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.

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 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.

1 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.

1 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

I wouldn't characterize it as a vision or dream, it's simply that there are obviously 2 massive revolutions in the automobile industry.

1 is the transition to electrification, and then the other is autonomy.

And it became obvious to me that in the future, any car that does 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.

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 5 to 10 times more than a color which is not autonomous.

Depends what you mean by long term, but let's say at least for the next 5 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?

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 1 with the system and understanding what the system is capable of.

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

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.

1 is augmented vision, where, 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 gibberish to most people.

What's your view on how to best distribute effort?

So there's 3, 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 the strain 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 3?

We automatically get fast amounts of data because all of our cars have 8 external facing cameras and radar and usually 12 ultrasonic sensors, GPS obviously, and IMU.

And so we basically have a fleet that has, and 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.

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.

And then it's taken us about 3 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 1 of the systems.

The hard, the full self-driving computer is really 2 computers, 2 systems on a chip that are fully redundant.

So you could put a boat through basically any part of that system and it still works.

The redundancy, are they perfect copies of each other?

Or, so it's purely for redundancy as opposed to an arguing machine kind of architecture where they're both making decisions.

I think it's 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 1.

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 functionality across both SOCs.

We can actually just run a full duplicate on each 1.

Do you haven't really explored or hit the limit of the speed?

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, 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, if there's something, all input is error.

That's a powerful line to think of it that way.

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.

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 a certain 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.

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 1, navigating autopilot without having to confirm, is a huge leap.

It also automatically overtakes slow cars.

So it's both navigation and seeking the fastest lane.

So it'll overtake slow 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.

You just mentioned some things that are inkling of a step towards full autonomy.

What would you say are the biggest technological roadblocks to full self-driving?

Actually, 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 there update.

The thing that's really profound and I'll be emphasizing at the sort of 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...

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.

what's your intuition on the software side?

How hard are the remaining steps to get it to where, you know, the experience, not just the safety, but the full experience is something that people would enjoy.

I think people enjoy it very much 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, you know, 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.

If you look at the current level 4 vehicles being tested on road like Waymo and so on, they're only technically autonomous.

They're really level 2 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 a 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 6 months or something like that from here.

Really it's a question of like, 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?

You know, and this is a debate that 1 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 2 or 300% safer than a person.

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, you know, 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

And you think there's the ability to have a healthy discourse with the regulatory bodies on this topic?

I mean, there's no question that regulators pay a disproportionate amount of attention to that which generates press.

So, in the United States, there's I think almost 40,000 automotive deaths per year.

But if there are 4 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 3 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

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,

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?

Do you think, 1 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.

So the fact that a human may, some percent of the population may exhibit a vigilance decrement will not affect 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.

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 1.

But I also have to ask, from a user experience and from a safety perspective, 1 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 out or below a human level of 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 the elevator between floors?

Okay, you're optimistic about the pace of improvement of the system, from what you've seen with the full self driving car, computer.

So 1 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 supercruise 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.

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.

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.

In the future, people will be like, I can't believe anyone was just allowed to drive for 1 of these 2 ton death machines and they just drive wherever they wanted.

Just like elevators, you just like move the elevator with the lever wherever you want.

It can stop at halfway between floors if you want.

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.

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.

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?

So can you elaborate on the confidence behind that answer?

Well, the, you know, a neural net is just like basically a bunch of matrix math.

You have to be like 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.

So learn both on the validated and the invalid data.

So basically learn on the adversarial examples to be able to exclude them.

Yeah, like 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.

People have no idea of neural nets really.

They probably think neural nets involves like fishing net or something.

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.

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Elon Musk: Tesla Autopilot | Lex Fridman Podcast #18