#217 – Rodney Brooks: Robotics

Rodney Brooks is a roboticist, former head of CSAIL at MIT, and co-founder of iRobot, Rethink Robotics, and Robust. AI. Please support this podcast by checking out our sponsors: – Paperspace: https://gradient.run/lex to get $15 credit – GiveDirectly: https://givedirectly.org/lex to get gift matched up to $300 – BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off – Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off – SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera EPISODE LINKS: Rodney’s Twitter: https://twitter.com/rodneyabrooks Rodney’s Blog: http://rodneybrooks.com/blog/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:33) – First robots (28:58) – Brains and computers (1:01:47) – Self-driving cars (1:21:57) – Believing in the impossible (1:32:47) – Predictions (1:43:49) – iRobot (2:11:11) – Sharing an office with AI experts (2:23:21) – Advice for young people (2:27:07) – Meaning of life

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 Rodney Brooks, one of the greatest roboticists in history. He led the Computer Science and Artificial Intelligence Laboratory at MIT, then co-founded iRobot, which is one of the most successful robotics companies ever. Then he co-founded Rethink Robotics that created some amazing collaborative robots like Baxter and Sawyer. Finally, he co-founded robust.ai, whose mission is to teach robots common sense, which is a lot harder than it sounds. To support this podcast, please check out our sponsors in the description. As a side note, let me say that Rodney is someone I've looked up to

• 0:41Lex Fridman?

  for many years in my now over two-decade journey in robotics. Because, one, he's a legit great engineer of real-world systems. And, two, he's not afraid to state controversial opinions that challenge the way we see the AI world. But of course, while I agree with him on some of his critical views of AI, I don't agree with some others. And he's fully supportive of such disagreement. Nobody ever built anything great by being fully agreeable. There's always respect and love behind our interactions. And when a conversation is recorded, like it was for this podcast, I think a little bit of disagreement is fun.

• 1:24Lex Fridman?

  As usual, I'll do a few minutes of ads now, no ads in the middle. I try to make these interesting, so hopefully you don't skip. But if you do, please still check out the sponsor links in the description. It is the best way to support this podcast. I use their stuff and enjoy it, maybe you will too. This show is brought to you by Paperspace Gradient. These guys are amazing. It's a platform that lets you build, train, and deploy machine learning models of any size and complexity. I love how powerful and intuitive it is. I'm likely going to use Paperspace for a couple of machine learning experiments I'm doing as part of an upcoming video. Fast.ai, of course, I highly recommend uses it.

• 2:04Lex Fridman?

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• 2:46Lex Fridman?

  And I hope to make a bunch of machine learning videos in the near future. This show is also brought to you by GiveDirectly, a nonprofit that lets you send money directly to people living in extreme poverty. GiveDirectly donors includes previous guests of this very podcast like Jack Dorsey, Elon Musk, Vitalik Buterin, Will MacAskill, and Peter Singer. It may seem like there's more optimal ways to do it, but that's not actually the case. Hundreds of independent studies have shown that direct giving can have positive impacts on health, nutrition, income, education, and more. Studies show giving cash unconditionally can more than double incomes, increase school enrollment and entrepreneurship, decrease skipped meals, illness and depression,

• 3:30Lex Fridman?

  and cut domestic violence by one-third. It does not decrease hours worked or increase spending on temptations like tobacco and alcohol. There's a spillover effect to where every dollar given amounts to $2.60 in the local economy. In the last decade, GiveDirectly has delivered $400 million to over 900,000 recipients across nine countries. Visit givedirectly.org/lex and your first gift will be matched up to $300. That's givedirectly.org/lex.

• 4:03Lex Fridman?

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• 4:47Lex Fridman?

  And BiOptimizers is just an easy way to get it right. It's kind of incredible when you get this part right, how easy it is to do the keto or the fasting. Anyway, go to magbreakthrough.com/lex for a special discount. That's magbreakthrough.com/lex. This show is also sponsored by Four Sigmatic, the maker of delicious mushroom coffee and plant-based protein. Does the coffee taste like mushrooms, you ask? No, it does not. It's delicious and also just the ritual of drinking hot coffee that has this aromatic flavor and obviously smell to it in the morning.

• 5:30Lex Fridman?

  That's just something that's like a catalyst for my mind to get extremely focused in the morning. It's the caffeine, the aroma, the hot coffee, the cup with the steam coming off of it. That's when I know I'm ready to get in the zone. And the first two, three hours, I try to make sure I allocate to deep work and deep thinking, really focusing on the difficult tasks where my mind is sharp and jumping from one element of a task to another and getting stuff done. Anyway, get up to 40% off and free shipping on mushroom coffee bundles if you go to foursigmatic.com/lex. That's foursigmatic.com/lex.

• 6:10Lex Fridman?

  This show is also brought to you by SimpliSafe, a home security company designed to be simple and effective. It takes only 30 minutes to set up. You can customize the system for your needs on simplisafe.com/lex. I have it set up in my place and love it. It's the whole thing. The cameras, the monitoring, the response from the setup to the day to day operation. It's super simple. Like I've said many times before, I just like it when people design solutions and implement them really well. And in the home security company space, SimpliSafe is the best. You can go on the internet and ask around on Reddit and elsewhere, like, what is the best home security company? And a lot of people say SimpliSafe.

• 6:55Lex Fridman?

  Anyway, go to simplisafe.com/lex to customize your system and get a free security camera plus a 60 day risk free trial. Again, that's simplisafe.com/lex. This is the Lex Fridman Podcast, and here is my conversation with Rodney Brooks.

• 7:33Lex Fridman?

  What is the most amazing or beautiful robot that you've ever had the chance to work with?

• 7:39Speaker 01

  I think it was Domo, which was made by one of my grad students, Aaron Edsinger. It now sits in Daniela Roos's office, Director of CSAL. And it was just a beautiful robot. And Aaron was really clever. He didn't give me a budget ahead of time. He didn't tell me what he was going to do. He just started spending money. He spent a lot of money. He and Jeff Weber, who is a mechanical engineer, who Aaron insisted he bring with him when he became a grad student, built this beautiful gorgeous robot, Domo, which is an upper torso humanoid, two arms.

• 8:17Lex Fridman?

  With fingers?

• 8:18Speaker 01

  Three fingered hands. Uh, and face eyeballs. All, uh, not the, not the eyeballs, but everything else, series elastic actuators. You can interact with it. Cable driven, all the motors are inside and it's just gorgeous.

• 8:35Lex Fridman?

  The eyeballs are actuated too, or no?

• 8:37Speaker 01

  Oh yeah, the eyeballs are actuated with cameras and, you know, so had a visual attention mechanism, you know?

• 8:42Lex Fridman?

  Wow.

• 8:43Speaker 01

  Looking when people came in and looking in their face and talking with them.

• 8:48Lex Fridman?

  Wow, was it amazing?

• 8:50Speaker 01

  The beauty of it. You said, you said what was the most beautiful?

• 8:53Lex Fridman?

  What is the most beautiful?

• 8:54Speaker 01

  It's just mechanically gorgeous. As, as everything Aaron builds, has always been mechanically gorgeous. It's just exquisite in the detail.

• 9:02Lex Fridman?

  Are we talking about mechanically, like literally the amount of actuators?

• 9:06Speaker 01

  The actuators, the cables, he anodizes different parts, different colors. And it's just looks like a work of art.

• 9:15Lex Fridman?

  What about the face? Is that, do you find the face beautiful in robots?

• 9:19Speaker 01

  When you make a robot, it's making a promise for how well it will be able, able to interact. So I always encourage my students not to over promise, you know?

• 9:29Lex Fridman?

  Like even with its essence, like the, the thing it presents, it should not over promise.

• 9:33Speaker 01

  Yeah. So the, the joke I make, which I think you'll get is if your robot looks like Albert Einstein, it should be as smart as Albert Einstein. So, uh, the, the only thing in, in Domo's face is, um, the eyeballs. And, cause that's all it can do. It can look at you and pay attention. And so there is no, it's not like one of those, um, Japanese robots that looks exactly like a person at all.

• 10:01Lex Fridman?

  But see, the thing is us humans and dogs too, don't just use eyes for, as attentional mechanisms. They also use it to communicate, as part of the communication. Like a dog can look at you, look at another thing and look back at you. And that designates that we're going to be looking at that thing.

• 10:18Speaker 01

  Yeah, or, or intent, you know, and on, on, on both Baxter and Sawyer at Rethink Robotics. They had a screen with, you know, graphic eyes. So it wasn't actually where the cameras were pointing, but it, the, the eyes would look in the direction it was about to move its arm. So people in the factory nearby were not surprised by its motions because it gave that intent away.

• 10:42Lex Fridman?

  Before we talk about Baxter, which I think is a beautiful robot, let's go back to the beginning. When did you first fall in love with robotics? We're talking about beauty and love to open the conversation. This is great.

• 10:55Speaker 01

  I've got these, I was born in the end of 1954 and I grew up in Adelaide, South Australia. And I have these two books that are dated 1961. So I'm guessing my mother found them in a store in 62 or 63. How and Why Wonder Books? How and Why Wonder Book of Electricity? And How and Why Wonder Book of Giant Brains and Robots? And I learned how to build circuits, you know, when I was eight or nine, simple circuits. And I, and I read, you know, learned the binary system and saw all these drawings mostly of robots. And then I tried to build them for the rest of my childhood.

• 11:40Lex Fridman?

  Wait, 61, you said?

• 11:42Speaker 01

  This was when the two books, I've still got them at home.

• 11:45Lex Fridman?

  What does the robot mean in that context?

• 11:47Speaker 01

  No, they, they were, some of the robots that they had were arms, you know, big arms to move nuclear material around. But they had pictures of welding robots that looked like humans under the sea welding stuff underwater. So they weren't real robots, but they were, you know, what people were thinking about for robots.

• 12:09Lex Fridman?

  What were you thinking about? Were you thinking about humanoids? Were you thinking about arms with fingers? Were you thinking about faces or cars?

• 12:16Speaker 01

  No, actually, to be honest, I realized my limitation on building mechanical stuff. So I just built the brains mostly out of different technologies as I got older. I built a, a learning system, which was chemical based. And I had this ice cube tray. Each well was a cell. And by applying voltage to the two electrodes, it would build up a copper bridge. So over time, it would, it would learn a simple network. So I could teach it stuff. And that was.

• 12:54Speaker 01

  Mostly, things were driven by my budget. And nails as electrodes and a, and a, and a ice cream, I mean, an ice cube tray was, was about my budget at that stage. Later, I managed to buy transistors and then I could build gates and flip flops and stuff.

• 13:09Lex Fridman?

  So, so one, one of your first robots was an ice cube tray?

• 13:13Speaker 01

  Yeah.

• 13:15Speaker 01

  And it was very cerebral because it learned to add.

• 13:18Lex Fridman?

  Very nice. Uh, well, just a decade or so before, in, in 1950, Alan Turing wrote the paper that formulated the Turing test. And he opened that paper with the question, can machines think? So let me ask you this question. Can machines think? Can your ice cube tray one day think?

• 13:42Speaker 01

  Um, certainly, machines can think because I believe you're a machine and I'm a machine and I believe we both think. Um, I think, I think any other philosophical position is sort of a, a little ludicrous. What does think mean if, if it's not something that we do? Um, and we, and we are machines. Um, so yes, machines can, but do we have a clue how to build such machines? That's a very different question. Are we capable of building such machines? Uh, you know, are we smart enough? We think we're smart enough to do anything, but maybe we're not. Maybe, you know, we're just not, not smart enough to build stuff like us.

• 14:17Lex Fridman?

  The kind of computer that Alan Turing was thinking about, do you think there is something fundamentally, uh, or significantly different between a computer between our ears, the, the biological computer that humans use, and, uh, the computer that he was thinking about from a, from a sort of high level philosophical?

• 14:36Speaker 01

  Yeah, I, I believe that, uh, that it's very wrong. In fact, I'm halfway through a, I think it'll be about a 480-page book, um, titled, the working title is Not Even Wrong. And if I may, I'll tell you a bit about that book. So there's two, two, well, three thrusts to it. Um, one is the history of computation, what we call computation. Goes all the way back to, uh, uh, some manuscripts in Latin from 1614 and 1620 by Napier and Kepler through Babbage and Lovelace. And then-

• 15:13Speaker 01

  Turing's 1936 paper is what we think of as the invention of of of modern computation. And that paper, by the way, did not set out to, you know, invent computation. It set out to negatively answer one of Hilbert's three later set of problems. He called it an effective way of of of getting answers. And Hilbert, Hilbert really worked with rewriting rules, as did Church, who also, at the same time, a month earlier than Turing, disproved Hilbert's one of these three hypotheses.

• 15:56Speaker 01

  The other two had already been disproved by Gödel. So Turing set out to disprove it because that's it's always easier to disprove these things than to prove that there is an answer. And so he needed, and it really came from his professor, I was an undergrad at Cambridge, who'd said, who'd turned it into, is there a mechanical process? So he wanted to have a show a mechanical process that could calculate numbers. Because that was a mechanical process that people used to generate tables. They were called computers, the people at the time. And they followed a set of rules where they had paper and they would write numbers down. And based on the numbers, they'd keep writing other numbers.

• 16:41Speaker 01

  And they would produce numbers for these tables, engineering tables, that the more, the more iterations they did, the more significant digits came out. And so Turing, in that paper, set out to define what sort of machine could do that, mechanical machine, where it could produce an arbitrary number of digits in the same way a human computer did. And he came up with a very simple

• 17:13Speaker 01

  Set of constraints where there was an infinite supply of paper. This is the tape of the Turing machine. And each Turing machine had a set, I mean, came with a set of instructions that as a person could do with pencil and paper, write down things on the tape and erase them and put new things there. And he was able to show that that system was not able to do something that Hilbert had hypothesized. So he disproved it. But he had to show that this was, this, this system was good enough to do whatever could be done, but couldn't do this other thing. Yeah. And there, he said, and he says in the paper, I don't have any real arguments for this, but based on intuition.

• 17:57Speaker 01

  So that's how he defined computation. And then if you look over the next, from 1936 up until really around 1975, you see people struggling with, is this really what computation is? And so Marvin Minsky, very well known in AI, but also a fantastic mathematician. In his book Finite and Infinite Machines from the mid 60s, which is a beautiful, beautiful mathematical book.

• 18:26Speaker 01

  Says, says at the start of the book, well, what is computation? Turing says it's this and yeah, I sort of think it's that. It doesn't really matter whether the stuff's made of wood or plastic. It's just, you know, that relatively cheap stuff can do this stuff. And so, yeah, it seems like computation. And Donald Knuth, in his first volume of his, you know, Art of Computer Programming, in around 1968, says, well, what's computation? It's this stuff, like Turing says, that a person could do each step without too much trouble. And so one of his examples of what would be too much trouble was a step which required knowing whether Fermat's Last Theorem was true or not,

• 19:10Speaker 01

  because it was not known at the time. And that's too much trouble for a person to do as a step. And Hopcraft and Allman sort of said a similar thing later that year. And by 1975, in the Aho, Hopcraft and Allman book, they're saying, well, you know, we don't really know what computation is, but intuition says this is sort of about right. And this is what it is. That's computation. It's a sort of. Agreed upon thing which happens to be really easy to implement in silicon. And then we had Moore's law, which took off and it's been incredibly powerful tool. I certainly wouldn't argue with that. The version we have of computation incredibly powerful.

• 19:53Lex Fridman?

  Can we just take a pause? So what we're talking about is there's an infinite tape with some simple rules of how to write and on that tape and that's, that's what we're kind of thinking about. This is computation.

• 20:03Speaker 01

  Yeah, and it's modeled after humans, how humans do stuff. And I think it's a, Turing says in the thirty six paper, one of the critical facts here is that a human has a limited amount of memory. So that's what we're going to put onto our, our mechanical computers. So, so, you know, unlike mass, unlike mass or charge or, you know, it's not, it's not given by the universe. It was, this is what we're going to call computation.

• 20:30Lex Fridman?

  Yeah.

• 20:31Speaker 01

  And then it has this really, uh, you know, it had this really good implementation, which has completely changed our technological world. That's computation.

• 20:41Speaker 01

  Second part of the book, I, or, or argument in the book, I have this two by two matrix with science in the top row, engineering in the bottom row. Left column is intelligence, right column is life.

• 21:00Speaker 01

  So in the bottom row, the engineering, there's artificial intelligence and there's artificial life. In the top row, there's neuroscience and abiogenesis. How does living matter turn and how does non living matter become living matter?

• 21:14Lex Fridman?

  Yes.

• 21:14Speaker 01

  Four disciplines. These four disciplines all

• 21:20Speaker 01

  came into the current form in the period nineteen forty five to nineteen sixty five.

• 21:26Lex Fridman?

  That's interesting.

• 21:27Speaker 01

  There was neuroscience before, but it wasn't effective neuroscience. It was, you know, there was ganglia and there's electrical charges, but no one knows what to do with it. And furthermore, there are a lot of players who are common across them. I've got, I've identified common players except for artificial intelligence and abiogenesis. I don't have, but for any other pair, I can point to people who work in. And a whole bunch of them, by the way, were at the research lab for electronics at MIT, where Warren McCulloch held, held, held forth.

• 21:59Speaker 01

  And in fact, McCulloch, Pitts, Letton and Machiavelli wrote the first paper on functional neuroscience called What the Frog's Eye Tells the Frog's Brain, where instead of it just being this bunch of nerves, they sort of showed what different anatomical components were doing and telling other anatomical components and, you know, generating behavior in the frog.

• 22:25Lex Fridman?

  Would you put them as basically the fathers or the one of the early pioneers of what are now called artificial neural networks?

• 22:34Speaker 01

  Yeah, I mean, McCulloch and Pitts, Pitts was much younger than him, in 1943, had written a paper inspired by Bertrand Russell on a calculus for the ideas eminent in neural systems, where they had tried to, without any real proof, they had tried to give a formalism for neurons, basically in terms of logic and gates, all gates and not gates, with no real evidence that that was what was going on, but they talked about it. And that was picked up by Minsky for his 1953 dissertation on, which was a neural network,

• 23:18Speaker 01

  we would call it today. It was picked up by John von Neumann when he was designing the Ed Back computer in 1945. He talked about its components being neurons, based on, and in references, he's only got three references, and one of them is the McCulloch-Pitts paper. So all these people, and then the AI people and the artificial life people, which was John von Neumann originally.

• 23:43Lex Fridman?

  It's like overlap, which you know.

• 23:45Speaker 01

  They're all going around at the same time. And three of these four disciplines turned to computation as their primary metaphor. So I've got a couple of chapters in the book. One is titled, Wait, Computers Are People? Because that's where our computers came from.

• 24:02Lex Fridman?

  Yeah.

• 24:03Speaker 01

  And, you know, from people who were computing stuff. And then I've another chapter, Wait, People Are Computers?, which is about computational neuroscience.

• 24:12Lex Fridman?

  Yes.

• 24:13Speaker 01

  So there's this whole circle here. And that computation is it. And, you know, I have talked to people about, no, maybe it's not computation that goes on in the head. Of course it is.

• 24:26Lex Fridman?

  Yeah.

• 24:26Speaker 01

  Okay, well.

• 24:29Speaker 01

  When Elon Musk's rocket goes up, is it computing? Is that how it gets into orbit, by computing? But we've got this idea, if you want to build an AI system, you write a computer program.

• 24:41Lex Fridman?

  Yeah, in a sense, so the word computation very quickly starts doing a lot of work that was not initially intended to do. It's the second of saying, if you talk about the universe as essentially performing a computation.

• 24:55Speaker 01

  Yeah, right. Wolfram does this. He turns it into computation. You don't turn rockets into computation.

• 25:01Lex Fridman?

  Yeah. By the way, when you say computation in our conversation, do you tend to think of computation narrowly in the way Turing thought of computation?

• 25:09Speaker 01

  It's gotten very, you know, squishy.

• 25:16Lex Fridman?

  Yeah.

• 25:16Speaker 01

  Squishy.

• 25:17Lex Fridman?

  Okay.

• 25:19Speaker 01

  But computation in the way Turing thinks about it and the way most people think about it actually fits very well with thinking like a hunter-gatherer. There are places and there can be stuff in places and the stuff in places can change and it stays there until someone changes it. And it's this metaphor of place and container, which, you know, is a combination of our place cells in our hippocampus and our cortex. But this is, this is how we use metaphors for mostly to think about. And when we get outside of our metaphor range, we have to invent tools which we can sort of switch on to use. So calculus is an example of a tool.

• 26:04Speaker 01

  It can do stuff that our raw reasoning can't do. And we've got conventions of when you can use it or not. But sometimes, you know, people try to, all the time, we always try to get physical metaphors for things, which is why quantum mechanics has been such a problem for 100 years. Because it's a particle. No, it's a wave. It's got to be something we understand. And I say, no, it's some weird mathematical object that's different from those. But we want that metaphor. Well, you know, I suspect that, that, you know, 100 years or 200 years from now, neither quantum mechanics nor dark matter will be talked about in the same terms, you know, in the same way that.

• 26:45Speaker 01

  Flodgen's theory eventually went away because it just wasn't an adequate explanatory metaphor. That metaphor was the stuff, there is stuff in the burning, the burning is in the matter. It turns out the burning was outside the matter, it was the oxygen.

• 27:03Lex Fridman?

  So our desire for metaphor and combined with our limited cognitive capabilities gets us into trouble.

• 27:10Speaker 01

  That's my argument in this book. Now, and people say, well, what is it then? And I say, well, I wish I knew that, I'd write a book about that, but I, you know, give some ideas. But so, so there's the three things. Computation is sort of a particular thing we use.

• 27:26Speaker 01

  Oh, can I tell you one beautiful thing?

• 27:28Lex Fridman?

  Yes.

• 27:29Speaker 01

  So, you know, I used an example of a thing that's different from computation. You hit a drum and it vibrates and there are some, some stationary points on the drum's surface. You know, because the waves are going up and down the stationary points. Now, you could compute them to arbitrary precision. But the drum just knows them, the drum doesn't have to compute. What was the very first computer program ever written by Ada Lovelace? To compute Bernoulli numbers and Bernoulli numbers are exactly what you need to find those stable points in the drum's surface.

• 28:03Lex Fridman?

  Wow.

• 28:04Speaker 01

  Anyway, and there was a bug in the program. The arguments to divide were reversed in one place.

• 28:12Lex Fridman?

  And it still worked?

• 28:13Speaker 01

  Well, no, she never got to run it. They never built the analytical engine. She wrote the program without, without a, you know.

• 28:21Lex Fridman?

  So, so your computation?

• 28:23Speaker 01

  Computation is sort of, you know, a thing that's become dominant as a metaphor. But is it the right metaphor? All three of these four fields adopted computation and, you know, the, a lot of it swirls around Warren McCulloch and his, all his students and he funded a lot of people. And.

• 28:47Speaker 01

  And and our human metaphors, our limitations to human thinking will play into this. Those are three themes of the book. So I have a little to say about computation.

• 28:58Lex Fridman?

  So you're saying that there is a gap between the computer or the the the machine that performs computation and this machine that appears to have consciousness and intelligence.

• 29:15Speaker 01

  Yeah.

• 29:15Lex Fridman?

  Can we?

• 29:16Speaker 01

  That piece of meat in your head.

• 29:18Lex Fridman?

  Piece of meat.

• 29:18Speaker 01

  And maybe it's not just the meat in your head, it's the rest of you too. I mean, you have you have you actually have a neural system in your gut.

• 29:27Lex Fridman?

  I tend to also believe, not believe, but we're now dancing around things we don't know. But I tend to believe other humans are important. Like, so we're almost like, I I just don't think we would ever have achieved the level of intelligence we have with other humans. I'm not saying so confidently, but I have an intuition that some of the intelligence is in the interaction.

• 29:53Speaker 01

  Yeah, and and I think, you know, I think it's seems to be very likely, again, we, you know, this is speculation. But we, our species, and probably probably Neanderthals to some extent, because you can find old bones where they seem to be counting on them by putting notches that were in the Neanderthals had done. We are able to put some of our stuff outside our body into the world, and then other people can share it. And then we get these tools that become shared tools. And so there's a whole coupling that would not occur in, you know, the single deep learning network, which was fed, you know, all of literature or something.

• 30:35Lex Fridman?

  Yeah, the the neural network can't step outside of itself. But is there is there some? Can we explore this dark room a little bit and try to get at something? What is the magic? Where does the magic come from in the human brain that creates the mind? What's your sense? As scientists that try to understand it and try to build it, what are the directions that if followed might be productive? Is it creative, interactive robots? Is it creating large deep neural networks that do like self-supervised learning? And just like we'll, we'll, we'll discover that when you make something large enough,

• 31:20Lex Fridman?

  some interesting things will emerge? Is it through physics and chemistry and biology, like artificial life angle? Like we'll sneak up in this four quadrant matrix that you mentioned? Is there anything you're most? If you had to bet all your money, financial.

• 31:36Speaker 01

  I wouldn't.

• 31:37Lex Fridman?

  Okay.

• 31:38Speaker 01

  So every intelligence we know, I mean, who's, you know, animal intelligence, dog intelligence, you know, octopus intelligence, which is a very different sort of architecture from, from us.

• 31:52Speaker 01

  All the intelligences we know perceive the world in some way and then have action in the world. But they're able to perceive objects in a way which is actually pretty damn phenomenal and surprising. You know, we tend to think, you know, that, that, that the box over here between us, which is a sound box, I think, is a blue box. But blueness is something that we construct with, with color constancy.

• 32:32Speaker 01

  It's not, it's not, it's not, the blueness is not a direct function of the photons we're receiving. It's actually context, you know, which is why.

• 32:43Speaker 01

  You can turn, you know, you maybe seen the examples where

• 32:49Speaker 01

  someone turns a stop sign into a some other sort of sign by just putting a couple of marks on them and the deep learning system gets it wrong. And everyone says, but the stop sign's red, you know, why is it, why is it thinking it's the other sort of sign? Because redness is not intrinsic in just the photons. It's actually a construction of an understanding of the whole world and the relationship between objects. The color constancy. But our tendency, in order that we get an archive paper really quickly, is you just show a lot of data and give the labels and hope it figures it out. But it's not figuring it out in the same way we do. We have a very complex perceptual understanding of the world. Dogs have a very different perceptual understanding based on smell. They go smell, smell a post, they can tell

• 33:33Speaker 01

  how many, you know, different dogs have visited it in the last 10 hours and how long ago. There's all sorts of stuff that we just don't perceive about the world. And just taking a single snapshot is not perceiving about the world. It's not seeing the, the registration between us and the object. And registration is a philosophical concept. Brian Cantwell Smith talks about a lot, very difficult,

• 33:59Speaker 01

  squirmy thing to understand. But I think none of our systems do that. We've always talked in AI about the symbol grounding problem, how our symbols that we talk about are grounded in the world. And when deep learning came along and started labeling images, people said, ah, the grounding problem has been solved. No, the labeling problem was solved with some percentage accuracy, which is different from the grounding problem.

• 34:22Lex Fridman?

  So you, uh, there's, uh, you agree with Hans Marvick and what's called the Marvick's paradox that highlights this counterintuitive notion that reasoning is easy, but perception and mobility are hard.

• 34:41Speaker 01

  Yeah, we shared an office when, um, when I was working on computer vision and he was working on his, his first mobile robot.

• 34:48Lex Fridman?

  What were those conversations like?

• 34:50Speaker 01

  They were great.

• 34:52Lex Fridman?

  So do you still kind of, maybe you can elaborate, do you still believe this kind of notion that perception is really, uh, hard? And like, can you make sense of why we humans have this poor intuition about what's hard and not?

• 35:06Speaker 01

  Well, well, let me, let me give a sort of a.

• 35:10Speaker 01

  And another, another story.

• 35:12Lex Fridman?

  Sure.

• 35:13Speaker 01

  If you go back to, you know, the original, um, you know, teams working on AI,

• 35:20Speaker 01

  from the late 50s into the 60s, you know, and you go to the AI lab at MIT. Who was it that was doing that? Was a bunch of really smart kids got into MIT and they were intelligent. So what's intelligence about? Well, the stuff they were good at, playing chess, doing integrals. That was, that was hard stuff.

• 35:43Lex Fridman?

  Yeah.

• 35:43Speaker 01

  But, you know, a baby could see stuff. That wasn't, that wasn't intelligent. I mean, anyone could do that. That's not intelligence. And so it, you know, this, there was this intuition that the hard stuff is the things they were good at. And the easy stuff was the stuff that everyone could do.

• 36:00Lex Fridman?

  Yeah.

• 36:00Speaker 01

  And maybe I'm overplaying it a little bit, but I think there's an element of that.

• 36:03Lex Fridman?

  Yeah, I mean, there, I don't know how much truth there is to, uh, like chess, for example, has, was for the longest time seen as the highest, um, level of intellect. Right?

• 36:16Speaker 01

  Until we got computers that were better at it than people. And then we realized, you know, but if you go back to the 90s, you'll see, you know, the stories in the press around when, when, Kasparov was beaten by Deep Blue. Oh, this is the end of all sorts of things. Computers are going to be able to do anything from now on. And we saw exactly the same stories with Alpha Zero, the, the Go playing program.

• 36:38Lex Fridman?

  Yeah. But still, to me, reasoning is a special thing. And perhaps.

• 36:44Speaker 01

  No, we, actually, we're, we're really bad at reasoning. We just use these analogies based on our hunter gatherer intuitions.

• 36:50Lex Fridman?

  But why is that not, don't you think the ability to construct metaphor is a really powerful thing?

• 36:56Speaker 01

  Oh, yeah, it is.

• 36:56Lex Fridman?

  Stories.

• 36:57Speaker 01

  It is. That's, it's the constructing the metaphor and registering that to something constant in our brains.

• 37:03Lex Fridman?

  Like, isn't that what we're doing with, with vision too? And we're, we're telling our stories. We're constructing good models of the world.

• 37:10Speaker 01

  Yeah, yeah. But I, but, um, I think we, we jumped between what we're capable of and how we're doing it right there. There was a little confusion that went on.

• 37:20Lex Fridman?

  Sure.

• 37:21Speaker 01

  As we're telling each other stories.

• 37:23Lex Fridman?

  Yes, exactly. Trying to delude each other. No, I, I just think, uh, I'm not exactly, so I'm trying to pull apart this Marvex paradox.

• 37:32Speaker 01

  I don't view it as a paradox.

• 37:35Speaker 01

  What did evolution, what did evolution spend its time on?

• 37:38Lex Fridman?

  Yes.

• 37:38Speaker 01

  It spent its time on getting us to perceive and move in the world. That was, you know, 600 million years as multi-cell creatures doing that. And then it was, you know, relatively recent that we, that we, you know, were able to hunt or gather or, you know, even, even animals hunting. That's much more recent. And then, and then anything that we, you know, speech, uh, language, those things are, you know, just a couple of hundred thousand years, probably if, if, if, if that long. And then agriculture, 10,000 years, you know? All that stuff was built on top of those earlier things, which took a long time to develop.

• 38:16Lex Fridman?

  So if you then look at the engineering of these things, so building it into robots, what's the hardest part of robotics, do you think? As the decades that you worked on robots, in the context of what we're talking about, vision, like a perception, the actual sort of the, the biomechanics of movement. I'm kind of drawing parallels here between humans and machines always. Like, uh, what do you think is the hardest part of robotics?

• 38:48Speaker 01

  I sort of think all of them. There are no easy paths to do well. We, we sort of go reductionist and we reduce it. If only we had all the, the location of all the points in 3D.

• 39:01Lex Fridman?

  Yeah.

• 39:01Speaker 01

  Things would be great. You know, if only we had labels on the, on the images, you know? Things would be great, but you know, as, as we see, that's not good enough. Uh, some deeper understanding.

• 39:15Lex Fridman?

  But if you, if I came to you and I could solve one category of problems in robotics, instantly, what would give you the greatest pleasure?

• 39:30Lex Fridman?

  I mean, is it, uh, you know, you, you look at robots that manipulate objects. What, what's hard about that? You know, is it the perception? Is it the, uh, the reasoning about the world, like common sense reasoning? Is it the actual building a robot that's able to interact with the world? Is it like human aspects of a robot that's interacting with humans and that that game theory of how they work well together?

• 39:58Speaker 01

  Well, let's talk about manipulation for a second because I had this really blinding moment.

• 40:05Speaker 01

  You know, I'm a grandfather, so grandfathers had blinding moments.

• 40:08Lex Fridman?

  Yes.

• 40:08Speaker 01

  Just three or four miles from here.

• 40:12Speaker 01

  Last year, my sixteen month old grandson was in his new house, first time, right? First time in this house. And he'd never been able to get to a window before, but this had some low windows. And he goes up to this window with a handle on it that he's never seen before. And he's got one hand pushing the window and the other hand turning the handle to open the window. He knew two different hands, two different things he knew how to how to put together.

• 40:45Lex Fridman?

  Yeah.

• 40:46Speaker 01

  And he's sixteen months old.

• 40:47Lex Fridman?

  And there you are watching in awe.

• 40:54Speaker 01

  In an environment, environment he'd never seen before.

• 40:56Lex Fridman?

  How did he do that?

• 40:58Speaker 01

  Yes, that's a good question. How did he do that? That's why.

• 41:02Lex Fridman?

  It's like, okay, like you could see the the leap of genius from using one hand to perform a task to combining, doing, I mean, first of all, in manipulation, that's really difficult. It's like two hands, both necessary to complete the action.

• 41:18Speaker 01

  Completely different, and he'd never seen a window open before.

• 41:24Speaker 01

  But he inferred somehow handle open something.

• 41:27Lex Fridman?

  Yeah, there may have been a lot of slightly different failure cases that you didn't see. Yeah, not with a window, but with other objects of turning and twisting and handles.

• 41:38Speaker 01

  Oh, you know, there's a there's a great counter to,

• 41:42Speaker 01

  you know, reinforce reinforcement learning will just give, you know, the the robot, or you give the robot plenty of time to try everything.

• 41:51Lex Fridman?

  Yes.

• 41:51Speaker 01

  Actually, can I tell a little side story here? So I'm in DeepMind in London. This is three, four years ago, where, you know, there's a big Google building. And then you go inside and you go through this more security. And then you get to DeepMind where the other Google employees can't go.

• 42:10Lex Fridman?

  Yeah.

• 42:11Speaker 01

  And I'm in a. I'm in a conference room, a bare conference room with some of the people. And they tell me about their reinforcement learning experiment with robots,

• 42:23Speaker 01

  which are just trying stuff out. And they're my robots. They're Sawyers that we sold them.

• 42:31Speaker 01

  And they really like them because Sawyers are compliant and consents forces. So they don't break when they're bashing into walls. They they stop and they do all the stuff. And, you know, so you just let the robot do stuff and eventually it figures stuff out.

• 42:44Lex Fridman?

  By the way, so we're talking about robot manipulation, so robot arms and so on.

• 42:49Speaker 01

  Yeah, so it's so he's a robot.

• 42:51Lex Fridman?

  Just what's Sawyer?

• 42:53Speaker 01

  So here's a robot arm that my company, Rethink Robotics, built.

• 42:57Lex Fridman?

  Thank you for the context.

• 42:58Speaker 01

  Yeah, sorry.

• 42:59Lex Fridman?

  Okay, cool. So we're in DeepMind.

• 43:01Speaker 01

  And, you know, it's in the next room. These robots are just bashing around to try and use reinforcement learning to learn how to act. And what can I go see them? Oh, no, they're secret. They were my robots that were secret.

• 43:13Lex Fridman?

  That's hilarious. Okay.

• 43:14Speaker 01

  Anyway, the point is, you know, this idea that you just let reinforcement learning figure everything out is so counter to how a kid does stuff. So again, story about my grandson. I gave him this this box that had lots of different lock mechanisms. He didn't randomly, you know, and he was 18 months old. He didn't randomly try to touch every surface or push everything. He found he could see what where the mechanism was. And he started exploring the mechanism for each of these different lock mechanisms. And there was reinforcement, no doubt, of some sort going on there. But he applied a pre-filter, which cut down the search space dramatically.

• 43:57Lex Fridman?

  I wonder to what level we're able to introspect what's going on. Because what's also possible is you have something like reinforcement learning going on in the mind in the space of imagination. So like you have a good model of the world you're predicting and you may be running those tens of thousands of like loops. But you're like, as a human, you're just looking at yourself, trying to tell a story of what happened. And it might seem simple, but maybe there's a lot of computation going on.

• 44:26Speaker 01

  Whatever it is, but there's also a mechanism that's being built up. It's not just random search. That mechanism prunes it dramatically.

• 44:36Lex Fridman?

  Yeah, that pruning, that pruning step, but it doesn't.

• 44:42Lex Fridman?

  It's possible that that's, so you don't think that's akin to a neural network inside a reinforcement learning algorithm? Is it possible?

• 44:52Speaker 01

  It's, yeah, until, it's possible, but, but I, you know,

• 44:59Speaker 01

  I, I, I'll be incredibly surprised if that happens. I'll also be incredibly surprised that, you know, after all the decades that I've been doing this, where every, every few years, someone thinks, now we've got it, now we've got it. You know, four or five years ago, I was saying, I don't think we've got it yet. And everyone was saying, you don't understand how powerful AI is. I had people tell me, you don't understand how powerful it is. I, you know, I, I, I sort of had a, a track record of what the world had done to think, well, this is no different from before. Oh, we had bigger computers. We had bigger computers in the 90s and we could do more shit stuff.

• 45:40Lex Fridman?

  But, okay, so let me, let me, let me push back, because I'm, I'm generally sort of optimistic and try to find the beauty in things.

• 45:48Lex Fridman?

  I think there's a lot of surprising and beautiful things that neural networks, this new generation of deep learning revolution has revealed. To me, has continually been very surprising, the kind of things it's able to do. Now, generalizing that over, saying like this, we've solved intelligence, that's another big leap, but is there something surprising and beautiful to you about neural networks that, where actually you sat back and said, I did not expect this?

• 46:20Speaker 01

  Oh, I think, I think their performance, their performance on ImageNet was shocking.

• 46:26Lex Fridman?

  So computer vision, those early days was just very like, wow, okay.

• 46:30Speaker 01

  That doesn't mean that they're solving everything in computer vision we need to solve, or in vision for robots.

• 46:37Lex Fridman?

  What about AlphaZero and self-play mechanisms and reinforcement learning? Isn't that?

• 46:41Speaker 01

  Yeah, that was all in, in Donald Mickey's 1961 paper. Everything there was there, which introduced reinforcement learning.

• 46:50Lex Fridman?

  No, but come on, so, no, you're talking about the actual techniques, but isn't it surprising to you, the level it's able to achieve with no human supervision? Of chess play like I to me, there's a big, big difference.

• 47:06Speaker 01

  Maybe maybe.

• 47:07Lex Fridman?

  Deep blue and.

• 47:07Speaker 01

  Maybe what that's saying is how overblown our view of ourselves is.

• 47:15Lex Fridman?

  You know, we. The chess is easy.

• 47:17Speaker 01

  Is that? Yeah, I mean, I, I, I, uh, I came across this nineteen forty six report that, um, and I'd seen this as a kid in one of those books that my mother had given me, actually. Nineteen forty six report, which pitted, uh, uh, someone with an abacus against an electronic calculator. And he beat the electronic calculator, you know, so there at that point was, well, humans are still better than machines are calculating. Are you surprised today that a machine can, you know, do a billion floating point operations a second? And, you know, you're, you're puzzling for, for minutes through one. So, you know.

• 48:03Lex Fridman?

  I, I am, I mean, I, I don't know, but I am certainly surprised. There's something, uh, to me, different about learning. So a system that's able to learn.

• 48:14Speaker 01

  Learning now, see, see now you're getting into one of the deadly sins.

• 48:18Lex Fridman?

  Because of using terms overly broadly.

• 48:22Speaker 01

  Yeah, I mean, there's so many different forms of learning.

• 48:25Lex Fridman?

  Yeah.

• 48:25Speaker 01

  And so many different forms, you know, I learnt my way around the city. I learnt to play chess. I learnt Latin. I learnt to ride a bicycle. All of those are, you know, are very different capabilities.

• 48:36Lex Fridman?

  Yeah.

• 48:37Speaker 01

  And if someone, you know, has a, well, in, you know, in the old days, people would write a paper about learning something. Now the corporate, uh, press office puts out a press release about how company X has, has is leading the world because they have a system that can.

• 48:58Lex Fridman?

  Yeah, but he- here's the thing. Okay, so what is learning? When I refer.

• 49:02Speaker 01

  Learning is many things.

• 49:04Lex Fridman?

  But.

• 49:05Speaker 01

  It's a suitcase word.

• 49:06Lex Fridman?

  It's a suitcase word, but loosely, there's a dumb system. And over time, it becomes smart.

• 49:15Speaker 01

  Well, it becomes less dumb at the thing that it's doing.

• 49:18Lex Fridman?

  Yeah.

• 49:18Speaker 01

  Smart is a, smart is a loaded word.

• 49:21Lex Fridman?

  Yes, less, less dumb at the thing it's doing.

• 49:23Speaker 01

  Gets better performance under some measure.

• 49:25Lex Fridman?

  Yeah.

• 49:26Speaker 01

  Under some set of conditions at that thing. And, and most of these learning algorithms, um, uh, learning systems fail when you change the conditions just a little bit in a way that humans don't. So.

• 49:40Lex Fridman?

  Right.

• 49:41Speaker 01

  I was at DeepMind, um, the AlphaGo had just come out. And I said, what would have happened if you'd given it a 21 by 21 board instead of a 19 by 19 board? They said, fail totally. But a human player would actually, you know, well, would actually be able to play a game.

• 49:58Lex Fridman?

  Funny enough, if you look at DeepMind's work since then, uh, they are, uh, they're presenting a lot of algorithms that would do, uh, the, that would do well at the, at the bigger board. So they're slowly expanding this generalization. I mean, to me, there's a core element there. I, it is very surprising to me that even in a constrained game of chess or Go, that through self-play by a system playing itself, that can, it can achieve superhuman level performance through learning alone. So like.

• 50:32Speaker 01

  Okay, so, so you know, you.

• 50:34Lex Fridman?

  It's so fundamentally different in search of.

• 50:36Speaker 01

  You didn't, you didn't like it when I referred to Donald Mickey's 1961 paper? There, in the second part of it, which came a year later, they had self-play on an electronic computer at tic-tac-toe. Okay, it's not as, but it learnt to play tic-tac-toe through self-play.

• 50:54Lex Fridman?

  That, that's not.

• 50:55Speaker 01

  And it learnt to play optimally.

• 50:56Lex Fridman?

  What I'm saying is, uh, I, okay, I have a little bit of a bias, but I, I, I find ideas beautiful, but only when they actually. Realize the promise. That's another level of beauty. Like, for example, uh, what uh, uh, Bezos and Elon Musk are doing with rockets. We had rockets for a long time, but doing reusable, cheap rockets is very impressive. The same way, I, okay.

• 51:23Speaker 01

  Yeah.

• 51:23Lex Fridman?

  I would have not predicted, first of all, when I was uh, started and fell in love with AI, the game of Go was seen to be impossible to solve. Okay. So I thought, maybe, you know, I, maybe it'd be possible to maybe have big leaps in a Moore's Law style of way in computation, I'll be able to solve it. But I would never have guessed that you can learn your way, however.

• 51:51Lex Fridman?

  I mean, in the narrow sense of learning, learn your way to, to, to beat the best people in the world at the game of Go, without human supervision, not studying the game of experts.

• 52:02Speaker 01

  But, okay, so, so using, using a different learning technique.

• 52:06Lex Fridman?

  Yes.

• 52:07Speaker 01

  Arthur Samuel, in the early sixties, and he was the first person to use machine learning, got, had a program that could beat the world champion at checkers. Now.

• 52:18Lex Fridman?

  Yes.

• 52:19Speaker 01

  So, and that at the time was considered amazing. By the way, Arthur Samuel had some fantastic advantages.

• 52:25Lex Fridman?

  Yep.

• 52:25Speaker 01

  Do you want to hear Arthur Samuel's advantage? Two things. One, he was at the nineteen fifty-six AI conference. I knew Arthur later in life. Uh, he was at Stanford when I was graduating there. He wore a tie and a jacket every day. The rest of us didn't. Um, delightful man, delightful man. Um, uh, it turns out Claude Shannon, in a nineteen fifty Scientific American article, uh, outlined, on chess playing, outlined the learning mechanism that Arthur Samuel used, um, and they had met in nineteen fifty-six. I assume there was some communication, but I don't know that for sure. But Arthur Samuel. I'd been a vacuum tube engineer, getting reliably all the vacuum tubes,

• 53:08Speaker 01

  and then had overseen the first transistorized computers at IBM. And in those days, before you shipped a computer, you ran it for a week to see, to get early failures. So he had this whole farm of computers running random code for hours and hours. A week for each computer, he had a whole bunch of them. So he ran his chess learning program with self-play on IBM's production line. He had more computation available to him than anyone else in the world. And then he was able to produce a chess playing program, I mean, a checkers playing program that could beat the world champion.

• 53:52Lex Fridman?

  So that's amazing. The question is, what I mean surprised, I don't just mean it's nice to have that accomplishment. Is there is a stepping towards something that feels more intelligent than before?

• 54:08Speaker 01

  Yeah, but that's, that's in your view of the world.

• 54:10Lex Fridman?

  Okay, well, let me then, it doesn't mean I'm wrong.

• 54:14Speaker 01

  No, no, it doesn't.

• 54:16Lex Fridman?

  So the question is, if we keep taking steps like that, how far that takes us? Are we going to build a better recommender systems? Are we going to build better robots?

• 54:25Speaker 01

  Okay.

• 54:26Lex Fridman?

  Or will we solve intelligence?

• 54:28Speaker 01

  So, you know, I'm putting my bet on, we're still missing a whole lot.

• 54:35Lex Fridman?

  A lot.

• 54:36Speaker 01

  And, and why would I say that? Well, in these games, they're all, you know, 100% information games. But again, but each of these systems is a very short description of the current state, which is different from registering and perception in the world. Which gets back to the Maravix paradox.

• 54:57Lex Fridman?

  I'm definitely not saying that. Chess is somehow harder than perception. Or any kind of robotics in the physical world. I definitely think is way harder than the game of chess. So I was always much more impressed by the workings of the human mind. It's incredible. The human mind is incredible. I believed that from the very beginning. I wanted to be a psychiatrist for the longest time. I always thought that's way more incredible than the game of chess. I think the game of chess is a, I love the Olympics. It's just another example of us humans picking a task and then agreeing that a million humans will dedicate their whole life to that task. And that's the cool thing that the human mind is able to focus on one task and then

• 55:41Lex Fridman?

  compete against each other and achieve like weirdly incredible levels of performance. That's the aspect of chess that's super cool. Not that chess in itself is really difficult. It's like the Fermat's Last Theorem is not in itself to me that interesting. The fact that thousands of people have been struggling to solve that particular problem is fascinating.

• 56:00Speaker 01

  So can I tell you my disease in this way?

• 56:02Lex Fridman?

  Sure.

• 56:03Speaker 01

  Which actually is closer to what you're saying. So as a child, you know, I was building various, I called them computers. They weren't general purpose computers.

• 56:11Lex Fridman?

  Ice cube tray.

• 56:12Speaker 01

  The ice cube tray was one, but I built other machines. And what I liked to build was machines that could beat adults at a game. And they couldn't, the adults couldn't beat my machine.

• 56:21Lex Fridman?

  Yes. So you were like, that's powerful. Like that's a, that's a way to rebel.

• 56:27Speaker 01

  Yeah.

• 56:27Lex Fridman?

  I, by the way, um, did you, when was the first time you built something that outperformed you? Do you remember like?

• 56:36Speaker 01

  Well, I knew how it worked. I was probably nine years old and I built a thing that was a game where you take turns in taking matches from a pile and either the one who takes the last one or the one who doesn't take the last one wins, I forget. And so it was pretty easy to build that out of wires and nails and little coils that were like plugging in the number and a few light bulbs. The one, the one I was prouder of, I was 12 when I built a thing out of old telephone switchboard switches that could always win at tic tac toe. And that was a much harder circuit to design. But again, was just.

• 57:18Speaker 01

  There was no active components. It was just three position switches, empty X, zero, oh, and um, and nine of them and and a light bulb on which which move it wanted next.

• 57:31Lex Fridman?

  And then the human would go and move that. See, there's magic in that creation.

• 57:35Speaker 01

  There was, yeah, yeah.

• 57:36Lex Fridman?

  I tend to uh, I tend to see magic in robots that

• 57:42Lex Fridman?

  like I, I also think that intelligence is a, is a little bit overrated. I think we can have deep connections with robots very soon.

• 57:51Speaker 01

  And we'll, we'll come back to connections, bro.

• 57:54Lex Fridman?

  Sure.

• 57:54Speaker 01

  But, but I do want to say, I, I don't, I, I think people, too many people make the mistake of seeing that magic and thinking, well, we'll just continue, you know? But each, each one of those is a hard fought battle for the next step, the next step.

• 58:09Lex Fridman?

  Yes. My, the open question here is, and this is why I'm playing devil's advocate, but I often do when I read your blog post in my mind, because I have like this eternal optimism, is it's not clear to me, so I don't do what obviously the journalists do or like give into the hype, but it's not obvious to me how many steps away we are from

• 58:31Lex Fridman?

  from a truly transformational understanding of what it means to build intelligent systems like, or how to build intelligent systems. I'm also aware the whole history of artificial intelligence, which is where your deep grounding of this is, is there's been an optimism for decades.

• 58:53Lex Fridman?

  And that optimism, just like reading old optimism is absurd because people were like, this is, they were saying things are trivial for decades, since the sixties. They're saying everything is true, computer vision is trivial. But I think my mind is working crisply enough to where, I, I mean, we can dig into if, if you want. I'm, I'm really surprised by the things DeepMind has done. I don't think they're so, they're yet

• 59:20Lex Fridman?

  close to solving intelligence, but I'm not sure it's not ten to, ten years away. What I'm referring to is interesting to see when the engineering,

• 59:31Lex Fridman?

  it takes that idea to scale and the idea works.

• 59:35Speaker 01

  And no, it fools people.

• 59:37Lex Fridman?

  Okay, honestly, Rodney, if it was you, me and Demis inside a room, forget the press, forget all those things, just as a scientist, as a roboticist, you don't, that wasn't surprising to you that at scale, so we're talking about very large now, we'll get, okay, let's pick one that's the most surprising to you. Okay, please don't yell at me. GPT-3. Okay.

• 59:59Speaker 01

  I was gonna, I was gonna bring that up.

• 1:00:01Lex Fridman?

  Okay, thank you. Alpha zero, Alpha Go, Alpha Go zero, Alpha zero, and then Alpha Fold one and two. So are any, do any of these kind of have this core of, not, not, forget usefulness or application or so on, which you could argue for Alpha Fold, like as a scientist, was those surprising to you that it worked as well as it did?

• 1:00:26Speaker 01

  Okay, so if we're gonna make the distinction between surprise and usefulness and, and, and I'll, I'll, I have to explain this. I would say Alpha Fold. And one of the problems at the moment with Alpha Fold is, you know, it gets a lot of them right, which is a surprise to me because they're a really complex thing. But you don't know which ones it gets right,

• 1:00:52Speaker 01

  which then is a, is a bit of a problem. Now they've come out with a recent-

• 1:00:55Lex Fridman?

  You mean the structure of the protein, it gets a lot of those right?

• 1:00:58Speaker 01

  Yeah, it's a, it's surprising number of them right. It's been a really hard problem. So that was a surprise how many it gets right. So far, the usefulness is limited because you don't know which ones are right or not. And now they've come out with a, a thing in the last few weeks, which is trying to get a useful tool out of it, and they may well do it.

• 1:01:17Lex Fridman?

  In that sense, at least Alpha Fold is different because your Alpha Fold 2 is different because now it's producing data sets that are actually, you know, potentially revolutionizing competition biology, like they will actually help a lot of people. But

• 1:01:33Speaker 01

  You'd say potentially revolutionizing, we don't know yet, but yeah.

• 1:01:38Lex Fridman?

  That's true. Yeah. But there, you know, but I, I, I got you. I mean, this is, okay. So you know what? This is gonna be so fun. So let's go right into it. Speaking of robots that operate in the real world, let's talk about self-driving cars.

• 1:01:54Speaker 01

  Oh.

• 1:01:56Lex Fridman?

  Okay. Because you do, you have built robotics companies. You, you're one of the greatest roboticists in history, and that's not in space of, just in the space of ideas. We'd also probably talk about that, but in the actual building and execution of businesses that make robots that are useful for people and that actually work in the real world and make money.

• 1:02:20Lex Fridman?

  You also sometimes are critical of Mr. Elon Musk, or let's more specifically focus on this particular technology, which is autopilot inside Teslas.

• 1:02:31Lex Fridman?

  What are your thoughts about Tesla autopilot, or more generally, vision-based machine learning approach to semi-autonomous driving?

• 1:02:40Lex Fridman?

  These are robots. They're being used in the real world by hundreds of thousands of people. And

• 1:02:48Lex Fridman?

  if you want to go there, I can go there, but that's not too much, which there, let's say they're on par safety-wise as humans currently, meaning human alone versus human plus robot.

• 1:03:00Speaker 01

  Okay. So first, let me say I really like the car I came here, in here today.

• 1:03:05Lex Fridman?

  Which is?

• 1:03:08Speaker 01

  2021

• 1:03:11Speaker 01

  model Mercedes E450. I am impressed by the machine vision. So now other things, I'm impressed by what it can do. I'm really impressed with many aspects of it. And, and I'm

• 1:03:31Lex Fridman?

  It's able to stay in lane. Is it?

• 1:03:33Speaker 01

  Oh, yeah. It does, it does the lane stuff. It, you know, it's, it's looking on either side of me. It's telling me about nearby cars.

• 1:03:42Lex Fridman?

  Blind spots and so on.

• 1:03:43Speaker 01

  Yeah. When I, when I, when I, when I'm going in close to something in the park, I get this beautiful, gorgeous top-down view of the world. I am impressed. Up the wazoo of how, you know, registered and metrical.

• 1:03:58Lex Fridman?

  Oh, so it's like multiple cameras and it's all ready together to produce the 360 view.

• 1:04:02Speaker 01

  360 view, you know, synthesized, so it's above the car and it is unbelievable. I, I got this car in January. It's the longest I've ever owned a car without digging it. So it's better than me. Or me and it together are better. So I'm not saying technology's bad or not useful.

• 1:04:26Speaker 01

  But here's my point. Yes.

• 1:04:29Speaker 01

  It's this, it's a replay of the same movie. Okay. So maybe you've seen me ask this question before, but when,

• 1:04:42Speaker 01

  when, when did the first car go over 55 miles an hour for over,

• 1:04:52Speaker 01

  over 10 miles on a public freeway with other traffic around, driving completely autonomously? When did that happen?

• 1:05:01Lex Fridman?

  Was it CMU in the eighties or something? It was a long time ago.

• 1:05:04Speaker 01

  It was actually 1987 in, in Munich.

• 1:05:07Lex Fridman?

  A Munich fan.

• 1:05:08Speaker 01

  At the Bundesfahrt.

• 1:05:11Speaker 01

  So they, they had it running in 1987. When do you think, and Elon has said he's going to do this. When do you think we'll have the first car drive coast to coast in the US? Hands off the wheel, hands off the wheel, feet off the pedals, coast to coast.

• 1:05:28Lex Fridman?

  As far as I know, a few people have claimed to do it.

• 1:05:30Speaker 01

  1995. That was Carnegie Mellon.

• 1:05:32Lex Fridman?

  I didn't know, but oh, that was the.

• 1:05:34Speaker 01

  Yeah.

• 1:05:35Lex Fridman?

  They didn't claim, did they claim 100%?

• 1:05:37Speaker 01

  Not, not 100%, not 100%.

• 1:05:39Lex Fridman?

  But, but. And then there's a few marketing people who have claimed 100%.

• 1:05:43Speaker 01

  But my point is that, you know, I, I, what I see happening again is someone sees a demo and they over generalize and say, we must be almost there. But we've been, we've been working on it for 35 years.

• 1:05:57Lex Fridman?

  So that's demos, but this is going to take us back to the same conversation with Alpha Zero. Are you not? Okay. I'll just say what I am because I thought, okay, when I first started interacting with, with the Mobile Eye implementation of Tesla Autopilot. I've driven a lot of car, you know, I've been in Google stuff, driving cars since the beginning. I thought there was no way before I sat and used Mobileye. I thought there just knowing computer vision. I thought there's no way it could work as well as it was working. So my model of the limits of computer vision was was way more limited than the actual implementation of Mobileye. I was so that's one example. I was really surprised.

• 1:06:42Lex Fridman?

  It's like, wow, that was that was incredible. The second surprise came when Tesla threw away Mobileye and started from scratch. I thought there's no way they can catch up to Mobileye. I thought what Mobileye was doing was kind of incredible, like the amount of work and the annotation.

• 1:06:59Speaker 01

  Yeah, well, Mobileye was started by Amman Sheshua and and used a lot of traditional, you know, hard fought computer vision techniques.

• 1:07:07Lex Fridman?

  But they also did a lot of good sort of like non researched stuff like actual like just good like what you do to make a successful product, right? It scaled all that kind of stuff. And so I was very surprised when they from scratch were able to catch up to that. That's very impressive. And I've talked to a lot of engineers that was involved. This is that was impressive. And the recent progress, especially under well, with the involvement of Andre Kapathi, the what they were, what they're doing with the data engine, which is converting into the driving task into these multiple tasks and then doing this edge case discovery when they're pulling back like the level of engineering made me rethink what's possible.

• 1:07:52Lex Fridman?

  I don't, I still, you know, I don't know to that intensity, but I always thought it was very difficult to solve a ton was driving with all the sensors, with all the computation. I just thought it's a very difficult problem. But I've been continuously surprised how much you can engineer. First of all, the data acquisition problem, because I thought, you know, just because I worked with a lot of car companies and they're they're so a little, a little bit old school to where I didn't think they could do this at scale like AWS style data collection. So when Tesla was able to do that, I started to think, okay, so what are the limits of this? I still believe that.

• 1:08:38Lex Fridman?

  Driver like sensing and the interaction with the driver and like studying the human factors, psychology problem is essential. It's it's always going to be there. It's always going to be there, even with fully autonomous driving. But I've been surprised, what is the limit, especially of vision based alone, how far that can take us. So that's my levels of surprise. Now, can you can you explain, in the same way you said like Alpha Zero, that's a homework problem that's scaled large in his chest, like who cares, go with it. Here's actual people using an actual car and driving, many of them drive more than half their miles using the system.

• 1:09:23Speaker 01

  Right. So, yeah, they're doing well with with pure vision.

• 1:09:27Lex Fridman?

  With pure vision, yeah.

• 1:09:28Speaker 01

  And, you know, they

• 1:09:30Lex Fridman?

  And now no radar, which is

• 1:09:32Speaker 01

  I suspect that can't go all the way. And one reason is, well, without without new cameras that have a dynamic range closer to the human eye, because human eye has incredible dynamic range. And we make use of that dynamic range in in its eleven orders of magnitude or some crazy number like that. The cameras don't have that, which is why you see the the the bad cases where the sun on a white thing and it blinds it in a way it wouldn't blind a person.

• 1:10:03Speaker 01

  I think there's a bunch of things to think about before you say, this is so good, it's just gonna work. Okay. And I'll come at it from multiple angles. And I know you've got a lot of time.

• 1:10:17Lex Fridman?

  Yeah. Okay, let's

• 1:10:19Speaker 01

  I have thought about these things.

• 1:10:21Lex Fridman?

  Yeah, I know. You've been writing a lot of great blog posts about it for a while before Tesla had autopilot, right? So you've been thinking about autonomous driving for a while from every angle.

• 1:10:33Speaker 01

  So so a few things. You know, in the US, I think that the the death rate from motor vehicle accidents is about thirty five thousand a year.

• 1:10:46Speaker 01

  Which is an outrageous number, not outrageous compared to COVID deaths, but you know, there is no rationality. And that's part of the thing. People have said, engineers say to me, well, if we cut down the number of deaths by 10% by having autonomous driving, that's going to be great. Everyone will love it. And my prediction is that if autonomous vehicles kill more than 10 people a year, they'll be screaming and hollering, even though 35,000 people a year have been killed by human drivers. It's not rational. It's a different set of expectations, and that will probably continue. So there's that

• 1:11:27Speaker 01

  aspect of it. The other aspect of it is that when we introduce new technology, we often change the rules of the game. So when we introduced cars first into our daily lives, we completely rebuilt our cities and we changed all the laws. Jaywalking was not an offense. That was pushed by the car companies so that people would stay off the road so there wouldn't be deaths from pedestrians getting hit. We completely changed the structure of our cities and had these foul smelling things, you know, everywhere around us. And you know, and now you see pushback in cities like Barcelona is really trying to exclude

• 1:12:12Speaker 01

  cars, et cetera. So I think that to get to self-driving, we will, large adoption, it's not going to be just take the current situation, take out the driver and put the same car doing the same stuff because the end cases too many. Here's an interesting question. How many fully autonomous train systems do we have in the US?

• 1:12:48Lex Fridman?

  I mean, do you count them as fully autonomous? I don't know, because they're usually as a driver, but they're kind of autonomous, right?

• 1:12:54Speaker 01

  No, well, let's get rid of the driver.

• 1:12:58Lex Fridman?

  Okay, I don't know.

• 1:12:59Speaker 01

  It's either fifteen or sixteen. Most, most of them are in airports.

• 1:13:03Lex Fridman?

  Okay.

• 1:13:03Speaker 01

  Um, there's a few that go about five, two that go about five kilometers out of airports.

• 1:13:09Lex Fridman?

  Yeah.

• 1:13:10Speaker 01

  Um, uh, when do, when, when is the first fully autonomous train system for mass transit expected to operate fully autonomously?

• 1:13:22Speaker 01

  With no driver?

• 1:13:23Lex Fridman?

  Uh.

• 1:13:23Speaker 01

  In, in a US city? It's expected to operate in twenty seventeen in Honolulu.

• 1:13:30Lex Fridman?

  Oh, wow.

• 1:13:31Speaker 01

  It's delayed, but they will get there. But, by the way, it was originally going to be autonomous, uh, here in the Bay Area.

• 1:13:37Lex Fridman?

  I mean, they're all very close to fully autonomous, right?

• 1:13:40Speaker 01

  Yeah, but, but the, the getting the close is the thing. And I have, I have, I've often gone on a fully autonomous train in Japan. Um, one that goes, uh, out to that fake island in the middle of Tokyo Bay. I forget the name of the, uh, uh, and, and what do you, what do you see when you look at that? What do you see when you go to a fully autonomous train in a, in a, in a, oh, um, an airport? It's not like regular trains. There's, uh, at every station, there's this double set of doors. Um, so that there's a door of the train and this door off the, the, um, off the, the platform.

• 1:14:18Lex Fridman?

  Yeah.

• 1:14:19Speaker 01

  Um, and, and it's really visible in this Japanese one because it goes out in, in amongst buildings. The whole track is built so that people can't climb onto it.

• 1:14:30Lex Fridman?

  Yeah.

• 1:14:30Speaker 01

  So there's an engineering that then makes the system safe and makes them acceptable. I think we'll see similar sorts of things happen in the US. What surprised me, I thought wrongly, um, that we would have special purpose lanes on one oh one, uh, in, in the Bay Area, the, the leftmost lane. So that it would be normal for Teslas or other cars to move into that lane and then say, okay, now it's autonomous and have that dedicated lane. I was expecting movement to that, you know, five years ago, I was expecting we'd have a lot more movement towards that. We haven't. And it may be because Tesla's been overpromising by saying this,

• 1:15:15Speaker 01

  you know, calling their system fully self-driving. I think they may have been gotten there quicker by collaborating to change the infrastructure. This is one of the problems with long haul tracking being autonomous. I think it makes sense on freeways at night for the trucks to go autonomously.

• 1:15:42Speaker 01

  But then there's the how to get on to and off of the freeway. What sort of infrastructure do you need for that? Do you need to have the human in there to do that? Or can you get rid of the human? So I think there's ways to get there, but it's an infrastructure argument. Because the long tail of cases is very long and the acceptance of it will not be at the same level as human drivers.

• 1:16:08Lex Fridman?

  So I'm with you still and I was with you for a long time, but I am surprised how well, how many edge cases of machine learning and vision-based methods can cover. This is what I'm trying to get at is I think there's something fundamentally different with vision-based methods and Tesla Autopilot and any company that's trying to do the same.

• 1:16:34Speaker 01

  Okay, well, I'm not, I'm not going to argue with you because, you know, I, I, we're speculating.

• 1:16:41Lex Fridman?

  Yes.

• 1:16:41Speaker 01

  But I, you know, my gut feeling tells me it's going to be.

• 1:16:48Speaker 01

  Things will, things will speed up when there is engineering of the environment, because that's what happened with every other technology.

• 1:16:54Lex Fridman?

  I'm a, I'm a bit, I don't know about you, but I'm a bit cynical that infrastructure, which relies on, on government to help out in these cases. If you just look at infrastructure in all domains, it's just government always drags behind on infrastructure. There's like, there's so many just-

• 1:17:15Speaker 01

  Well, in this country.

• 1:17:17Lex Fridman?

  In the, sure, sorry, yes, in the, in this country. And of course, there's many, many countries that are actually much worse on infrastructure.

• 1:17:23Speaker 01

  Oh, yes, many are much worse than the some that, you know, like high-speed rail. The other countries have done much better.

• 1:17:30Lex Fridman?

  I guess my question is like, which is at the core, what I was trying to think through here and ask you is like, how hard is the driving problem?

• 1:17:41Lex Fridman?

  As it currently stands, so you, you mentioned like, we don't want to just take the human out and duplicate whatever the human was doing. But if we were to try to do that, what, how hard is that problem? Because I used to think is way harder. Like I, I used to think it's, uh, with vision alone, it, it, it would be three decades, four decades.

• 1:18:04Speaker 01

  Okay, so I, I don't know the answer to this thing I'm about to pose. But I do notice that on Highway 280 here in the Bay Area, which largely has concrete surface rather than blacktop surface. The white lines that are painted there now have black boundaries around them.

• 1:18:25Speaker 01

  And my lane drift system in my car would not work without those black boundaries.

• 1:18:32Lex Fridman?

  Interesting.

• 1:18:33Speaker 01

  So I don't know whether they've started doing it to help the lane drift, whether it is an instance of infrastructure following the technology. But it, but it, my car would not perform as well without that change in the way they paint the line.

• 1:18:47Lex Fridman?

  Unfortunately, really good lane keeping is not as valuable. Like it's orders of magnitude more valuable to have a fully autonomous system, like-

• 1:18:57Speaker 01

  Yeah, but, but for me, lane keeping is really helpful because I'm lousy at it.

• 1:19:02Lex Fridman?

  But you wouldn't pay 10 times, like- Um, the problem is there's not financial, like it doesn't make sense to, to, to, uh, revamp the infrastructure to make lane keeping easier. It does make sense to revamp the infrastructure.

• 1:19:18Speaker 01

  Oh, I see.

• 1:19:19Lex Fridman?

  If you have a large fleet of autonomous vehicles, now you change what it means to own cars. You change the nature of transportation that mean, but that, that for that you need autonomous vehicles. Let me ask you about Waymo then. Uh, I've gotten a bunch of chances to, to ride in, in a Waymo, um, self-driving car. And they're, I don't know if you'd call them self-driving, but.

• 1:19:43Speaker 01

  Well, I mean, I, I rode in one before they were called Waymo.

• 1:19:46Lex Fridman?

  Yeah.

• 1:19:46Speaker 01

  So at X.

• 1:19:47Lex Fridman?

  So there's currently, this was a big leap. Another surprising leap, I didn't think would happen, which is they have no driver currently.

• 1:19:55Speaker 01

  Yeah, in, in Chandler.

• 1:19:57Lex Fridman?

  In Chandler, Arizona, and I think they're thinking of doing that in Austin as well. But they're, they're expanding.

• 1:20:02Speaker 01

  Although, although, you know, and I, I do an annual, um, checkup on this. So as of late last year, they were aiming for hundreds of rides a week.

• 1:20:14Lex Fridman?

  Yep.

• 1:20:14Speaker 01

  Not thousands. And, um, there is no one in the car, but there's certainly, uh, um, safety, uh, people in the loop. And it's not clear how many, you know, what the ratio of cars to safety people is.

• 1:20:29Lex Fridman?

  I, it wasn't, uh, obviously they're not a hundred percent transparent about this.

• 1:20:33Speaker 01

  No, none of them are a hundred percent transparent.

• 1:20:35Lex Fridman?

  This is the problem.

• 1:20:35Speaker 01

  Untransparent.

• 1:20:36Lex Fridman?

  But I, at least the way they're, I, I don't want to make definitively, but they're saying there's no teleoperation.

• 1:20:43Speaker 01

  Um.

• 1:20:44Lex Fridman?

  So like they're, I mean, okay.

• 1:20:47Speaker 01

  And, and, and, and that sort of fits with, with, um, YouTube videos I've seen of people being trapped in the car.

• 1:20:54Lex Fridman?

  Yeah.

• 1:20:54Speaker 01

  Um, by a red cone on the, on the street. And they do, they do have rescue vehicles that come.

• 1:21:01Lex Fridman?

  Yeah.

• 1:21:01Speaker 01

  And then a person gets in and drives it.

• 1:21:04Lex Fridman?

  Yeah. But isn't it incredible to you? It was to me to get in a car with no driver and watch the steering wheel turn. Like for somebody who has been studying, at least certainly the human side of autonomous vehicles for many years, and you've been doing it for way longer, like it was incredible to me that this was actually could happen. I don't care if that scale is 100 cars. This is not a demo. This is not, this is me as a regular.

• 1:21:30Speaker 01

  Now, the argument I have is that people make interpolations from that.

• 1:21:35Lex Fridman?

  Interpolations.

• 1:21:35Speaker 01

  That are, you know, it's here, it's done. You know, it's just, you know, we've solved it. No, we haven't yet. And that's my argument.

• 1:21:44Lex Fridman?

  Okay, so I'd like to go to, you, you keep a list of predictions.

• 1:21:48Speaker 01

  Yeah.

• 1:21:49Lex Fridman?

  On your amazing blog post, it'd be fun to go through them. But before then, let me ask you about this. You have, you have a harshness to you sometimes in your criticisms of what is, see, as hype. And so like, because people extrapolate, like you said, and they, they kind of buy into the hype and then they, they kind of start to think that the technology's way better than it is. But let me ask you maybe a difficult question.

• 1:22:25Speaker 01

  Sure.

• 1:22:26Lex Fridman?

  Do you think, if you look at history of progress, don't you think to achieve the quote impossible, you have to believe that it's possible?

• 1:22:36Speaker 01

  Absolutely. Yeah. Look, his, his, his, his, two great runs. Great. Unbelievable. 1903, first human power, human, you know, heavier than air flight.

• 1:22:51Lex Fridman?

  Yeah.

• 1:22:52Speaker 01

  1969, we land on the moon. That's 66 years. I'm 66 years old. In my lifetime, that span of my lifetime, barely, you know, flying, I don't know what it was, 50 feet or the length of the first flight or something, to landing on the moon. Unbelievable. Fantastic.

• 1:23:11Lex Fridman?

  But that requires, by the way, one of the Wright brothers, both of them, but one of them didn't believe it's even possible like a year before. Right? So like, not just possible soon, but like ever.

• 1:23:22Speaker 01

  So, so, so, you know.

• 1:23:24Lex Fridman?

  How important is it to believe and be optimistic is what I guess.

• 1:23:27Speaker 01

  Oh, yeah. It is important. It's when it goes crazy. When, when, when I, you know, you said it. What, what was the word you used for my bad?

• 1:23:35Lex Fridman?

  Harshness.

• 1:23:36Speaker 01

  Harshness. Yes.

• 1:23:43Speaker 01

  I just get so frustrated.

• 1:23:44Lex Fridman?

  Yes.

• 1:23:45Speaker 01

  When, when people make these leaps and tell me that I'm, that I don't understand.

• 1:23:51Lex Fridman?

  Right.

• 1:23:51Speaker 01

  I, you know, yeah.

• 1:23:55Speaker 01

  There's just from iRobot, which I was co-founder of.

• 1:23:59Lex Fridman?

  Yeah.

• 1:23:59Speaker 01

  I don't know the exact numbers now because I haven't, it's 10 years since I stepped off the board. But I believe it's well over 30 million robots cleaning houses from that one company. Then now there's lots of other companies.

• 1:24:10Lex Fridman?

  Yes.

• 1:24:12Speaker 01

  Was that a crazy idea that we had to believe?

• 1:24:17Speaker 01

  In 2002, when we released it? Yeah, that was, we, we had, we had to, you know, believe that it could be done.

• 1:24:25Lex Fridman?

  Let me ask you about this. So iRobot, one of the greatest robotic companies ever. In terms of manufacturing, creating a robot that actually works in the real world is probably the greatest robotic company ever. You were the co-founder of it.

• 1:24:40Lex Fridman?

  If, if the Rodney Brooks of today talked to the Rodney of back then, what would you tell him? Cause I have a sense that, would you pat him on the back and say, what you're doing is going to fail, but go at it anyway? That's what I'm referring to with the harshness. You've accomplished an incredible thing there. One of the several things we'll talk about. Well, like that's what I'm trying to get at, that line.

• 1:25:08Speaker 01

  No, it's, it's when, my harshness is reserved for people who are not doing it. Who claim it's just, well, this shows that it's just going to happen.

• 1:25:18Lex Fridman?

  But here, here's the thing.

• 1:25:20Speaker 01

  This show.

• 1:25:20Lex Fridman?

  But you have that harshness for Elon too.

• 1:25:25Speaker 01

  And no.

• 1:25:26Lex Fridman?

  No, it's a different harshness.

• 1:25:28Speaker 01

  No, it's, it's a different argument with Elon. You know, I, I, I think SpaceX is amazing company. On the other hand, you know, I, in one of my blog posts, I said, what's easy and what's hard? I said SpaceX, vertically landing rockets, it had been done before. Grid fins had been done since the sixties. Every Soyuz has them.

• 1:25:54Speaker 01

  Reusable space, DCX reused those rockets that landed vertically. There's a whole insurance industry in place for rocket launches, so all sorts of infrastructure.

• 1:26:09Speaker 01

  That was doable. It took a great entrepreneur, a great personal expense. He almost drove himself, you know, bankrupt doing it. A great belief to do it. Whereas Hyperloop, uh, there's a whole bunch more stuff that's never been thought about, never been demonstrated. So my estimation is Hyperloop is a long, long, lot further off. But and if I've got a criticism of Elon, it's that he doesn't make distinctions between when the technology's coming along and ready. And then he'll go off and mouth off about other things, which then people go and compete about

• 1:26:52Speaker 01

  and try and do. And so.

• 1:26:55Lex Fridman?

  See, this is where I, I understand what you're saying. I tend to draw a different distinction. I, I have a similar kind of harshness towards people who are not telling the truth, who are basically fabricating stuff to make money or to.

• 1:27:12Speaker 01

  Well, he believes what he says. I just think he's wrong.

• 1:27:14Lex Fridman?

  To me, that's a very important difference.

• 1:27:15Speaker 01

  Yeah, I'm not, I'm not.

• 1:27:17Lex Fridman?

  Because I think in order to fly, in order to get to the moon, you have to believe, uh, even when, uh, most people tell you you're wrong and most likely you're wrong. But sometimes you're right. I mean, that's the same thing I have with Tesla Autopilot. I, I think that's an interesting one. I was, especially when I was, you know, um, at MIT and just the entire human factors in the robotics community were very negative towards Elon. It was very interesting for me to observe colleagues at MIT.

• 1:27:47Lex Fridman?

  I wasn't sure what to make of that. That was very upsetting to me because I understood where that, where that's coming from. And I, I agreed with them and I kind of almost felt the same thing in the beginning until I kind of opened my eyes and, and realized there's a lot of interesting ideas here. There might be overhype, you know? If, if you focus yourself on the idea that, um, You shouldn't call a system full self-driving when it's obviously not autonomous, fully autonomous. You're going to miss the magic.

• 1:28:19Speaker 01

  Oh, you are gonna miss the magic, but at the same time, there are people who buy it, literally pay money for it.

• 1:28:26Lex Fridman?

  Yeah.

• 1:28:26Speaker 01

  And take those words as given.

• 1:28:29Lex Fridman?

  So it's.

• 1:28:29Speaker 01

  And that's.

• 1:28:30Lex Fridman?

  Uh, but I haven't, so that I take words as given is one thing. I haven't actually seen people that use autopilot that believe that the behavior is really important, like the actual action. So like, this is, I had to push back on the very thing that you're frustrated about, which is like journalists and general people, uh, buying all the hype and going out. In the same way, I think there's a lot of hype about the negatives of this too, that people are buying without using. People use the way, this is what, this was, this opened my eyes, actually. The way people use a product is very different than the way they talk about it. This is true with robotics, with everything. Everybody

• 1:29:11Lex Fridman?

  has dreams of how a particular product might be used or so on. There's, and then when it meets reality, there's a lot of fear of robotics, for example, that robots are somehow dangerous and all those kinds of things. But when you actually have robots in your life, whether it's in the factory or in the home, making your life better, that's going to be, that's way different. Your perceptions of it are going to be way different. And so my just tension was, was like, here's an innovator, um, uh, like, uh, uh, what is it? Sorry. Super Cruise from Cadillac was super interesting too. That's a really interesting system. There's, and we should like be excited by those innovations.

• 1:29:49Speaker 01

  Okay. So can I tell you something that's really annoyed me recently? It's really annoyed me that the press and friends of mine on Facebook are going, these billionaires and their space games, you know, why are they doing that?

• 1:30:04Lex Fridman?

  Yeah, that's been very frustrating.

• 1:30:05Speaker 01

  Really pisses me off. I, I must say, I, I applaud that.

• 1:30:09Lex Fridman?

  Yeah.

• 1:30:09Speaker 01

  I applaud it.

• 1:30:10Lex Fridman?

  Yeah.

• 1:30:10Speaker 01

  It's the taking, and not necessarily the people who are doing the things, but you know, that I keep having to push back against unrealistic expectations when these things can become real.

• 1:30:24Lex Fridman?

  Yeah. I, this was interesting on a, because there's been a particular focus for me is autonomous driving. Elon's prediction of when certain milestones will be hit.

• 1:30:37Lex Fridman?

  There's several things to be said there that I always I thought about because whenever you said them, it was obvious that's not going to me as a person that kind of not inside the system is obviously it's unlikely to hit those. There's two comments I want to make. One, he legitimately believes it. And two, much more importantly, I think that having ambitious deadlines drives people to do the best work of their life, even when the odds of those deadlines are very low.

• 1:31:13Speaker 01

  To a point, and I'm not, I'm not talking about anyone here. I'm just saying.

• 1:31:16Lex Fridman?

  So there's a line there, right?

• 1:31:17Speaker 01

  You have to have a line because you overextend and it's it's demoralizing.

• 1:31:23Lex Fridman?

  Yeah.

• 1:31:24Speaker 01

  But I will say that there's an additional thing here that those words also drive the stock market.

• 1:31:34Lex Fridman?

  Yeah.

• 1:31:35Speaker 01

  And, you know, we have because of the way that rich people in the past have manipulated the rubes through investment. We have, we have developed wars about what, you know, what you're allowed to say. And you know, there's an area here which is.

• 1:31:54Lex Fridman?

  I tend to be, maybe I'm naive, but I tend to believe that like engineers, innovators, people like that, they're not, they're my, they don't think like that, like manipulating the price, the stock price, but it's possible that I'm, I'm certain it's possible that I'm wrong. It's a very cynical view of the world because I don't, I think most people that run companies and build, like especially original founders.

• 1:32:27Lex Fridman?

  They

• 1:32:29Speaker 01

  Yeah, I'm not saying that's the intent. I'm saying it's a

• 1:32:31Lex Fridman?

  Eventually, it's kind of you You you you fall into that kind of behavior pattern. I don't know. I tend to

• 1:32:39Speaker 01

  I wasn't saying I wasn't saying it's falling into that intent. It's just a You also have to protect investors in this in this market.

• 1:32:46Lex Fridman?

  Yeah. Okay, so you have first of all, you have an amazing blog that people should check out. But you also have this in that blog, a set of predictions. Such a cool idea. I don't know how long ago you started, like three, four years ago.

• 1:33:00Speaker 01

  It was January 1, 2018.

• 1:33:03Lex Fridman?

  Eighteen. Yeah.

• 1:33:05Speaker 01

  And I made these predictions and I said that every January 1, I was going to check back on how my predictions.

• 1:33:11Lex Fridman?

  That's such a great thought experiment.

• 1:33:12Speaker 01

  For 32 years.

• 1:33:14Lex Fridman?

  Oh, you said 32 years.

• 1:33:15Speaker 01

  I said 32 years because it's still that'll be January 1, 2050.

• 1:33:18Lex Fridman?

  Yeah.

• 1:33:19Speaker 01

  I'll be I will just turn 95.

• 1:33:26Lex Fridman?

  Nice.

• 1:33:27Speaker 01

  You know, so

• 1:33:29Lex Fridman?

  And so people know that your predictions, at least for now, are in the space of artificial intelligence.

• 1:33:35Speaker 01

  Yeah, I didn't say I was going to make new predictions. I was just going to measure this set of predictions that I made. Because I was sort of I was sort of annoyed that everyone could make predictions. They didn't come true and everyone forgot. So I should hold myself to a high standard.

• 1:33:46Lex Fridman?

  Yeah, but also just putting years and like date ranges on things. It's a good thought exercise.

• 1:33:52Speaker 01

  Yeah.

• 1:33:52Lex Fridman?

  Like and like reasoning your thoughts out. And so the topics are Artificial intelligence, autonomous vehicles and space.

• 1:34:00Speaker 01

  Yep.

• 1:34:03Lex Fridman?

  I was wondering if we could just go through some that stand out, maybe from memory, I can just mention to you some. Let's talk about self-driving cars, like some predictions that you're particularly proud of or are particularly interesting. From flying cars to the other element here is Like how widespread the location where the deployment of the autonomous vehicles is. And there's also just a few fun ones. Is there something that jumps to mind that you remember from the predictions?

• 1:34:34Speaker 01

  Well, I did, I think I did put in there that there would be a dedicated self-driving lane on 101 by some year, and I think I was over optimistic on that one.

• 1:34:44Lex Fridman?

  Yeah, actually, yeah, I actually do remember that, but you, I think you were mentioning like difficulties at different cities.

• 1:34:50Speaker 01

  Yeah, yeah.

• 1:34:51Lex Fridman?

  So Cambridge, Massachusetts, I think was an example.

• 1:34:54Speaker 01

  Yeah, like in Cambridge Port, you know.

• 1:34:56Lex Fridman?

  Yeah.

• 1:34:56Speaker 01

  I, I lived in Cambridge Port for a number of years and, you know, the roads are narrow and getting, getting anywhere as a human driver is incredibly frustrating when you start to put, and, and people drive the wrong way on one-way streets there. It's just.

• 1:35:12Lex Fridman?

  So your prediction was driverless taxi services operating on all streets in Cambridge Port, Massachusetts in 2035?

• 1:35:23Speaker 01

  Yeah, and that may have been too optimistic.

• 1:35:27Lex Fridman?

  You think so?

• 1:35:28Speaker 01

  You know, I've gotten a little more pessimistic since I made these internally on some of these things.

• 1:35:33Lex Fridman?

  So what, can you put a year to a major milestone of deployment of a taxi service in, in a few major cities, like something where you feel like autonomous vehicles are here?

• 1:35:49Speaker 01

  So let's, let's take the grid streets of San Francisco, north of Market.

• 1:35:57Lex Fridman?

  Okay.

• 1:35:58Speaker 01

  Okay.

• 1:36:02Speaker 01

  Relatively benign environment. The streets are wide. The major problem is delivery trucks stopping everywhere, which has made things more complicated. A taxi system there with somewhat designated pickup and drop-offs, unlike with Uber and Lyft where you can sort of get to any place and the drivers will figure out how to. Get in there. Um. We're still a few years away. I, you know, I live in that area. So I see, you know, the self-driving car companies cars, multiple, multiple ones every day.

• 1:36:47Speaker 01

  Out there at Cruise.

• 1:36:50Speaker 01

  Um, uh, uh, Zooks less often, uh, Waymo all the time. Um, different and different ones come and go.

• 1:36:58Lex Fridman?

  And there's always a driver.

• 1:37:00Speaker 01

  There's always a driver at the moment, although I have noticed that, um, so sometimes the driver does not have the authority to take over without talking to the home office because they will sit there waiting for a long time.

• 1:37:16Lex Fridman?

  Yeah.

• 1:37:16Speaker 01

  And clearly something's going on where the home office is making a decision.

• 1:37:21Lex Fridman?

  Um, that's fast.

• 1:37:22Speaker 01

  So they're, you know, and, and so you can see whether they've got their hands on the wheel or not. And, and it's the incident resolution time that tells you, gives you some clues.

• 1:37:33Lex Fridman?

  So what year do you think, what's your intuition, what date range are you currently thinking? San Francisco would be autonomous, uh, taxi service from any point A to any point B without a driver. Are you, are you still, are you thinking 10 years from now, 20 years from now, 30 years from now?

• 1:37:55Speaker 01

  Certainly not 10 years from now. It's going to be longer. If you're allowed to go south of market, way longer. Um, and, and unless it's re-engineering of roads.

• 1:38:06Lex Fridman?

  By the way, what's the biggest challenge? You can mention a few. Is it, uh, the, is it the delivery trucks? Is it the edge cases, the computer perception, uh?

• 1:38:15Speaker 01

  Well, here's, here's a case that I saw outside my house, uh, a few weeks ago. Um, about 8:00 PM on a Friday night, it was getting dark, it was before the solstice. Um. It was a cruise vehicle come down the hill, uh, turned right, um, and stopped dead covering the crosswalk. Why did it stop dead? Cause there was a human just two feet from it.

• 1:38:40Lex Fridman?

  Mm hmm.

• 1:38:41Speaker 01

  Now, I just glanced. I knew what was happening. The human was, uh, was a woman, was at the door of her car, trying to unlock it with one of those things that, you know, when you don't have a key.

• 1:38:51Lex Fridman?

  Yes.

• 1:38:52Speaker 01

  The car thought, oh, she could jump out in front of me any second.

• 1:38:56Lex Fridman?

  Yeah.

• 1:38:57Speaker 01

  As a human, I could tell, no, she's not gonna jump out. She's busy trying to unlock her. She's lost her keys. She's trying to get in the car. And it, it stayed there for, until I got bored. Um.

• 1:39:08Lex Fridman?

  Yeah.

• 1:39:10Speaker 01

  And so the, the human driver in there did not take over.

• 1:39:15Speaker 01

  But here's the kicker to me. A guy comes down the hill with a stroller. I assume there's a baby in there. And now the crosswalk's blocked by this cruise vehicle.

• 1:39:27Lex Fridman?

  Mm hmm.

• 1:39:28Speaker 01

  What's he gonna do? Cleverly, I think, he decided not to go in front of the car.

• 1:39:33Lex Fridman?

  Mm hmm.

• 1:39:35Speaker 01

  He went, but he had to go behind it. He had to get off the crosswalk out into the intersection

• 1:39:39Lex Fridman?

  Wow.

• 1:39:40Speaker 01

  to push his baby around this car, which was stopped there and no human driver would have stopped there for that length of time. Um, they would have got out and out of the way. And that's another one of my pet peeves. That safety is being compromised for individuals who didn't sign up for having this happen in their neighborhood.

• 1:40:02Lex Fridman?

  Yeah, but.

• 1:40:03Speaker 01

  Now, you can say that's an edge case, but.

• 1:40:06Lex Fridman?

  Yeah, well, I'm in general not a fan which, uh, of anecdotal evidence for stuff like this is one of my biggest problems with the discussion of autonomous vehicles in general, people that criticize them or support them are using edge case.

• 1:40:22Speaker 01

  Okay.

• 1:40:23Lex Fridman?

  Are using.

• 1:40:24Speaker 01

  So let me.

• 1:40:25Lex Fridman?

  But I got you.

• 1:40:25Speaker 01

  You know, you know, your question is when is it going to happen in San Francisco? I say not soon, but it's going to be one of them. But when where it is going to happen is in uh, limited domains, uh, campuses of various sorts, gated communities, where the other drivers are are are not arbitrary people. There are people who know about these things. They, they, you know, it's been warned about them. And at velocities where it's always safe to stop dead.

• 1:40:57Lex Fridman?

  Yeah.

• 1:40:58Speaker 01

  Um, you can't do that on the freeway. That I think we're going to start to see.

• 1:41:04Speaker 01

  And they may not be shaped like,

• 1:41:07Speaker 01

  you know, current cars, they may be, you know, things like, you know, May Mobility has those things and various companies have these.

• 1:41:14Lex Fridman?

  Yeah, I wonder if that's a compelling experience. To me, it's always important. It's not just about automation. It's about creating a product that like, that makes your, it's not just cheaper, but makes your this fun to ride. Uh, one of the most, one of the least fun things is for a car that stops and like waits. There's something deeply frustrating for us humans for the rest of the world to take advantage of us as we wait.

• 1:41:37Speaker 01

  But

• 1:41:41Speaker 01

  think about, uh, you know, not you as the customer, but someone who's in their eighties in an, uh, you know, retirement village. Whose kids have said, you're not driving anymore. And this gives you the freedom to go to the market.

• 1:41:56Lex Fridman?

  That's a hugely beneficial thing, but it's a very, uh, few orders of magnitude less impact on the world. It's not, it's just a few people in a small community using cars as opposed to the entirety of the world.

• 1:42:10Lex Fridman?

  I like that, uh, the first time that a car equipped with some version of a solution to the trolley problem is, uh, what's NIML stand for?

• 1:42:18Speaker 01

  Not in my life.

• 1:42:19Lex Fridman?

  Not in my life.

• 1:42:19Speaker 01

  I define my lifetime as.

• 1:42:21Lex Fridman?

  As twenty fifty.

• 1:42:22Speaker 01

  Twenty fifty. Yeah. You know, I ask, I ask you, when, when have you had to decide which person shall I kill? No, you put the brakes on and you brake as hard as you can.

• 1:42:35Lex Fridman?

  I mean.

• 1:42:35Speaker 01

  You're not making that decision.

• 1:42:37Lex Fridman?

  It is, you know, I do think autonomous vehicles or semi autonomous vehicles do need to solve the whole pedestrian problem that has elements of the trolley problem within it, but it's not.

• 1:42:47Speaker 01

  Yeah, well, so here's, and I talk about it in one of my, the articles or blog posts that I wrote. His, his, and people have told me, one of my coworkers has told me he does this. He, he tortures autonomously driven vehicles and pedestrians will, will torture them. They'll, you know, once they realize that, you know, putting one foot off the curb makes the car think that they might walk into the road, kids, teenagers will be doing that all the time. They will.

• 1:43:14Lex Fridman?

  I, by the way, one of my, and it's a whole nother discussion because my main issue with robotics is HRI, human robot interaction. I believe that robots that interact with humans will have to push back. Like they can't just be bullied because that creates a very uncompelling experience for the humans.

• 1:43:34Speaker 01

  Yeah, well, you know, Waymo, before it was called Waymo, discovered that, you know, they had to do that at, at four-way intersections. They had to, they had to nudge forward to give the cue that they were going to go because otherwise the other drivers would just beat them all the time.

• 1:43:49Lex Fridman?

  So you co-founded iRobot, as we mentioned, one of the most successful robotics companies ever. What are you most proud of with that company? And the approach you took to robotics?

• 1:44:03Speaker 01

  Well, there's something I am quite proud of there, which may be a surprise, but I was still on the board when this happened. It was March 2011. And we sent robots to Japan and they were used to shut, help shut down the Fukushima, Fukushima Daiichi nuclear power plant, which was, everything was, I've been there since. I was there in 2014 to the, and the robots, some of the robots were still there. I was, I was proud that we were able to do that. Why were we able to do that? And, and you know, people have said, well, you know, Japan is so good at robotics.

• 1:44:45Speaker 01

  It was because we had had about sixty five hundred robots deployed in Iraq and Afghanistan, teleopt, but with intelligence, dealing with roadside bombs. So we had, I think it was at that time, nine years of in field experience with the robots in harsh conditions. Whereas the Japanese robots, which were, you know, getting, you know, this goes back to what annoys me so much, getting all the hype. Look at that. Look at that Honda robot, it can walk. Wow, the future's here. Couldn't do a thing because they weren't deployed, but we had deployed in really harsh conditions for a long time.

• 1:45:26Speaker 01

  And so we're able to, to do something very positive in a very bad situation.

• 1:45:33Lex Fridman?

  What about just the simple, and for people who don't know, one of the things that iRobot has created is the Roomba vacuum cleaner.

• 1:45:44Lex Fridman?

  What about the simple robot that, that is the Roombas, quote unquote, simple, that's deployed in tens of millions of, in tens of millions of homes? What do you think about that?

• 1:45:58Speaker 01

  Well, I make the joke that I started out life as a pure mathematician and turned into a vacuum cleaner salesman. So if you're going to be an entrepreneur, be ready for, be ready to do anything. But I was, you know, there was a, there was a wacky lawsuit that I got proposed for not too many years ago. And I was the only one who had email from the nineteen nineties and, and no one in the company had it. So I went and went through my email and, and it reminded me of.

• 1:46:34Speaker 01

  You know, the the joy of what we were doing and and what what was I doing? What was I doing at the time we were building um building uh the Roomba? One of the things was we had this incre- you know, incredibly tight budget because we wanted to to put it on the shelves at two hundred dollars. There was another home cleaning robot at the time. It was the uh uh Electrolux uh Trilobite, which sold for two thousand euros. And to us, that was not going to be a consumer product. So we had reason to believe that two hundred dollars was a was a thing that people would buy at.

• 1:47:14Speaker 01

  That was our aim. But that meant we had, you know, that's that's on the shelf making profit. Uh, that means the cost of goods has to be minimal. So I find all these emails of me going, uh, you know, I'd be in Taipei for a MIT meeting. And I'd stay a few extra days and go down to Shinhu and talk to these little tiny companies, lots of little tiny companies outside of TSMC, Taiwan Semiconductor, Taiwan Semiconductor Manufacturing Corporation, which let all these little companies be fabulous. They didn't have to have their own fab so they could innovate. And then um they were building, their innovations were to build stripped down sixty eight O twos.

• 1:47:57Speaker 01

  Sixty eight O two is what was in an Apple one. Get rid of half the silicon and still have it be viable. And I'd I'd previously got some of those for some earlier failed products of of iRobot. And then that was um in Hong Kong, going to all these um uh companies that built, you know, the the they weren't gaming in the current sense. There were these handheld games that you would play. Um, or or birthday cards, because we had about a fifty cent budget for computation. So I'm tracking from place to place, looking at their chips, looking at what they'd removed. Ah, the interrupt, their interrupt handling is too weak for a a general purpose.

• 1:48:40Speaker 01

  So I was going deep technical detail. And then I found this one from a company called Windbond, which had, and I'd forgotten it had this much RAM. It had five hundred and twelve bytes of RAM. And it was in our budget and it had all the capabilities we needed. So. And you were excited. Yeah, and I I was reading all these emails, Colin, I found this.

• 1:49:04Speaker 01

  So.

• 1:49:04Lex Fridman?

  Did you think, did you ever think that you guys could be so successful? Like eventually this company would be so successful, did you, could you possibly have imagined?

• 1:49:13Speaker 01

  Um, no, we never did think that. We'd had fourteen failed business models up to two thousand and two. And then we had two winners the same year. Um, uh, no, and then, you know. We, I remember the board. Um, cause by this time we had some venture capital in. The board went along with us building, um.

• 1:49:39Speaker 01

  Some robots for, you know, aiming at the Christmas two thousand and two market and. We went three times over what they authorized and built seventy thousand of them and sold them all. Uh, in that first, cause we released on September eighteenth and they were all sold by Christmas. So it was, uh. So we were gutsy, but.

• 1:50:02Lex Fridman?

  But, but yeah, you didn't think this will take over the world. Well, this is a.

• 1:50:09Lex Fridman?

  So a lot of amazing robotics companies have gone under over the past few decades. Why do you think it's so damn hard to, uh, run a successful?

• 1:50:21Speaker 01

  Well, there's a.

• 1:50:21Lex Fridman?

  Robotics company.

• 1:50:22Speaker 01

  There's a few things. Um. One is. Expectations of capabilities by the. Founders that are off base.

• 1:50:34Lex Fridman?

  The founders, not the consumer, the founders.

• 1:50:36Speaker 01

  Yeah, expectations of what, what can be delivered.

• 1:50:38Lex Fridman?

  Sure.

• 1:50:40Speaker 01

  Mispricing. And what a customer thinks is a valid price is not rational necessarily.

• 1:50:47Lex Fridman?

  Yeah.

• 1:50:49Speaker 01

  And expectations of customers. Um, and.

• 1:50:55Speaker 01

  Just the sheer hardness of getting people to adopt a new technology. And I've suffered from all three of these. Uh, you know, I've had uh more failures and successes.

• 1:51:09Lex Fridman?

  Yeah.

• 1:51:09Speaker 01

  In terms of companies, I've suffered from all three. Um, so.

• 1:51:16Lex Fridman?

  Do you think um

• 1:51:19Lex Fridman?

  one day there will be a robotics company? And by robotics company, I mean where your primary source of income is from robots. That will be a trillion plus dollar company. And if so, what would that company do?

• 1:51:39Speaker 01

  I can't, you know, because I'm still starting robot companies.

• 1:51:42Lex Fridman?

  Yeah.

• 1:51:45Speaker 01

  I'm I'm not making any such predictions in my own mind. I'm not thinking about a trillion dollar company. And by the way, I don't think, you know, in the nineties, anyone was thinking that Apple would ever be a trillion dollar company. So these are, these are very hard to predict.

• 1:51:59Lex Fridman?

  But sorry to interrupt, but don't you, because I kind of have a vision in a, in a small way, and it's a big vision in a small way, that I see that there will be robots in the home

• 1:52:12Lex Fridman?

  at scale, like Roomba, but more. And that's trillion dollar.

• 1:52:17Speaker 01

  Right, and I, and I think there's a, there's a real market pull for them because of the um demographic inversion. You know, who's, who's going to do all the stuff for the older people? Um, there's too many, I'm, you know, I'm, I'm leading here.

• 1:52:33Speaker 01

  This is going to be too many of us. And um, um, but we don't have capable enough robots to, to make that economic argument at this point. Do I expect that that will happen? Yes, I expect it will happen, but I got to tell you, we introduced the Roomba in two thousand and two, and I stayed another nine years. We were always trying to find what the next home robot would be in. Still today, the primary product of 20 years, almost 20 years later, 19 years later, the primary product is still LaRumba. So iRobot hasn't found the next one.

• 1:53:09Lex Fridman?

  Do you think it's possible for one person in the garage to build it versus like Google launching Google self-driving car that turns into Waymo? Do you think it's possible, like this is almost like what it takes to build a successful robotics company? Do you think it's possible to go from the ground up? Or is it just too much capital investment?

• 1:53:28Speaker 01

  Yeah, so it's very hard to get there without a lot of capital. And we're starting to see, you know, fair chunks of capital for some robotics companies. You know, Series Bs, I just saw one yesterday for $80 million, I think it was for CoVarient.

• 1:53:51Speaker 01

  But it can take real money to get into these things and you may fail along the way. I've certainly failed at Rethink Robotics and we've all lost $150 million in capital there.

• 1:54:03Lex Fridman?

  So, okay, so Rethink Robotics is another amazing robotics company you co-founded. So what was the vision there? What was the dream? And what are you most proud of with Rethink Robotics?

• 1:54:17Speaker 01

  I'm most proud of the fact that we got robots out of the cage in factories that was safe, absolutely safe for people and robots to be next to each other.

• 1:54:28Lex Fridman?

  So these are robotic arms?

• 1:54:29Speaker 01

  Robotic arms.

• 1:54:30Lex Fridman?

  They're able to pick up stuff and interact with humans?

• 1:54:33Speaker 01

  Yeah, and that the humans could re-task them without writing code. And now that's sort of become an expectation for a lot of other little companies and big companies are advertising they're doing.

• 1:54:45Lex Fridman?

  That's both an interface problem and also a safety problem.

• 1:54:48Speaker 01

  Yeah, yeah. So I'm most proud of that.

• 1:54:54Speaker 01

  I completely, I let.

• 1:54:57Speaker 01

  Myself be talked out of what I wanted to do. And you know, you've always got, you know, I can't replay the tape. You know, I can't replay it. Maybe, maybe I, you know, if I'd been stronger on, and I remember the day. I remember the exact meeting. Can you take me through that meeting? Yeah. Um, so I'd said that I'd set as a target for the company that we were going to build three thousand dollar robots with force feedback. Um, that was safe for people to be around. Wow. That was my goal. And we built, uh, so we started in two thousand eight.

• 1:55:38Speaker 01

  And we had prototypes built of plastic, uh, plastic gearboxes. And at a three thousand dollar, you know, uh, lifetime or three thousand dollar. I was saying, we're going to go after not the people who already have robot arms in factories, the people who would never have a robot arm. We're going to go after a different market. So we don't have to meet their expectations. Mm hmm. And, and so we're going to build it out of plastic. It doesn't have to have a thirty five thousand hour lifetime. It's going to be so cheap that it's OpEx, not CapEx. Mm hmm. Um.

• 1:56:14Speaker 01

  And so we had, we had a prototype that worked reasonably well. But the control engineers were complaining about these plastic gearboxes. It was a beautiful little planetary gearbox. Um, but, uh, we could use something called series elastic actuators. We embedded them in there. We can measure forces. We knew when we hit something, et cetera. The control engineers were saying, yeah, but this, this is torque ripple. You know, because these plastic gears, they're not great gears. And there's this ripple and trying to do force control around this ripple is so hard. And I'm not going to name names, but I remember one of the mechanical engineers saying,

• 1:56:56Speaker 01

  we'll just build a metal gearbox with spur gears and it'll take six weeks. We'll be done. Problem solved.

• 1:57:05Speaker 01

  Two years later, we got the gear, the spur gearbox working.

• 1:57:08Lex Fridman?

  Yeah.

• 1:57:09Speaker 01

  Um, we, we, we cost reduced in every possible way we could.

• 1:57:13Lex Fridman?

  Um, yeah.

• 1:57:14Speaker 01

  But now the price went up to, and then the CEO at the time said, well, we have to have two arms, not one arm. So our first robot product Baxter now costs $25,000.

• 1:57:28Speaker 01

  And the only people who are going to look at that were people who had arms in factories because that was somewhat cheaper for two arms than arms in factories. But they were used to um, point one millimeter reproduce, uh, re, reproducibility of motion. And certain velocities. And we, I kept thinking, but that's not what we're giving you. You don't need position repeatability. You use force control like a human does. No, no, but we want, we want that repeatability. We want that repeatability. All the other robots have that repeatability. Why don't you have that repeatability?

• 1:58:00Lex Fridman?

  So can you clarify, force controls, you can grab the arm and you can move it?

• 1:58:04Speaker 01

  Well, you can move it around, but, but suppose you, um, can you see that?

• 1:58:09Lex Fridman?

  Yes.

• 1:58:09Speaker 01

  Suppose you want to.

• 1:58:12Lex Fridman?

  Yes.

• 1:58:13Speaker 01

  Suppose this, this thing is a, you know, precise thing that's got to fit here in this right angle.

• 1:58:17Lex Fridman?

  Yeah.

• 1:58:18Speaker 01

  Um, under position control, you sent your, you, you have fixed where this is, you know where this is precisely. And you just move it, oh, you know, and it goes there. If force control, you would do something like slide it over here till we feel that and slide it in there. And that's how a human gets precise precision.

• 1:58:36Lex Fridman?

  Yeah.

• 1:58:36Speaker 01

  They use force feedback.

• 1:58:38Lex Fridman?

  Yes.

• 1:58:38Speaker 01

  And get the things to mate rather than just go straight to it.

• 1:58:42Lex Fridman?

  Yeah.

• 1:58:44Speaker 01

  Couldn't convince, couldn't convince our customers who were in factories and were used to thinking about things a certain way. And they wondered, wondered, wondered. So then we said, okay, we're going to build an arm that gives you that. So now we ended up building a $35,000 robot with one arm with, um, um. Oh, what are they called? Um.

• 1:59:07Speaker 01

  Um, a certain sort of gearbox made by a company whose name I can't remember right now, but it's the name of the gearbox. Um, and, um, but it's, it's got talk ripple in it. So now there was an extra two years of solving the problem of doing the force with the talk ripple. So we had to do the, the thing we had avoided. And for the plastic gearbox, as we ended up having to do, the robot was now overpriced. And, um,

• 1:59:37Lex Fridman?

  And that was your intuition from the very beginning, kind of that this is not,

• 1:59:42Lex Fridman?

  you're opening a door to, to solve a lot of problems that you're, you're eventually going to have to solve this problem anyway.

• 1:59:47Speaker 01

  Yeah. And also I was aiming at a low price to go into a different market.

• 1:59:51Lex Fridman?

  Low price.

• 1:59:52Speaker 01

  That, that didn't have.

• 1:59:52Lex Fridman?

  Three thousand dollars would be amazing.

• 1:59:54Speaker 01

  Yeah, I think we could have done it for five. Um, but you know, you said, talked about setting the goal a little too far for the engineers.

• 2:00:02Lex Fridman?

  Exactly.

• 2:00:05Lex Fridman?

  So why, why would you say that company, um, not failed, but went under?

• 2:00:12Speaker 01

  We had buyers and, um,

• 2:00:16Speaker 01

  there's this thing called the Committee on Foreign Investment in the U S, CFIUS.

• 2:00:21Speaker 01

  And, um, that had previously been invoked twice.

• 2:00:26Speaker 01

  Around where the government could stop, uh, foreign money coming into a U S company. Based on, um, defense requirements. We went through due diligence multiple times. We were going to get acquired.

• 2:00:42Speaker 01

  But every consortium had Chinese money in it. And all the bankers would say at the last minute, you know, I'm going to get past CFIUS. And the investors would go away. And then we had two buyers, once we're about to run out of money, two buyers. And one used heavy handed legal stuff with the other one.

• 2:01:05Speaker 01

  Said they were gonna take it, pay more, dropped out when we were out of cash and then bought the assets at one thirtieth of the price they had offered a week before. It was a tough week.

• 2:01:19Lex Fridman?

  Do you, um, does it hurt to think about? Like, an amazing company that didn't?

• 2:01:28Lex Fridman?

  You know, like iRobot didn't find a way?

• 2:01:31Speaker 01

  Yeah, it was tough. Um, I said I was never gonna start another company. I was pleased that everyone liked what we did so much that the teams, the team was hired by, um, three companies within a week. Everyone had a job in one of these three companies. Some stayed in their same desks because the company, another company came in and rented the space. So, so I felt good about people not being out on the street.

• 2:01:57Lex Fridman?

  So Baxter has a screen with a face. What, uh, that's a revolutionary idea for a robot manipulation, like for robotic arm. How much opposition did you get?

• 2:02:10Speaker 01

  Well, first, the screen was also used during, um, codeless programming, where you taught by demonstration, it showed you what its understanding of the task was. So it had two roles. Um, some customers hated it, and so we made it so that when the robot was running, it could be showing graphs of what was happening and not show the eyes. Other com, other people, and some of them surprised me, who they were, saying, "Well, this one doesn't look as human as the old one. We like the human looking."

• 2:02:41Lex Fridman?

  Yeah.

• 2:02:42Speaker 01

  So there was a mixed bag.

• 2:02:45Lex Fridman?

  But you think that's, uh, I don't know, I, I'm kind of disappointed whenever I talk to, um, to roboticists, like, the best robotics people in the world, they seem to not want to do the eyes type of thing. Like, they seem to see it as a machine, as opposed to a machine that can also have a human connection. I'm not sure what to do with that. It seems like a lost opportunity. I think the trillion dollar company will have to do the human connection very well, no matter what it does.

• 2:03:13Speaker 01

  Yeah, I agree.

• 2:03:15Lex Fridman?

  Can I ask you a ridiculous question?

• 2:03:17Speaker 01

  Sure. I'm not giving ridiculous answers.

• 2:03:21Lex Fridman?

  Uh, do you think, uh, well, ma- maybe by way of asking the question, let me first mention that you're kind of critical of the idea of the Turing test as a test of intelligence.

• 2:03:34Lex Fridman?

  Let me first ask this question. Do you think we'll be able to build an AI system that humans fall in love with and it falls in love with the human? Like romantic love?

• 2:03:49Speaker 01

  Well, we've had that with humans falling in love with cars, even back in the 50s.

• 2:03:54Lex Fridman?

  It's a different love, right? Well, I think, I think there's a lifelong partnership where you can communicate and grow like.

• 2:04:02Speaker 01

  I think we're a long way from that. I think we're a long, long way. I think Blade Runner was, you know, had the time scale totally wrong.

• 2:04:13Lex Fridman?

  Yeah, but so to me, honestly, the most difficult part is the thing that you said with the Marvex paradox is to create a human form that interacts and perceives the world. But if we just look at a voice, like the movie Her, or just like an Alexa type voice, I tend to think we're not that far away.

• 2:04:34Speaker 01

  Well, for some, for some people, maybe not, but I, I, you know, I, I.

• 2:04:45Speaker 01

  You know, as humans, as we think about the future, we always try to, and this is the premise of most science fiction movies. You've got the world just as is today and you change one thing. Right. But that's not how, and it's the same with a self-driving car. You change one thing. No, you, everything changes.

• 2:05:01Lex Fridman?

  Yes.

• 2:05:02Speaker 01

  Everything grows together. So surprisingly, it might be surprising to you or might not. I think the best movie about this stuff was Bicentennial Man. And what was happening there? It was schmaltzy and, you know, but what was happening there? As the robot was trying to become more human, the humans were adopting the technology of the robot and changing their bodies.

• 2:05:27Lex Fridman?

  Yeah.

• 2:05:28Speaker 01

  So there was a convergence happening in a sense. So we will not be the same. You know, we're already talking about genetically modifying our babies. You know, there's a, there's more and more stuff happening around that. We will, we will want to modify ourselves even more for all sorts of things. We put all sorts of technology in our bodies to improve it, you know? I've got. I've got things in my ears so that I can sort of hear you.

• 2:05:59Lex Fridman?

  Yeah.

• 2:06:02Speaker 01

  So we're always modifying our bodies. So, so, you know, I think it's hard to imagine exactly what it will be like in the future.

• 2:06:09Lex Fridman?

  But on the Turing test side, do you think, so forget about love for a second. Let's talk about just like the Alexa Prize. Actually, I was invited to be a, what is the interviewer for the Alexa Prize or whatever that's in two days. Their idea is success looks like a person wanting to talk to an AI system for a prolonged period of time, like 20 minutes.

• 2:06:37Lex Fridman?

  How far away are we? And why is it difficult to build an AI system with which you'd want to have a beer and talk for an hour or two hours, like not for to check the weather or to check music, but just like to, to talk as friends.

• 2:06:55Speaker 01

  Yeah, well, you know, we saw, we saw Weizenbaum back in the 60s with his program Eliza. Being shocked at how much people would talk to Eliza. And I, I remember, you know, in the 70s, typing, you know, stuff to Eliza to see what it would come back with. You know, I think right now, and this is a thing that

• 2:07:20Speaker 01

  Amazon's been trying to improve with Alexa. There is no continuity of, of, of, of topic. There's not, you can't refer to what we talked about yesterday. It's not the same as talking to a person where there seems to be an ongoing existence.

• 2:07:35Lex Fridman?

  Right.

• 2:07:36Speaker 01

  Changes.

• 2:07:37Lex Fridman?

  We share moments together and they last in our memory together.

• 2:07:40Speaker 01

  Yeah, but there's none of that. And there's no sort of intention of these systems that they have any goal in life, even if it's to be happy, you know, they don't, they don't even have a semblance of that. Now, I'm not saying this can't be done. I'm just saying, I think this is why we don't feel that way about them. Or that's a, a sort of a minimal requirement. If you want the sort of interaction you're talking about, it's a minimal requirement. Whether it's going to be sufficient, I don't know. We haven't seen it yet. We don't know what.

• 2:08:16Speaker 01

  Feels like.

• 2:08:16Lex Fridman?

  I tend to be, I tend to think it's, uh, it's not, uh, as difficult as solving intelligence, for example, and I think it's achievable in the near term.

• 2:08:29Lex Fridman?

  But on the Turing test, why don't you think the Turing test is a good test of intelligence?

• 2:08:35Speaker 01

  Oh, I, I, because, you know, again, the Turing, if you read the paper, Turing wasn't saying this is a good test. He was using as a rhetorical device to argue that if you can't tell the difference between a computer and a person, you must say that the computer's thinking because can't tell the difference, you know, then it's thinking. You can't, you can't say something different. What it has become is this sort of weird game of fooling people. So back at the AI lab in the late eighties, we had this thing that still goes on called the AI Olympics.

• 2:09:17Speaker 01

  And one of the events we had one year was the original imitation game, as Turing talked about. Cause he starts by saying, can you tell whether it's a man or a woman? So we did that at the, at the lab. We had, you know, you'd go and type and the thing would come back and you had to tell whether it was a man or a woman. And the, the, uh, one of the, one of the, one of the, uh, one man came up with a question that he could ask, which was always a dead giveaway of whether the other person was really a man or a woman, you know?

• 2:09:57Speaker 01

  What he would ask them, did you have, um, green plastic toy soldiers as a kid? Yeah. What'd you do with them? And a woman, a woman trying to be a man would say, oh, I lined them up. We had wars, we had battles. And the man, just being a man, I stumped on them. I burnt them.

• 2:10:17Speaker 01

  So, you know, that's what, that's what the Turing test, the Turing test with computers has become. What's the trick question? What's the, that's why I say it's sort of devolved into this weirdness.

• 2:10:31Lex Fridman?

  Nevertheless, conversation not formulated as a test is a pretty, is a fascinatingly challenging dance. That's a really hard problem. To me, conversation when non poses a test is a, is a more intuitive illustration how far away we are from solving intelligence than like computer vision. It's hard, computer vision is harder for me to pull apart. But with language, with conversation, you could see.

• 2:10:57Speaker 01

  Well, because language is so human.

• 2:10:59Lex Fridman?

  It's so human. We can, we can so, we can so clearly see it.

• 2:11:06Lex Fridman?

  Shit, you mentioned something I was gonna go on, off on. Okay.

• 2:11:11Lex Fridman?

  I mean, I have to ask you, because you were the head of C cell AI lab for a long time. You're, I don't know, to me, when I came to MIT, you're like one of the greats at MIT. So what was that time like? Like what? And, and plus you, with your, I don't know, friends with, but you knew Minsky and all the, all the folks there, all the legendary AI people of which you're one. So what was that time like? Like what are memories that stand out to you from that time? From your time at MIT, from the AI lab, from the dreams that the AI lab represented, to the actual like revolutionary work?

• 2:11:55Speaker 01

  Well, let, let me tell you first a disappointment in myself. You know, as I've been researching this book, and so many of the players, you know, were active in the 50s and 60s, I knew many of them when they were older. And I didn't ask them all the questions now I wish I had asked. I'd sit with them at our Thursday lunches, which we had a faculty lunch. And, and I didn't ask them so many questions that now I wish I had.

• 2:12:21Lex Fridman?

  Can I ask you that question? Because, because you wrote that. You wrote that you were fortunate to know and rub shoulders with many of the greats, those who founded AI, robotics and computer science and the world wide web. And you wrote that your big regret nowadays is that often I have questions for those who have passed on.

• 2:12:38Speaker 01

  Yeah.

• 2:12:39Lex Fridman?

  And I didn't think to ask them any of these questions.

• 2:12:42Speaker 01

  Right.

• 2:12:43Lex Fridman?

  Uh, even as I saw them and said hello to them on a daily basis. So maybe also another question I want to ask. If you could talk to them today, what question would you ask? What questions would you ask?

• 2:12:56Speaker 01

  I, well, Lick Leiter, I, I would, I would ask him, you know, he had the vision for humans and, and computers working together and he really founded that at DARPA and he gave the money to MIT, which started Project Mac in 1963.

• 2:13:15Speaker 01

  And I, I would have talked to him about what, what the successes were, what the failures were, what he saw as progress, et cetera.

• 2:13:23Speaker 01

  I, I would have asked him more, more questions about that because now I could use it in my book. But I think it's lost. It's lost forever. A lot of the motivations are lost.

• 2:13:37Speaker 01

  I, I, I should have asked Marvin why he, he and Seymour Papert came down so hard on neural networks in 1968 in their book Perceptrons because Marvin's PhD thesis was on neural networks.

• 2:13:51Lex Fridman?

  Yeah. How, how do you make sense of that?

• 2:13:53Speaker 01

  That book destroyed the field for.

• 2:13:55Lex Fridman?

  He probably, do you think he knew the, the effect that book would have?

• 2:14:02Speaker 01

  All the theorems are negative theorems.

• 2:14:05Lex Fridman?

  Yeah.

• 2:14:07Speaker 01

  Um, yeah. So, yeah.

• 2:14:09Lex Fridman?

  That's just the way of, that's, that's the way of life.

• 2:14:14Speaker 01

  Yeah.

• 2:14:14Lex Fridman?

  But still, it's kind of tragic that he was both a proponent and the destroyer of neural networks.

• 2:14:19Speaker 01

  Yeah.

• 2:14:21Lex Fridman?

  Is there other memories stand out from the, the robotics and the AI work at MIT?

• 2:14:30Speaker 01

  Well, yeah, but you gotta be more specific.

• 2:14:33Lex Fridman?

  Well, I mean, like it's such a magical place. I mean, it, to me, it's a little bit also heartbreaking that. You know, with Google and Facebook, like DeepMind and so on, so much of the talent, you know, doesn't stay necessarily for prolonged periods of time in these universities.

• 2:14:52Speaker 01

  Oh, yeah. I mean, some of the companies are more guilty than others of paying fabulous salaries to some of the highest producers and then just you never hear from them again. They're not allowed to give public talks. They're sort of locked away. And it's sort of like collecting, you know, Hollywood stars or something. And they're not allowed to make movies anymore. I own them.

• 2:15:17Lex Fridman?

  Yeah, that's tragic because I mean, there's an openness to the university setting where you do research to both in the space of ideas and like publication, all those kinds of things.

• 2:15:27Speaker 01

  Yeah, you know, and, you know, there's the publication and all that and often, you know, although these places say they publish.

• 2:15:35Lex Fridman?

  There's pressure.

• 2:15:36Speaker 01

  And, but I think for instance, you know, net, net, I think

• 2:15:45Speaker 01

  Google buying those eight or nine robotics company was bad for the field because it locked those people away. They didn't have to make the company succeed anymore, lock them away for years and then sort of all fledged away.

• 2:16:01Lex Fridman?

  Yeah.

• 2:16:02Speaker 01

  So.

• 2:16:05Lex Fridman?

  Do you have hope for MIT?

• 2:16:08Lex Fridman?

  For MIT?

• 2:16:09Speaker 01

  Yeah, why shouldn't I?

• 2:16:10Lex Fridman?

  Well, I could be harsh and say that

• 2:16:14Lex Fridman?

  I'm not sure I would say MIT is leading the world in AI. Or even Stanford or Berkeley. I would say, I would say, DeepMind, Google AI, Facebook AI.

• 2:16:28Speaker 01

  See, I would take a slightly different approach, a different answer. I'll come back to Facebook in a minute, but I think those other places are following a dream of.

• 2:16:42Speaker 01

  One of the founders, uh, and I'm not sure that it's well-founded, the dream. And I'm not sure that it's going to have the impact that he believes it is.

• 2:16:57Lex Fridman?

  You're talking about Facebook and Google and so on.

• 2:16:59Speaker 01

  I'm talking about Google.

• 2:17:00Lex Fridman?

  Google. But the thing is, those research labs aren't, there's the big dream. And I'm, I'm, I'm usually a fan of, no matter what the dream is, a big dream is a unifier. Cause what happens is you have a lot of bright minds working together, uh, on a dream. What results is a lot of like adjacent ideas. And this is how so much progress is made.

• 2:17:22Speaker 01

  Yeah, I'm, I'm, I'm, so I'm not saying they're actually leading. I'm not, I'm not saying that the universities are leading.

• 2:17:28Lex Fridman?

  Yeah.

• 2:17:28Speaker 01

  But I don't think those companies are leading in general because they're, you know, the, and we, we saw this incredible spike in, you know, attendees at Europe's and as I said in my January first review this year for two thousand and twenty, two thousand and twenty will not be remembered as a watershed year for machine learning or AI. You know, there was nothing surprising happened anyway, unlike when deep learning hit ImageNet. That was a, that was a shake.

• 2:18:04Speaker 01

  And there's a lot more people writing papers, but the papers are fundamentally boring.

• 2:18:10Lex Fridman?

  Yeah.

• 2:18:10Speaker 01

  And uninteresting.

• 2:18:11Lex Fridman?

  Incremental work.

• 2:18:12Speaker 01

  Yeah.

• 2:18:16Lex Fridman?

  Is there particular memories you have with Minsky or somebody else at MIT that stand out? Funny stories. I mean, unfortunately, he's another one that's passed away.

• 2:18:28Lex Fridman?

  You've known some of the biggest minds in AI.

• 2:18:31Speaker 01

  Yeah. And, you know, they, they did amazing things and some, sometimes they were grumpy.

• 2:18:38Lex Fridman?

  Well, he was, uh, he was interesting because he was very grumpy, but that, that was, is, uh, I remember him saying in an interview that the key to success or being, to keep being productive is to hate everything you've ever done in the past.

• 2:18:53Speaker 01

  Maybe, maybe that explains the Perceptron book. There it was. He, he told you exactly.

• 2:19:01Lex Fridman?

  But he, meaning like, just like, I mean, maybe that's the way to not treat yourself too seriously. Just always be moving forward. That was his idea. I mean, that crankiness, I mean, there's a

• 2:19:16Speaker 01

  So let me, let me, let me tell you, you know, what really,

• 2:19:21Speaker 01

  you know, the joy memories are about having access to technology before anyone else had seen it. So, so, you know, I got to Stanford in 1977 and we had, you know, we had terminals that could show live video on them, digital, digital sound system. We had a Xerox graphics printer. We could print,

• 2:19:49Speaker 01

  it wasn't, you know, it wasn't like a typewriter ball hitting in characters. It could print arbitrary things, only in, you know, one bit, you know, black or white, but arbitrary pictures. This was science fiction sort of stuff.

• 2:20:04Speaker 01

  At, at MIT, the, the list machines, which, you know, they were the first personal computers. And, you know, they were, it cost a hundred thousand dollars each and I could, you know, I got there early enough in the day, I got one for the day. Couldn't, couldn't stand up. Had to keep working.

• 2:20:23Lex Fridman?

  So having that like direct glimpse into the future.

• 2:20:27Speaker 01

  Yeah. And, and, you know, I've had email every day since 1977. And, you know, the, the host field was only eight bits in that many places. But we, I could send email to other people at a few places. So that was, that was pretty exciting to be in that world, so different from what the rest of the world knew.

• 2:20:52Lex Fridman?

  Let me ask you, probably edit this out, but just in case you have a story.

• 2:20:58Lex Fridman?

  I'm hanging out with Don Knuth for a while tomorrow. Did you ever get a chance? It's such a different world than yours. He's a very kind of theoretical computer science, the puzzle of computer science and mathematics. And you're so much about the magic of robotics, like the practice of it. You mentioned him earlier for like, not, you know, about computation. Did your worlds cross?

• 2:21:21Speaker 01

  They did, you know, I know him now. We talked, you know, but let me tell you my Donald Knuth story. So, you know, besides, you know, analysis of algorithms, he's well known for writing tech, which is in LaTeX, which is the academic publishing system. So he did that at the AI lab and he would do it, he would work overnight at the AI lab. And one, one day, the, one night, the, the mainframe computer went down. And a guy named Robert Poore was there. He did his PhD at the Media Lab at MIT. And he was a, you know, engineer.

• 2:22:06Speaker 01

  And so I, he and I, you know, tracked down what were the problem was. It was one of this big refrigerator sized or washing machine sized disk drives had failed. And that's what brought the whole system down. So we got panels pulled off and we're pulling, you know, circuit cards out. And Donald Knuth, who's a really tall guy, walks in and he's looking down and says, when will it be fixed? You know, because he wanted to get back to writing his tech system.

• 2:22:31Lex Fridman?

  Great.

• 2:22:31Speaker 01

  Donald Knuth. And so we, we figured out, you know, it was a particular chip, 7400 series chip, which were socketed. We popped it out, we put a replacement in, put it back in, smoke comes out. Cause we put it in backwards because we were so nervous that Donald Knuth was standing over us. Anyway, we eventually got it fixed and got the, the mainframe running again.

• 2:22:55Lex Fridman?

  So that was your little, when was that again?

• 2:22:58Speaker 01

  That, well, that must've been before October 79 because we moved out of that building then. So sometime, probably 78, sometime early 79.

• 2:23:07Lex Fridman?

  Yeah, those, all those figures is just fascinating. All the people who've passed through MIT is really fascinating. Is there a, let me ask you to put on your big wise man hat. Is there advice that you can give to young people today, whether in high school or college, who are thinking about their career? Who are thinking about life?

• 2:23:31Lex Fridman?

  How to live a life they're proud of, a successful life?

• 2:23:38Speaker 01

  Yeah, so, so many people ask me for advice and have asked, and I give, I talk to a lot of people all the time.

• 2:23:46Speaker 01

  And there is no one way.

• 2:23:50Speaker 01

  You know, there's a lot of pressure

• 2:23:53Speaker 01

  to produce papers

• 2:23:58Speaker 01

  that will be acceptable and be published.

• 2:24:02Speaker 01

  Maybe I was, maybe I come from an age where I would, I could be a rebel against that and, and still succeed. Maybe it's harder today. But I think it's important not to get too caught up with what everyone else is doing. And

• 2:24:22Speaker 01

  if you, if, well, it depends what you want of life. If you want to have real impact, you have to be ready to fail a lot of times. So you have to make a lot of unsafe decisions. And the only way to make that work is to make, keep doing it for a long time. And then one of them will be work out. And so that, that, that will make something successful.

• 2:24:45Lex Fridman?

  Or not.

• 2:24:47Speaker 01

  Or not, or you may, or you just may, you know, end up, you know, not having a, you know, having a lousy career. I mean, it's certainly possible.

• 2:24:54Lex Fridman?

  Taking the risk is the thing.

• 2:24:55Speaker 01

  Yeah, so, but it, it, but there's no way to, to make all safe decisions and actually really contribute.

• 2:25:08Lex Fridman?

  Do you think about your death, about your mortality?

• 2:25:14Speaker 01

  I gotta say, when COVID hit, I did, because we, you know, in the early days, we didn't know how bad it was going to be. And I, that, that made me work on my book harder for a while. But then I'd started this company and now I'm doing full time, more than full time at the company. So the book's on hold. But I do want to finish this book.

• 2:25:32Lex Fridman?

  When you think about it, are you afraid of it?

• 2:25:37Speaker 01

  I'm afraid of dribbling. You know, I'm losing it.

• 2:25:44Lex Fridman?

  The details of, okay.

• 2:25:46Speaker 01

  Yeah, yeah.

• 2:25:47Lex Fridman?

  But the fact that the ride ends.

• 2:25:51Speaker 01

  I've known that for a long time. Um, so it's.

• 2:25:55Lex Fridman?

  Yeah, but there's knowing and knowing. It's such a.

• 2:25:59Speaker 01

  Yeah, and it.

• 2:26:00Lex Fridman?

  It really sucks.

• 2:26:00Speaker 01

  It feels, it feels a lot closer. So my, in, in my, my blog with my predictions, my sort of pushback against that was I said, I'm going to review these every year for 32 years. That puts me into my mid 90s. So, you know, it's my.

• 2:26:17Lex Fridman?

  That puts the whole, every, every time you write the blog posts, you're getting closer and closer to your own prediction.

• 2:26:23Speaker 01

  That's, that's true.

• 2:26:24Lex Fridman?

  Of your death.

• 2:26:25Speaker 01

  Yeah.

• 2:26:26Lex Fridman?

  What, what do you hope your legacy is?

• 2:26:30Lex Fridman?

  You're one of the greatest roboticist AI researchers of all time.

• 2:26:36Speaker 01

  What I hope is that I actually finish writing this book. And that there's, there's one person who reads it and sees something about changing the way they're thinking. And that leads to the next big.

• 2:26:58Lex Fridman?

  And then they'll be on a podcast 100 years from now saying, I once read that book.

• 2:27:04Lex Fridman?

  And that changed everything. Uh, what do you think is the meaning of life? This whole thing, the existence, the, the, the, all the hurried things we do on this planet. What do you think is the meaning of it all?

• 2:27:17Speaker 01

  Ah, well, you know, I think we're all really bad at it.

• 2:27:21Lex Fridman?

  Life or finding meaning or both?

• 2:27:23Speaker 01

  Yeah, we get caught up in, in, in the, it's easier to get, easier to do the stuff that's immediate and not do the stuff that's not immediate. Um, so.

• 2:27:32Lex Fridman?

  The big picture we're bad at.

• 2:27:34Speaker 01

  Yeah.

• 2:27:35Lex Fridman?

  Do you have a sense of what that big picture is? Like, why? You ever look up to the stars and ask, why the hell are we here?

• 2:27:45Speaker 01

  You know, my atheism tells me it's just random. But, you know, I want to understand the way random in that's what I talk about in this book, how order comes from disorder.

• 2:27:59Lex Fridman?

  Yeah.

• 2:28:02Lex Fridman?

  But it kind of sprung up, like most of the whole thing is random. But this little pocket of complexity they will call Earth. That like, why the hell does that happen?

• 2:28:12Speaker 01

  And what we don't know is how common that those pockets of complexity are, or how often. Because they may not last forever.

• 2:28:24Lex Fridman?

  Which is more exciting slash sad to you? If we're alone, or if there's infinite number of?

• 2:28:32Speaker 01

  Oh, I think it's impossible for me to believe that we're alone. That would just be too horrible, too cruel.

• 2:28:43Lex Fridman?

  Could be like the sad thing, it could be like a graveyard of intelligent civilizations.

• 2:28:48Speaker 01

  Oh, everywhere. Yeah, that may be the most likely outcome.

• 2:28:52Lex Fridman?

  And for us too.

• 2:28:53Speaker 01

  Yeah, exactly. Yeah.

• 2:28:55Lex Fridman?

  And all of this will be forgotten.

• 2:28:56Speaker 01

  Yeah.

• 2:28:57Lex Fridman?

  Including all the robots you build. Everything forgotten.

• 2:29:03Speaker 01

  Well, on average, everyone has been forgotten in history.

• 2:29:07Lex Fridman?

  Yeah.

• 2:29:08Speaker 01

  Right?

• 2:29:09Lex Fridman?

  Yeah.

• 2:29:09Speaker 01

  Most people are not remembered beyond a generation or two.

• 2:29:14Lex Fridman?

  I mean, yeah, well, not just on average, basically. Very close to 100% of people who have ever lived are forgotten.

• 2:29:20Speaker 01

  Yeah, I mean.

• 2:29:21Lex Fridman?

  In a long arc.

• 2:29:22Speaker 01

  I don't know anyone alive who remembers my great grandparents because we didn't meet them. So.

• 2:29:30Lex Fridman?

  Still, this life is pretty fun somehow.

• 2:29:34Speaker 01

  Yeah.

• 2:29:35Lex Fridman?

  Even the immense absurdity and at times meaninglessness of it all. It's pretty fun. And one of the, for me, one of the most fun things is robots. And I've looked up to your work. I've looked up to you for a long time.

• 2:29:48Speaker 01

  That's very kind.

• 2:29:49Lex Fridman?

  Rod, it's an honor that you would spend your valuable time with me today, talking. It was an amazing conversation. Thank you so much for being here.

• 2:29:58Speaker 01

  Well, thanks for, thanks for talking with me. I've enjoyed it.

• 2:30:02Lex Fridman?

  Thanks for listening to this conversation with Rodney Brooks. To support this podcast, please check out our sponsors in the description. And now, let me leave you with the three laws of robotics from Isaac Asimov. One, a robot may not injure a human being or, through inaction, allow a human being to come to harm. Two, a robot must obey the orders given to it by human beings, except when such orders would conflict with the first law. And three, a robot must protect its own existence, as long as such protection does not conflict with the first or the second laws. Thank you for listening. I hope to see you next time.