The following is a conversation with George Hotz.
He's the founder of Comma AI,
a machine learning based vehicle automation company.
He is most certainly an outspoken personality
in the field of AI and technology in general.
He first gained recognition for being the first person
to carry on lock and iPhone.
And since then, he's done quite a few interesting things
at the intersection of hardware and software.
This is the artificial intelligence podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on iTunes, support it on Patreon,
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Alex Friedman, spelled F R I D M A N.
And I'd like to give a special thank you to Jennifer
from Canada for her support of the podcast on Patreon.
Merci beaucoup, Jennifer.
She's been a friend and an engineering colleague
for many years since I was in grad school.
Your support means a lot and inspires me
to keep this series going.
And now here's my conversation with George Hotz.
Do you think we're living in a simulation?
Yes, but it may be unfalsifiable.
What do you mean by unfalsifiable?
So if the simulation is designed in such a way
that they did like a formal proof
to show that no information can get in and out.
And if their hardware is designed for the anything
in the simulation to always keep the hardware in spec,
it may be impossible to prove
whether we're in a simulation or not.
So they've designed it such that it's a closed system,
you can't get outside the system.
Well, maybe it's one of three worlds.
We're either in a simulation which can be exploited,
we're in a simulation which not only can't be exploited,
but like the same thing's true about VMs.
A really well designed VM,
you can't even detect if you're in a VM or not.
That's brilliant.
So we're, yeah, so the simulation is running
on a virtual machine.
But now in reality, all VMs have ways to detect.
That's the point.
I mean, is it, you've done quite a bit of hacking yourself.
So you should know that really any complicated system
will have ways in and out.
So this isn't necessarily true going forward.
I spent my time away from comma,
I learned a cock, it's a dependently typed,
like it's a language for writing math proofs.
And if you write code that compiles in a language like that,
it is correct by definition.
The types check it's correctance.
So it's possible that the simulation
is written in a language like this, in which case, yeah.
Yeah, but that can't be sufficiently expressive
of language like that.
Oh, it can.
It can be?
Oh, yeah.
Okay, well, so, all right, so.
The simulation doesn't have to be tearing complete
if it has a scheduled end date.
Looks like it does actually with entropy.
I mean, I don't think that a simulation
that results in something as complicated as the universe
would have a formal proof of correctness, right?
It's possible, of course.
We have no idea how good their tooling is.
And we have no idea how complicated
the universe computer really is.
It may be quite simple.
It's just very large, right?
It's very, it's definitely very large.
But the fundamental rules might be super simple.
Yeah, Conway's gonna like kinda stop.
Right, so if you could hack,
so imagine the simulation that is hackable,
if you could hack it,
what would you change about the universe?
Like how would you approach hacking a simulation?
The reason I gave that talk?
By the way, I'm not familiar with the talk you gave.
I just read that you talked about escaping the simulation
or something like that.
So maybe you can tell me a little bit
about the theme and the message there too.
It wasn't a very practical talk
about how to actually escape a simulation.
It was more about a way of restructuring
an us versus them narrative.
If we continue on the path we're going with technology,
I think we're in big trouble,
like as a species and not just as a species,
but even as me as an individual member of the species.
So if we could change rhetoric to be more like,
to think upwards,
like to think about that we're in a simulation
and how we could get out,
already we'd be on the right path.
What you actually do once you do that,
well, I assume I would have acquired way more intelligence
in the process of doing that, so I'll just ask that.
So the thinking upwards,
what kind of ideas,
what kind of breakthrough ideas do you think thinking
in that way could inspire?
And why did you say upwards?
Upwards.
Into space?
Are you thinking sort of exploration in all forms?
The space narrative that held for the modernist generation
doesn't hold as well for the postmodern generation.
What's the space narrative?
Are we talking about the same space?
The three dimensional space?
No, no, space, like going up space,
like building like Elon Musk,
like we're going to build rockets,
we're going to go to Mars,
we're going to colonize the universe.
And the narrative you're referring,
I was born in the Soviet Union,
you're referring to the race to space?
The race to space, yeah.
Yes, explore, okay.
That was a great modernist narrative.
Yeah.
It doesn't seem to hold the same weight in today's culture.
I'm hoping for good postmodern narratives that replace it.
So let's think, so you work a lot with AI.
So AI is one formulation of that narrative.
There could be also,
I don't know how much you do in VR and AR.
Yeah.
That's another, I know less about it,
but every time I play with it and our research,
it's fascinating, that virtual world.
Are you interested in the virtual world?
I would like to move to virtual reality.
In terms of your work?
No, I would like to physically move there.
The apartment I can rent in the cloud
is way better than the apartment I can rent in the real world.
Well, it's all relative, isn't it?
Because others will have very nice apartments too,
so you'll be inferior in the virtual world as well.
But that's not how I view the world, right?
I don't view the world.
I mean, that's a very like, almost zero summish way
to view the world.
Say like, my great apartment isn't great
because my neighbor has one too.
No, my great apartment is great
because like, look at this dishwasher, man.
You just touch the dish and it's washed, right?
And that is great in and of itself
if I had the only apartment
or if everybody had the apartment.
I don't care.
So you have fundamental gratitude.
The world first learned of Geohot, George Hots
in August 2007, maybe before then,
but certainly in August 2007
when you were the first person to unlock,
carry on lock an iPhone.
How did you get into hacking?
What was the first system you discovered
vulnerabilities for and broke into?
So that was really kind of the first thing.
I had a book in 2006 called Gray Hat Hacking.
And I guess I realized that
if you acquired these sort of powers
you could control the world.
But I didn't really know that much
about computers back then.
I started with electronics.
The first iPhone hack was physical.
Cardware.
You had to open it up and pull an address line high.
And it was because I didn't really know
about software exploitation.
I learned that all in the next few years
and I got very good at it.
But back then I knew about like
how memory chips are connected to processors and stuff.
But you knew about software and programming.
You didn't know.
Oh really, so your view of the world
and computers was physical, was hardware.
Actually, if you read the code that I released with that
in August 2007, it's atrocious.
What language was it?
C.
C, nice.
And in a broken sort of state machine, ask C.
I didn't know how to program.
Yeah.
So how did you learn to program?
What was your journey?
I mean, we'll talk about it.
You've live streamed some of your programming.
This chaotic, beautiful mess.
How did you arrive at that?
Years and years of practice.
I interned at Google after,
the summer after the iPhone unlock.
And I did a contract for them
where I built a hardware for Street View
and I wrote a software library to interact with it.
And it was terrible code.
And for the first time I got feedback
from people who I respected saying,
no, like, don't write code like this.
Now, of course, just getting that feedback is not enough.
The way that I really got good was,
I wanted to write this thing that could emulate
and then visualize like arm binaries
because I wanted to hack the iPhone better.
And I didn't like that I couldn't see what the,
I couldn't single step through the processor
because I had no debugger on there,
especially for the low level things like the boot ROM
and the boot loader.
So I tried to build this tool to do it.
And I built the tool once and it was terrible.
I built the tool second times, it was terrible.
I built the tool third time.
This by the time I was at Facebook, it was kind of okay.
And then I built the tool fourth time
when I was a Google intern again in 2014.
And that was the first time I was like,
this is finally usable.
How do you pronounce this, Kira?
Kira, yeah.
So it's essentially the most efficient way
to visualize the change of state of the computer
as the program is running.
That's what you mean by debugger.
Yeah, it's a timeless debugger.
So you can rewind just as easily as going forward.
Think about, if you're using GDB,
you have to put a watch on a variable.
If you want to see if that variable changes.
In Kira, you can just click on that variable.
And then it shows every single time
when that variable was changed or accessed.
Think about it like get for your computer's, the run lock.
So there's like a deep log of the state of the computer
as the program runs and you can rewind.
Why isn't that, maybe it is, maybe you can educate me.
Why isn't that kind of debugging used more often?
Because the tooling's bad.
Well, two things.
One, if you're trying to debug Chrome,
Chrome is a 200 megabyte binary
that runs slowly on desktops.
So that's gonna be really hard to use for that.
But it's really good to use for like CTFs
and for boot ROMs and for small parts of code.
So it's hard if you're trying to debug like massive systems.
What's a CTF and what's a boot ROM?
A boot ROM is the first code that executes
the minute you give power to your iPhone.
Okay.
And CTF were these competitions that I played.
Capture the flag.
I was gonna ask you about that.
What are those, those look at,
I watched a couple of videos on YouTube.
Those look fascinating.
What have you learned about maybe at the high level
in the vulnerability of systems from these competitions?
I feel like in the heyday of CTFs,
you had all of the best security people in the world
challenging each other and coming up
with new toy exploitable things over here.
And then everybody, okay, who can break it?
And when you break it, you get like,
there's like a file in the server called flag.
And then there's a program running,
listening on a socket that's vulnerable.
So you write an exploit, you get a shell,
and then you cat flag, and then you type the flag
into like a web based scoreboard and you get points.
So the goal is essentially to find an exploit in the system
that allows you to run shell,
to run arbitrary code on that system.
That's one of the categories.
That's like the Poneable category.
Poneable?
Yeah, Poneable.
It's like, you know, you Pone the program.
It's a program.
Oh, yeah.
You know, first of all, I apologize, I'm gonna say,
it's because I'm Russian,
but maybe you can help educate me.
Some video game like misspelled
to own way back in the day.
Yeah, and it's just,
I wonder if there's a definition
and I'll have to go to Urban Dictionary for it.
Yeah, it'd be interesting to see what it says.
Okay, so what was the heyday of CTL, by the way,
but was it, what decade are we talking about?
I think like, I mean, maybe I'm biased
because it's the era that I played,
but like 2011 to 2015,
because the modern CTF scene
is similar to the modern competitive programming scene.
You have people who like do drills.
You have people who practice.
And then once you've done that,
you've turned it less into a game of generic computer skill
and more into a game of, okay, you memorize,
you drill on these five categories.
And then before that, it wasn't,
it didn't have like as much attention as it had.
I don't know, they were like,
I won $30,000 once in Korea
for one of these competitions.
Holy crap.
Yeah, they were, they were, that was...
So that means, I mean, money is money,
but that means there was probably good people there.
Exactly, yeah.
Are the challenges human constructed
or are they grounded in some real flaws in real systems?
Usually they're human constructed,
but they're usually inspired by real flaws.
What kind of systems are imagined
is really focused on mobile?
Like what has vulnerabilities these days?
Is it primarily mobile systems like Android?
Oh, everything does.
Yeah, of course.
The price has kind of gone up
because less and less people can find them.
And what's happened in security is now,
if you want to like jailbreak an iPhone,
you don't need one exploit anymore, you need nine.
Nine change together?
What would you mean?
Yeah, wow.
Okay, so it's really, what's the benefit?
Speaking higher level philosophically about hacking.
I mean, it sounds from everything I've seen about you,
you just love the challenge and you don't want to do anything.
You don't want to bring that exploit out into the world
and do any actual, let it run wild.
You just want to solve it
and then you go on to the next thing.
Oh yeah, I mean, doing criminal stuff's not really worth it.
And I'll actually use the same argument
for why I don't do defense for why I don't do crime.
If you want to defend a system,
say the system has 10 holes, right?
If you find nine of those holes as a defender,
you still lose because the attacker gets in
through the last one.
If you're an attacker,
you only have to find one out of the 10.
But if you're a criminal,
if you log on with a VPN nine out of the 10 times,
but one time you forget, you're done.
Because you're caught, okay.
Because you only have to mess up once
to be caught as a criminal.
That's why I'm not a criminal.
But okay, let me,
cause I was having a discussion with somebody
just at a high level about nuclear weapons,
actually why we're having blown ourselves up yet.
And my feeling is all the smart people in the world,
if you look at the distribution of smart people,
smart people are generally good.
And then the Southern person,
I was talking to Sean Carroll, the physicist,
and he was saying no good and bad people
are evenly distributed amongst everybody.
My sense was good hackers are in general good people
and they don't want to mess with the world.
What's your sense?
I'm not even sure about that.
Like, I have a nice life.
Crime wouldn't get me anything.
But if you're good and you have these skills,
you probably have a nice life too, right?
Right, you can use the father things.
But is there an ethical,
is there a little voice in your head that says,
well, yeah, if you could hack something
to where you could hurt people
and you could earn a lot of money doing it though,
not hurt physically perhaps,
but disrupt their life in some kind of way.
Isn't there a little voice that says,
Well, two things.
One, I don't really care about money.
So like the money wouldn't be an incentive.
The thrill might be an incentive.
But when I was 19, I read crime and punishment.
That was another great one
that talked me out of ever really doing crime.
Cause it's like, that's gonna be me.
I'd get away with it, but it would just run through my head.
Even if I got away with it, you know?
And then you do crime for long enough,
you'll never get away with it.
That's right, in the end.
That's a good reason to be good.
I wouldn't say I'm good, I would just say I'm not bad.
You're a talented programmer and a hacker
in a good positive sense of the word.
You've played around, found vulnerabilities
in various systems.
What have you learned broadly
about the design of systems and so on
from that whole process?
You learn to not take things
for what people say they are,
but you look at things for what they actually are.
Yeah.
I understand that's what you tell me it is,
but what does it do?
And you have nice visualization tools
to really know what it's really doing.
Oh, I wish I'm a better programmer now than I was in 2014.
I said, Kira, that was the first tool
that I wrote that was usable.
I wouldn't say the code was great.
I still wouldn't say my code is great.
So how was your evolution as a programmer?
Except practice.
You started with C,
what point did you pick up Python?
Because you're pretty big in Python now.
Now, yeah, in college,
I went to Carnegie Mellon when I was 22.
I went back, I'm like,
I'm gonna take all your hardest CS courses
and we'll see how I do, right?
Like, did I miss anything
by not having a real undergraduate education?
Took operating systems, compilers, AI,
and they're like a freshman Weeder math course.
And some of those classes you mentioned,
pretty tough, actually.
They're great.
At least when the 2012,
circa 2012 operating systems and compilers
were two of the best classes I've ever taken in my life.
Because you write an operating system
and you write a compiler.
I wrote my operating system in C
and I wrote my compiler in Haskell,
but somehow I picked up Python that semester as well.
I started using it for the CTFs, actually.
That's when I really started to get into CTFs
and CTFs, you're all to race against the clock.
So I can't write things and see.
Oh, there's a clock component.
So you really want to use the programming language
just so you can be fastest.
48 hours.
Pwn as many of these challenges as you can.
Pwn.
Yeah.
You got like 100 points of challenge,
whatever team gets the most.
You were both at Facebook and Google for a brief stint.
Yeah.
With Project Zero, actually, at Google for five months
where you develop Kira.
What was Project Zero about in general?
Just curious about the security efforts in these companies.
Well, Project Zero started the same time I went there.
What year is it there?
2015.
So that was right at the beginning of Project Zero.
It's small.
It's Google's offensive security team.
I'll try to give the best public facing explanation
that I can.
So the idea is basically,
these vulnerabilities exist in the world.
Nation states have them.
Some high powered bad actors have them.
Sometimes people will find these vulnerabilities
and submit them in bug bounties to the companies.
But a lot of the companies don't only care.
They don't even fix the bug.
It doesn't hurt for there to be a vulnerability.
So Project Zero is like, we're going to do it different.
We're going to announce a vulnerability
and we're going to give them 90 days to fix it.
And then whether they fix it or not,
we're going to drop the Zero Day.
Oh, wow.
We're going to drop the weapon on the textbook.
That's so cool.
That is so cool.
I love that deadlines.
Oh, that's so cool.
Give them real deadlines.
Yeah.
And I think it's done a lot for moving the industry forward.
I watched your coding sessions on the streamed online.
You code things up, the basic projects, usually from scratch.
I would say, sort of as a programmer myself,
just watching you, that you type really fast
and your brain works in both brilliant and chaotic ways.
I don't know if that's always true,
but certainly for the live streams.
So it's interesting to me because I'm much slower
and systematic and careful.
And you just move probably in order of magnitude faster.
So I'm curious, is there a method to your madness?
Or is it just who you are?
There's pros and cons.
There's pros and cons to my programming style.
And I'm aware of them.
If you ask me to get something up and working quickly
with an API that's kind of undocumented,
I will do this super fast because I will throw things
at it until it works.
If you ask me to take a vector and rotate it 90 degrees
and then flip it over the X, Y plane,
I'll spam program for two hours and won't get it.
Oh, because it's something that you
could do with a sheet of paper or think through design
and then just you really just throw stuff at the wall
and you get so good at it that it usually works.
I should become better at the other kind as well.
Sometimes I will do things methodically.
It's nowhere near as entertaining on the Twitch streams.
I do exaggerate it a bit on the Twitch streams as well.
The Twitch streams, I mean, what do you want to see a game
or you want to see actions permit, right?
I'll show you APM for programming too.
Yeah, I'd recommend people go to it.
I think I watched probably several hours that you put,
like I've actually left you programming in the background
while I was programming because you made me,
it was like watching a really good gamer.
It's like energizes you because you're like moving so fast
and so it's awesome, it's inspiring.
It made me jealous that like,
because my own programming is inadequate
in terms of speed, so I was like.
So I'm twice as frantic on the live streams
as I am when I code without, oh.
It's super entertaining.
So I wasn't even paying attention to what you were coding,
which is great, it's just watching you switch windows
and Vim, I guess is the most way.
Yeah, does Vim on screen?
I've developed a workload Facebook and stuck with it.
How do you learn new programming tools,
ideas, techniques these days?
What's your like methodology for learning new things?
So I wrote for comma,
the distributed file systems out in the world
are extremely complex.
Like if you want to install something like like like Ceph,
Ceph is I think the like open infrastructure
distributed file system or there's like newer ones
like seaweed FS, but these are all like 10,000
plus line projects.
I think some of them are even 100,000 line
and just configuring them as a nightmare.
So I wrote, I wrote one, it's 200 lines
and it uses like engine X of the line servers
and has this little master server that I wrote and go.
And the way I go, this, if I would say
that I'm proud per line of any code I wrote,
maybe there's some exploits that I think are beautiful
and then this, this is 200 lines
and just the way that I thought about it,
I think was very good and the reason it's very good
is because that was the fourth version of it that I wrote
and I had three versions that I threw away.
You mentioned, did you say go?
I wrote a go, yeah.
And go.
Is that a functional language?
I forget what go is.
Go is Google's language.
Right.
It's not functional.
It's some, it's like, in a way it's C++, but easier.
It's strongly typed.
It has a nice ecosystem around it.
When I first looked at it, I was like,
this is like Python, but it takes twice as long
to do anything.
Now that I've open pilot is migrating to C,
but it still has large Python components,
I now understand why Python doesn't work
for large code bases and why you want something like go.
Interesting.
So why, why doesn't Python work for,
so even most, speaking for myself at least,
like we do a lot of stuff, basically demo level work
with autonomous vehicles and most of the work is Python.
Why doesn't Python work for large code bases?
Because, well, lack of type checking is a big one.
So errors creep in.
Yeah, and like you don't know,
the compiler can tell you like nothing, right?
So everything is either, you know,
like syntax errors, fine,
but if you misspell a variable in Python,
the compiler won't catch that.
There's like linters that can catch it some of the time.
There's no types.
This is really the biggest downside.
And then we'll Python slow, but that's not related to it.
Well, maybe it's kind of related to it, so it's lack of.
So what's in your toolbox these days?
Is it Python or what else?
Go.
I need to move to something else.
My adventure into dependently typed languages,
I love these languages.
They just have like syntax from the 80s.
What do you think about JavaScript?
ES6, like the modern type script?
JavaScript is, the whole ecosystem
is unbelievably confusing.
NPM updates a package from 022 to 025
and that breaks your Babel linter,
which translates your ES5 into ES6, which doesn't run on.
So why do I have to compile my JavaScript again, huh?
It may be the future though.
You think about, I mean,
I've embraced JavaScript recently
just because just like I've continually embraced PHP,
it seems that these worst possible languages live on
for the longest, like cockroaches never die.
Yeah, well, it's in the browser and it's fast.
It's fast.
Yeah.
It's in the browser and compute might stay become,
you know, the browser,
it's unclear what the role of the browser is
in terms of distributed computation in the future.
So.
JavaScript is definitely here to stay.
Yeah.
It's interesting if autonomous vehicles
will run on JavaScript one day.
I mean, you have to consider these possibilities.
Well, all our debug tools are JavaScript.
We actually just open source them.
We have a tool explorer, which you can annotate
your disengagements and we have tool Kibana,
which lets you analyze the can traffic from the car.
So basically any time you're visualizing something
about the log using JavaScript.
Well, the web is the best UI toolkit by far.
Yeah.
So, and then, you know what?
You're coding in JavaScript.
We have a React guy.
He's good.
React, nice.
Let's get into it.
So let's talk autonomous vehicles.
You found a comma AI.
Let's, at a high level,
how did you get into the world of vehicle automation?
Can you also just, for people who don't know,
tell the story of comma AI?
Sure.
So I was working at this AI startup
and a friend approached me and he's like,
dude, I don't know where this is going,
but the coolest applied AI problem today
is self driving cars.
I'm like, well, absolutely.
You wanna meet with Elon Musk
and he's looking for somebody to build a vision system
for autopilot.
This is when they were still on AP one.
They were still using Mobileye.
Elon back then was looking for a replacement.
And he brought me in and we talked about a contract
where I would deliver something
that meets Mobileye level performance.
I would get paid $12 million if I could deliver it tomorrow
and I would lose $1 million for every month I didn't deliver.
So I was like, okay, this is a great deal.
This is a super exciting challenge.
You know what?
It takes me 10 months, I get $2 million, it's good.
Maybe I can finish up in five.
Maybe I don't finish it at all and I get paid nothing
and I'll work for 12 months for free.
So maybe just take a pause on that.
I'm also curious about this
because I've been working in robotics for a long time.
And I'm curious to see a person like you just step in
and sort of somewhat naive, but brilliant, right?
So that's the best place to be
because you basically full steam take on a problem.
How confident, from that time,
because you know a lot more now,
at that time, how hard do you think it is
to solve all of autonomous driving?
I remember I suggested to Elon in the meeting
on putting a GPU behind each camera
to keep the compute local.
This is an incredibly stupid idea.
I leave the meeting 10 minutes later and I'm like,
I could have spent a little bit of time
thinking about this problem before I went in.
Why is this a stupid idea?
Oh, just send all your cameras to one big GPU.
You're much better off doing that.
Oh, sorry, you said behind every camera.
Every camera.
Every small GPU.
I was like, oh, I'll put the first few layers
of my comms there.
Like why did I say that?
That's possible.
It's possible, but it's a bad idea.
It's not obviously a bad idea.
Pretty obviously bad.
But whether it's actually a bad idea or not,
I left that meeting with Elon, like beating myself up.
I'm like, why did I say something stupid?
Yeah, you haven't, like you haven't at least
like thought through every aspect fully.
He's very sharp too.
Like usually in life, I get away with saying stupid things
and then kind of course,
right away he called me out about it.
And like, usually in life,
I get away with saying stupid things.
And then like people will, you know,
a lot of times people don't even notice.
And I'll like correct it and bring the conversation back.
But with Elon, it was like, nope, like, okay.
Well, that's not at all why the contract fell through.
I was much more prepared the second time I met him.
Yeah.
But in general, how hard did you think it,
like 12 months is a tough timeline?
Oh, I just thought I'd clone Mobileye IQ three.
I didn't think I'd solve level five self driving
or anything.
So the goal there was to do lane keeping,
good lane keeping.
I saw my friend showed me the outputs from Mobileye.
And the outputs from Mobileye was just basically two lanes
and a position of a lead car.
I'm like, I can gather a data set
and train this net in weeks.
And I did.
Well, first time I tried the implementation of Mobileye
in a Tesla, I was really surprised how good it is.
It's quite incredibly good.
Cause I thought it's just cause I've done
a lot of computer vision.
I thought it'd be a lot harder to create a system
that that's stable.
So I was personally surprised.
Just, you know, have to admit it.
Cause I was kind of skeptical before trying it.
Cause I thought it would go in and out a lot more.
It would get disengaged a lot more.
And it's pretty robust.
So what, how, how, how hard is the problem
when you, when you tackled it?
So I think AP one was great. Like Elon talked
about disengagements on the 405 down in LA
with like the lane marks were kind of faded
and the Mobileye system would drop out.
Like I had something up and working
that I would say was like the same quality in three months.
Same quality, but how do you know?
You say stuff like that confidently, but you can't,
and I love it, but the question is you can't,
you're kind of going by feel cause you just,
You're going by feel, absolutely, absolutely.
Like, like I would take, I hadn't,
I borrowed my friend's Tesla.
I would take AP one out for a drive.
And then I would take my system out for a drive.
And seems reasonably like the same.
So the 405, how hard is it to create something
that could actually be a product that's deployed?
I mean, I've read an article where Elon, this respond,
it said something about you saying that
to build autopilot is more complicated
than a single George Hodds level job.
How hard is that job to create something
that would work across the globally?
Why don't the global is the challenge,
but Elon followed that up by saying
it's going to take two years and a company of 10 people.
And here I am four years later with a company of 12 people.
And I think we still have another two to go.
Two years.
So yeah, so what do you think,
what do you think about how Tesla's progressing
with autopilot of V2, V3?
I think we've kept pace with them pretty well.
I think navigating autopilot is terrible.
We had some demo features internally of the same stuff
and we would test it and I'm like,
I'm not shipping this even as like open source software
to people.
What do you think is terrible?
Consumer Reports does a great job of describing it.
Like when it makes a lane change,
it does it worse than a human.
You shouldn't ship things like autopilot, open pilot,
they lane keep better than a human.
If you turn it on for a stretch of highway,
like an hour long, it's never going to touch a lane line.
Human will touch probably a lane line twice.
You just inspired me.
I don't know if you're grounded in data on that.
I read your paper.
Okay, but no, but that's interesting.
I wonder actually how often we touch lane lines
a little bit because it is.
I could answer that question pretty easily
with the common data side.
Yeah, I'm curious.
I've never answered it.
I don't know.
I just too was like my personal.
It feels right, but that's interesting
because every time you touch a lane,
that's a source of a little bit of stress
and kind of lane keeping is removing that stress.
That's ultimately the biggest value add
honestly is just removing the stress
of having to stay in lane.
And I think I don't think people fully realize
first of all that that's a big value add,
but also that that's all it is.
And that not only I find it a huge value add.
I drove down when we moved to San Diego,
I drove down in an enterprise rental car
and I missed it.
So I missed having the system so much.
It's so much more tiring to drive
without it.
It's, it is that lane centering.
That's the key feature.
Yeah.
And in a way it's the only feature
that actually adds value to people's lives
in autonomous vehicles today.
Waymo does not add value to people's lives.
It's a more expensive, slower Uber.
Maybe someday it'll be this big cliff where it adds value,
but I don't usually.
It's fascinating.
I haven't talked to, this is good.
Cause I haven't, I have intuitively,
but I think we're making it explicit now.
I actually believe that really good lane keeping
is a reason to buy a car.
Will be a reason to buy a car.
It is a huge value add.
I've never, until we just started talking about it,
haven't really quite realized it,
that I've felt with Elon's chase of level four
is not the correct chase.
It was on, cause you should just say Tesla has the best
as if from a Tesla perspective say,
Tesla has the best lane keeping.
Kama AI should say Kama AI is the best lane keeping.
And that is it.
Yeah.
Do you think?
You have to do the longitudinal as well.
You can't just lane keep.
You have to do ACC,
but ACC is much more forgiving than lane keep,
especially on the highway.
By the way, are you Kama AI's camera only, correct?
No, we use the radar.
We, from the car, you're able to get to, okay.
We can do it camera only now.
It's gotten to the point,
but we leave the radar there as like a,
it's fusion now.
Okay, so let's maybe talk through
some of the system specs on the hardware.
What's the hardware side of what you're providing?
What's the capabilities on the software side
with OpenPilot and so on?
So OpenPilot as the box that we sell that it runs on,
it's a phone in a plastic case.
It's nothing special.
We sell it without the software.
So you're like, you know, you buy the phone,
it's just easy.
It'll be easy set up,
but it's sold with no software.
OpenPilot right now is about to be 0.6.
When it gets to 1.0,
I think we'll be ready for a consumer product.
We're not gonna add any new features.
We're just gonna make the lane keeping really, really good.
Okay, I got it.
So what do we have right now?
It's a Snapdragon 820.
It's a Sony IMX 298 forward facing camera,
driver monitoring camera.
It's just a selfie cam on the phone.
And a can transceiver,
maybe it's a little thing called pandas.
And they talk over USB to the phone
and then they have three can buses
that they talk to the car.
One of those can buses is the radar can bus.
One of them is the main car can bus.
And the other one is the proxy camera can bus.
We leave the existing camera in place.
So we don't turn AEB off.
Right now we still turn AEB off
if you're using our longitudinal,
but we're gonna fix that before 1.0.
Got it.
Wow, that's cool.
So in its can both ways.
So how are you able to control vehicles?
So we proxy the vehicles that we work with
already have a lane keeping assist system.
So lane keeping assist can mean a huge variety of things.
It can mean it will apply a small torque
to the wheel after you've already crossed a lane line
by a foot, which is the system in the older Toyotas.
Versus like, I think Tesla still calls it lane keeping assist
where it'll keep you perfectly in the center of the lane
on the highway.
You can control like you with the joystick, the cars.
So these cars already have the capability of drive by wire.
So is it, is it trivial to convert a car
that it operates with?
It open pilot is able to control the steering.
Oh, a new car or a car that we,
so we have support now for 45 different makes of cars.
What are the cars in general?
Mostly Honda's and Toyotas.
We support almost every Honda and Toyota made this year.
And then bunch of GM's, bunch of Subaru's.
But it doesn't have to be like a Prius.
It could be Corolla as well.
Oh, the 2020 Corolla is the best car with open pilot.
It just came out there.
The actuator has less lag than the older Corolla.
I think I started watching a video with you.
I mean, the way you make videos is awesome.
It's just literally at the dealership streaming.
I had my friend to follow him.
I probably want to stream for an hour.
Yeah, and basically like if stuff goes a little wrong,
you just like, you just go with it.
Yeah, I love it.
It's real.
Yeah, it's real.
That's so beautiful and it's so in contrast to the way
other companies would put together a video like that.
Kind of why I like to do it like that.
Good.
I mean, if you become super rich one day and successful,
I hope you keep it that way because I think that's actually
what people love, that kind of genuine.
Oh, it's all that has value to me.
Money has no, if I sell out to like make money,
I sold out.
It doesn't matter.
What do I get?
Yacht, I don't want a yacht.
And I think Tesla actually has a small inkling of that
as well with autonomy day.
They did reveal more than, I mean, of course,
there's marketing communications, you could tell,
but it's more than most companies would reveal,
which is I hope they go towards that direction
more other companies, GM, Ford.
Oh, Tesla's going to win level five.
They really are.
So let's talk about it.
You think, you're focused on level two currently, currently.
We're going to be one to two years behind Tesla
getting to level five.
OK.
We're Android, right?
We're Android.
You're Android.
I'm just saying once Tesla gets it,
we're one to two years behind.
I'm not making any timeline on when Tesla's going to get it.
That's right.
You did.
That's brilliant.
I'm sorry, Tesla investors, if you
think you're going to have an autonomous robot taxi
fleet by the end of the year, I'll bet against that.
So what do you think about this?
The most level four companies are kind of just
doing their usual safety driver, doing full autonomy kind
of testing.
And then Tesla does basically trying
to go from lane keeping to full autonomy.
What do you think about that approach?
How successful would it be?
It's a ton better approach.
Because Tesla is gathering data on a scale
that none of them are.
They're putting real users behind the wheel of the cars.
It's, I think, the only strategy that works, the incremental.
Well, so there's a few components to Tesla approach
that's more than just the incremental.
What you spoke with is the software,
so over the air software updates.
Necessity.
I mean, Waymo crews have those too.
Those aren't.
But no.
Those differentiate from the automakers.
Right.
No lane keeping systems have no cars with lane keeping system
have that except Tesla.
Yeah.
And the other one is the data, the other direction,
which is the ability to query the data.
I don't think they're actually collecting
as much data as people think, but the ability
to turn on collection and turn it off.
So I'm both in the robotics world, in the psychology,
human factors world.
Many people believe that level two autonomy
is problematic because of the human factor.
Like the more the task is automated,
the more there's a vigilance decrement.
You start to fall asleep.
You start to become complacent, start texting more and so on.
Do you worry about that?
Because if you're talking about transition from lane keeping
to full autonomy, if you're spending 80% of the time
not supervising the machine, do you
worry about what that means for the safety of the drivers?
One, we don't consider OpenPilot to be 1.0
until we have 100% driver monitoring.
You can cheat right now, our driver monitoring system.
There's a few ways to cheat it.
They're pretty obvious.
We're working on making that better.
Before we ship a consumer product that can drive cars,
I want to make sure that I have driver monitoring
that you can't cheat.
What's a successful driver monitoring system look like?
Is it all about just keeping your eyes on the road?
Well, a few things.
So that's what we went with at first for driver monitoring.
I'm checking.
I'm actually looking at where your head is looking.
The camera's not that high.
Resolution eyes are a little bit hard to get.
Well, head is big.
I mean, that's just.
Head is good.
And actually, a lot of it, just psychology wise,
to have that monitor constantly there,
it reminds you that you have to be paying attention.
But we want to go further.
We just hired someone full time to come on
to do the driver monitoring.
I want to detect phone in frame, and I
want to make sure you're not sleeping.
How much does the camera see of the body?
This one, not enough.
Not enough.
The next one, everything.
What's interesting, FishEye, is we're
doing just data collection, not real time.
But FishEye is a beautiful being able to capture the body.
And the smartphone is really the biggest problem.
I'll show you.
I can show you one of the pictures from our new system.
Awesome.
So you're basically saying the driver monitoring
will be the answer to that.
I think the other point that you raised in your paper
is good as well.
You're not asking a human to supervise a machine
without giving them the they can take over at any time.
Our safety model, you can take over.
We disengage on both the gas or the brake.
We don't disengage on steering.
I don't feel you have to.
But we disengage on gas or brake.
So it's very easy for you to take over.
And it's very easy for you to reengage.
That switching should be super cheap.
The cars that require, even autopilot,
requires a double press.
That's almost, I see, I don't like that.
And then the cancel.
To cancel in autopilot, you either
have to press cancel, which no one knows where that is.
So they press the brake.
But a lot of times you don't want to press the brake.
You want to press the gas.
So you should cancel on gas or wiggle the steering wheel,
which is bad as well.
Wow, that's brilliant.
I haven't heard anyone articulate that point.
Oh, there's a lot I think about.
Because I think actually Tesla has done a better job
than most automakers at making that frictionless.
But you just described that it could be even better.
I love Super Cruise as an experience.
Once it's engaged.
I don't know if you've used it, but getting the thing
to try to engage.
Yeah, I've used the driven Super Cruise a lot.
So what's your thoughts on the Super Cruise system in general?
You disengage Super Cruise, and it falls back to ACC.
So my car is still accelerating.
It feels weird.
Otherwise, when you actually have Super Cruise engaged
on the highway, it is phenomenal.
We bought that Cadillac.
We just sold it.
But we bought it just to experience this.
And I wanted everyone in the office to be like,
this is what we're striving to build.
GM pioneering with the driver monitoring.
You like their driver monitoring system?
It has some bugs.
If there's a sun shining back here, it'll be blind to you.
But overall, mostly, yeah.
That's so cool that you know all this stuff.
I don't often talk to people that because it's such a rare car,
unfortunately, currently.
We bought one explicitly for that.
We lost like $25K in the deprecation,
but it feels worth it.
I was very pleasantly surprised that our GM system
was so innovative and really wasn't advertised much,
wasn't talked about much.
And I was nervous that it would die, that it would disappear.
Well, they put it on the wrong car.
They should have put it on the bolt and not some weird Cadillac
that nobody bought.
I think that's going to be into, they're saying at least
it's going to be into their entire fleet.
So what do you think about, as long as we're
on the driver monitoring, what do you think
about Elon Musk's claim that driver monitoring is not needed?
Normally, I love his claims.
That one is stupid.
And he's not going to have his level five fleet
by the end of the year.
Hopefully, he's like, OK, I was wrong.
I'm going to add driver monitoring.
Because when these systems get to the point
that they're only messing up once every 1,000 miles,
you absolutely need driver monitoring.
So let me play, because I agree with you,
but let me play devil's advocate.
One possibility is that without driver monitoring,
people are able to self regulate, monitor themselves.
So your idea is, I'm just.
You're seeing all the people sleeping in Teslas?
Yeah.
Well, I'm a little skeptical of all the people sleeping
in Teslas because I've stopped paying attention to that kind
of stuff because I want to see real data.
It's too much glorified.
It doesn't feel scientific to me.
So I want to know how many people are really sleeping
in Teslas versus sleeping.
I was driving here, sleep deprived,
in a car with no automation.
I was falling asleep.
I agree that it's hypey.
It's just like, you know what?
If Elon put driver monitoring, my last autopilot experience
was I rented a Model 3 in March and drove it around.
The wheel thing is annoying.
And the reason the wheel thing is annoying.
We use the wheel thing as well, but we
don't disengage on wheel.
For Tesla, you have to touch the wheel just enough
to trigger the torque sensor to tell it that you're there,
but not enough as to disengage it, which don't use it
for two things.
Don't disengage on wheel.
You don't have to.
That whole experience, wow, beautifully put.
All those elements, even if you don't have driver monitoring,
that whole experience needs to be better.
Driver monitoring, I think would make,
I mean, I think supercruise is a better experience
once it's engaged over autopilot.
I think supercruise is a transition to engagement
and disengagement are significantly worse.
There's a tricky thing, because if I were to criticize
supercruise, it's a little too crude.
And I think it's like six seconds or something.
If you look off road, it'll start warning you.
It's some ridiculously long period of time.
And just the way, I think it's basically, it's a binary.
It should be adapted.
Yeah, it needs to learn more about you.
It needs to communicate what it sees about you more.
I'm not, you know, Tesla shows what it sees
about the external world.
It would be nice if supercruise would tell us
what it sees about the internal world.
It's even worse than that.
You press the button to engage
and it just says supercruise unavailable.
Yeah, why?
Why?
Yeah, that transparency is good.
We've renamed the driver monitoring packet
to driver state.
Driver state.
We have car state packet, which has the state of the car
and driver state packet, which has state of the driver.
So what is it?
Estimate their BAC.
What's BAC?
Blood alcohol, kind of.
You think that's possible with computer vision?
Absolutely.
It's a, to me, it's an open question.
I haven't looked into too much.
Actually, I quite seriously looked at the literature.
It's not obvious to me that from the eyes and so on,
you can tell.
You might need stuff from the car as well.
You might need how they're controlling the car, right?
And that's fundamentally at the end of the day
what you care about.
But I think, especially when people are really drunk,
they're not controlling the car nearly as smoothly
as they would look at them walking, right?
They're, the car is like an extension of the body.
So I think you could totally detect.
And if you could fix people who are drunk,
distracted, asleep, if you fix those three.
Yeah, that's a huge, that's huge.
So what are the current limitations of OpenPilot?
What are the main problems that still need to be solved?
We're hopefully fixing a few of them in zero six.
We're not as good as autopilot at stop cars.
So if you're coming up to a red light at like 55,
so it's the radar stopped car problem,
which is responsible for two autopilot accidents,
it's hard to differentiate a stopped car
from a like signpost.
Yeah, static object.
So you have to fuse, you have to do this visually.
There's no way from the radar data to tell the difference.
Maybe you can make a map,
but I don't really believe in mapping at all anymore.
Wait, wait, wait, what?
You don't believe in mapping?
No.
So you're basically, the OpenPilot solution is saying,
react to the environment as you see it,
just like human doing beings do.
And then eventually when you want to do navigate
on OpenPilot, I'll train the net to look at ways.
I'll run ways in the background,
I'll train and come down a way.
Are you using GPS at all?
We use it to ground truth.
We use it to very carefully ground truth the paths.
We have a stack which can recover relative
to 10 centimeters over one minute.
And then we use that to ground truth
exactly where the car went in that local part
of the environment, but it's all local.
How are you testing in general?
Just for yourself, like experiments and stuff.
Where are you located?
San Diego.
Yeah.
Okay.
So you basically drive around there,
collect some data and watch the performance?
We have a simulator now and we have,
our simulator is really cool.
Our simulator is not,
it's not like a Unity based simulator.
Our simulator lets us load in real estate.
What do you mean?
We can load in a drive and simulate
what the system would have done on the historical data.
Ooh, nice.
Interesting.
Right now we're only using it for testing,
but as soon as we start using it for training.
That's it.
That's all set up for us.
What's your feeling about the real world versus simulation?
Do you like simulation for training?
If this moves to training?
So we have to distinguish two types of simulators, right?
There's a simulator that like is completely fake.
I could get my car to drive around in GTA.
I feel that this kind of simulator is useless.
You're never, there's so many.
My analogy here is like, okay, fine.
You're not solving the computer vision problem,
but you're solving the computer graphics problem.
Right.
And you don't think you can get very far
by creating ultra realistic graphics?
No, because you can create ultra realistic graphics
or the road, now create ultra realistic behavioral models
of the other cars.
Oh, well, I'll just use myself driving.
No, you won't.
You need real, you need actual human behavior
because that's what you're trying to learn.
The driving does not have a spec.
The definition of driving is what humans do when they drive.
Whatever Waymo does, I don't think it's driving.
Right.
Well, I think actually Waymo and others,
if there's any use for reinforcement learning,
I've seen it used quite well.
I studied pedestrians a lot too,
is try to train models from real data
of how pedestrians move and try to use reinforcement learning
models to make pedestrians move in human like ways.
By that point, you've already gone so many layers,
you detected a pedestrian.
Did you hand code the feature vector of their state?
Right.
Did you guys learn anything from computer vision
before deep learning?
Well, okay, I feel like this is...
So perception to you is the sticking point.
I mean, what's the hardest part of the stack here?
There is no human understandable feature vector
separating perception and planning.
That's the best way I can put that.
There is no...
So it's all together and it's a joint problem.
So you can take localization.
Localization and planning,
there is a human understandable feature vector
between these two things.
I mean, okay, so I have like three degrees position,
three degrees orientation and those derivatives,
maybe those second derivatives, right?
That's human understandable, that's physical.
The between perception and planning.
So like Waymo has a perception stack and then a planner.
And one of the things Waymo does right
is they have a simulator that can separate those two.
They can like replay their perception data
and test their system,
which is what I'm talking about
about like the two different kinds of simulators.
There's the kind that can work on real data
and there's the kind that can't work on real data.
Now, the problem is that I don't think
you can hand code a feature vector, right?
Like you have some list of like,
well, here's my list of cars in the scenes.
Here's my list of pedestrians in the scene.
This isn't what humans are doing.
What are humans doing?
Global.
Some, some.
You're saying that's too difficult to hand engineer.
I'm saying that there is no state vector.
Given a perfect, I could give you the best team
of engineers in the world to build a perception system
and the best team to build a planner.
All you have to do is define the state vector
that separates those two.
I'm missing the state vector that separates those two.
What do you mean?
So what is the output of your perception system?
Output of the perception system.
It's, there's, okay, well, there's several ways to do it.
One is the slam component is localization.
The other is drivable area, drivable space.
Drivable space, yep.
And then there's the different objects in the scene.
Yep.
And different objects in the scene over time maybe
to give you input to then try to start
modeling the trajectories of those objects.
Sure.
That's it.
I can give you a concrete example of something you missed.
What's that?
So say there's a bush in the scene.
Humans understand that when they see this bush
that there may or may not be a car behind that bush.
Drivable area and a list of objects does not include that.
Humans are doing this constantly
at the simplest intersections.
So now you have to talk about occluded area.
Right.
Right, but even that, what do you mean by occluded?
Okay, so I can't see it.
Well, if it's the other side of a house, I don't care.
What's the likelihood that there's a car
in that occluded area, right?
And if you say, okay, we'll add that,
I can come up with 10 more examples that you can't add.
Certainly occluded area would be something
that simulator would have because it's simulating
the entire, you know, occlusion is part of it.
Occlusion is part of a vision stack.
Vision stack.
But what I'm saying is if you have a hand engineered,
if your perception system output can be written
in a spec document, it is incomplete.
Yeah, I mean, I certainly, it's hard to argue with that
because in the end, that's going to be true.
Yeah, and I'll tell you what the output
of our perception system is.
What's that?
It's a 1024 dimensional vector.
Transparent neural net.
Oh, you know that.
No, that's the 1024 dimensions of who knows what.
Because it's operating on real data.
Yeah.
And that's the perception.
That's the perception state, right?
Think about an autoencoder for faces, right?
If you have an autoencoder for faces
and you say it has 256 dimensions in the middle,
and I'm taking a face over here
and projecting it to a face over here.
Can you hand label all 256 of those dimensions?
Well, no, but those are generated automatically.
But even if you tried to do it by hand,
could you come up with a spec between your encoder
and your decoder?
No, no, because it wasn't designed, but they're...
No, no, no, but if you could design it,
if you could design a face reconstructor system,
could you come up with a spec?
No, but I think we're missing here a little bit.
I think you're just being very poetic
about expressing a fundamental problem of simulators,
that they are going to be missing so much
that the feature of actually
would just look fundamentally different
from in the simulated world than the real world.
I'm not making a claim about simulators.
I'm making a claim about the spec division
between perception and planning.
And planning.
Even in your system.
Just in general.
Right, just in general.
If you're trying to build a car that drives,
if you're trying to hand code
the output of your perception system,
like saying, here's a list of all the cars in the scene.
Here's a list of all the people.
Here's a list of the occluded areas.
Here's a vector of drivable areas.
It's insufficient.
And if you start to believe that,
you realize that what Waymo and Cruz are doing is impossible.
Currently, what we're doing is the perception problem
is converting the scene into a chessboard.
And then you reason some basic reasoning
around that chessboard.
And you're saying that really there's a lot missing there.
First of all, why are we talking about this?
Because isn't this a full autonomy?
Is this something you think about?
Oh, I want to win self driving cars.
So your definition of win includes the full five.
I don't think level four is a real thing.
I want to build the AlphaGo of driving.
So AlphaGo is really end to end.
Yeah.
Is, yeah, it's end to end.
And do you think this whole problem,
is that also kind of what you're getting at
with the perception and the planning?
Is that this whole problem, the right way to do it,
is really to learn the entire thing?
I'll argue that not only is it the right way,
it's the only way that's going to exceed human performance.
Well, it's certainly true for Go.
Everyone who tried to hand code Go things
built human inferior things.
And then someone came along and wrote some 10,000 line thing
that doesn't know anything about Go that beat everybody.
It's 10,000 lines.
True, in that sense.
The open question then that maybe I can ask you
is driving is much harder than Go.
The open question is how much harder?
So how, because I think the Elon Musk approach here
with planning and perception is similar
to what you're describing,
which is really turning into not some kind of modular thing,
but really do formulate as a learning problem
and solve the learning problem with scale.
So how many years, put one,
how many years would it take to solve this problem
or just how hard is this freaking problem?
Well, the cool thing is,
I think there's a lot of value
that we can deliver along the way.
I think that you can build lame keeping assist
actually plus adaptive cruise control plus, okay,
looking at ways extends to like all of driving.
Yeah, most of driving, right?
Oh, your adaptive cruise control treats red lights
like cars, okay.
So let's jump around with you mentioned
that you didn't like navigate an autopilot.
What advice, how would you make it better?
Do you think as a feature that if it's done really well,
it's a good feature?
I think that it's too reliant on like hand coded hacks
for like, how does navigate an autopilot do a lane change?
It actually does the same lane change every time
and it feels mechanical.
Humans do different lane changes.
Humans, sometimes we'll do a slow one,
sometimes do a fast one.
Navigate an autopilot at least every time I use it
is it the identical lane change?
How do you learn?
I mean, this is a fundamental thing actually
is the breaking and accelerating,
something that still, Tesla probably does it better
than most cars, but it still doesn't do a great job
of creating a comfortable natural experience
and navigate an autopilot is just lane changes
and extension of that.
So how do you learn to do natural lane change?
So we have it and I can talk about how it works.
So I feel that we have the solution for lateral
but we don't yet have the solution for longitudinal.
There's a few reasons longitudinal is harder than lateral.
The lane change component, the way that we train on it
very simply is like our model has an input
for whether it's doing a lane change or not.
And then when we train the end to end model,
we hand label all the lane changes because you have to.
I've struggled a long time about not wanting to do that
but I think you have to.
Or the training data.
For the training data, right?
We actually have an automatic ground truth
or which automatically labels all the lane changes.
Was that possible?
To automatically label lane changes?
Yeah.
And detect the lane I see when it crosses it, right?
And I don't have to get that high percent accuracy
but it's like 95 good enough.
Okay.
Now I set the bit when it's doing the lane change
in the end to end learning.
And then I set it to zero when it's not doing a lane change.
So now if I want us to do a lane change a test time,
I just put the bit to a one and it'll do a lane change.
Yeah, but so if you look at the space of lane change,
you know some percentage, not a hundred percent,
that we make as humans is not a pleasant experience
because we messed some part of it up.
It's nerve wracking to change.
If you look, you have to see,
it has to accelerate.
How do we label the ones that are natural and feel good?
You know, that's the,
because that's your ultimate criticism,
the current navigate and autopilot just doesn't feel good.
Well, the current navigate and autopilot
is a hand coded policy written by an engineer in a room
who probably went out and tested it a few times on the 280.
Probably a more, a better version of that.
But yes.
That's how we would have written it.
Yeah.
Maybe Tesla did a Tesla, they tested it in.
That might have been two engineers.
Two engineers.
Yeah.
No, but so if you learn the lane change,
if you learn how to do a lane change from data,
just like you have a label that says lane change
and then you put it in when you want it to do the lane change,
it'll automatically do the lane change
that's appropriate for the situation.
Now, to get at the problem of some humans
do bad lane changes,
we haven't worked too much on this problem yet.
It's not that much of a problem in practice.
My theory is that all good drivers are good in the same way
and all bad drivers are bad in different ways.
And we've seen some data to back this up.
Well, beautifully put.
So you just basically, if that's true hypothesis,
then your task is to discover the good drivers.
The good drivers stand out
because they're in one cluster
and the bad drivers are scattered all over the place
and your net learns the cluster.
Yeah.
So you just learn from the good drivers
and they're easy to cluster.
In fact, we learned from all of them
and the net automatically learns the policy
that's like the majority.
But we'll eventually probably have to build some out.
So if that theory is true, I hope it's true
because the counter theory is there is many clusters,
maybe arbitrarily many clusters of good drivers.
Because if there's one cluster of good drivers,
you can at least discover a set of policies.
You can learn a set of policies
which would be good universally.
Yeah.
That would be nice if it's true.
And you're saying that there is some evidence that...
Let's say lane changes can be clustered into four clusters.
Right.
There's a finite level of...
I would argue that all four of those are good clusters.
All the things that are random are noise and probably bad.
And which one of the four you pick?
Or maybe it's 10 or maybe it's 20.
You can learn that.
It's context dependent.
It depends on the scene.
And the hope is it's not too dependent on the driver.
Yeah, the hope is that it all washes out.
The hope is that the distribution is not bimodal.
The hope is that it's a nice Gaussian.
So what advice would you give to Tesla?
How to fix, how to improve, navigate an autopilot?
That's the lessons that you've learned from Kamii.
The only real advice I would give to Tesla
is please put driver monitoring in your cars.
With respect to improving it.
You can't do that anymore.
I started to interrupt.
But there's a practical nature of many of hundreds of thousands
of cars being produced that don't have a good driver facing camera.
The Model 3 has a selfie cam.
Is it not good enough?
Did they not have put IR LEDs for night?
That's a good question.
But I do know that it's fish eye
and it's relatively low resolution.
So it's really not designed.
It wasn't designed for driver monitoring.
You can hope that you can kind of scrape up
and have something from it.
But why didn't they put it in today?
Put it in today.
Every time I've heard Carpathian talk about the problem
and talking about like software 2.0
and how the machine learning is gobbling up everything,
I think this is absolutely the right strategy.
I think that he didn't write and navigate on autopilot.
I think somebody else did and kind of hacked it on top of that stuff.
I think when Carpathian says, wait a second,
why did we hand code this lane change policy
with all these magic numbers?
We're going to learn it from data.
They'll fix it.
They already know what to do there.
Well, that's Andre's job is to turn everything
into a learning problem and collect a huge amount of data.
The reality is, though, not every problem
can be turned into a learning problem in the short term.
In the end, everything will be a learning problem.
The reality is, like if you want to build L5 vehicles today,
it will likely involve no learning.
And that's the reality is, so at which point does learning start?
It's the crutch statement that LiDAR is a crutch.
Which point will learning get up to part of human performance?
It's over human performance on ImageNet, classification,
on driving, it's a question still.
It is a question.
I'll say this, I'm here to play for 10 years.
I'm not here to try to.
I'm here to play for 10 years and make money along the way.
I'm not here to try to promise people
that I'm going to have my L5 taxi network up and working
in two years.
Do you think that was a mistake?
Yes.
What do you think was the motivation behind saying
that other companies are also promising L5 vehicles
with their different approaches in 2020, 2021, 2022?
If anybody would like to bet me that those things do not pan out,
I will bet you.
Even money, even money, I'll bet you as much as you want.
So are you worried about what's going to happen?
Because you're not in full agreement on that.
What's going to happen when 2022, 2021 come around
and nobody has fleets of autonomous vehicles?
Well, you can look at the history.
If you go back five years ago, they
were all promised by 2018 and 2017.
But they weren't that strong of promises.
I mean, Ford really declared.
I think not many have declared as definitively
as they have now these dates.
Well, OK.
So let's separate L4 and L5.
Do I think that it's possible for Waymo
to continue to hack on their system
until it gets to level four in Chandler, Arizona?
Yes.
No safety driver?
Chandler, Arizona?
Yeah.
By which year are we talking about?
Oh, I even think that's possible by like 2020, 2021.
But level four, Chandler, Arizona, not level five,
New York City.
Level four, meaning some very defined streets.
It works out really well.
Very defined streets.
And then practically, these streets are pretty empty.
If most of the streets are covered in Waymos,
Waymo can kind of change the definition of what driving is.
Right?
If your self driving network is the majority
of cars in an area, they only need
to be safe with respect to each other,
and all the humans will need to learn to adapt to them.
Now go drive in downtown New York.
Oh, yeah, that's.
I mean, already.
You can talk about autonomy and like on farms,
it already works great, because you can really just
follow the GPS line.
So what does success look like for Kama AI?
What are the milestones like where
you can sit back with some champagne
and say, we did it, boys and girls?
Well, it's never over.
Yeah, but don't be so.
You must drink champagne every time you celebrate.
So what is good?
What are some wins?
A big milestone that we're hoping for by mid next year
is profitability of the company.
And we're going to have to revisit the idea of selling
a consumer product.
But it's not going to be like the Kama One.
When we do it, it's going to be perfect.
OpenPilot has gotten so much better in the last two years.
We're going to have a few features.
We're going to have 100% driver monitoring.
We're going to disable no safety features in the car.
Actually, I think it'd be really cool what we're doing right
now, our project this week is we're analyzing the data set
and looking for all the AEB triggers
from the manufacturer systems.
We have better data set on that than the manufacturers.
How much does Toyota have 10 million miles of real world
driving to know how many times they're AEB triggered?
So let me give you, because you asked, financial advice.
Because I work with a lot of automakers
and one possible source of money for you,
which I'll be excited to see you take on, is basically
selling the data, which is something that most people,
and not selling in a way where here, here at Automaker,
but creating.
We've done this actually at MIT, not for money purposes,
but you could do it for significant money purposes
and make the world a better place
by creating a consortia where automakers would pay in
and then they get to have free access to the data.
And I think a lot of people are really hungry for that
and would pay significant amount of money for it.
Here's the problem with that.
I like this idea all in theory.
It'd be very easy for me to give them access to my servers.
And we already have all open source tools to access this data.
It's in a great format.
We have a great pipeline.
But they're going to put me in the room
with some business development guy.
And I'm going to have to talk to this guy.
And he's not going to know most of the words I'm saying.
I'm not willing to tolerate that.
OK, Mick Jagger.
No, no, no, no.
But I think I agree with you.
I'm the same way.
But you just tell them the terms
and there's no discussion needed.
If I could just tell them the terms, then like, all right.
Who wants access to my data?
I will sell it to you for, let's say,
you want a subscription?
I'll sell you for 100k a month.
Anyone?
100k a month?
I'll give you access to the data subscription?
Yeah.
Yeah, I think that's kind of fair.
Came up with that number off the top of my head.
If somebody sends me like a three line email where it's like,
we would like to pay 100k a month to get access to your data.
We would agree to like reasonable privacy terms
of the people who are in the data set.
I would be happy to do it.
But that's not going to be the email.
The email is going to be, hey, do you
have some time in the next month where we can sit down
and we can, I don't have time for that.
We're moving too fast.
You could politely respond to that email,
but not saying I don't have any time for your bullshit.
You say, oh, well, unfortunately, these are the terms.
And so this is what we try to, we brought the cost down
for you in order to minimize the friction, the communication.
Yeah, absolutely.
Here's the whatever it is, $1, $2 million a year.
And you have access.
And it's not like I get that email from like,
but OK, am I going to reach out?
Am I going to hire a business development person
who's going to reach out to the automakers?
No way.
Yeah.
OK, I got you.
I admire.
If they reached into me, I'm not
going to ignore the email.
I'll come back with something like, yeah,
if you're willing to pay $100,000 for access to the data,
I'm happy to set that up.
That's worth my engineering time.
That's actually quite insightful of you.
You're right.
Probably because many of the automakers
are quite a bit old school, there
will be a need to reach out.
And they want it, but there will need
to be some communication.
You're right.
Mobileye circa 2015 had the lowest R&D spend of any chipmaker.
Like per, and you look at all the people who work for them,
and it's all business development people
because the car companies are impossible to work with.
Yeah, so you have no patience for that,
and you're a legit Android, huh?
I have something to do, right?
Like, it's not like I don't mean to be a dick and say,
I don't have patience for that, but it's like,
that stuff doesn't help us with our goal of winning
self driving cars.
If I want money in the short term,
if I showed off the actual learning tech that we have,
it's somewhat sad.
It's years and years ahead of everybody else's.
Maybe not Tesla's.
I think Tesla has similar stuff to us, actually.
I think Tesla has similar stuff, but when you compare it
to what the Toyota Research Institute has,
you're not even close to what we have.
No comments, but I also can't.
I have to take your comments.
I intuitively believe you, but I have
to take it with a grain of salt because,
I mean, you are an inspiration because you basically
don't care about a lot of things that other companies care
about.
You don't try to bullshit, in a sense, like make up stuff,
so to drive up valuation.
You're really very real, and you're
trying to solve the problem, and I admire that a lot.
What I don't necessarily fully can't trust you on about your
respect is how good it is, right?
I can only, but I also know how bad others are.
I'll say two things about, trust, but verify, right?
I'll say two things about that.
One is try, get in a 2020 Corolla,
and try OpenPilot 0.6 when it comes out next month.
I think already, you'll look at this,
and you'll be like, this is already really good.
And then, I could be doing that all with hand labelers
and all with the same approach that Mobileye uses.
When we release a model that no longer
has the lanes in it, that only outputs a path,
then think about how we did that machine learning,
and then right away, when you see,
and that's going to be an OpenPilot,
that's going to be an OpenPilot before 1.0,
when you see that model, you'll know
that everything I'm saying is true,
because how else did I get that model?
Good.
You know what I'm saying is true about the simulator.
Yeah, yeah, yeah, this is super exciting.
That's super exciting.
But I listened to your talk with Kyle,
and Kyle was originally building the aftermarket system,
and he gave up on it because of technical challenges,
because of the fact that he's going
to have to support 20 to 50 cars.
We support 45, because what is he
going to do when the manufacturer ABS system triggers?
We have alerts and warnings to deal with all of that
and all the cars, and how is he going to formally verify it?
Well, I got 10 million miles of data.
It's probably better verified than the spec.
Yeah, I'm glad you're here talking to me.
I'll remember this day, because it's interesting.
If you look at Kyle's from Cruise,
I'm sure they have a large number of business development
folks, and he's working with GM.
He could work with Argo AI, worked with Ford.
It's interesting, because chances that you fail businesswise,
like bankrupt, are pretty high.
And yet, it's the Android model,
is you're actually taking on the problem.
So that's really inspiring.
Well, I have a long term way for comedy to make money, too.
And one of the nice things when you really take on the problem,
which is my hope for autopilot, for example,
is things you don't expect, ways to make money,
or create value that you don't expect will pop up.
I've known how to do it since 2017 is the first time I said it.
Which part to know how to do which part?
Our long term plan is to be a car insurance company.
Insurance.
Yeah, I love it.
I make driving twice as safe.
Not only that, I have the best data
such to know who statistically is the safest drivers.
And oh, oh, we see you.
We see you driving unsafely.
We're not going to insure you.
And that causes a bifurcation in the market,
because the only people who can't get common insurance
or the bad drivers, Geico can insure them.
Their premiums are crazy high, our premiums are crazy low.
We win car insurance.
Take over that whole market.
OK, so if we win, if we win, but that's
I'm saying like how do you turn comma into a $10 billion
company is that.
That's right.
So you Elon Musk, who else?
Who else is thinking like this and working like this
in your view?
Who are the competitors?
Are there people seriously?
I don't think anyone that I'm aware of is seriously
taking on lane keeping, like to where it's a huge business that
turns eventually to full autonomy that then creates
other businesses on top of it and so on.
Thinks insurance, thinks all kinds of ideas like that.
Do you know anyone else thinking like this?
Not really.
That's interesting.
I mean, my sense is everybody turns to that in like four
or five years.
Like Ford, once the autonomy doesn't fall through.
But at this time.
Elon's the iOS.
By the way, he paved the way for all of us.
It's not iOS, true.
I would not be doing comma AI today if it was not
for those conversations with Elon.
And if it were not for him saying like,
I think he said like, well, obviously we're not
going to use LiDAR, we use cameras, humans use cameras.
So what do you think about that?
How important is LiDAR?
Everybody else's on L5 is using LiDAR.
What are your thoughts on his provocative statement
that LiDAR is a crutch?
See, sometimes they'll say dumb things like the driver
monitoring thing, but sometimes they'll say absolutely
completely 100% obviously true things.
Of course LiDAR is a crutch.
It's not even a good crutch.
You're not even using it.
They're using it for localization,
which isn't good in the first place.
If you have to localize your car to centimeters
in order to drive, that's not driving.
Currently not doing much machine learning.
I thought LiDAR data, meaning like to help you
in the task of general task of perception.
The main goal of those LiDARs on those cars
I think is actually localization more than perception,
or at least that's what they use them for.
Yeah, that's true.
If you want to localize to centimeters,
you can't use GPS.
The fancies GPS in the world can't do it,
especially if you're under tree cover and stuff.
LiDAR you can do this pretty easily.
So really they're not taking on,
I mean in some research they're using it for perception,
but and they're certainly not, which is sad,
they're not fusing it well with vision.
They do use it for perception.
I'm not saying they don't use it for perception,
but the thing that they have vision based
and radar based perception systems as well.
You could remove the LiDAR and keep around
a lot of the dynamic object perception.
You want to get centimeter accurate localization.
Good luck doing that with anything else.
So what should a cruise Waymo do?
Like what would be your advice to them now?
I mean Waymo is actually, they're serious.
Waymo out of the ball of them,
are quite serious about the long game.
If L5 is a lot, is requires 50 years,
I think Waymo will be the only one left standing at the end
with a given the financial backing that they have.
They're boo Google box.
I'll say nice things about both Waymo and cruise.
Let's do it.
Nice is good.
Waymo is by far the furthest along with technology.
Waymo has a three to five year lead
on all the competitors.
If the Waymo looking stack works,
maybe three year lead.
If the Waymo looking stack works,
they have a three year lead.
Now, I argue that Waymo has spent too much money
to recapitalize, to gain back their losses
in those three years.
Also self driving cars have no network effect like that.
Uber has a network effect.
You have a market, you have drivers and you have riders.
Self driving cars, you have capital and you have riders.
There's no network effect.
If I want to blanket a new city in self driving cars,
I buy the off the shelf Chinese knockoff self driving cars
and I buy enough of them in the city.
I can't do that with drivers.
And that's why Uber has a first mover advantage
that no self driving car company will.
Can you just a thing, let a little bit.
Uber, you're not talking about Uber,
the autonomous vehicle Uber.
You're talking about the Uber cars.
Yeah.
I'm Uber.
I open for business in Austin, Texas, let's say.
I need to attract both sides of the market.
I need to both get drivers on my platform
and riders on my platform.
And I need to keep them both sufficiently happy, right?
Riders aren't going to use it
if it takes more than five minutes for an Uber to show up.
Drivers aren't going to use it
if they have to sit around all day and there's no riders.
So you have to carefully balance a market.
And whenever you have to carefully balance a market,
there's a great first mover advantage
because there's a switching cost for everybody, right?
The drivers and the riders
would have to switch at the same time.
Let's even say that, let's say, Uber shows up.
And Uber somehow agrees to do things at a bigger,
we've done it more efficiently, right?
Uber only takes 5% of a car
instead of the 10% that Uber takes.
No one is going to switch
because the switching cost is higher than that 5%.
So you actually can, in markets like that,
you have a first mover advantage.
Yeah.
Autonomous vehicles of the level five variety
have no first mover advantage.
If the technology becomes commoditized,
say I want to go to a new city, look at the scooters.
It's going to look a lot more like scooters.
Every person with a checkbook
can blanket a city in scooters
and that's why you have 10 different scooter companies.
Which one's going to win?
It's a race to the bottom.
It's a terrible market to be in
because there's no market for scooters.
And the scooters don't get a say
in whether they want to be bought
and deployed to a city or not.
Right.
So yeah.
We're going to entice the scooters with subsidies
and deals.
So whenever you have to invest that capital,
it doesn't...
It doesn't come back.
Yeah.
They can't be your main criticism of the Waymo approach.
Oh, I'm saying even if it does technically work.
Even if it does technically work, that's a problem.
Yeah.
I don't know if I were to say, I would say,
you're already there.
I haven't even thought about that.
But I would say the bigger challenge
is the technical approach.
So Waymo's cruise is...
And not just the technical approach,
but of creating value.
I still don't understand how you beat Uber,
the human driven cars.
In terms of financially,
it doesn't make sense to me
that people want to get an autonomous vehicle.
I don't understand how you make money.
In the long term, yes, like real long term,
but it just feels like there's too much
capital investment needed.
Oh, and they're going to be worse than Ubers
because they're going to stop
for every little thing everywhere.
I'll say a nice thing about cruise.
That was my nice thing about Waymo.
They're three years ahead of me.
It was a nice...
Oh, because they're three years.
They're three years technically ahead of everybody.
Their tech stack is great.
My nice thing about cruise is GM buying them
was a great move for GM.
For $1 billion,
GM bought an insurance policy against Waymo.
They put cruise is three years behind Waymo.
That means Google will get a monopoly
on the technology for at most three years.
And if technology works,
you might not even be right about the three years.
It might be less.
Might be less.
Cruise actually might not be that far behind.
I don't know how much Waymo has waffled around
or how much of it actually is just that long tail.
Yeah, okay.
If that's the best you could say in terms of nice things,
that's more of a nice thing for GM
that that's a smart insurance policy.
It's a smart insurance policy.
I mean, I think that's how...
I can't see cruise working out any other.
For cruise to leapfrog Waymo would really surprise me.
Yeah, so let's talk about the underlying assumptions
of everything is...
We're not gonna leapfrog Tesla.
Tesla would have to seriously mess up
for us to leapfrog them.
Okay, so the way you leapfrog, right,
is you come up with an idea
or you take a direction, perhaps secretly,
that the other people aren't taking.
And so cruise, Waymo, even Aurora...
I don't know, Aurora, Zooks is the same stack as well.
They're all the same code base even.
They're all the same DARPA Urban Challenge code base.
It's...
So the question is, do you think there's a room
for brilliance and innovation there
that will change everything?
Like say, okay, so I'll give you examples.
It could be if revolution and mapping, for example,
that allow you to map things,
do HD maps of the whole world,
all weather conditions somehow really well,
or revolution and simulation,
to where all the way you said before becomes incorrect.
That kind of thing.
Any room for breakthrough innovation?
What I said before about,
oh, they actually get the whole thing, well,
I'll say this about we divide driving into three problems.
And I actually haven't solved the third yet,
but I haven't had any idea how to do it.
So there's the static.
The static driving problem is assuming
you are the only car on the road, right?
And this problem can be solved 100%
with mapping and localization.
This is why farms work the way they do.
If all you have to deal with is the static problem,
and you can statically schedule your machines, right?
It's the same as like statically scheduling processes.
You can statically schedule your tractors
to never hit each other on their paths, right?
Because then you know the speed they go at.
So that's the static driving problem.
Maps only helps you with the static driving problem.
Yeah, the question about static driving,
you've just made it sound like it's really easy.
Static driving is really easy.
How easy?
How, well, because the whole drifting out of lane,
when Tesla drifts out of lane,
it's failing on the fundamental static driving problem.
Tesla is drifting out of lane?
The static driving problem is not easy for the world.
The static driving problem is easy for one route.
One route in one weather condition
with one state of lane markings
and like no deterioration, no cracks in the road.
Well, I'm assuming you have a perfect localizer.
So that's all for the weather condition
and the lane marking condition.
But that's the problem.
How do you have a perfect localizer?
You can build, perfect localizers are not that hard to build.
Okay, come on now, with LIDAR.
LIDAR, yeah.
With LIDAR, okay.
LIDAR, yeah, but you use LIDAR, right?
Like you use LIDAR, build a perfect localizer.
Building a perfect localizer without LIDAR,
it's gonna be hard.
You can get 10 centimeters without LIDAR,
you can get one centimeter with LIDAR.
I'm not even concerned about the one or 10 centimeters.
I'm concerned if every once in a while you just weigh off.
Yeah, so this is why you have to carefully
make sure you're always tracking your position.
You wanna use LIDAR camera fusion,
but you can get the reliability of that system
up to 100,000 miles
and then you write some fallback condition
where it's not that bad if you're way off, right?
I think that you can get it to the point,
it's like ASL D that you're never in a case
where you're way off and you don't know it.
Yeah, okay, so this is brilliant.
So that's the static.
Static.
We can, especially with LIDAR and good HD maps,
you can solve that problem.
It's easy.
The static, the static problem is so easy.
It's very typical for you to say something's easy.
I got it.
It's not as challenging as the other ones, okay.
Well, okay, maybe it's obvious how to solve it.
The third one's the hardest.
And a lot of people don't even think about the third one
and even see it as different from the second one.
So the second one is dynamic.
The second one is like, say there's an obvious example,
it's like a car stopped at a red light, right?
You can't have that car in your map
because you don't know whether that car
is gonna be there or not.
So you have to detect that car in real time
and then you have to do the appropriate action, right?
Also, that car is not a fixed object.
That car may move and you have to predict
what that car will do, right?
So this is the dynamic problem.
Yeah.
So you have to deal with this.
This involves, again, like you're gonna need models
of other people's behavior.
Do you, are you including in that?
I don't wanna step on the third one.
Oh, but are you including in that your influence
on people?
Ah, that's the third one.
Okay.
That's the third one.
We call it the counterfactual.
Yeah, brilliant.
And that.
I just talked to Judea Pro who's obsessed
with counterfactuals.
Counterfactual, oh yeah, yeah, I read his books.
So the static and the dynamic are our approach right now
for lateral will scale completely to the static and dynamic.
The counterfactual, the only way I have to do it yet,
the thing that I wanna do once we have all of these cars
is I wanna do reinforcement learning on the world.
I'm always gonna turn the exploiter up to max.
I'm not gonna have them explore.
But the only real way to get at the counterfactual
is to do reinforcement learning
because the other agents are humans.
So that's fascinating that you break it down like that.
I agree completely.
I've spent my life thinking about this problem.
This is beautiful.
And part of it, cause you're slightly insane,
because not my life, just the last four years.
No, no, you have some non zero percent of your brain
has a madman in it, which is a really good feature.
But there's a safety component to it
that I think when there's sort of
with counterfactuals and so on,
that would just freak people out.
How do you even start to think about this in general?
I mean, you've had some friction with NHTSA and so on.
I am frankly exhausted by safety engineers.
The prioritization on safety over innovation
to a degree where it kills, in my view,
kills safety in the longterm.
So the counterfactual thing,
they just actually exploring this world
of how do you interact with dynamic objects and so on?
How do you think about safety?
You can do reinforcement learning without ever exploring.
And I said that, like,
so you can think about your, in like reinforcement learning,
it's usually called like a temperature parameter.
And your temperature parameter
is how often you deviate from the argmax.
I could always set that to zero and still learn.
And I feel that you'd always want that set to zero
on your actual system.
Gotcha.
But the problem is you first don't know very much
and so you're going to make mistakes.
So the learning, the exploration happens through mistakes.
We're all ready, yeah, but.
Okay, so the consequences of a mistake.
OpenPilot and Autopilot are making mistakes left and right.
We have 700 daily active users,
1,000 weekly active users.
OpenPilot makes tens of thousands of mistakes a week.
These mistakes have zero consequences.
These mistakes are,
oh, I wanted to take this exit and it went straight.
So I'm just going to carefully touch the wheel.
The humans catch them.
And the human disengagement is labeling
that reinforcement learning in a completely
consequence free way.
So driver monitoring is the way you ensure they keep.
Yes.
They keep paying attention.
How's your messaging?
Say I gave you a billion dollars,
so you would be scaling it now.
Oh, if I could scale, I couldn't scale with any amount of money.
I'd raise money if I could, if I had a way to scale it.
Yeah, you're not, no, I'm not focused on scale.
I don't know how to do.
Oh, like, I guess I could sell it to more people,
but I want to make the system better.
Better, better.
And I don't know how to.
But what's the messaging here?
I got a chance to talk to Elon.
And he basically said that the human factor doesn't matter.
You know, the human doesn't matter
because the system will perform.
There'll be sort of a, sorry to use the term,
but like a singular, like a point
where it gets just much better.
And so the human, it won't really matter.
But it seems like that human catching the system
when it gets into trouble is like the thing
which will make something like reinforcement learning work.
So how do you, how do you think messaging for Tesla,
for you, for the industry in general, should change?
I think my messaging is pretty clear,
at least like our messaging wasn't that clear
in the beginning and I do kind of fault myself for that.
We are proud right now to be a level two system.
We are proud to be level two.
If we talk about level four,
it's not with the current hardware.
It's not going to be just a magical OTA upgrade.
It's going to be new hardware.
It's going to be very carefully thought out right now.
We are proud to be level two.
And we have a rigorous safety model.
I mean, not like, like, okay, rigorous.
Who knows what that means?
But we at least have a safety model
and we make it explicit.
It's in safety.md and open pilot.
And it says, seriously though.
Safety.md.
This is really, this is so Android.
So, well, this is, this is the safety model
and I like to have conversations like if, like, you know,
sometimes people will come to you and they're like,
your system's not safe.
Okay.
Have you read my safety docs?
Would you like to have an intelligent conversation
about this?
And the answer is always no.
They just like scream about, it runs Python.
Okay. What?
So you're saying that, that because Python's not real time,
Python not being real time never causes disengagement.
Disengagement's are caused by, you know, the model is QM.
But safety.md says the following.
First and foremost,
the driver must be paying attention at all times.
I don't consider, I do,
I still consider the software to be alpha software
until we can actually enforce that statement.
But I feel it's very well communicated to our users.
Two more things.
One is the user must be able to easily take control
of the vehicle at all times.
So if you step on the gas or brake with open pilot,
it gives full manual control back to the user
or press the cancel button.
Step two, the car will never react so quickly.
We define so quickly to be about one second
that you can't react in time.
And we do this by enforcing torque limits,
braking limits and acceleration limits.
So we have like our torque limits way lower than Tesla's.
This is another potential.
If I could tweak autopilot,
I would lower their torque limit
and I would add driver monitoring.
Because autopilot can jerk the wheel hard.
Open pilot can't.
It's, we limit and all this code is open source, readable.
And I believe now it's all MISRA C compliant.
What's that mean?
MISRA is like the automotive coding standard.
At first, I've come to respect,
I've been reading like the standards lately
and I've come to respect them.
They're actually written by very smart people.
Yeah, they're brilliant people actually.
They have a lot of experience.
They're sometimes a little too cautious,
but in this case, it pays off.
MISRA is written by like computer scientists
and you can tell by the language they use.
You can tell by the language they use.
They talk about like whether certain conditions in MISRA
are decidable or undecidable.
And you mean like the halting problem?
And yes, all right, you've earned my respect.
I will read carefully what you have to say
and we want to make our code compliant with that.
All right, so you're proud level two, beautiful.
So you were the founder and I think CEO of Kama AI,
then you were the head of research.
What the heck are you now?
What's your connection to Kama AI?
I'm the president, but I'm one of those like
unelected presidents of like a small dictatorship country,
not one of those like elected presidents.
Oh, so you're like Putin when he was like the, yeah,
I got you.
So there's, what's the governance structure?
What's the future of Kama AI finance?
I mean, yeah, as a business, do you want,
are you just focused on getting things right now,
making some small amount of money in the meantime
and then when it works, it works a new scale.
Our burn rate is about 200 K a month
and our revenue is about 100 K a month.
So we need to forex our revenue,
but we haven't like tried very hard at that yet.
And the revenue is basically selling stuff online.
Yeah, we sell stuff shop.com.ai.
Is there other, well, okay.
So you'll have to figure out.
That's our only, see, but to me,
that's like respectable revenues.
We make it by selling products to consumers
for honest and transparent about what they are.
Most actually level four companies, right?
Cause you could easily start blowing up like smoke,
like overselling the hype and feeding into,
getting some fundraisers.
Oh, you're the guy, you're a genius
because you hacked the iPhone.
Oh, I hate that.
I hate that.
Yeah, I can trade my social capital for more money.
I did it once.
I almost regret it doing it the first time.
Well, on a small tangent,
what's your, you seem to not like fame
and yet you're also drawn to fame.
What's, where have you on, where are you on that currently?
Have you had some introspection, some soul searching?
Yeah.
I actually, I've come to a pretty stable position on that.
Like after the first time,
I realized that I don't want attention from the masses.
I want attention from people who I respect.
Who do you respect?
I can give a list of people.
So are these like Elon Musk type characters?
Yeah.
Actually, you know what?
I'll make it more broad than that.
I won't make it about a person.
I respect skill.
I respect people who have skills, right?
And I would like to like be,
I'm not gonna say famous,
but be like known among more people
who have like real skills.
Who in cars, do you think have skill?
Not do you respect?
Oh, Kyle Voat has skill.
A lot of people at Waymo have skill.
And I respect them.
I respect them as engineers.
Like I can think, I mean,
I think about all the times in my life
where I've been like dead set on approaches
and they turn out to be wrong.
Yeah.
So I mean, this might, I might be wrong.
I accept that, I accept that there's a decent chance
that I'm wrong.
And actually, I mean, having talked to Chris Armson,
Sterling Anderson, those guys,
I mean, I deeply respect Chris.
I just admire the guy.
He's legit.
When you drive a car through the desert
when everybody thinks it's impossible, that's legit.
And then I also really respect the people
who are like writing the infrastructure of the world,
like the Linus Torvalds and the Chris Ladin.
Oh yeah, they were doing the real work.
I know they're doing the real work.
Having talked to Chris Ladin,
you realize, especially when they're humble,
it's like, you realize, oh, you guys,
we're just using your...
Oh yeah.
All the hard work that you did.
Yeah, that's incredible.
What do you think, Mr. Anthony Lewandowski?
What do you, he's a, he's another mad genius.
Sharp guy.
Oh yeah.
What, do you think he might long term become a competitor?
Oh, to comma?
Well, so I think that he has the other right approach.
I think that right now, there's two right approaches.
One is what we're doing and one is what he's doing.
Can you describe, I think it's called Pronto AI,
he's starting using, do you know what the approach is?
I actually don't know.
Embark is also doing the same sort of thing.
The idea is almost that you want to,
so if you're, I can't partner with Honda and Toyota.
Honda and Toyota are like 400,000 person companies.
It's not even a company at that point.
Like I don't think of it like, I don't personify it.
I think of it like an object, but a trucker drives for a fleet.
Maybe that has like, some truckers are independent.
Some truckers drive for fleets with a hundred trucks.
There are tons of independent trucking companies out there.
Start a trucking company and drive your costs down
or figure out how to drive down the cost of trucking.
Another company that I really respect is Nauto.
Actually, I respect their business model.
Nauto sells a driver monitoring camera
and they sell it to fleet owners.
If I owned a fleet of cars and I could pay 40 bucks a month
to monitor my employees,
this is gonna like reduces accidents 18%.
It's so like that in the space,
that is like the business model that I like most respect
because they're creating value today.
Yeah, which is, that's a huge one.
How do we create value today with some of this?
And the length keeping thing is huge.
And it sounds like you're creeping in
or full steam ahead on the drive of monitoring too.
Which I think actually where the short term value,
if you can get right.
I still, I'm not a huge fan of the statement
that everything is to have drive of monitoring.
I agree with that completely,
but that statement usually misses the point
that to get the experience of it right is not trivial.
Oh, no, not at all.
In fact, like, so right now we have,
I think the timeout depends on speed of the car,
but we want to depend on like the scene state.
If you're on like an empty highway,
it's very different if you don't pay attention
than if like you're like coming up to a traffic light.
And long term, it should probably learn from the driver
because that's to do, I watched a lot of video.
We've built a smartphone detector
just to analyze how people are using smartphones
and people are using it very differently.
And there's this, it's a texting styles.
We haven't watched nearly enough of the videos.
We haven't, I got millions of miles
of people driving cars.
In this moment, I spent a large fraction of my time
just watching videos because it's never fails to learn.
Like I've never failed from a video watching session
to learn something I didn't know before.
In fact, I usually, like when I eat lunch, I'll sit,
especially when the weather is good
and just watch pedestrians.
With an eye to understand like from a computer vision eye,
just to see, can this model, can you predict
what are the decisions made?
And there's so many things that we don't understand.
This is what I mean about state vector.
Yeah, it's, I'm trying to always think like,
because I'm understanding in my human brain,
how do we convert that into,
how hard is the learning problem here?
I guess is the fundamental question.
So something that's from a hacking perspective,
this is always comes up, especially with folks.
Well, first, the most popular question is
the trolley problem, right?
So that's not a sort of a serious problem.
There are some ethical questions, I think that arise.
Maybe you wanna, do you think there's any ethical,
serious ethical questions?
We have a solution to the trolley problem at com.ai.
Well, so there is actually an alert
in our code, ethical dilemma detected.
It's not triggered yet.
We don't know how yet to detect the ethical dilemmas,
but we're a level two system.
So we're going to disengage and leave
that decision to the human.
You're such a troll.
No, but the trolley problem deserves to be trolled.
Yeah, that's a beautiful answer actually.
I know, I gave it to someone who was like,
sometimes people ask like you asked about the trolley problem.
Like you can have a kind of discussion about it.
Like when you get someone who's like really like
earnest about it, because it's the kind of thing where
if you ask a bunch of people in an office,
whether we should use a SQL stack or no SQL stack,
if they're not that technical, they have no opinion.
But if you ask them what color they want to paint the office,
everyone has an opinion on that.
And that's why the trolley problem is.
I mean, that's a beautiful answer.
Yeah, we're able to detect the problem
and we're able to pass it on to the human.
Wow, I've never heard anyone say it.
This is your nice escape route.
Okay, but...
Proud level two.
I'm proud level two.
I love it.
So the other thing that people have some concern about
with AI in general is hacking.
So how hard is it, do you think, to hack an autonomous vehicle
either through physical access or through the more sort of
popular now, these adversarial examples on the sensors?
Okay, the adversarial examples one.
You want to see some adversarial examples
that affect humans, right?
Oh, well, there used to be a stop sign here,
but I put a black bag over the stop sign
and then people ran it, adversarial, right?
Like, there's tons of human adversarial examples too.
The question in general about security, if you saw,
something just came out today and there are always
such hypey headlines about how navigate on autopilot
was fooled by a GPS spoof to take an exit.
Right.
At least that's all they could do was take an exit.
If your car is relying on GPS in order
to have a safe driving policy, you're doing something wrong.
If you're relying, and this is why V2V
is such a terrible idea, V2V now relies on both parties
getting communication right.
This is not even, so I think of safety,
security is like a special case of safety, right?
Safety is like we put a little, you know,
piece of caution tape around the hole
so that people won't walk into it by accident.
Security is like put a 10 foot fence around the hole
so you actually physically cannot climb into it
with barbed wire on the top and stuff, right?
So like if you're designing systems
that are like unreliable, they're definitely not secure.
Your car should always do something safe
using its local sensors.
And then the local sensor should be hardwired.
And then could somebody hack into your can boss
and turn your steering wheel on your brakes?
Yes, but they could do it before comma AI too, so.
Let's think out of the box and some things.
So do you think teleoperation has a role in any of this?
So remotely stepping in and controlling the cars?
No, I think that if the safety operation
by design requires a constant link to the cars,
I think it doesn't work.
So that's the same argument used for V2I, V2V.
Well, there's a lot of non safety critical stuff
you can do with V2I.
I like V2I, I like V2I way more than V2V
because V2I is already like,
I already have internet in the car, right?
There's a lot of great stuff you can do with V2I.
Like for example, you can,
well, where I already have V2V, Waze is V2I, right?
Waze can route me around traffic jams.
That's a great example of V2I.
And then, okay, the car automatically talks
to that same service, like it works.
So it's improving the experience,
but it's not a fundamental fallback for safety.
No, if any of your things that require
wireless communication are more than QM,
like have an ASL rating, you shouldn't.
You previously said that life is work
and that you don't do anything to relax.
So how do you think about hard work?
Well, what is it?
What do you think it takes to accomplish great things?
And there's a lot of people saying
that there needs to be some balance.
You know, you need to,
in order to accomplish great things,
you need to take some time off,
you need to reflect and so on.
And then some people are just insanely working,
burning the candle on both ends.
How do you think about that?
I think I was trolling in the Suraj interview
when I said that off camera,
but right before I spoke to a little bit of weed,
like, you know, come on, this is a joke, right?
Like I do nothing to relax.
Look where I am, I'm at a party, right?
Yeah, yeah, yeah, sure.
That's true.
So no, no, of course I don't.
When I say that life is work though,
I mean that like, I think that
what gives my life meaning is work.
I don't mean that every minute of the day
you should be working.
I actually think this is not the best way
to maximize results.
I think that if you're working 12 hours a day,
you should be working smarter and not harder.
Well, so it gives work gives you meaning
for some people, other sorts of meaning
is personal relationships, like family and so on.
You've also in that interview with Suraj
or the trolling mentioned that one of the things
you look forward to in the future is AI girlfriends.
Yes.
So that's a topic that I'm very much fascinated by,
not necessarily girlfriends,
but just forming a deep connection with AI.
Yeah.
What kind of system do you imagine
when you say AI girlfriend,
whether you were trolling or not?
No, that one I'm very serious about.
And I'm serious about that on both a shallow level
and a deep level.
I think that VR brothels are coming soon
and are gonna be really cool.
It's not cheating if it's a robot.
I see the slogan already.
Um, but...
There's a, I don't know if you've watched
or just watched the Black Mirror episode.
I watched the latest one, yeah.
Yeah, yeah.
Oh, the Ashley 2 one?
Or the...
No, where there's two friends
who were having sex with each other in...
Oh, in the VR game.
In the VR game, it's the two guys,
but one of them was a female, yeah.
Yeah, the...
Which is another mind blowing concept.
That in VR, you don't have to be the form.
You can be two animals having sex, it's weird.
I mean, I'll see how nice
that the software maps the nerve endings, right?
Yeah, it's weird.
I mean, yeah, they sweep a lot of the fascinating,
really difficult technical challenges under the rug,
like assuming it's possible
to do the mapping of the nerve endings, then...
I wish, yeah, I saw that.
The way they did it with the little like stim unit
on the head, that'd be amazing.
So, well, no, no, on a shallow level,
like you could set up like almost a brothel
with like real dolls and Oculus quests,
write some good software.
I think it'd be a cool novelty experience.
But no, on a deeper, like emotional level.
I mean, yeah, I would really like to fall in love
with the machine.
Do you see yourself having a long term relationship
of the kind monogamous relationship that we have now
with the robot, with the AI system, even?
Not even just the robot.
So, I think about maybe my ideal future.
When I was 15, I read Eliezer Yudkowsky's early writings
on the singularity and like that AI
is going to surpass human intelligence massively.
He made some Moore's law based predictions
that I mostly agree with.
And then I really struggled
for the next couple of years of my life.
Like, why should I even bother to learn anything?
It's all gonna be meaningless when the machine show up.
Right.
Well, maybe when I was that young,
I was still a little bit more pure
and really like clung to that.
And then I'm like, well, the machine's ain't here yet.
You know, and I seem to be pretty good at this stuff.
Let's try my best, you know,
like what's the worst that happens?
But the best possible future I see
is me sort of merging with the machine.
And the way that I personify this
is in a longterm and augments relationship with the machine.
Oh, you don't think there's room
for another human in your life
if you really truly merge with another machine?
I mean, I see merging.
I see like the best interface to my brain
is like the same relationship interface
to merge with an AI, right?
What does that merging feel like?
I've seen couples who've been together for a long time
and like, I almost think of them as one person.
Like couples who spend all their time together and...
That's fascinating.
You're actually putting,
what does that merging actually looks like?
It's not just a nice channel.
Like a lot of people imagine it's just an efficient link,
search link to Wikipedia or something.
I don't believe in that.
But it's more, you're saying that there's the same kind of,
the same kind of relationship you have with another human
as a deep relationship is that's what merging looks like.
That's pretty...
I don't believe that link is possible.
I think that that link, so you're like,
oh, I'm gonna download Wikipedia right to my brain.
My reading speed is not limited by my eyes.
My reading speed is limited by my inner processing loop.
And to like bootstrap that
sounds kind of unclear how to do it and horrifying.
But if I am with somebody, and I'll use somebody
who is making a super sophisticated model of me
and then running simulations on that model,
I'm not gonna get into the question
whether the simulations are conscious or not.
I don't really wanna know what it's doing.
But using those simulations to play out hypothetical futures
for me, deciding what things to say to me
to guide me along a path and that's how I envision it.
So on that path to AI of super human level intelligence,
you've mentioned that you believe in the singularity,
that singularity is coming.
Again, could be trolling, could be not, could be part...
All trolling has truth in it.
I don't know what that means anymore.
What is the singularity?
So yeah, so that's really the question.
How many years do you think before the singularity
of what form do you think it will take?
Does that mean fundamental shifts in capabilities of AI?
Does it mean some other kind of ideas?
Maybe that's just my roots, but...
So I can buy a human being's worth of computers
for things worth of compute for like a million bucks a day.
It's about one TPU pod V3.
I want like, I think they claim a hundred pay to flops.
That's being generous.
I think humans are actually more like 20.
So that's like five humans.
That's pretty good.
Google needs to sell their TPUs.
But no, I could buy GPUs.
I could buy a stack of like, I buy 1080TIs,
build data center full of them.
And for a million bucks, I can get a human worth of compute.
But when you look at the total number of flops in the world,
when you look at human flops,
which goes up very, very slowly with the population,
and machine flops, which goes up exponentially,
but it's still nowhere near.
I think that's the key thing
to talk about when the singularity happened.
When most flops in the world are silicon
and not biological, that's kind of the crossing point.
Like they are now the dominant species on the planet.
And just looking at how technology is progressing,
when do you think that could possibly happen?
Do you think it would happen in your lifetime?
Oh yeah, definitely in my lifetime.
I've done the math.
I like 2038 because it's the UNIX timestamp roll over.
Yeah, beautifully put.
So you've said that the meaning of life is to win.
If you look five years into the future,
what does winning look like?
So...
I can go into technical depth to what I mean by that, to win.
It may not mean...
I was criticized for that in the comments.
Like, doesn't this guy want to save the penguins in Antarctica?
Or like, oh man, listen to what I'm saying.
I'm not talking about like I have a yacht or something.
I am an agent.
I am put into this world.
And I don't really know what my purpose is.
But if you're a reinforcement, if you're an intelligent agent
and you're put into a world, what is the ideal thing to do?
Well, the ideal thing, mathematically,
you can go back to like Schmidt Hoover theories about this,
is to build a compressive model of the world.
To build a maximally compressive to explore the world
such that your exploration function maximizes
the derivative of compression of the past.
Schmidt Hoover has a paper about this.
And like, I took that kind of as like a personal goal function.
So what I mean to win, I mean like,
maybe this is religious, but like I think that in the future
I might be given a real purpose.
Or I may decide this purpose myself.
And then at that point, now I know what the game is
and I know how to win.
I think right now I'm still just trying to figure out what the game is.
But once I know...
So you have...
You have imperfect information.
You have a lot of uncertainty about the reward function
and you're discovering it.
Exactly.
But the purpose is...
That's a better way to put it.
The purpose is to maximize it
while you have a lot of uncertainty around it.
And you're both reducing the uncertainty
and maximizing at the same time.
And so that's at the technical level.
What is the...
If you believe in the universal prior,
what is the universal reward function?
That's the better way to put it.
So that win is interesting.
I think I speak for everyone in saying that
I wonder what that reward function is for you.
And I look forward to seeing that in five years and ten years.
I think a lot of people including myself are cheering you on, man.
So I'm happy you exist.
And I wish you the best of luck.
Thanks for talking today, man.
Thank you.
This was a lot of fun.