Elon Musk: Tesla Autopilot

Elon Musk is the CEO of Tesla, SpaceX, Neuralink, and a co-founder of several other companies. Video version is available on YouTube. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  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.

• 0:43Lex Fridman?

  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.

• 1:26Lex Fridman?

  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

• 2:10Lex Fridman?

  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.

• 2:52Lex Fridman?

  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 in the cars, what was the vision, the dream?

• 3:06Elon Musk?

  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.

• 3:46Elon Musk?

  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.

• 4:18Lex Fridman?

  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?

• 4:37Elon Musk?

  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 and ultrasonics, GPS and so forth.

• 4:54Elon Musk?

  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.

• 5:19Lex Fridman?

  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...

• 5:50Elon Musk?

  Probabilities associated with, say, image recognition or something like that?

• 5:53Lex Fridman?

  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.

• 6:09Elon Musk?

  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, 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.

• 6:39Elon Musk?

  That does not show up 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.

• 6:56Lex Fridman?

  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.

• 7:05Elon Musk?

  It's like if you have no idea what, 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, you know, if you're a development engineer, or if you're, you know, if you're, if you have the development build like I do, then you can see, you know, all the debug information. But those would just be like total gibberish to most people.

• 7:27Lex Fridman?

  Right. 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 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 three?

• 8:01Elon Musk?

  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, and we've got about 400,000 cars on the road that have that level of data. Actually, I think you keep quite close track of it, actually.

• 8:32Lex Fridman?

  Yes.

• 8:33Elon Musk?

  Yeah, so we're approaching half a million cars on the road that have the full sensor suite.

• 8:39Lex Fridman?

  Yeah.

• 8:40Elon Musk?

  So this is, I'm not sure how many other cars on the road have this sensor suite, but I'd be surprised if it's more than 5,000, which means that we have 99% of all the data.

• 8:52Lex Fridman?

  So there's this huge inflow of data.

• 8:55Elon Musk?

  Absolutely, massive inflow of data. And then we, it's taken us about three years, but now we've finally developed our full self-driving computer, which can Process in order to make 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, it's a, in fact, we're not even, we're still exploring the boundaries of its capabilities. 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.

• 9:36Elon Musk?

  The hard, 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.

• 9:47Lex Fridman?

  The redundancy, are they perfect copies of each other?

• 9:50Elon Musk?

  Yeah.

• 9:51Lex Fridman?

  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.

• 9:58Elon Musk?

  I think more like it's, if you have a twin-engine aircraft, a 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 duplicate on each one.

• 10:34Lex Fridman?

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

• 10:37Elon Musk?

  Not yet, the limit now.

• 10:39Lex Fridman?

  So the magic of deep learning is that it gets better with data. You said there's a huge inflow of data, but

• 10:46Elon Musk?

  Yeah.

• 10:47Lex Fridman?

  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

• 11:02Elon Musk?

  Yes.

• 11:03Lex Fridman?

  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?

• 11:17Elon Musk?

  Well, there's a lot of things that are learned. There are certainly edge cases where I say somebody's on autopilot and they they take over. And then, okay, that that that's a trigger that goes to our system that 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 then the ones where there are no interventions and are the right ones. So you then say, okay, when it looks like this, do the following. And then and then you get the optimal spline for a complex, now getting a complex intersection.

• 12:01Lex Fridman?

  So that's for this, so 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.

• 12:18Elon Musk?

  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, it does something, all input is error.

• 12:30Lex Fridman?

  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 all autopilot is not currently designed to do, then the driver takes over. How do you know?

• 12:45Elon Musk?

  Yeah, that's going to change with Navigate and Autopilot, which we've just released, and with that store confirm. So the navigation, like lane change based, like asserting control in order to do a lane change, or exit a freeway, or or doing a highway interchange, the vast majority of that will go away with the release that just went out.

• 13:05Lex Fridman?

  Yeah, so that that I don't think people quite understand how big of a step that is.

• 13:11Elon Musk?

  Yeah, they don't. If you drive the car, then you do.

• 13:15Lex Fridman?

  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, 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. Confirm without having to confirm is a huge leap.

• 13:37Elon Musk?

  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.

• 14:13Lex Fridman?

  So those are the steps, right? You just mentioned something sort of inkling of a step towards full autonomy. What would you say are the biggest technological roadblocks to full self-driving?

• 14:26Elon Musk?

  Actually, I don't think, I think we just, the full self-driving computer that we just, that the Tesla, what we call the FSD computer, that's now in production. So if you order any Model SRX 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 where I'll be emphasizing at the sort of what, that Investor Day that we're having focused on autonomy,

• 15:10Elon Musk?

  is that the cars currently being produced, or the hardware currently being produced is capable of full self-driving.

• 15:17Lex Fridman?

  But capable is an interesting word because.

• 15:21Elon Musk?

  Like the hardware is.

• 15:22Lex Fridman?

  Yeah.

• 15:22Elon Musk?

  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 a car. 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.

• 15:50Lex Fridman?

  So that's a really important statement there because if hardware is capable enough, that's the hard thing to upgrade.

• 15:56Elon Musk?

  Yes.

• 15:56Lex Fridman?

  Usually.

• 15:57Elon Musk?

  Exactly.

• 15:57Lex Fridman?

  So then the rest is a software problem.

• 16:00Elon Musk?

  Yes. Software has no marginal cost, really.

• 16:04Lex Fridman?

  But 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?

• 16:26Elon Musk?

  I think people would enjoy it very much on the highways. It's a total game changer for quality of life for using, you know, 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.

• 17:03Elon Musk?

  And then if, and then you can just, it could just drop you off and find a parking spot by itself.

• 17:09Lex Fridman?

  Yeah, in terms of enjoyability and something that people would, would actually find a lot of use from, the parking lot is a really, you know, it's, 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 Tesla 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.

• 17:49Elon Musk?

  Right.

• 17:50Lex Fridman?

  Do you see? Tesla's full self-driving is 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.

• 18:14Elon Musk?

  I think it will require detecting hands-on wheel for at least six months or something like that from here.

• 18:26Elon Musk?

  Really, it's a question of like, from a regulatory standpoint,

• 18:32Elon Musk?

  how much safer than a person does autopilot need to be for it to be okay to not monitor the car?

• 18:41Elon Musk?

  And this is a debate that one can have. And then, but you need a large sample, a large amount of data, 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 200 or 300% safer than a person.

• 19:06Lex Fridman?

  And how do you prove that?

• 19:08Elon Musk?

  Incidents per mile.

• 19:09Lex Fridman?

  Incidents per mile. So crashes and fatalities.

• 19:13Elon Musk?

  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, then probability of death. And all of those need to be much better than a person, by at least Perhaps 200%.

• 19:49Lex Fridman?

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

• 19:56Elon Musk?

  I mean, there's no question that regulators pay disproportionate amounts 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 1,000 times more press than anyone else.

• 20:25Lex Fridman?

  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.

• 21:00Elon Musk?

  Yeah, right.

• 21:01Lex Fridman?

  Can I talk you through what we found?

• 21:03Elon Musk?

  Sure.

• 21:03Lex Fridman?

  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.

• 21:44Lex Fridman?

  Do you think, one of the criticisms is that there's a small minority of drivers that may be highly responsible, where their vigilance decrement would increase with autopilot use?

• 21:57Elon Musk?

  I think this is all really going to be swept. I mean, the system's improving so much, so fast, that this is going to be a moot point very soon.

• 22:09Elon Musk?

  Where vigilance is, I guess, 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.

• 22:27Lex Fridman?

  That's really interesting. So the, 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.

• 22:39Elon Musk?

  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 the person, having a human intervene will decrease safety.

• 22:56Elon Musk?

  Decrease. It's like, imagine if you're in an elevator. Now, it used to be that there were elevator operators and, and you couldn't go in an elevator by yourself and, 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.

• 23:26Lex Fridman?

  So that's a, that's a really powerful statement and 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, 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 believe you have to have camera-based driver monitoring. Do you think there could be benefit gained from driver monitoring?

• 23:58Elon Musk?

  If you have a system that's, that's out of, that's out or below a human level reliability, then driver monitoring makes sense.

• 24:07Elon Musk?

  But if, if your system is dramatically better, more reliable than, than a human, then driver monitoring, monitoring is not, does not help much. And like I said, you, you're just like as, you wouldn't want someone into, like, you wouldn't want someone in the elevator, if you're in an elevator, do you really want someone with a big lever, some, some random person operating the elevator between floors? They could, I wouldn't trust that, or I'd rather have the buttons.

• 24:34Lex Fridman?

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

• 24:42Elon Musk?

  The rate of improvement is exponential.

• 24:45Lex Fridman?

  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.

• 25:00Lex Fridman?

  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.

• 25:17Elon Musk?

  Looks like ADD.

• 25:19Lex Fridman?

  Yeah.

• 25:21Lex Fridman?

  That's good. That's a good line. What was the design decision? What's 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 of 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.

• 25:55Elon Musk?

  Anywhere that could detect lanes with confidence.

• 25:58Lex Fridman?

  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?

• 26:11Elon Musk?

  Well, I mean, I think it's frankly, it's pretty crazy giving, 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 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 it halfway between floors if you want.

• 26:39Elon Musk?

  It's pretty crazy. So it's gonna seem like a mad thing in the future that people were driving cars.

• 26:49Lex Fridman?

  So I have a bunch of questions about the human psychology, about behavior and so on.

• 26:54Elon Musk?

  I don't know.

• 26:54Lex Fridman?

  That would become that.

• 26:56Elon Musk?

  Yeah, moot, totally moot.

• 26:57Lex Fridman?

  Because you have faith in the AI system, not faith, but the, both on the hardware side and the deep learning approach of learning from data will make it just far safer than humans.

• 27:12Elon Musk?

  Yeah, exactly.

• 27:14Lex Fridman?

  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 broader, for the industry?

• 27:32Elon Musk?

  Sure.

• 27:33Lex Fridman?

  So, yeah. Can you elaborate on the, on the confidence behind that answer?

• 27:40Elon Musk?

  Well, the, you know, a neural net is just like a basic bunch of matrix math. You have to be like a very sophisticated, somebody who really understands neural nets and like basically reverse engineer how the matrix is being built and then create a little thing that's just exactly causes the matrix math to be slightly off. But it's very easy to then block, block that by having basically anti, negative recognition. It's like if, if the system sees something that looks like a matrix hack excluded, it's such an easy thing to do.

• 28:19Lex Fridman?

  So learn both on the, the valid data and the invalid data. So basically learn on the adversarial examples to be able to exclude them.

• 28:26Elon Musk?

  Yeah, you like, you basically want to both know what is, 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, like, you know, fishing net or something.

• 28:46Lex Fridman?

  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?

• 29:12Elon Musk?

  I think we're missing a few key ideas for general intelligence, general, artificial general intelligence.

• 29:22Elon Musk?

  But it's going to be upon us very quickly. And then we'll need to figure out what shall we do if we even have that choice?

• 29:32Elon Musk?

  But it's amazing how people can't differentiate between, say, the narrow AI that, you know, allows a car to figure out what a lane line is and, and, you know, 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.

• 29:55Lex Fridman?

  You're confident with Tesla you can create the world's best toaster?

• 30:00Elon Musk?

  The world's best toaster, yes. The world's best self-driving.

• 30:05Elon Musk?

  I'm, yes. To me, right now, this seems game, set, match. I don't, I mean, that's, I don't want to be complacent or overconfident, but that's what it appears. That is just literally what it, how it appears right now. I could be wrong, but it appears to be the case that Tesla is vastly ahead of everyone.

• 30:27Lex Fridman?

  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?

• 30:37Elon Musk?

  I think AI will be capable of convincing you to fall in love with it very well.

• 30:42Lex Fridman?

  And that's different than us humans?

• 30:46Elon Musk?

  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 but from a physics standpoint, I tend to think of things, you know, like physics was my main sort of training. And 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.

• 31:14Lex Fridman?

  It's a physics view of love.

• 31:16Elon Musk?

  Yeah. If there's no, if you cannot prove that it does not, if there's no test that you can apply that would make it,

• 31:30Elon Musk?

  make, allow you to tell the difference, then there is no difference.

• 31:34Lex Fridman?

  Right, 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.

• 31:46Elon Musk?

  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, you know, pause the simulation, start a new simulation, or do one of any other things that then corrects for that error.

• 32:10Lex Fridman?

  So when maybe you or somebody else creates an AGI system and you get to ask her one question, what would that question be?

• 32:33Elon Musk?

  What's outside the simulation?

• 32:38Lex Fridman?

  Elon, thank you so much for talking today. It was a pleasure.

• 32:41Elon Musk?

  All right, thank you.