Elon Musk: Tesla Autopilot
嘉宾：Elon Musk
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§01Transcript · 00:00–09:27
The following is a conversation with Elon Musk.
He's the CEO of Tesla, SpaceX, Neuralink,
and a co-founder of several other companies.
This conversation is part
of the Artificial Intelligence podcast.
The series includes leading researchers
in academia and industry,
including CEOs and CTOs of automotive, robotics,
AI, and technology companies.
This conversation happened after the release
of the paper from our group at MIT
on driver functional vigilance
during use of Tesla's autopilot.
The Tesla team reached out to me,
offering a podcast conversation with Mr. Musk.
I accepted with full control of questions I could ask
and the choice of what is released publicly.
I ended up editing out nothing of substance.
I've never spoken with Elon before this conversation,
publicly or privately.
Neither he nor his companies have any influence
on my opinion, nor on the rigor and integrity
of the scientific method that I practice
in my position at MIT.
Tesla has never financially supported my research,
and I've never owned a Tesla vehicle.
I've never owned Tesla stock.
This podcast is not a scientific paper.
It is a conversation.
I respect Elon as I do all other leaders
and engineers I've spoken with.
We agree on some things and disagree on others.
My goal is always with these conversations
is to understand the way the guest sees the world.
One particular point of disagreement in this conversation
was the extent to which camera-based driver monitoring
will improve outcomes, and for how long
it will remain relevant for AI-assisted driving.
As someone who works on and is fascinated
by human-centered artificial intelligence,
I believe that if implemented and integrated effectively,
camera-based driver monitoring is likely to be of benefit
in both the short-term and the long-term.
In contrast, Elon and Tesla's focus
is on the improvement of autopilot
such that its statistical safety benefits
override any concern of human behavior and psychology.
Elon and I may not agree on everything,
but I deeply respect the engineering and innovation
behind the efforts that he leads.
My goal here is to catalyze a rigorous, nuanced,
and objective discussion in industry and academia
on AI-assisted driving, one that ultimately makes
for a safer and better world.
And now, here's my conversation with Elon Musk.
What was the vision, the dream of autopilot
when, in the beginning, the big picture system level
when it was first conceived and started being installed
in 2014, the hardware and the cars?
What was the vision, the dream?
- I wouldn't characterize it as a vision or dream,
simply that there are obviously two massive revolutions
in the automobile industry.
One is the transition to electrification,
and then the other is autonomy.
And it became obvious to me that in the future,
any car that did not have autonomy
would be about as useful as a horse.
Which is not to say that there's no use, it's just rare,
and somewhat idiosyncratic if somebody
has a horse at this point.
It's just obvious that cars will drive themselves completely,
it's just a question of time.
And if we did not participate in the autonomy revolution,
then our cars would not be useful to people
relative to cars that are autonomous.
I mean, an autonomous car is arguably worth
five to 10 times more than a car which is not autonomous.
- In the long term?
- Depends what you mean by long term,
but let's say at least for the next five years,
perhaps 10 years.
- So there are a lot of very interesting design choices
with Autopilot early on.
First is showing on the instrument cluster,
or in the Model 3 on the center stack display,
what the combined sensor suite sees.
What was the thinking behind that choice?
Was there debate?
What was the process?
- The whole point of the display is to provide
a health check on the vehicle's perception of reality.
So the vehicle's taking in information
from a bunch of sensors, primarily cameras,
but also radar and ultrasonics, GPS, and so forth.
And then that information is then rendered
into vector space, and that, you know,
with a bunch of objects, with properties,
like lane lines and traffic lights and other cars,
and then in vector space, that is re-rendered
onto a display so you can confirm whether the car
knows what's going on or not by looking out the window.
- Right, I think that's an extremely powerful thing
for people to get an understanding,
so to become one with the system
and understanding what the system is capable of.
Now, have you considered showing more?
So if we look at the computer vision,
you know, like road segmentation, lane detection,
vehicle detection, object detection underlying the system,
there is, at the edges, some uncertainty.
Have you considered revealing the parts,
the uncertainty in the system, the sort of--
- Probabilities associated with, say,
image recognition or something like that?
- Yeah, so right now it shows, like,
the vehicles in the vicinity, a very clean, crisp image,
and people do confirm that there's a car in front of me
and the system sees there's a car in front of me,
but to help people build an intuition
of what computer vision is
by showing some of the uncertainty.
- Well, I think it's, in my car,
I always look at the sort of the debug view,
and there's two debug views.
One is augmented vision, which I'm sure you've seen,
where it's basically, we draw boxes and labels
around objects that are recognized,
and then there's what we call the visualizer,
which is basically a vector space representation
summing up the input from all sensors.
That does not show any pictures,
but it shows all of the,
it basically shows the car's view
of the world in vector space.
But I think this is very difficult for people to,
normal people to understand.
They would not know what they're looking at.
- So it's almost an HMI challenge
to the current things that are being displayed
is optimized for the general public understanding
of what the system's capable of.
- It's like if you have no idea
how computer vision works or anything,
you can still look at the screen
and see if the car knows what's going on.
And then if you're a development engineer,
or if you have the development build like I do,
then you can see all the debug information.
But those would just be total jerk to most people.
- What's your view on how to best distribute effort?
So there's three, I would say,
technical aspects of autopilot that are really important.
So it's the underlying algorithms,
like the neural network architecture,
there's the data, so that's trained on,
and then there's the hardware development.
There may be others, but,
so look, algorithm, data, hardware.
You only have so much money, only have so much time.
What do you think is the most important thing
to allocate resources to?
Or do you see it as pretty evenly distributed
between those three?
- We automatically get fast amounts of data
because all of our cars have
eight external facing cameras and radar
and usually 12 ultrasonic sensors,
GPS, obviously, and IMU.
And so we basically have a fleet that has,
we've got about 400,000 cars on the road
that have that level of data.
I think you keep quite close track of it, actually.
- Yes.
- Yeah, so we're approaching half a million cars
on the road that have the full sensor suite.
So this is, I'm not sure how many other cars on the road
have the sensor suite,
but I'd be surprised if it's more than 5,000,
which means that we have 99% of all the data.
- So there's this huge inflow of data.
- Absolutely, massive inflow of data.
And then it's taken us about three years,
but now we've finally developed
our full self-driving computer,
which can process an order of magnitude
as much as the NVIDIA system
that we currently have in the cars.
And it's really just to use it,
you unplug the NVIDIA computer
and plug the Tesla computer in, and that's it.
And it's, in fact, we're not even,
we're still exploring the boundaries of its capabilities,
but we're able to run the cameras at full frame rate,
full resolution, not even crop the images,
and it's still got headroom, even on one of the systems.
The full self-driving computer is really two computers,
two systems on a chip that are fully redundant.
So you could put a bolt through basically
any part of that system and it still works.
§02Transcript · 09:30–19:35
- The redundancy, are they perfect copies of each other?
- Yeah.
- Also, it's purely for redundancy
as opposed to an arguing machine kind of architecture
where they're both making decisions.
This is purely for redundancy.
- I think it would more like,
if you have a twin-engine aircraft, commercial aircraft,
the system will operate best if both systems are operating,
but it's capable of operating safely on one.
So, but as it is right now, we can just run,
we haven't even hit the edge of performance.
So, there's no need to actually distribute
the functionality across both SOCs.
We can actually just run a full duplicate on each one.
- You haven't really explored or hit the limit of the--
- Not yet, hit the limit now.
- So, the magic of deep learning
is that it gets better with data.
You said there's a huge inflow of data,
but the thing about driving,
the really valuable data to learn from is the edge cases.
So, how do you, I mean, I've heard you talk somewhere
about autopilot disengagements
being an important moment of time to use.
Is there other edge cases,
or perhaps can you speak to those edge cases,
what aspects of them might be valuable,
or if you have other ideas,
how to discover more and more and more edge cases in driving?
- Well, there's a lot of things that are learned.
There are certainly edge cases where,
I say somebody's on autopilot and they take over,
and then, okay, that's a trigger that goes to our system
that says, okay, did they take over for convenience,
or did they take over
because the autopilot wasn't working properly?
There's also, like, let's say we're trying to figure out
what is the optimal spline for traversing an intersection.
Then, the ones where there are no interventions
are the right ones.
So, you then say, okay, when it looks like this,
do the following, and then you get the optimal spline
for a complex, navigating a complex intersection.
- So, that's for, so there's kind of the common case.
You're trying to capture a huge amount of samples
of a particular intersection, when things went right,
and then there's the edge case where, as you said,
not for convenience, but something didn't go exactly right.
- Somebody took over, somebody asserted manual control
from autopilot, and really, like, the way to look at this
is view all input as error.
If the user had to do input, there's something,
all input is error.
- That's a powerful line to think of it that way,
'cause it may very well be error,
but if you want to exit the highway,
or if you want to, it's a navigation decision
that autopilot's not currently designed to do,
then the driver takes over.
How do you know the difference?
- Yeah, that's gonna change with navigate and autopilot,
which were just released, and without still confirm.
So, the navigation, like, lane change-based,
like, asserting control in order to do a lane change,
or exit a freeway, or do a highway interchange,
the vast majority of that will go away
with the release that just went out.
- Yeah, so that, I don't think people quite understand
how big of a step that is.
- Yeah, they don't.
So, if you drive the car, then you do.
- So, you still have to keep your hands
on the steering wheel currently,
when it does the automatic lane change.
What are, so there's these big leaps
through the development of autopilot, through its history,
and what stands out to you as the big leaps?
I would say this one, navigate and autopilot
without having to confirm, is a huge leap.
- It is a huge leap.
- And it also automatically overtakes slow cars.
So, it's both navigation and seeking the fastest lane.
So, it'll overtake slower cars, and exit the freeway,
and take highway interchanges,
and then we have traffic light recognition,
which is introduced initially as a warning.
I mean, on the development version that I'm driving,
the car fully stops and goes at traffic lights.
- So, those are the steps, right?
You've just mentioned some things
that are an inkling of a step towards full autonomy.
What would you say are the biggest technological roadblocks
to full self-driving?
- Actually, I don't think, I think we just,
the full self-driving computer that we just,
the Tesla, what we call the FSD computer,
that's now in production.
So, if you order any Model S or X,
or any Model 3 that has the full self-driving package,
you'll get the FSD computer.
That's important to have enough base computation.
Then refining the neural net and the control software,
but all of that can just be provided as an over-the-air update.
The thing that's really profound,
and what I'll be emphasizing at the,
sort of that investor day that we're having
focused on autonomy,
is that the cars currently being produced,
or the hardware currently being produced,
is capable of full self-driving.
- But capable is an interesting word, because--
- Like the hardware is.
And as we refine the software,
the capabilities will increase dramatically,
and then the reliability will increase dramatically,
and then it will receive regulatory approval.
So, essentially, buying a car today
is an investment in the future.
You're essentially buying,
I think the most profound thing is that
if you buy a Tesla today,
I believe you are buying an appreciating asset,
not a depreciating asset.
- So, that's a really important statement there,
because if hardware is capable enough,
that's the hard thing to upgrade, usually.
So, then the rest is a software problem.
- Software has no marginal cost, really.
But, what's your intuition on the software side?
How hard are the remaining steps
to get it to where
the experience, not just the safety,
but the full experience,
is something that people would enjoy?
- I think people enjoy it very much so, on the highways.
It's a total game changer for quality of life.
For using Tesla autopilot on the highways.
So, it's really just extending that functionality
to city streets,
adding in the traffic light recognition,
navigating complex intersections,
and then being able to navigate complicated parking lots,
so the car can exit a parking space
and come and find you,
even if it's in a complete maze of a parking lot.
And then it can just drop you off
and find a parking spot by itself.
- Yeah, in terms of enjoyability
and something that people would actually find a lot of use
from, the parking lot, is a really,
it's rich of annoyance when you have to do it manually,
so there's a lot of benefit to be gained
from automation there.
So, let me start injecting the human
into this discussion a little bit.
So, let's talk about full autonomy.
If you look at the current level four vehicles
being tested on road, like Waymo and so on,
they're only technically autonomous.
They're really level two systems
with just a different design philosophy,
because there's always a safety driver in almost all cases
and they're monitoring the system.
Do you see Tesla's full self-driving
as still for a time to come,
requiring supervision of the human being?
So, its capabilities are powerful enough to drive,
but nevertheless requires the human
to still be supervising, just like a safety driver is
in other fully autonomous vehicles.
- I think it'll require detecting hands on wheel
for at least six months or something like that from here.
Really, it's a question of, from a regulatory standpoint,
how much safer than a person does autopilot need to be
for it to be okay to not monitor the car?
And this is a debate that one can have,
and then, but you need a large sample,
a large amount of data so that you can prove
with high confidence, statistically speaking,
that the car is dramatically safer than a person,
and that adding in the person monitoring
does not materially affect the safety.
So, it might need to be like 200 or 300% safer than a person.
- And how do you prove that?
- Incidents per mile.
- Incidents per mile, so crashes and fatalities.
- Yeah, fatalities would be a factor,
but there are just not enough fatalities
to be statistically significant at scale,
but there are enough crashes,
there are far more crashes than there are fatalities.
So, you can assess what is the probability of a crash,
then there's another step, which is probability of injury,
and probability of permanent injury,
and probability of death.
And all of those need to be much better than a person
by at least perhaps 200%.
- And you think there's the ability
to have a healthy discourse with the regulatory bodies
§03Transcript · 19:38–29:18
on this topic?
- I mean, there's no question that regulators
pay a disproportionate amount of attention
to that which generates press,
this is just an objective fact,
and Tesla generates a lot of press.
So, in the United States,
there's I think almost 40,000 automotive deaths per year.
But if there are four in Tesla,
they'll probably receive a thousand times more press
than anyone else.
- So, the psychology of that is actually fascinating.
I don't think we'll have enough time to talk about that,
but I have to talk to you about the human side of things.
So, myself and our team at MIT recently released a paper
on functional vigilance of drivers while using Autopilot.
This is work we've been doing since Autopilot
was first released publicly over three years ago,
collecting video of driver faces and driver body.
So, I saw that you tweeted a quote from the abstract,
so I can at least guess that you've glanced at it.
- Yeah, I read it.
- Can I talk you through what we found?
- Sure. - Okay.
So, it appears that in the data that we've collected,
that drivers are maintaining functional vigilance
such that we're looking at 18,000 disengagements
from Autopilot, 18,900,
and annotating were they able to take over control
in a timely manner.
So, they were there, present, looking at the road
to take over control.
Okay, so this goes against what many would predict
from the body of literature on vigilance with automation.
Now, the question is, do you think these results
hold across the broader population?
So, ours is just a small subset.
Do you think, one of the criticism is that,
you know, there's a small minority of drivers
that may be highly responsible,
where their vigilance decrement would increase
with Autopilot use?
- I think this is all really gonna be swept.
I mean, the system's improving so much, so fast,
that this is gonna be a moot point very soon.
Where vigilance is, like, if something's many times
safer than a person, then adding a person does,
the effect on safety is limited.
And, in fact, it could be negative.
- That's really interesting.
So, the fact that a human may, some percent of the population
may exhibit a vigilance decrement will not affect
the overall statistics numbers of safety.
- No, in fact, I think it will become, very, very quickly,
maybe even towards the end of this year,
but I'd say, I'd be shocked if it's not next year,
at the latest, that having a human intervene
will decrease safety.
Decrease.
I can imagine if you're in an elevator.
Now, it used to be that there were elevator operators,
and you couldn't go in an elevator by yourself
and work the lever to move between floors.
And now, nobody wants an elevator operator,
because the automated elevator that stops the floors
is much safer than the elevator operator.
And, in fact, it would be quite dangerous
to have someone with a lever that can move
the elevator between floors.
- So, that's a really powerful statement,
and a really interesting one.
But I also have to ask, from a user experience
and from a safety perspective,
one of the passions for me, algorithmically,
is camera-based detection of just sensing the human,
but detecting what the driver's looking at,
cognitive load, body pose.
On the computer vision side, that's a fascinating problem,
but do you, and there's many in industry
who believe you have to have
camera-based driver monitoring.
Do you think there could be benefit gained
from driver monitoring?
- If you have a system that's at or below
a human-level reliability,
then driver monitoring makes sense.
But if your system is dramatically better,
more reliable than a human,
then driver monitoring does not help much.
And, like I said, you wouldn't want someone in the elevator,
if you're in an elevator,
do you really want someone with a big lever,
some random person operating an elevator between floors?
I wouldn't trust that.
I would rather have the buttons.
- Okay, you're optimistic about the pace
of improvement of the system,
from what you've seen with a full self-driving car,
computer, the rate of improvement is exponential.
- So one of the other very interesting design choices
early on that connects to this
is the operational design domain of autopilot.
So where autopilot is able to be turned on.
So contrast another vehicle system that we're studying
is the Cadillac Super Cruise system.
That's, in terms of ODD,
very constrained to particular kinds of highways,
well-mapped, tested,
but it's much narrower than the ODD of Tesla vehicles.
- It's like ADD.
(laughing)
- That's good, that's a good line.
What was the design decision
in that different philosophy of thinking where,
so there's pros and cons.
What we see with a wide ODD
is Tesla drivers are able to explore more
the limitations of the system, at least early on,
and they understand,
together with the instrument cluster display,
they start to understand what are the capabilities.
So that's a benefit.
The con is you're letting drivers use it basically anywhere.
- Well, anywhere that could detect lanes with confidence.
- Was there a philosophy,
design decisions that were challenging
that were being made there?
Or from the very beginning,
was that done on purpose with intent?
- Well, I mean, I think,
frankly, it's pretty crazy letting people
drive a two-ton death machine manually.
That's crazy.
In the future, people will be like,
I can't believe anyone was just allowed
to drive one of these two-ton death machines,
and they could just drive wherever they wanted.
Just like elevators, you could just move the elevator
with that lever wherever you want.
It could stop at halfway between floors if you want.
It's pretty crazy.
So, it's gonna seem like a mad thing in the future
that people were driving cars.
- So I have a bunch of questions about the human psychology,
about behavior and so on, that would become--
- That mood's totally moot.
- Because you have faith in the AI system.
Not faith, but both on the hardware side
and the deep learning approach of learning from data
will make it just far safer than humans.
- Yeah, exactly.
- Recently, there are a few hackers
who tricked Autopilot to act in unexpected ways
with adversarial examples.
So, we all know that neural network systems
are very sensitive to minor disturbances
to these adversarial examples on input.
Do you think it's possible to defend against
something like this for the industry?
- Sure.
(laughing)
- Can you elaborate on the confidence behind that answer?
- Well, a neural net is just a bunch of matrix math.
You have to be a very sophisticated,
somebody who really understands neural nets
and basically reverse engineer how the matrix
is being built and then create a little thing
that just exactly causes the matrix math
to be slightly off.
But it's very easy to then block that
by having basically negative recognition.
It's like if the system sees something
that looks like a matrix hack, exclude it.
It's such an easy thing to do.
- So, learn both on the valid data and the invalid data.
So, basically learn on the adversarial examples
to be able to exclude them.
- Yeah, you basically want to both know
what is a car and what is definitely not a car.
You train for this is a car and this is definitely not a car.
Those are two different things.
People have no idea of neural nets, really.
They probably think neural nets involves
like fishing net or something.
(laughing)
- So, as you know, so taking a step beyond
just Tesla and autopilot, current deep learning approaches
still seem in some ways to be far
from general intelligence systems.
Do you think the current approaches
will take us to general intelligence
or do totally new ideas need to be invented?
- I think we're missing a few key ideas
for general intelligence,
general artificial general intelligence.
But it's gonna be upon us very quickly
and then we'll need to figure out what shall we do
if we even have that choice.
But it's amazing how people can't differentiate
between say the narrow AI that allows a car
§04Transcript · 29:22–32:38
to figure out what a lane line is
and navigate streets versus general intelligence.
Like these are just very different things.
Like your toaster and your computer are both machines
but one's much more sophisticated than another.
- You're confident with Tesla you can create
the world's best toaster?
- The world's best toaster, yes.
The world's best self-driving.
Yes.
To me, right now, this seems game, set, match.
I mean, I don't want to be complacent or overconfident
but that is just literally how it appears right now.
I could be wrong but it appears to be the case
that Tesla is vastly ahead of everyone.
- Do you think we will ever create an AI system
that we can love and loves us back
in a deep meaningful way like in the movie Her?
- I think AI will be capable of convincing you
to fall in love with it very well.
- And that's different than us humans?
- You know, we start getting into a metaphysical question
of like do emotions and thoughts exist
in a different realm than the physical?
And maybe they do, maybe they don't, I don't know.
But from a physics standpoint, I tend to think of things,
like physics was my main sort of training
and from a physics standpoint, essentially,
if it loves you in a way that you can't tell
whether it's real or not, it is real.
- It's a physics view of love.
- Yeah.
If you cannot prove that it does not,
if there's no test that you can apply
that would make it,
allow you to tell the difference,
then there is no difference.
- And it's similar to seeing our world as simulation.
There may not be a test to tell the difference
between what the real world and the simulation
and therefore, from a physics perspective,
it might as well be the same thing.
- Yes.
And there may be ways to test whether it's a simulation.
There might be, I'm not saying there aren't,
but you could certainly imagine that a simulation
could correct that once an entity in the simulation
found a way to detect the simulation,
it could either restart, pause the simulation,
start a new simulation or do one of many other things
that then corrects for that error.
- So when maybe you or somebody else creates an AGI system
and you get to ask her one question,
what would that question be?
- What's outside the simulation?
- Elon, thank you so much for talking today.
It was a pleasure.
- All right, thank you.
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