Elon Musk: Tesla Autopilot | Lex Fridman Podcast #18
 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 the 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 a 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 information from a bunch of sensors, primarily cameras, but also radar, ultrasonics, GPS and so forth. And then that information is then rendered into vector space and that 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, 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, 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 more- Will these associated with say image recognition or something like that? Yeah, so right now it shows like the vehicles are in the vehicle's view, 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 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 cause 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 is 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 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 that it's trained on, and then there's the hardware development, and maybe others. 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 a vast 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, 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. Yes. So we're approaching half a million cars on the road that have the full sensor suite. Yeah. So this is, I'm not sure how many other cars on the road have this sensor suite, but I would 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, in 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. We were able to run the cameras at full frame rate, full resolution, not even crop of 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 bulb through basically any part of that system, and it still works. The redundancy, are they perfect copies of each other? Yeah. Also, it's purely for redundancy, as opposed to an argue machine kind of architecture where they're both making decisions. This is purely for redundancy. 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 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 duplication of the system and duplicate on each one. You haven't really explored or hit the limit of the system. Not yet, hit the limit, no. 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 disengagement 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's 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 and says, okay, did they take over for convenience, or do 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, there's kind of the common case. You're trying to capture a huge amount of samples of a particular intersection, how, 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, 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 because 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 is not currently designed to do, then the driver takes over. How do you know the difference? That's gonna change with Navigator and autopilot, which we've just released, 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. 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, navigating autopilot without having to confirm, is a huge leap. It is a huge leap. It also automatically overtakes slow cars. So it's both navigation and seeking the fastest lane. So it'll overtake the 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. So those are the steps, right? You've just mentioned some things that an inkling of a step towards full autonomy. What would you say are the biggest technological roadblocks to full-cell driving? Actually, I don't think, I think we just, the full-cell driving computer that we just, that has 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-cell driving, the full-cell 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 what I'll be emphasizing at the sort of what that investor day that we're having focused on autonomy is that the car is 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. Yeah, the hardware. 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, you're 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. Exactly. So then the rest is a software problem. Yes, 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 would 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, 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 test 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 it's 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 will require detecting hands on wheel for at least six 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? And this is a debate that one can have it. But you need a large samples, a large amount of data so that you can prove with high confidence that you can drive a car. So 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 two or 300% safer than a person. And how do you prove that? Incidence per mile. Incidence 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 probability of injury and probability of permanent injury and the 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 on this topic? I mean, there's no question that regulators pay 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 mood point very soon where vigilance is, like if something's many times safer than a person, then adding a person, 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 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 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 elevator between floors? I wouldn't trust that. I'd rather have the buttons. Okay, you're optimistic about the pace of improvement of the system, that from what you've seen with the full-size 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. What's, there's- It's like ADD. Yeah. That's good. That's a good line. What was the design decision in that different philosophy of thinking where, 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. Any way that can 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 it's, frankly, it's pretty crazy giving it, letting people drive a two-ton death machine manually. That's crazy. Like in the future, people will be like, I can't believe anyone was just allowed to drive for one of these two-ton death machines, and they just drive wherever they wanted. Just like elevators, you just like move the elevator with that lever wherever you want. It can 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. That mood, totally mood. 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. Absolutely. Can you elaborate on the confidence behind that answer? Well, the 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 anti-negative recognition. It's like if the system sees something that looks like a matrix hack excluded, 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 us 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 neural nets really. They probably think neural nets involves fishing net or something. So as you know, 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, 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 to figure out what a lane line is and navigate streets versus general intelligence. 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 don't want to be complacent or overconfident but that's what it appears. That is the way it is. I don't want to be overconfident but that's what it appears. 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. It'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. That'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, 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 is in the simulation and therefore from a physics perspective it might as well be the same thing. Yes. There may be ways to test whether it's a simulation. There might be, I'm not saying there aren't. But you can 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 any 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.
 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 the 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 a 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 information from a bunch of sensors,
 primarily cameras, but also radar, ultrasonics,
 GPS and so forth.
 And then that information is then rendered into vector space
 and that 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,
 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,
 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 more-
 Will these associated with say image recognition
 or something like that?
 Yeah, so right now it shows like the vehicles
 are in the vehicle's view,
 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 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 cause 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 is 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 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 that it's trained on,
 and then there's the hardware development,
 and maybe others.
 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 a vast 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,
 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.
 Yes.
 So we're approaching half a million cars on the road
 that have the full sensor suite.
 Yeah.
 So this is, I'm not sure how many other cars on the road
 have this sensor suite,
 but I would 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,
 in 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.
 We were able to run the cameras at full frame rate,
 full resolution, not even crop of 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 bulb through basically
 any part of that system, and it still works.
 The redundancy, are they perfect copies of each other?
 Yeah.
 Also, it's purely for redundancy,
 as opposed to an argue machine kind of architecture
 where they're both making decisions.
 This is purely for redundancy.
 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 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 duplication of the system
 and duplicate on each one.
 You haven't really explored or hit the limit of the system.
 Not yet, hit the limit, no.
 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 disengagement
 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's 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
 and says, okay, did they take over for convenience,
 or do 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, there's kind of the common case.
 You're trying to capture a huge amount of samples
 of a particular intersection, how, 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, 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
 because 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 is not currently designed to do,
 then the driver takes over.
 How do you know the difference?
 That's gonna change with Navigator and autopilot,
 which we've just released, 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.
 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, navigating autopilot
 without having to confirm, is a huge leap.
 It is a huge leap.
 It also automatically overtakes slow cars.
 So it's both navigation and seeking the fastest lane.
 So it'll overtake the 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.
 So those are the steps, right?
 You've just mentioned some things
 that an inkling of a step towards full autonomy.
 What would you say are the biggest technological roadblocks
 to full-cell driving?
 Actually, I don't think, I think we just,
 the full-cell driving computer that we just,
 that has 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-cell driving,
 the full-cell 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 what I'll be emphasizing at the sort of what
 that investor day that we're having focused on autonomy
 is that the car is 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.
 Yeah, the hardware.
 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, you're 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.
 Exactly.
 So then the rest is a software problem.
 Yes, 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 would 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,
 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 test 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 it's 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 will require detecting hands on wheel
 for at least six 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?
 And this is a debate that one can have it.
 But you need a large samples, a large amount of data
 so that you can prove with high confidence
 that you can drive a car.
 So 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 two or 300% safer than a person.
 And how do you prove that?
 Incidence per mile.
 Incidence 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 probability of injury
 and probability of permanent injury
 and the 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 on this topic?
 I mean, there's no question that regulators pay
 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 mood point very soon
 where vigilance is, like if something's many times safer
 than a person, then adding a person,
 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 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
 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 elevator between floors?
 I wouldn't trust that.
 I'd rather have the buttons.
 Okay, you're optimistic about the pace
 of improvement of the system,
 that from what you've seen
 with the full-size 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.
 What's, there's-
 It's like ADD.
 Yeah.
 That's good.
 That's a good line.
 What was the design decision
 in that different philosophy of thinking where,
 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.
 Any way that can 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 it's,
 frankly, it's pretty crazy giving it,
 letting people drive a two-ton death machine manually.
 That's crazy.
 Like in the future, people will be like,
 I can't believe anyone was just allowed to drive
 for one of these two-ton death machines,
 and they just drive wherever they wanted.
 Just like elevators, you just like move the elevator
 with that lever wherever you want.
 It can 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.
 That mood, totally mood.
 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.
 Absolutely.
 Can you elaborate on the confidence behind that answer?
 Well, the 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 anti-negative recognition.
 It's like if the system sees something that looks like
 a matrix hack excluded,
 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 us 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 neural nets really.
 They probably think neural nets involves
 fishing net or something.
 So as you know, 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,
 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
 to figure out what a lane line is
 and navigate streets versus general intelligence.
 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 don't want to be complacent or overconfident
 but that's what it appears.
 That is the way it is.
 I don't want to be overconfident but that's what it appears.
 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.
 It'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.
 That'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,
 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 is in the simulation
 and therefore from a physics perspective
 it might as well be the same thing.
 Yes.
 There may be ways to test whether it's a simulation.
 There might be, I'm not saying there aren't.
 But you can 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 any 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.