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Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot | Lex Fridman Podcast #49
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Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot | Lex Fridman Podcast #49

Lex Fridman and Elon Musk on elon Musk on Neuralink, AI Safety, Tesla Autonomy, and Humanity’s Future.

Lex FridmanhostElon Muskguest
Nov 12, 201936mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 2:01

    Part two setup: first-principles engineering and why Neuralink matters

    1. LF

      The following is a conversation with Elon Musk, part two. The second time we spoke on the podcast, with parallels, if not in quality, then in outfit, to the objectively speaking, greatest sequel of all time, Godfather Part II. As many people know, Elon Musk is a leader of Tesla, SpaceX, Neuralink, and The Boring Company. What may be less known is that he's a world-class engineer and designer, constantly emphasizing first principles thinking in taking on big engineering problems that many before him would consider impossible. As scientists and engineers, most of us don't question the way things are done. We simply follow the momentum of the crowd. But revolutionary ideas that change the world on the small and large scales happen when you return to the fundamentals and ask, "Is there a better way?" This conversation focuses on the incredible engineering and innovation done in brain computer interfaces at Neuralink. This work promises to help treat neurobiological diseases, to help us further understand the connection between the individual neuron to the high level of function of the human brain, and finally, to one day expand the capacity of the brain through two-way communication with computational devices, the internet, and artificial intelligence systems. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe by YouTube, Apple Podcasts, Spotify, support on Patreon, or simply connect with me on Twitter, @lexfridman, spelled F-R-I-D-M-A-N. And now, as an anonymous YouTube commenter referred to our previous conversation, as the, quote, "Historical first video of two robots conversing without supervision," here's the second time, the second conversation with Elon Musk. Let's start with a easy question about consciousness.

  2. 2:01 – 2:53

    Consciousness, panpsychism, and what science can actually test

    1. LF

      In your view, is consciousness something that's unique to humans, or is it something that permeates all matter, almost like a fundamental force of physics?

    2. EM

      I don't think consciousness permeates all matter.

    3. LF

      Panpsychists believe that.

    4. EM

      Yeah.

    5. LF

      There's a philosophical...

    6. EM

      How would you tell? (laughs)

    7. LF

      (laughs) That's true. That's a good point.

    8. EM

      I believe in scientific method. I don't want to blow your mind or anything, but the scientific method is like, if you cannot test a hypothesis, then you cannot reach meaningful conclusion that it is true.

    9. LF

      Do you think consciousness, understanding consciousness, is within the reach of science, of the scientific method?

    10. EM

      We can dramatically improve our understanding of consciousness. You know, I would be hard-pressed to say that we understand anything with complete accuracy, but can we dramatically improve our understanding of consciousness? I believe the answer is yes.

  3. 2:53 – 5:57

    Does superintelligent AI need consciousness—or just convincing simulation?

    1. LF

      Does an AI system, in your view, have to have consciousness in order to achieve human level or superhuman level intelligence? Does it need to have some of these human qualities like consciousness, maybe a body, maybe a fear of mortality, capacity to love, those kinds of silly human things?

    2. EM

      (sighs) E- b- there's- it's- it's different... O- o- you know, there's this- there's the scientific method, which I very much believe in, where something is true to the degree that it is testably so. And- and otherwise, you're really just talking about, you know, preferences or un- untestable beliefs or that, you know, that kind of thing. So ends up being somewhat of a semantic question, where we are conflating a lot of things with the word intelligence. If we parse them out and say, you know, w- o- are we headed, uh, towards the future where an AI will be able to outthink us in every way? Then the answer is unequivocally yes.

    3. LF

      In order for an AI system that needs to outthink us in every way, it also needs to have a- a capacity to have consciousness, self-awareness, and understanding.

    4. EM

      Well, it will be self-aware, yes. That's different from consciousness. I mean, to me, in terms of what- what consciousness feels like, it feels like consciousness is in a different dimension. But this is, this is- could be just an illusion. You know, if- if you dama- damage your brain in some way physically, you get- you- you damage your consciousness, which implies that consciousness is a physical phenomenon, in- in my view. The thing is that- that I think are really quite, quite likely is that digital intelligence will out- be able to outthink us, uh, in- in every way, and it will soon be able to simulate what we consider consciousness, uh, so- to- to a degree that you would not be able to tell the difference.

    5. LF

      And from the- from the aspect of the scientific method, it's- might as well be consciousness if we can simulate it perfectly.

    6. EM

      If you can't tell the difference, and this is sort of the- the Turing test, but think of a more sort of advanced version of the Turing test. If you- i- if you're- if you're talking to a d- digital superintelligence and can't tell if that is a computer or a human, like let's say you're just having a conversation over the phone or a video conference or something where you're- you're- you think you're talking, loo- looks like a person, makes all of the right, uh, uh, inflections and movements and- and all the small subtleties that constitute a human, uh, and, uh, talks like a human, makes mistakes like a human, like, the- at that- and- and you literally just can't tell, is this- are you video conferencing with a person or- or an AI?

    7. LF

      Might as well-

    8. EM

      Might as well.

  4. 5:57 – 9:38

    Escaping AI existential risk: safety regulation, slow government, and regulatory capture

    1. LF

      ...be human. So on a darker topic, you've expressed serious concern about existential threats.... of AI. It's perhaps one of the greatest challenges our civilization faces. But since I would say we're kind of an optimistic descendants of apes, perhaps we can find several paths of escaping the harm of AI. So if I can give you three options, maybe you can comment which do you think is the most promising. So one is scaling up efforts on a- AI safety and beneficial AI research in, in hope of finding an algorithmic or maybe a policy solution. Two, is becoming a multi-planetary species as quickly as possible. And three, is merging with AI and, and riding the wave of that increasing intelligence, uh, as it continuously improves. What do you think is most promising, most interesting, as a civilization, that we should invest in?

    2. EM

      I think there's, there's a lot... a tremendous amount of investment going on in AI. Where there's a lack of investment is in AI safety. And there should be, in my view, a government agency that oversees anything related to AI to confirm that it is... does not represent a public safety risk. Just as there is a regulatory authority for... it's like the Food and Drug Administration is NITSA for automo- automotive safety, there's the FAA for aircraft safety, we generally come to the conclusion that it is important to have a government referee or a, a referee that is serving the public interest in, in ensuring that, uh, um, things are safe when, when there's a potential danger to the public. Um, I would argue that, uh, AI is unequivocally, uh, something that has potential to be dangerous to the public, and therefore should have a regulatory agency just as other things that are dangerous to the public have a regulatory agency. But let me tell you, the problem with this is that the government moves very slowly and the, the rate of... the rate that... usually the way a regulatory agency, uh, comes into being, is that something terrible happens, there's a huge public outcry and years after that, there's a regulatory agency or a rule put in place. Take something like, like seat belts. It was known for, I don't know, a decade or more, that seat belts would have a massive im- impact on, uh, safety and, and save so many lives and serious injuries, and the car industry fought the requirement to put seat belts in tooth and nail. That's crazy.

    3. LF

      Yeah.

    4. EM

      And, and I don't know, hundreds of thousands of people probably died because of that. And they said people wouldn't buy cars if they had seat belts, which is obviously absurd. You know, or look at the bat- tobacco industry and how long they fought anything about smoking. That's part of why I helped make that movie Thank You For Smoking. You can sort of see just how pernicious it can be when you have these companies effectively achieve regulatory capture of, of government

    5. NA

      Yeah.

    6. EM

      ... the bad. People in the AI community refer to the advent of digital super intelligence as a singularity. That, that is not to say that it is good or bad but it, that it is very difficult to predict what will happen after that point and, and that there's some probability it will be bad, some probably it'll be, it'll be good. We obviously want it to affect that probability and have it be more good than bad.

  5. 9:38 – 11:52

    Neuralink as a ‘factory door’ into the brain: precision sensing + stimulation

    1. LF

      Well, let me, on the merger with AI question and, and the incredible work that's being done at Neurolink, there, there's a lot of fascinating innovation here across different disciplines going on. So the flexible wires, the robotic sewing machine that responds to brain movement and everything around ensuring safety and so on. So we currently understand very little about the human brain. Do you also hope that the work at Neurolink will help us understand more about our... about the human mind, about the brain?

    2. EM

      Yeah, I think the work in Neurolink will definitely shed a lot of insight into how the brain and the mind works. Right now, just the data we have regarding how the brain works is, is very limited. You know, we've got fMRI which is... that, that's kind of like putting a, you know, stethoscope o- on the outside of a factory wall and then putting it like all over the factory wall and you can sort of hear the sounds but you don't know what the machines are doing really. It's hard... you can infer a few things but it's very broad brush stroke. In order to really know what's going on in the brain, you really need... you have to have high precision sensors and then you want to have stimulus and response, like if you trigger a neuron, what... how do you feel? What do you see? How does it change your perception of the world?

    3. LF

      You're speaking to physically just getting close to the brain, being able to measure signals from the brain-

    4. EM

      Yeah.

    5. LF

      ... will give us sort of open the door in- inside the factory?

    6. EM

      Yes, exactly. Being able to have high precision sensors that tell you what individual neurons are doing and then being able to trigger the neuron and see what the response is in the brain so you can see the consequences of, of a... if you fire this neuron, what happens? How do you feel? What does it change? It'll be really profound to have this in people because people can articulate their change, like if there's a change in mood or if they've... you know, if they can tell you if they can see better or hear better or be able to form sentences better or worse or, you know, their memories are jogged or that... you know, that kind of thing.

  6. 11:52 – 13:59

    Brain vs. machine plasticity: the interface must adapt on the machine side

    1. LF

      So on the human side, there's this incredible general malleability, plasticity of the human brain. The human brain adapts, adjusts and so on. So there's-

    2. EM

      It's not that plastic, to be totally frank.

    3. LF

      So there, there's a firm structure but-

    4. EM

      There is.

    5. LF

      ... nevertheless-... there is some plasticity, and the open question is, sort of if I could ask a broad question is, how much that plasticity can be utilized? Sort of on the human side, there's some plasticity in the human brain, and on the machine side, we have, uh, neural networks, machine learning, artificial intelligence. It's able to adjust and figure out signals. So, there's a mysterious language that we don't perfectly understand that's within the human brain, and then we're trying to understand that language to communicate both directions. So, the brain is adjusting a little bit. We don't know how much. And the machine is adjusting. Where do you see, as they try to sort of reach together, almost like with an alien species, try to find a protocol, a communication protocol that works, where do you see the biggest, uh, the biggest benefit arriving from? On the machine side or the human side? Do you see both of them working together?

    6. EM

      I actually think the machine side is far more malleable than the biological side, by a, by a huge amount. So, it'll be the- the machine that adapts to the brain. It ha- that's the only thing that's possible. The brain can't adapt that well to- to- to the machine. You can't have neurons start to regard an electrode as noth- another neuron, 'cause like, neurons just... they just like the pulse, and so something else is pulsing. So- so there's- there is that- there is that- that elasticity in the interface, which we believe is- is something that can- can happen. But the vast majority of malleability will have to be on the machine side.

    7. LF

      But it's interesting when you look at that synaptic plasticity at the int- interface side, there might be like an emergent plasticity. B- 'cause it's a whole nother... it's not like in the brain. It's a whole nother extension of the brain. You know, we might have to redefine what it means to be malleable for the brain. So, maybe the brain is able to adjust to external interfaces.

  7. 13:59 – 17:52

    Cortex vs. limbic system: the ‘monkey brain + computer’ model (and why it matters)

    1. EM

      There will be some adjustment to the brain 'cause there's- there's gonna be something reading and simulating the- the brain, and so it will adjust to- to that thing. But- but most, the vast majority of the adjustment will be on the machine side. This is just, this is just... it has to be that. Otherwise, it will not work. Ultimately, like, we know, we currently operate on two layers. We have sort of a limbic, like prime primitive brain layer, which is where all of our kind of impulses are coming from. It's sort of like we've got, we've got like a monkey brain with a computer stuck on it.

    2. LF

      (laughs)

    3. EM

      That's- that's the human brain. And a lot of our impulses and everything are driven by the monkey brain, and the- the computer, or the cortex, uh, is constantly trying to make the monke- monkey brain happy. It's not the cortex that's steering the monkey brain. It's the monkey brain steering the cortex, you know? So...

    4. LF

      But the cortex is the part that tells the story of the whole thing, so we convince ourselves it's- it's, uh, more interesting than just the monkey brain.

    5. EM

      (laughs) The cortex is like what we call like human intelligence, you know? So it's like the, that's like the advanced computer relative to other creatures. Uh, other cr- other creatures do not have either... r- really, they don't- they don't have the computer. Or they have a very weak computer relative to humans. But- but it's... just, it's like, it sort of seems like sh- surely the really smart things should control the dumb thing, but actually the dumb thing controls the smart thing. (laughs)

    6. LF

      So, do you think some of the same kind of machine learning methods, uh, whether that's natural language processing applications, are going to be applied for the communication between the machine and the brain? To- to learn how to do certain things like movement of the body, how to process visual stimuli and so on? Do you see the value of using machine learning to understand the language of the- the two-way communication with the brain?

    7. EM

      Sure, yeah, absolutely. I mean, we're a neural net and- and that, you know, AI is basically a neural net. So it's like digital neural net will interface with biological neural net and hopefully bring us along for the ride, you know? But the vast majority of our int- of our- of our intelligence will be digital.

    8. LF

      Does it...

    9. EM

      Like, like so... like think of like the- the difference in intelligence between your- your cortex and your limbic system is gigantic. Your- your- your limbic system really has no comprehension of what the hell the cortex is doing. Um, you know, it's just literally hungry, you know, or tired, or angry, or sexy, or something, you know?

    10. LF

      (laughs)

    11. EM

      That's... and- and just... and- and then it... that communicates that- that impulse to the cortex and tells the cortex to go satisfy that. (laughs) Then a lot of... a great deal of... like, a massive amount of thinking, like truly stupendous amount of thinking has gone into sex.

    12. LF

      Okay.

    13. EM

      Without purpose, without procreation- without procreation.

    14. LF

      Yeah.

    15. EM

      Which- which is actually quite a silly action in the absence of procreation. It's- it's a bit silly.

    16. LF

      Well...

    17. EM

      So, why are you doing it?

    18. LF

      Assuming everything-

    19. EM

      Because it makes the limbic system happy. That's why.

    20. LF

      That's why.

    21. EM

      But it's pretty absurd, really. (laughs)

    22. LF

      (laughs) Well, the whole of existence is pretty absurd in some kind of sense.

    23. EM

      Yeah. But- but I mean, this is a lot of computation has gone into, "How can I do more of that?"

    24. LF

      (laughs)

    25. EM

      With procreation not even being a factor. This is, I think, a very important area of research for NSFW.

    26. LF

      (laughs) Uh, an agency that should receive a lot of funding, especially after this conversation.

    27. EM

      (laughs) I propose the formation of a new agency.

    28. LF

      Oh boy. Uh (laughs)

    29. EM

      (laughs)

  8. 17:52 – 19:45

    Neuralink’s near-term medical wins and long-term goal: joining the AI wave

    1. LF

      What is the most exciting or some of the most exciting things that you see in the future impact of Neuralink, both in the science, the engineering and societal broad impact?

    2. EM

      So Neuralink, I think at first, will solve a lot of brain-related diseases. So-... uh, it could be anything from, like, autism, schizophrenia, memory loss. Like, everyone experiences mem- memory loss at, at certain points in- in age. Parents can't remember their- their kids' names and that kind of thing. So there's, I think, a tremendous amount of good that, uh, Neuralink can do in solving, uh, critical, uh, uh, critical damage to the brain or the spinal cord. There's a lot that can be done to improve quality of life of individuals, and that will be- those will be steps along the way. And then ultimately, it's intended to address the ex- the risk- the existential risk associated with, uh, digital superintelligence. Um, like, we will not be able to f- be smarter than a- a- a- es- a digital supercomputer. Um, so therefore, if you cannot beat 'em, join 'em. And at least we won't have that option.

    3. LF

      So you- you- you have hope that Neuralink will be able to be a kind of, um, connection to allow us to- to merge, to ride the wave of the improving, uh, AI systems?

    4. EM

      I think the chance is above 0%.

    5. LF

      So it's non-zero?

    6. EM

      Yes.

    7. LF

      There's a chance, and that's, uh-

    8. EM

      So what I'm s- have you seen Dumb and Dumber? (laughs)

    9. LF

      Yes, yes.

    10. EM

      So I'm saying there's a chance.

    11. LF

      So you're saying one in a billion or one in a million, whatever it was in Dumb and Dumber...

    12. EM

      You know, it went from maybe one in a million to improving, maybe it'll be one in a thousand, and then one in a hundred, then one in ten. It depends on the rate of improvement of Neuralink and how fast we're able to do- make progress, you know?

    13. LF

      Well, I've talked to a few folks here that are quite brilliant engineers, so I'm- I'm excited.

  9. 19:45 – 22:06

    A third layer of intelligence: peaceful coexistence with digital superintelligence

    1. EM

      Yeah, I- I think it's, like, fundamentally good, you know? Who- you know, giving somebody back full motor control after they've had a spinal cord injury, you know, restoring brain functionality after a stroke, um, solving debilitating, genetically-oriented brain diseases, these are all incredibly great, I think. And in order to do these, you have to be able to interface with the neurons at the detail level and you need to be able to, um, fire the r- right neurons, read the right neurons, and- and then effectively, you can create a- a circuit, replace what's broken with- with silicon, and essentially fill in the- the missing functionality. And then over time, we can have- we develop a tertiary layer. So if, like, the limbic system is a primary layer, then the cortex is like a sec- the second layer. Um, and I said that, you know, obviously the cortex is vastly more intelligent than the limbic system, but people generally like the fact that they have a limbic system and a cortex. I haven't met anyone who wants to delete either one of them. There's- they're like, "Okay, I'll keep 'em both." That's cool.

    2. LF

      The limbic system's kind of fun.

    3. EM

      That's where all the fun is, yep, absolutely, um, and then you- you- pe- people generally don't wanna, uh, lose the cortex either, right? So they like having the cortex and the limbic system.

    4. LF

      Yeah.

    5. EM

      Uh, and- and then there's a tertiary layer, which will be digital superintelligence, and, uh, I think there's room for optimism given that the cortex, the su- the- the- the cortex is very intelligent and the limbic system is not, and yet they work together well. Perhaps they can be a tertiary layer, uh, wh- where- where digital superintelligence lies, and that- that will be vastly more intelligent than the cortex but still coexist peacefully and in- in a benign manner with the cortex and limbic system.

    6. LF

      That's a super exciting future, both in the low-level engineering that I saw is being done here and the actual possibility in the next few decades.

    7. EM

      It- it's important that Neuralink solve this problem sooner rather than later, because the point at which we have digital superintelligence, that's when we pass the singularity and- and things become just very uncertain. It doesn't mean that they're necessarily bad or good, but the point at which we pass singularity, things become extremely unstable. So we want to have a human brain interface before the singularity, or at least not long after it, to minimize existential risk for humanity and consciousness as we know it.

  10. 22:06 – 25:00

    Neuralink engineering reality: materials, chips, heat, microfabrication, and automation

    1. LF

      So, but there's a lot of fascinating actual engineering low-level problems here at Neuralink that-

    2. EM

      Yeah.

    3. LF

      ... are quite- uh, quite exciting. What, uh-

    4. EM

      The- the problems that we face at Neuralink are material science, electrical engineering, software, mechanical engineering, microfabrication. It's a bunch of engineering disciplines, essentially. That's what it comes down to, is that you have to have a- a- a tiny electrode, so small- so small it doesn't hurt- hurt neurons, um, but it's gotta last for as long as a person, so it's gotta last for decades, uh, and then you've gotta take that signal and you've got to, uh, process that signal loc- signal locally at low power, so we need a lot of chip design, uh, engineers that are- y- you know, 'cause we gotta do, uh, signal processing and do so in- in a very power-efficient way so that we don't heat your brain up, um, 'cause the brain's very heat sensitive. Um, and then- and then we've gotta take those signals and we've gotta do something with them, and then we've gotta stimulate in res- stimulate the, uh, back to- to, you know, so you could... um, bidirectional communication. Um, so if somebody's good at material science, software, um, mechanical engineering, electrical engineering, chip design, microfabrication, uh, that's what- those are the things we need to work on. We need to be good at material science so that the- that we can have tiny electrodes that last a long time, and it's- and it's the tough thing with the- the material science problem. It's a tough one because you're trying to, uh, read and stimulate electrically in a- an el- an electrically active area. Your brain is very electrically active, l- and electrochemically active. So how do you have a- a- a, say, a coating on the electrode that doesn't dissolve over time, um, and, uh, and- and is safe in the brain? This is a very hard problem, and then- and then- and then how do you, um, collect those signals in a way that is most efficient? 'Cause you- you really just have very tiny amounts of power to process those signals.... you know, and then we need to automate the whole thing so it's like LASIK, you know? So it's, so it's- it's not ... y- y- if this is done by neurosurgeons, there's no way it can scale to large numbers of people, and it needs to scale to large numbers of people 'cause I think ultimately, we want the future be d- to be determined by a large number of- th- the- uh, humans.

    5. LF

      Do you think that this has a chance to revolutionize surgery period? So neurosurgery and-

    6. EM

      Yeah, yeah, absolutely.

    7. LF

      ... surgery all across-

    8. EM

      Yeah, for sure. It's gotta be like LASIK. Like ima- if LASIK had to be hand-done, d-done by hand, by a person, that wouldn't be great.

    9. LF

      Yeah.

    10. EM

      You know? So ... it's- it's done by a robot, uh, and the ophthalmologist kind of just needs to make sure y- your- your head's in the right position and then they just press a button and go.

  11. 25:00 – 27:17

    Tesla autonomy as mass robotics education: Smart Summon and ‘car personality’

    1. LF

      So Smart Summon, and soon Auto Park, takes on the full beautiful mess of parking lots and their human-to-human nonverbal communication. I think it has, actually, uh, the potential to have a profound impact in changing how our civilization looks at AI and robotics-

    2. EM

      Mm-hmm.

    3. LF

      ... because this is the first time human beings, people that don't own a Tesla, may have never seen a Tesla or heard about a Tesla, get to watch hundreds of thousands of cars without a driver.

    4. EM

      Yeah.

    5. LF

      Do you see it this way, almost like an education tool for the world about AI? Do you feel the burden of that, the excitement of that? Or do you just think it's a smart parking feature?

    6. EM

      I- I do think you're- you are getting at something important there, which is most people have never really seen a robot, right? Or ... and- and what- what is the car that is autonomous? It's a four-wheeled robot.

    7. LF

      Right. Yeah, but it- it communicates a certain sort of message with everything from safety to the possibility of what AI could bring, to its current limitations, its current challenges, its- what's possible. Do you feel the burden of that, almost like a communicator/educator to the world about AI?

    8. EM

      We're just really trying to make people's lives easier with au- autonomy, but now that you mention it, I think it will be an eye-opener to people about robotics 'cause they've d- really never seen ... most people have never seen a robot and there are hundreds of thousands of Teslas, won't be long before there's a million of 'em, that have autonomous capability and they drive without a person in it, uh, and you- you'll- you can see the kind of evolution of the car's personality and- and thinking, um, with each iteration of, uh, of Autopilot. You can see it's- it's, uh, uncertain about this or it gets ... and- but now it's more certain. Um, now- now it's moving in a slightly different way. Um, like I can tell immediately if a car is o- on Tesla Autopilot 'cause it's got just little nuances of movement. It just moves in a slightly different way. Um, it's- it- it ... cars on- on Tesla Autopilot, for example, on the highway, are far more precise about being in the center of the lane than a person. Uh, if you drive down the highway and look at how, at where cars are ... the human-driven cars are in- within their lane, th- they're like bumper cars. They're like moving all over the place. The car on Autopilot? Dead center.

  12. 27:17 – 30:52

    Path to full self-driving: highway maturity, low-speed autonomy, and the perception bottleneck

    1. LF

      Yeah, so th- the incredible work that's going into that neural network, it's learning fast. Uh, autonomy is still very, very hard. We don't actually know how hard it is fully, of course. Uh, you look at the ... uh, at most problems you tackle, this one included, in- uh, f- with an exponential lens, but even with an exponential improvement, things can take longer than expected sometimes. So where does Tesla currently stand on its quest for full autonomy? What- what's your sense? When can we see successful deployment of full autonomy?

    2. EM

      Well, on the highway already, the y- the probability of a- an intervention is extremely low.

    3. LF

      Yes.

    4. EM

      Um, so f- for highway autonomy, um, with the latest release especially, the probability of needing to intervene, um, is- is qu- is really quite low. In fact, I'd say for stop-and-go traffic, th- it's m- it's far safer than a person right now in stop-and-go. The probability of an injury or an impact is much, much lower for Autopilot than a person. And then with Navigator and Autopilot, you can change lanes, take highway interchanges and then w- we're coming at it from the other direction, which is low speed, full autonomy. And in a way this is like, it's like how- how does a person learn to drive? You learn to drive in the parking lot, you know? You don't ... first time you learned to drive probably wasn't jumping on Market Street in San Francisco. That would be crazy. You learn to drive in- in the parking lot, get things- get things right at low speed and, um, and then the missing piece that we're working on is traffic lights and stop streets. Stop streets- s- stop streets, I would say are actually also relatively easy because y- you know, you kind of know where the stop street is. Worst case, you can geocode it and then, uh, use visualization to see where the line is and stop at the line to l- eliminate the GPS error. Uh, so it's actually, I'd say it's probably complex traffic lights and very windy roads are the two things that need to get solved.

    5. LF

      What's harder, perception or control for these problems? So being able to perfectly perceive everything or figuring out a plan once you perceive everything, how to interact with all the agents in the environment? In your sense, from a learning perspective, is perception or action harder in that giant, beautiful, multitask learning neural network?

    6. EM

      The h- the hardest thing is having accurate representation of the physical objects in vector space. So trans- t- taking the visual input, primarily visual input, uh, some, uh, sonar and radar and- and then creating the ... an- an accurate vector space representation of the objects around you. Once you have an accurate vector space representation, the planning con- and control is relatively easier. I'd say it's relatively easy. Basically, once you have ...... accurate vector space representation, then, then you're, you're kind of like a video game, like cars in, like, Grand Theft Auto or something. Like, they work pretty well. They drive down the road, they don't crash, you know, pretty much, unless you crash into them. Um, that's because they've- they've got an accurate vector space representation of where the cars are, and they're just then, and then they're rendering that as the- as the output.

    7. LF

      Do you have a sense, high level, that Tesla's on track, uh, on being able to achieve full autonomy? So, on the highway-

    8. EM

      Yeah, yeah, absolutely.

    9. LF

      ... and still no driver state, driver sensing?

    10. EM

      We have driver sensing with torque on the wheel.

    11. LF

      That's right.

    12. EM

      Yeah.

  13. 30:52 – 35:54

    Fun, safety, and the cosmic perspective: karaoke, Carl Sagan, and the Pale Blue Dot

    1. LF

      By the way, just a quick comment on karaoke. Most people think it's fun, but I also think it is, it's a driving feature. I've been saying for a long time, singing in the car is really good for attention management and vigilance management.

    2. EM

      That's right. Tesla karaoke, uh, yeah, is great. It's one of the most fun features of the car.

    3. LF

      You think of a connection between fun and safety sometimes?

    4. EM

      Yeah, if you can do both at the same time, that's great.

    5. LF

      I just met with Ann Druyan, wife of, uh, Carl Sagan-

    6. EM

      Oh, yeah.

    7. LF

      ... who directed ci- Cosmos.

    8. EM

      I'm generally I'm a big fan of Carl Sagan. He's re- super cool and had a great way of putting things. All the consciousness of all civilization, everything we've ever known and done is on this tiny blue dot. People also get... They get too trapped in their, it's like squabbles amongst humans and just don't think of the big picture and they take, uh, civilization and our continued existence for granted. They shouldn't do that. Look at the history of civilizations. They rise and they fall. And now civilization is all... It's globalized. And so civilization, I think now, rises and falls together. There's no, there's not geographic isolation. This is a big risk. Things don't always go up. That should be... That's an important lesson of history.

    9. LF

      In 1990, at the request of Carl Sagan, the Voyager 1 spacecraft, which is a spacecraft that's reaching out farther than anything human made into space, uh, turned around to take a picture of Earth from 3.7 billion miles away, and as you're talking about the pale blue dot, that picture, the Earth takes up less than a single pixel in that image-

    10. EM

      Yes.

    11. LF

      ... appearing as a tiny blue dot as, uh, pale blue dot, as Carl Sagan called it. So he spoke about this dot of ours in 1994, and if you could humor me, I was wondering if, in the last two minutes, you could, uh, read the words that he wrote describing this pale blue dot.

    12. EM

      Sure. Yeah, so it's funny, the- the universe appears to be 13.8 billion years old. Earth is, like, four and a half billion years old. In another half billion years or so, the sun will expand and probably evaporate the oceans and make life impossible on Earth, which means that if it had taken consciousness 10% longer to evolve, it would never have evolved at all. Just 10% longer. Um, and I wonder, I wonder how many dead one-planet civilizations there are out there in the cosmos that never made it to the other planet and ultimately extinguished themselves or were destroyed by external factors. Probably a few. It's only just possible to tr- to travel to Mars, just barely. If g was 10% more, whew, wouldn't work, really. If g was 10% lower, it would be easy. Like, you can go single stage from the surface of Mars all the way to the surface of the Earth, because Mars is 37% Earth's gravity thereabout. You need a giant booster to get off the Earth. Channeling Carl Sagan, "Look again at that dot. That's here. That's home. That's us. On it, everyone you love, everyone you know, everyone you've ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every young couple in love, every mother and father, hopeful child, inventor, and explorer, every teacher of morals, every corrupt politician, every superstar, every supreme leader, every saint and sinner in the history of our species lived there, on a mote of dust suspended in a sunbeam. Our planet is a lonely speck in the great enveloping cosmic dark. In our obscurity, in all this vastness, there is no hint that help will come from elsewhere to save us from ourselves. The Earth is the only world known so far to harbor life. There is nowhere else, at least in the near future, to which our species could migrate." This is not true (laughs) . This is false. Mars.

    13. LF

      And I think Carl Sagan would agree with that. He couldn't even imagine it at that time. So thank you for making the world dream, and thank you for talking today. I really appreciate it. Thank you.

    14. EM

      Thank you.

Episode duration: 36:09

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