Elon Musk: Tesla Autopilot | Lex Fridman Podcast #18

1. 0:00 – 31:53

   Intro

   1. LFLex Fridman0:00

      The following is a conversation with Elon Musk. He's the CEO of Tesla, SpaceX, Neurolink, and a co-founder of several other companies. This conversation is part of the Artificial Intelligence podcast. This 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 with 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, uh, in the beginning, the big picture system level, when, uh, it was first conceived and started being installed in 2014 in the hardware and the cars? What was the vision, the dream?

   2. EMElon Musk2:49

      I wouldn't characterize it as a vision or dream, simply that there are obviously two massive revolutions in, in the, uh, automobile industry. One is the transition to elect- electrification, um, and then the other is autonomy. And, uh, it became obvious to me that, in the future, any, any car that does not have autonomy, uh, 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. So, it's 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 ten times more than a non- a car which is not autonomous.

   3. LFLex Fridman3:53

      In the long term.

   4. EMElon Musk3:55

      Depends what you mean by long term, but let's say at least for the next five years, perhaps 10 years.

   5. LFLex Fridman4:01

      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?

   6. EMElon Musk4:20

      The whole point of the t- display is to provide a health check on the r- the vehicle's perception of reality. So, the vehicle's, uh, taking in information from a bunch of sensors, primarily cameras, but also radar and ultrasonics, uh, GPS, and so forth. And then, uh, that, that information is then rendered into c- vector space, uh, and that, you know, with a bunch of objects with pr- with properties, like lane lines and traffic lights and other cars. Um, and then in vector space, that is re-rendered onto your display so you can confirm whether the car knows what's going on or not by looking out the window.

   7. LFLex Fridman5:02

      Right. I think that's a extremely powerful thing for people to get an understanding, sort of become one with the system and understanding what the system is capable of.

   8. EMElon Musk5:11

      Mm-hmm.

   9. LFLex Fridman5:11

      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 that, uh, the- the uncertainty in the system, the sort of more-

   10. EMElon Musk5:33

       The probabilities associated with, with say image recognition or something like that?

   11. LFLex Fridman5:36

       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.

   12. EMElon Musk5:53

       Well, I think it's, uh, yeah, my car, I always look- look at the sort of the- the debug view, and there's, there's two debug views, uh, o- one is...... augmented vision, uh, where, which I'm sure you've seen, where it basically, uh, we, we draw boxes and labels around objects that are recognized. And then there's, uh, what we call the visualizer, which is basically a vector-based representation summing up, uh, the input from all sensors. That, that does, does not show b- any pictures, but it shows, uh, all of the ... it basically shows the car's view of, of, of the world in vector space. Um, but I think this is very difficult for people to kno- normal people to understand. They would not know what the heck they're looking at.

   13. LFLex Fridman6:39

       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.

   14. EMElon Musk6:48

       Yeah. It, like, if you've no idea what, how computer vision works or anything, you can still look at the screen and p- 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, uh, you know, all the debug information. But those would just be, like, total gibberish to most people.

   15. LFLex Fridman7:10

       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 that to train on, and then there's the hardware development. There may be others, but ... so, look, algorithm, data, hardware. You on- you only have so much money, only have so much time. What do you think is the most important thing to, to, uh, allocate resources to? Or do you see it as pretty evenly distributed between those three?

   16. EMElon Musk7:44

       We automatically get fast amounts of data, because all of our cars have eight external-facing cameras and radar, and usually 12 ultrasonic sensors, uh, GPS obviously, um, and, uh, IMU. And so we, we basically have a fleet that has, um ... and we've got about 400,000 cars on the road that have that level of data. I g- actually, I think you keep quite close track of it, actually.

   17. LFLex Fridman8:15

       Yes.

   18. EMElon Musk8:16

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

   19. LFLex Fridman8:22

       Yeah.

   20. EMElon Musk8:22

       Um, the ... so this is ... I, I'm n- I, 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.

   21. LFLex Fridman8:36

       So, there's this huge-

   22. EMElon Musk8:37

       Um-

   23. LFLex Fridman8:37

       ... inflow of data.

   24. EMElon Musk8:38

       Absolutely. Massive inflow of data. And then we ... it's d- it's taken us about three years, but now we've finally developed our full self-driving computer, which can process, uh, an or- an order of magnitude as much as the NVIDIA system that we currently have in the, in the cars. And it's really just a ... to use it, you unplug the Nvi- NVIDIA computer and plug the Tesla computer in, and that's it. And it's, it's, uh ... in fact, we're not even qu- we're still exploring the boundaries of its capabilities. Uh, but we're able to run the cameras at full frame rate, full resolution, uh, not even crop the images, and, uh, it's still got headroom, even on one of the, the systems. The hard d- full, 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.

   25. LFLex Fridman9:30

       The redundancy, are they perfect copies of each other, or ...

   26. EMElon Musk9:33

       Yeah.

   27. LFLex Fridman9:34

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

   28. EMElon Musk9:41

       I- I think of it more like it's ... if you have, uh, a twin engine aircraft, um, commercial aircraft, the system will operate best if both systems are operating, but it's, it's capable of operating safely on one. So ... but a- a- as it is right now, we can just run ... we're h- we haven't even hit the, the, the e- the edge of performance, so there's no need to actually distribute functionality across both SOCs. We, we can actually just run a full duplicate on b- on, on each one.

   29. LFLex Fridman10:17

       So, you haven't really explored or hit the limit of this-

   30. EMElon Musk10:20

       We have not yet hit the limit, no.
2. 31:53 – 32:29

   First question for AGI

   1. LFLex Fridman31:53

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

   2. EMElon Musk32:17

      What's outside the simulation?

   3. LFLex Fridman32:21

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

   4. EMElon Musk32:24

      All right. Thank you.

Episode duration: 32:44