |
01:49:47.280
like, you know, come on, this is a joke, right?
link |
01:49:49.840
Like I do nothing to relax.
link |
01:49:50.880
Look where I am, I'm at a party, right?
link |
01:49:52.600
Yeah, yeah, yeah, that's true.
link |
01:49:55.240
So no, no, of course I don't.
link |
01:49:58.080
When I say that life is work though,
link |
01:49:59.840
I mean that like, I think that what gives my life meaning is work.
link |
01:50:04.200
I don't mean that every minute of the day
link |
01:50:05.720
you should be working.
link |
01:50:06.560
I actually think this is not the best way to maximize results.
link |
01:50:09.800
I think that if you're working 12 hours a day,
link |
01:50:12.040
you should be working smarter and not harder.
link |
01:50:14.900
Well, so work gives you meaning.
link |
01:50:17.880
For some people, other sorts of meaning
link |
01:50:20.520
is personal relationships, like family and so on.
link |
01:50:24.560
You've also, in that interview with Siraj,
link |
01:50:27.200
or the trolling, mentioned that one of the things
link |
01:50:30.680
you look forward to in the future is AI girlfriends.
link |
01:50:34.280
So that's a topic that I'm very much fascinated by,
link |
01:50:38.720
not necessarily girlfriends,
link |
01:50:39.760
but just forming a deep connection with AI.
link |
01:50:42.880
What kind of system do you imagine
link |
01:50:44.320
when you say AI girlfriend,
link |
01:50:46.160
whether you were trolling or not?
link |
01:50:47.720
No, that one I'm very serious about.
link |
01:50:49.640
And I'm serious about that on both a shallow level
link |
01:50:52.280
and a deep level.
link |
01:50:53.600
I think that VR brothels are coming soon
link |
01:50:55.600
and are going to be really cool.
link |
01:50:57.760
It's not cheating if it's a robot.
link |
01:50:59.680
I see the slogan already.
link |
01:51:03.120
But there's, I don't know if you've watched,
link |
01:51:06.160
or just watched the Black Mirror episode.
link |
01:51:08.320
I watched the latest one, yeah.
link |
01:51:09.320
Yeah, yeah.
link |
01:51:11.320
Oh, the Ashley 2 one?
link |
01:51:15.080
No, where there's two friends
link |
01:51:16.920
who are having sex with each other in...
link |
01:51:20.160
Oh, in the VR game.
link |
01:51:21.000
In the VR game.
link |
01:51:22.720
It's just two guys,
link |
01:51:23.560
but one of them was a female, yeah.
link |
01:51:27.200
Which is another mind blowing concept.
link |
01:51:29.520
That in VR, you don't have to be the form.
link |
01:51:33.240
You can be two animals having sex.
link |
01:51:37.160
It's weird.
link |
01:51:38.000
I mean, I'll see how nice that the software
link |
01:51:38.920
maps the nerve endings, right?
link |
01:51:40.240
Yeah, it's huge.
link |
01:51:41.600
I mean, yeah, they sweep a lot of the fascinating,
link |
01:51:44.440
really difficult technical challenges under the rug,
link |
01:51:46.400
like assuming it's possible
link |
01:51:48.320
to do the mapping of the nerve endings, then...
link |
01:51:51.120
I wish, yeah, I saw that,
link |
01:51:51.960
the way they did it with the little like stim unit
link |
01:51:53.800
on the head, that'd be amazing.
link |
01:51:56.800
So, well, no, no, on a shallow level,
link |
01:51:58.760
like you could set up like almost a brothel
link |
01:52:01.680
with like real dolls and Oculus Quests,
link |
01:52:05.160
write some good software.
link |
01:52:06.200
I think it'd be a cool novelty experience.
link |
01:52:09.280
But no, on a deeper, like emotional level,
link |
01:52:12.840
I mean, yeah, I would really like to fall in love
link |
01:52:17.000
with a machine.
link |
01:52:18.120
Do you see yourself having a long term relationship
link |
01:52:25.000
of the kind monogamous relationship that we have now
link |
01:52:28.800
with a robot, with a AI system even,
link |
01:52:31.360
not even just a robot?
link |
01:52:32.680
So I think about maybe my ideal future.
link |
01:52:38.120
When I was 15, I read Eliezer Yudkowsky's early writings
link |
01:52:43.120
on the singularity and like that AI
link |
01:52:49.120
is going to surpass human intelligence massively.
link |
01:52:53.600
He made some Moore's law based predictions
link |
01:52:55.440
that I mostly agree with.
link |
01:52:57.360
And then I really struggled
link |
01:52:59.320
for the next couple of years of my life.
link |
01:53:01.320
Like, why should I even bother to learn anything?
link |
01:53:03.320
It's all gonna be meaningless when the machines show up.
link |
01:53:06.080
Right.
link |
01:53:07.960
Maybe when I was that young,
link |
01:53:10.480
I was still a little bit more pure
link |
01:53:11.960
and really like clung to that.
link |
01:53:13.120
And then I'm like, well,
link |
01:53:13.960
the machines ain't here yet, you know,
link |
01:53:14.960
and I seem to be pretty good at this stuff.
link |
01:53:16.720
Let's try my best, you know,
link |
01:53:18.440
like what's the worst that happens.
link |
01:53:21.320
But the best possible future I see
link |
01:53:24.000
is me sort of merging with the machine.
link |
01:53:26.760
And the way that I personify this
link |
01:53:28.760
is in a long term monogamous relationship with a machine.
link |
01:53:32.880
Oh, you don't think there's a room
link |
01:53:34.040
for another human in your life,
link |
01:53:35.680
if you really truly merge with another machine?
link |
01:53:39.160
I mean, I see merging.
link |
01:53:40.840
I see like the best interface to my brain
link |
01:53:46.280
is like the same relationship interface
link |
01:53:48.680
to merge with an AI, right?
link |
01:53:49.920
What does that merging feel like?
link |
01:53:52.840
I've seen couples who've been together for a long time.
link |
01:53:55.920
And like, I almost think of them as one person,
link |
01:53:58.400
like couples who spend all their time together and...
link |
01:54:01.840
That's fascinating.
link |
01:54:02.680
You're actually putting,
link |
01:54:03.840
what does that merging actually looks like?
link |
01:54:06.040
It's not just a nice channel.
link |
01:54:08.120
Like a lot of people imagine it's just an efficient link,
link |
01:54:12.160
search link to Wikipedia or something.
link |
01:54:14.280
I don't believe in that.
link |
01:54:15.200
But it's more,
link |
01:54:16.040
you're saying that there's the same kind of relationship
link |
01:54:18.520
you have with another human,
link |
01:54:19.400
that's a deep relationship.
link |
01:54:20.760
That's what merging looks like.
link |
01:54:22.880
That's pretty...
link |
01:54:24.400
I don't believe that link is possible.
link |
01:54:26.600
I think that that link,
link |
01:54:27.680
so you're like, oh, I'm gonna download Wikipedia
link |
01:54:29.160
right to my brain.
link |
01:54:30.080
My reading speed is not limited by my eyes.
link |
01:54:33.280
My reading speed is limited by my inner processing loop.
link |
01:54:36.720
And to like bootstrap that sounds kind of unclear
link |
01:54:40.680
how to do it and horrifying.
link |
01:54:42.360
But if I am with somebody and I'll use a somebody
link |
01:54:46.480
who is making a super sophisticated model of me
link |
01:54:51.280
and then running simulations on that model,
link |
01:54:53.120
I'm not gonna get into the question
link |
01:54:54.040
whether the simulations are conscious or not.
link |
01:54:55.840
I don't really wanna know what it's doing.
link |
01:54:58.200
But using those simulations
link |
01:55:00.080
to play out hypothetical futures for me,
link |
01:55:01.840
deciding what things to say to me,
link |
01:55:04.840
to guide me along a path.
link |
01:55:06.240
And that's how I envision it.
link |
01:55:08.680
So on that path to AI of superhuman level intelligence,
link |
01:55:15.080
you've mentioned that you believe in the singularity,
link |
01:55:16.840
that singularity is coming.
link |
01:55:18.600
Again, could be trolling, could be not,
link |
01:55:20.200
could be part, all trolling has truth in it.
link |
01:55:23.000
I don't know what that means anymore.
link |
01:55:24.120
What is the singularity?
link |
01:55:25.920
Yeah, so that's really the question.
link |
01:55:28.040
How many years do you think before the singularity,
link |
01:55:30.520
what form do you think it will take?
link |
01:55:32.080
Does that mean fundamental shifts in capabilities of AI?
link |
01:55:35.440
Or does it mean some other kind of ideas?
link |
01:55:39.400
Maybe that's just my roots, but.
link |
01:55:41.360
So I can buy a human beings worth of compute
link |
01:55:43.880
for like a million bucks today.
link |
01:55:46.000
It's about one TPU pod V3.
link |
01:55:47.680
I want like, I think they claim a hundred pay to flops.
link |
01:55:50.120
That's being generous.
link |
01:55:50.960
I think humans are actually more like 20.
link |
01:55:52.240
So that's like five humans.
link |
01:55:53.080
That's pretty good.
link |
01:55:53.960
Google needs to sell their TPUs.
link |
01:55:56.720
But I could buy, I could buy, I could buy GPUs.
link |
01:55:58.560
I could buy a stack of like, I'd buy 1080 TIs,
link |
01:56:02.200
build data center full of them.
link |
01:56:03.760
And for a million bucks, I can get a human worth of compute.
link |
01:56:08.040
But when you look at the total number of flops in the world,
link |
01:56:12.160
when you look at human flops,
link |
01:56:14.400
which goes up very, very slowly with the population
link |
01:56:17.040
and machine flops, which goes up exponentially,
link |
01:56:19.760
but it's still nowhere near.
link |
01:56:22.360
I think that's the key thing to talk about
link |
01:56:24.560
when the singularity happened.
link |
01:56:25.880
When most flops in the world are Silicon and not biological,
link |
01:56:29.760
that's kind of the crossing point.
link |
01:56:32.280
Like they're now the dominant species on the planet.
link |
01:56:35.480
And just looking at how technology is progressing,
link |
01:56:38.720
when do you think that could possibly happen?
link |
01:56:40.360
You think it would happen in your lifetime?
link |
01:56:41.680
Oh yeah, definitely in my lifetime.
link |
01:56:43.600
I've done the math.
link |
01:56:44.440
I like 2038 because it's the Unix timestamp rollover.
link |
01:56:49.880
Yeah, beautifully put.
link |
01:56:52.640
So you've said that the meaning of life is to win.
link |
01:56:57.960
If you look five years into the future,
link |
01:56:59.520
what does winning look like?
link |
01:57:02.640
So,
link |
01:57:08.560
there's a lot of,
link |
01:57:10.120
I can go into like technical depth
link |
01:57:12.680
to what I mean by that, to win.
link |
01:57:15.760
It may not mean, I was criticized for that in the comments.
link |
01:57:18.280
Like, doesn't this guy wanna like save the penguins
link |
01:57:20.520
in Antarctica or like,
link |
01:57:22.520
oh man, listen to what I'm saying.
link |
01:57:24.920
I'm not talking about like I have a yacht or something.
link |
01:57:27.560
But I am an agent.
link |
01:57:30.520
I am put into this world.
link |
01:57:32.920
And I don't really know what my purpose is.
link |
01:57:37.480
But if you're an intelligent agent
link |
01:57:40.280
and you're put into a world,
link |
01:57:41.400
what is the ideal thing to do?
link |
01:57:43.160
Well, the ideal thing mathematically,
link |
01:57:44.800
you can go back to like Schmidt Hoover theories about this,
link |
01:57:47.080
is to build a compressive model of the world.
link |
01:57:50.480
To build a maximally compressive,
link |
01:57:51.840
to explore the world such that your exploration function
link |
01:57:55.600
maximizes the derivative of compression of the past.
link |
01:57:58.880
Schmidt Hoover has a paper about this.
link |
01:58:00.720
And like, I took that kind of
link |
01:58:02.040
as like a personal goal function.
link |
01:58:05.360
So what I mean to win, I mean like,
link |
01:58:07.720
maybe this is religious,
link |
01:58:09.080
but like I think that in the future,
link |
01:58:11.320
I might be given a real purpose
link |
01:58:13.040
or I may decide this purpose myself.
link |
01:58:14.680
And then at that point,
link |
01:58:16.160
now I know what the game is and I know how to win.
link |
01:58:18.240
I think right now,
link |
01:58:19.080
I'm still just trying to figure out what the game is.
link |
01:58:20.720
But once I know,
link |
01:58:21.800
so you have imperfect information,
link |
01:58:26.440
you have a lot of uncertainty about the reward function
link |
01:58:28.600
and you're discovering it.
link |
01:58:29.720
Exactly.
link |
01:58:30.560
But the purpose is...
link |
01:58:31.400
That's a better way to put it.
link |
01:58:33.120
The purpose is to maximize it
link |
01:58:34.440
while you have a lot of uncertainty around it.
link |
01:58:37.960
And you're both reducing the uncertainty
link |
01:58:39.400
and maximizing at the same time.
link |
01:58:41.160
Yeah.
link |
01:58:42.000
And so that's at the technical level.
link |
01:58:44.240
What is the, if you believe in the universal prior,
link |
01:58:47.440
what is the universal reward function?
link |
01:58:49.360
That's the better way to put it.
link |
01:58:51.320
So that win is interesting.
link |
01:58:53.680
I think I speak for everyone in saying that
link |
01:58:57.280
I wonder what that reward function is for you.
link |
01:59:01.920
And I look forward to seeing that in five years,
link |
01:59:05.920
in 10 years.
link |
01:59:07.040
I think a lot of people, including myself,
link |
01:59:08.680
are cheering you on, man.
link |
01:59:09.840
So I'm happy you exist and I wish you the best of luck.
link |
01:59:14.280
Thanks for talking to me, man.
link |
01:59:15.360
Thank you.
link |
01:59:16.200
Have a good one.