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 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.

This is the Lex Friedman Podcast, and here is my conversation with Rodney Brooks. What is the most amazing or beautiful robot that you've ever had the chance to work with? - I think it was Domo, which was made by one of my grad students, Aaron Edsinger. It now sits in Daniela Russo's office, director of CSAIL, 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 gonna 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 with three-fingered hands, and face, eyeballs, all, 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. - The eyeballs are actuated, too, or no? - Oh, yeah, the eyeballs are actuated with cameras, and so it had a visual attention mechanism, looking when people came in, and looking in their face, and talking with them.

- Why was it amazing? - The beauty of it. - You said what was the most beautiful? - The beauty, what is the most beautiful? - It's just mechanically gorgeous. As everything Aaron builds has always been mechanically gorgeous. It's just exquisite in the detail. - Oh, we're talking about mechanically, like literally the amount of actuators, the-- - The actuators, the cables, he anodizes different parts, different colors, and it just looks like a work of art.

- What about the face? Do you find the face beautiful in robots? - When you make a robot, it's making a promise for how well it will be able to interact, so I always encourage my students not to overpromise. - Even with its essence, like the thing it presents, it should not overpromise.

- Yeah, so 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 the only thing in Domo's face is the eyeballs, 'cause that's all it can do. It can look at you and pay attention.

So there is no, it's not like one of those Japanese robots that looks exactly like a person at all. - But see, the thing is, us humans and dogs too, don't just use eyes 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 together.

- Yeah, or intent. You know, on both Baxter and Sawyer at Rethink Robotics, they had a screen with graphic eyes, so it wasn't actually where the cameras were pointing, but 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, 'cause it gave that intent away.

- 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. - 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, when I was eight or nine, simple circuits, and I read, 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.

- Wait, '61 you said? - This was when the two books, I've still got them at home. - What does the robot mean in that context? - No, 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 what people were thinking about for robots. - What were you thinking about? Were you thinking about humanoids? Were you thinking about arms with fingers? Were you thinking about faces or cars? - 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 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 learn a simple network, so I could teach it stuff.

And that was, mostly things were driven by my budget, and nails as electrodes, and an ice cream, I mean, an ice cube tray was about my budget at that stage. Later, I managed to buy transistors, and then I could build gates and flip-flops and stuff. - So one of your first robots was an ice cube tray?

- Yeah. (laughs) And it was very cerebral, 'cause it went to add. - Very nice. Well, just a decade or so before, in 1950, Alan Turing wrote a 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? - Certainly machines can think, because I believe you're a machine, and I'm a machine, and I believe we both think. I think-- - Speak for yourself. - I think any other philosophical position is sort of a little ludicrous, what does think mean, if it's not something that we do?

And we are machines. 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? Are we smart enough? We think we're smart enough to do anything, but maybe we're not. Maybe we're just not smart enough to build stuff like us.

- The kind of computer that Alan Turing was thinking about, do you think there is something fundamentally or significantly different between a computer between our ears, the biological computer that humans use, and the computer that he was thinking about, from a sort of high-level philosophical? - Yeah, I believe that it's very wrong.

In fact, I'm halfway through a, I think it'll be about a 480-page book, 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, well, three thrusts to it. One is the history of computation, what we call computation.

Goes all the way back to some manuscripts in Latin from 1614 and 1620 by Napier and Kepler, through Babbage and Lovelace. And then Turing's 1936 paper is what we think of as the invention of modern computation. And that paper, by the way, did not set out to invent computation.

It set out to negatively answer one of Hilbert's three later set of problems. He called it an effective way of getting answers. And 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.

The other two had already been disproved by Gödel. So Turing set out to disprove it, 'cause 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, well, he was an undergrad at Cambridge, who turned it into, is there a mechanical process?

So he wanted to 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.

And they would produce numbers for these tables, engineering tables, that 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 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 of, 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 hypothesized. So he disproved it. But he had to show that this system was good enough to do whatever could be done, but couldn't do this other thing. And there he said, and he says in the paper, I don't have any real arguments for this, but based on intuition.

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, 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, because it was not known at the time.

And that's too much trouble for a person to do as a step. And Hopcroft and Ullman sort of said a similar thing later that year. And by 1975, in the A. O. Hopcroft and Ullman 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 an incredibly powerful tool. I certainly wouldn't argue with that. The version we have of computation, incredibly powerful. - 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 on that tape, and that's what we're kind of thinking about. This is computation. - Yeah, and it's modeled after humans, how humans do stuff. And I think it's, Turing says in the '36 paper, one of the critical facts here is that a human has a limited amount of memory.

So that's what we're gonna put onto our mechanical computers. So, mm, mm, mm. So, you know, unlike mass, unlike mass or charge or, you know. It's not given by the universe. It was, this is what we're gonna call computation. And then it has this really, you know, it had this really good implementation, which has completely changed our technological world.

That's computation. Second part of the book, 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. 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 in, how does non-living matter become living matter? Four disciplines. These four disciplines all came into the current form in the period of 1945 to 1965. - That's interesting. - 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 were a lot of players who are common across them. I've identified common players, except for artificial intelligence and abiogenesis. I don't have, but any other pair, I can point to people who worked on. And a whole bunch of them, by the way, were at the research lab for electronics at MIT, where Warren McCulloch held forth.

And in fact, McCulloch, Pitts, Letvin, and Maturana 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 generating behavior in the frog.

- Would you put them as basically the fathers or one of the early pioneers of what are now called artificial neural networks? - 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, OR 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, which was a neural network, we would call it today.

It was picked up by John von Neumann when he was designing the EDVAC 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.

- It was like overlap between all. - 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?" 'Cause that's where our computers came from, from people who were computing stuff.

And then I've got another chapter, "Wait, People Are Computers?" Which is about computational neuroscience. So there's this whole circle here, that computation is it. And I have talked to people about, "Well, maybe it's not computation that goes on in the head." Of course it is. Okay, well, 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 wanna build an AI system, you write a computer program. - Yeah, so the word computation very quickly starts doing a lot of work that it was not initially intended to do. Is this like in the same, if you talk about the universe as essentially performing a computation?

- Yeah, right. Wolfram does this. He turns it into computation. You don't turn rockets into computation. - 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? - It's gotten very, okay, squishy, squishy over time.

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 is a combination of our place cells in our hippocampus and our cortex. But 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. 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, 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, 'cause it's a particle.

No, it's a wave. It's gotta be something we understand. And I say, no, it's some weird mathematical logic that's different from those, but we want that metaphor. Well, I suspect that 100 years or 200 years from now, neither quantum mechanics nor dark matter will be talked about in the same terms, in the same way that Flodgerson's theory eventually went away, 'cause 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. But as it turns out, the burning was outside the matter. It was the oxygen. So our desire for metaphor, and combined with our limited cognitive capabilities, gets us into trouble. - 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 if I'd spoke about that. But I give some ideas. But so, there's three things. Computation is sort of a particular thing we use. Oh, can I tell you one beautiful thing? One beautiful thing I found?

- Yes, please. - 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 stationary points on the drum's surface, you know, 'cause 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. - Wow. - Anyway, and there was a bug in the program.

The arguments to divide were reversed in one place. - And it still worked? - Well, no, she never got to run it. They never built the analytical engine. She wrote the program without a, you know. - So, computation? - 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, a lot of it swirls around Warren McCulloch and all his students, and he funded a lot of people. And our human metaphors, our limitations to human thinking will play into this. Those are the three themes of the book.

So, I have a little to say about computation. (laughing) - So, you're saying that there is a gap between the computer, or the machine that performs computation, and this machine that appears to have consciousness and intelligence. - Yeah. - Can we-- - That piece of meat in your head.

- Piece of meat. - And maybe it's not just the meat in your head, it's the rest of you, too. I mean, you actually have a neural system in your gut. - 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 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. - Yeah, and I think it seems to be very likely, again, this is speculation, but we, our species, and probably Neanderthals, to some extent, because you can find old bones where they seem to be counting on them by putting notches, that when Neanderthals had done, we were 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 the single deep learning network, which was fed all of literature or something.

- Yeah, the neural network can't step outside of itself, but 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 if followed might be productive?

Is it creative, interactive robots? Is it creating large, deep neural networks that do self-supervised learning? And just like we'll discover that when you make something large enough, some interesting things will emerge. Is it through physics and chemistry and biology, like artificial life, and go like, we'll sneak up in this four quadrant matrix that you mentioned, is there anything, your most, if you had to bet all your money, financial, I wouldn't.

Okay. - So every intelligence we know, and who's, animal intelligence, dog intelligence, octopus intelligence, which is a very different sort of architecture from us. 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 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 color constancy. 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 you can turn, you know, maybe seen the examples where someone turns a stop sign into 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 to get 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 a post, they can tell 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 registration between us and the object. And registration is a philosophical concept. Brian Cantwell Smith talks about a lot, very difficult 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. - So you agree with Hans Marvek and what's called the Marvek's paradox that highlights this counterintuitive notion that reasoning is easy, but perception and mobility are hard.

- Yeah, we shared an office when I was working on computer vision and he was working on his first mobile robot. - What were those conversations like? - They were great. (laughing) - So do you still kind of, maybe you can elaborate, do you still believe this kind of notion that perception is really hard?

Can you make sense of why we humans have this poor intuition about what's hard and not? - Well, let me give a sort of another story. - Sure. - If you go back to the original teams working on AI from the late '50s into the '60s, and you go to the AI lab at MIT, who was it that was doing that?

Was a bunch of really smart kids who 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 hard stuff. But a baby could see stuff, that wasn't intelligent. Anyone could do that, that's not intelligence. And so 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.

- Yeah. - And maybe I'm overplaying it a little bit, but I think there's an element of that. - Yeah, I mean, I don't know how much truth there is to, like chess, for example, was for the longest time seen as the highest level of intellect, right? - Until we got computers that were better at it than people.

And then we realized, if you go back to the '90s, you'll see the stories in the press around when Kasparov was beaten by Deep Blue. Oh, this is the end of all sorts of things. Computers are gonna be able to do anything from now on. And we saw exactly the same stories with AlphaZero, the Go playing program.

- Yeah, but still to me, reasoning is a special thing and perhaps- - No, actually, we're really bad at reasoning. We just use these analogies based on our hunter-gatherer intuitions. - But why is that not, don't you think the ability to construct metaphor is a really powerful thing? - Oh, yeah, it is.

- To tell stories. - It is, it's the constructing the metaphor and registering that something constant in our brains. - Like, isn't that what we're doing with vision too? And we're telling our stories. We're constructing good models of the world. - Yeah, yeah. But I think we jumped between what we're capable of and how we're doing it.

- Right, there was a little confusion that went on. - Sure. - As we were telling each other stories. - Yes, exactly. - Trying to delude each other. No, I just think, I'm not exactly, so I'm trying to pull apart this Marv X paradox. - I don't view it as a paradox.

What did evolution spend its time on? It spent its time on getting us to perceive and move in the world. That was 600 million years as multi-celled creatures doing that. And then it was relatively recent that we were able to hunt or gather, or even animals hunting. That's much more recent.

And then anything that we, speech, language, those things are just a couple of hundred thousand years probably, if that long. And then agriculture, 10,000 years. All that stuff was built on top of those earlier things which took a long time to develop. - 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, perception, the actual sort of the biomechanics of movement. I'm kind of drawing parallels here between humans and machines always. Like, what do you think is the hardest part of robotics? - I sort of think all of them.

(laughing) There are no easy paths to do well. We sort of go reductionist and we reduce it. If only we had all the location of all the points in 3D, things would be great. If only we had labels on the images, things would be great. But as we see, that's not good enough.

Some deeper understanding. - But if I came to you and I could solve one category of problems in robotics instantly, what would give you the greatest pleasure? (laughing) You look at robots that manipulate objects. What's hard about that? Is it the perception? Is it 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 game theory of how they work well together? - Well, let's talk about manipulation for a second, 'cause I had this really blinding moment.

You know, I'm a grandfather, so grandfathers have blinding moments. Just three or four miles from here, last year, my 16-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 put together. And he's 16 months old!

- And there you are watching in awe. (both laughing) - In an environment he'd never seen before, a mechanism he'd never seen. - How did he do that? - Yes, that's a good question. How did he do that? That's why. - It's like, okay, like you could see the leap of genius from using one hand to perform a task, to combining, to doing, I mean, first of all, in manipulation, that's really difficult.

It's like two hands, both necessary to complete the action. - And completely different. And he'd never seen a window open before. But he inferred somehow a handle opened something. - 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.

- Oh, there's a great counter to, reinforcement learning will just give the robot, or you'll give the robot plenty of time to try everything. - Yes. - Actually, can I tell a little side story here? So I'm in DeepMind in London, this is three, four years ago, where there's a big Google building, and then you go inside and you go through, there's more security, and then you get to DeepMind, where the other Google employees can't go.

- Yeah. - And I'm in a conference room, Bayer conference room with some of the people, and they tell me about their reinforcement learning experiment with robots, which are just trying stuff out. And they're my robots, they're Sawyers, we sold them. And they really like them, 'cause Sawyers are compliant and consents forces, so they don't break when they're bashing into walls, they stop and they do all this stuff.

And so you just let the robot do stuff, and eventually it figures stuff out. - By the way, Sawyer, we're talking about robot manipulation, so robot arms and so on. - Yeah, Sawyer's a robot. - Just to go, what's Sawyer? - Sawyer's a robot arm that my company, Rethink Robotics built.

- Thank you for the context. (laughs) - Sorry. - Okay, cool, so we're in DeepMind. - And it's in the next room, these robots are just bashing around to try and use reinforcement learning to learn how to act. And can I go see them? Oh no, they're secret. They're my robots, they're secret.

- That's hilarious, okay. - Anyway, the point is, 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 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 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. - 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. - 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. - Yeah, that pruning step. But it doesn't, 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? - It's, yeah, it's possible. But I, you know, 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 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, "Oh, you don't understand how powerful AI is." I had people tell me, "You don't understand how powerful it is." You know, I sort of had a track record of what the world had done to think, "Well, this is no different from before." Well, we have bigger computers.

We had bigger computers in the '90s and we could do more shit stuff. - But, okay, so let me push back. 'Cause I'm generally sort of optimistic and try to find the beauty in things. 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, 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." - Oh, I think their performance, their performance on ImageNet was shocking. - So computer vision, those early days, it was just very like, "Wow, okay." - That doesn't mean that they're solving everything in computer vision we need to solve or in vision for robots.

- What about AlphaZero and self-play mechanisms and reinforcement learning? Isn't that- - Yeah, that was all in Donald Mickey's 1961 paper. Everything there was there, which introduced reinforcement learning. - No, but come on. So now you're talking about the actual techniques, but isn't this surprising to you? The level it's able to achieve with no human supervision of chess play?

To me, there's a big, big difference between Deep Blue and- - Maybe what that's saying is how overblown our view of ourselves is. - You know, the chess is easy. - Yeah, I mean, I came across this 1946 report that, and I'd seen this as a kid in one of those books that my mother had given me, actually.

1946 report, which pitted someone with an abacus against an electronic calculator, and he beat the electronic calculator. So there, at that point was, well, humans are still better than machines at calculating. Are you surprised today that a machine can, you know, do a billion floating point operations a second, and you're puzzling for minutes through one?

So, you know- - I am, I mean, I don't know, but I am certainly surprised. There's something, to me, different about learning. So a system that's able to learn- - Learning, now, see, now you're getting to one of the deadly sins. - Because of using terms overly broadly. - Yeah, I mean, there's so many different forms of learning.

- Yeah. - And so many different forms. You know, I learned my way around the city. I learned to play chess. I learned Latin. I learned to ride a bicycle. All of those are, you know, very different capabilities. And if someone, you know, has a, you know, in the old days, people would write a paper about learning something.

Now, the corporate press office puts out a press release about how company X has, is leading the world because they have a system that can. - Yeah, but here's the thing. Okay, so what is learning? When I refer to learning as many things, but- - It's a suitcase word.

- It's a suitcase word, but loosely, there's a dumb system. And over time, it becomes smart. - Well, it becomes less dumb at the thing that it's doing. - Yeah. - Smart is a loaded word. - Yes, less dumb at the thing it's doing. - It gets better performance under some measure, under some set of conditions at that thing.

And most of these learning algorithms, learning systems fail when you change the conditions just a little bit in a way that humans don't. So I was at DeepMind, 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, well, would actually be able to play that game.

- And actually, funny enough, if you look at DeepMind's work since then, they're presenting a lot of algorithms that would do well at the bigger board. So they're slowly expanding this generalization. I mean, to me, there's a core element there. 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 it can achieve superhuman level performance through learning alone.

So like-- - Okay, so you know, you didn't-- - It's so fundamentally different in search of-- - 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 learned to play tic-tac-toe through self-play. - That's not-- - And it learned to play optimally. - What I'm saying is, I, okay, I have a little bit of a bias, but I find ideas beautiful, but only when they actually realize the promise. That's another level of beauty.

Like, for example, what Bezos and Elon Musk are doing with rockets. We had rockets for a long time, but doing reusable, cheap rockets, it's very impressive. In the same way, I, okay. - Yeah. - I would have not predicted, first of all, when I was, started and fell in love with AI, the game of Go was seen to be impossible to solve.

Okay, so I thought maybe, maybe it'd be possible to maybe have big leaps in a Moore's law style of way in computation that would be able to solve it. But I would never have guessed that you could learn your way, however, I mean, in the narrow sense of learning, learn your way to beat the best people in the world at the game of Go without human supervision, not studying the game of experts.

- Okay, so using a different learning technique. - Yes. - Arthur Samuel in the early '60s, and he was the first person to use machine learning, got, had a program that could beat the world champion at checkers. Now, so, and that at the time was considered amazing. By the way, Arthur Samuel had some fantastic advantages.

Do you want to hear Arthur Samuel's advantages? Two things. One, he was at the 1956 AI conference. I knew Arthur later in life. He was at Stanford when I was graduating there. He wore a tie and a jacket every day. The rest of us didn't. Delightful man, delightful man.

It turns out Claude Shannon in a 1950 Scientific American article outlined on chess playing, outlined the learning mechanism that Arthur Samuel used. And they had met in 1956. I assume there was some communication, but I don't know that for sure. But Arthur Samuel had been a vacuum tube engineer, getting reliability of vacuum tubes, 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. 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. And the question is- - Yeah, but that's in your view of the world. - Okay, well, let me then, doesn't mean I'm wrong. - No, no, it doesn't. (laughs) - 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 a better robot? Or will we solve intelligence? - So, I'm putting my bet on, we're still missing a whole lot, a lot. And why would I say that? Well, in these games, they're all 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 my R-of-X paradox. I'm definitely not saying that chess is somehow harder than perception or any kind of, even 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 like the workings of the human mind, it's incredible. The human mind is incredible. I believe that from the very beginning, I wanted to be a psychiatrist for the longest time. I always thought that's way more incredible in the game of chess.

I think the game of chess is, 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 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. - So can I tell you my disease in this way?

- Sure. - Which actually is closer to what you're saying. So as a child, I was building various, I called them computers, they weren't general purpose computers. - Ice cube tray. - 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 the adults couldn't beat my machine.

- Yes. - So you were like, that's powerful. Like that's a way to rebel. - Yeah. - By the way, when was the first time you built something that outperformed you? Do you remember? - 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 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, it was just, it was no active components. It was just three position switches, empty, X, zero, O and nine of them and a light bulb on which move it wanted next and then the human would go and move that.

- See, there's magic in that creation. - There was, yeah, yeah. - I tend to see magic in robots that, like, I also think that intelligence is a little bit overrated. I think we can have deep connections with robots, very soon. And-- - Well, we'll come back to connections with robots.

- Sure. - But I do wanna say, I don't, I think too many people make the mistake of seeing that magic and thinking, well, we'll just continue, you know? But each one of those is a hard fought battle for the next step, the next step. - Yes, 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 this eternal optimism, is it's not clear to me, so I don't do what, obviously, the journalists do or give into the hype, but it's not obvious to me how many steps away we are from a truly transformational understanding of what it means to build intelligent systems, or how to build intelligent systems.

I'm also aware of the whole history of artificial intelligence, which is where your deep grounding of this is, is there has been an optimism for decades. 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 '60s.

They were saying everything is true, computer vision is trivial. But I think my mind is working crisply enough to where, I mean, we can dig into it if you want. I'm really surprised by the things DeepMind has done. I don't think they're yet close to solving intelligence, but I'm not sure it's not 10 years away.

What I'm referring to is interesting to see when the engineering, it takes that idea to scale, and the idea works. - And no, it fools people. - 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, that wasn't surprising to you that at scale, so we're talking about very large, okay, let's pick one that's the most surprising to you.

Please don't yell at me. GPT-3, okay. Hold on a second. - I was gonna bring that up. - Okay, thank you. Alpha zero, alpha go, alpha go zero, alpha zero, and then alpha fold one and two. So do any of these kind of have this core of forget usefulness or application and 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?

- Okay, so if we're gonna make the distinction between surprise and usefulness, and 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, 'cause they're a really complex thing, but you don't know which ones it gets right, which then is a bit of a problem.

Now, they've come out with a-- - You mean the structure of the protein, it gets a lot of those right? - Yeah, it's a 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 thing in the last few weeks, which is trying to get a useful tool out of it, and they may well do it.

- In that sense, at least alpha fold is different, because your alpha fold too is different, because now it's producing data sets that are actually potentially revolutionizing competition biology. Like they will actually help a lot of people. - You would say potentially revolutionizing, we don't know yet, but yeah.

- That's true, yeah. - But 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. - Oh. (laughing) - Okay, because you have built robotics companies.

You're one of the greatest roboticists in history, and that's not just in the space of ideas. We'll 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.

You also sometimes are critical of Mr. Elon Musk, or let's more specifically focus on this particular technology, which is autopilot inside Teslas. What are your thoughts about Tesla autopilot, or more generally vision-based machine learning approach to semi-autonomous driving? These are robots. They're being used in the real world by hundreds of thousands of people.

And if you wanna go there, I can go there, but that's not too much, which let's say they're on par safety-wise as humans currently, meaning human alone versus human plus robot. - Okay, so first let me say, I really like the car I came in here today, which is?

- 2021 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 I'm- - It's able to stay in lane, is it? - Oh, yeah, it does the lane stuff. It's looking on either side of me.

It's telling me about nearby cars. - Or blind spots and so on. - Yeah, 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 registered and metrical that is. - Oh, so it's like multiple cameras and it's all very- - Yeah, and it has that.

- To produce the 360 view kind of thing? - 360 view, you know, synthesized so it's above the car. I mean, it is unbelievable. 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. Well, me and it together are better.

So I'm not saying technology's bad or not useful, but here's my point. - Yes. - It's a replay of the same movie. - Okay, so maybe you've seen me ask this question before, but when did the first car go over 55 miles an hour for over 10 miles on a public freeway with other traffic around driving completely autonomously?

When did that happen? - Was it the new '80s or something? It was a long time ago. - It was actually in 1987 in Munich. - Oh, Munich, yeah, yeah. - At the Bundeswehr. - Yeah. - So they had it running in 1987. When do you think, and Elon has said he's gonna 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?

- As far as I know, a few people have claimed to do it. 1995, that was Carnegie Mellon. - I didn't know, oh, that was the, yeah, they didn't claim, did they claim 100%? - Not 100%, not 100%, but-- - And then there's a few marketing people who have claimed 100% since then.

- But my point is that, you know, what I see happening again is someone sees a demo and they overgeneralize and say, we must be almost there. Well, we've been working on it for 35 years. - So that's demos, but this is gonna take us back to the same conversation with the AlphaZero.

Are you not, okay, I'll just say what I am, because I thought, okay, when I first started interacting with the Mobileye implementation of Tesla Autopilot, I've driven a lot of cars, 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 way more limited than the actual implementation of Mobileye, so that's one example, I was really surprised.

It's like, wow, 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. - Yeah, well, Mobileye was started by Amnon Shasher and used a lot of traditional, you know, hard-fought computer vision techniques.

- But they also did a lot of good sort of, like, non-research stuff, like actual, like, just good, like what you do to make a successful product, right, it's 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, that was impressive. And the recent progress, especially under, well, with the involvement of Andrej Karpathy, 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.

I don't, I still, you know, I don't know to that intensity, but I always thought it was very difficult to solve autonomous driving with all the sensors, with all of the computation. I just thought it was a very difficult problem. But I've been continuously surprised how much you can engineer.

First of all, the data acquisition problem, 'cause I thought, you know, just because I worked with a lot of car companies, they're so 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 driver, like sensing and the interaction with the driver and like studying the human factors, psychology problem is essential. 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 a vision-based alone, how far that can take us.

So that's my levels of surprise. Now, can you explain in the same way you said, like alpha zero, that's a homework problem that's scaled large in its 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.

- So, yeah, they're doing well with pure vision. - With pure vision, yeah. - And now no radar, which is. - I suspect that can't go all the way. And one reason is without new cameras that have a dynamic range closer to the human eye, 'cause human eye has incredible dynamic range.

And we make use of that dynamic range in it's 11 orders of magnitude or some crazy number like that. The cameras don't have that, which is why you see the bad cases where the sun on a white thing and the blinds, in a way it wouldn't blind a person.

I think there's a bunch of things to think about before you say, this is so good, it's just going to work. (laughing) - Okay. - And I'll come at it from multiple angles. And I know you've got a lot of time. - Yeah, okay, let's do this. - I have thought about these things.

- 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. - So a few things. In the US, I think that the death rate from motor vehicle accidents is about 35,000 a year, which is an outrageous number.

Not outrageous compared to COVID deaths, but 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, there'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 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 everywhere around us. And now you see pushback in cities like Barcelona is really trying to exclude 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?

- I mean, do you count them as fully autonomous? I don't know. 'Cause they're usually as a driver, but they're kind of autonomous, right? - No, let's get rid of the driver. - Okay, I don't know. - It's either 15 or 16. Most of them are in airports. There's a few that go about five, two that go about five kilometers out of airports.

- Yeah. - When is the first fully autonomous train system for mass transit expected to operate fully autonomously with no driver in any US city? - It's expected to operate in 2017 in Honolulu. It's delayed, but they will get there. But by the way, it was originally gonna be autonomous here in the Bay Area.

- I mean, they're all very close to fully autonomous, right? - Yeah, but getting the closest to the thing. And I've often gone on a fully autonomous train in Japan, one that goes out to that fake island in the middle of Tokyo Bay. I forget the name of the...

And what do you see when you look at that? What do you see when you go to a fully autonomous train in an airport? It's not like regular trains. At every station, there's a double set of doors. So that there's a door of the train and there's a door off the platform.

And it's really visible in this Japanese one because it goes out in amongst buildings. The whole track is built so that people can't climb onto it. 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 that we would have special purpose lanes on 101 in the Bay Area, 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.

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 over-promising by saying this, calling their system fully self-driving. I think they may have gotten there quicker by collaborating to change the infrastructure. This is one of the problems with long-haul trucking being autonomous.

I think it makes sense on freeways at night for the trucks to go autonomously. But then there's the how do you get onto 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. - 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. - Okay, well, I'm not gonna argue with you 'cause we're speculating. - Yes, but-- - And my gut feeling tells me it's gonna be, things will speed up when there is engineering of the environment because that's what happened with every other technology.

- I'm a bit, I don't know about you, but I'm a bit cynical that infrastructure, which relies 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 so many-- - Well, in this country.

- In the, sorry, yes, in this country. And of course, there's many, many countries that are actually much worse on infrastructure. - Oh, yes, many of them are much worse, and there's some that, like high-speed rail, the other countries have done much better. - I guess my question is, which is at the core of what I was trying to think through here and ask you, is how hard is the driving problem as it currently stands?

So 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, how hard is that problem? Because I used to think it's way harder. Like I used to think it's, with vision alone, it would be three decades, four decades.

- Okay, so 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. And my lane drift system in my car would not work without those black boundaries.

- Interesting. - 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 my car would not perform as well without that change in the way they paint the line. - Unfortunately, really good lane keeping is not as valuable.

Like it's orders of magnitude more valuable to have a fully autonomous system. - But for me, lane keeping is really helpful 'cause I'm lousy at it. - But you wouldn't pay 10 times. Like the problem is there's not financial, like it doesn't make sense to revamp the infrastructure to make lane keeping easier.

It does make sense to revamp the infrastructure. - Oh, I see what you mean. - If you have a large fleet of autonomous vehicles, now you change what it means to own cars, you change the nature of transportation. I mean, but for that, you need autonomous vehicles. Let me ask you about Waymo then.

I've gotten a bunch of chances to ride in a Waymo self-driving car and they're, I don't know if you'd call them self-driving, but. - Well, I mean, I rode in one before that called Waymo. - Yeah. - Still at X. - So there's currently, was a big leap, another surprising leap I didn't think would happen, which is they have no driver currently.

- Yeah, in Chandler. - In Chandler, Arizona. And I think they're thinking of doing that in Austin as well. But they're like expanding. - Although, and I do an annual checkup on this. So as of late last year, they were aiming for hundreds of rides a week, not thousands.

And there is no one in the car, but there's certainly safety people in the loop. And it's not clear how many, what the ratio of cars to safety people is. - It wasn't, obviously they're not 100% transparent about this, but-- - No, none of them are 100% transparent. They're very untransparent.

- But at least the way they're, I don't wanna make definitively, but they're saying there's no teleoperation. So like, they're, I mean, okay. - And that sort of fits with YouTube videos I've seen of people being trapped in the car by a red cone on the street. And they do have rescue vehicles that come and then a person gets in and drives it.

- 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 human-- - The argument I have is that people make interpolations from that. - Interpolations. - That, 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. - Okay, so I'd like to go to, you keep a list of predictions. - Yeah, okay. - On your amazing blog posts. It'd be fun to go through them. But before then, let me ask you about this.

You have a harshness to you sometimes in your criticisms of what is perceived as hype. (both laughing) And so like, 'cause people extrapolate, like you said, and they kind of buy into the hype, and then they kind of start to think that the technology is way better than it is.

But let me ask you maybe a difficult question. - Sure. - 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? - Absolutely, yeah. Look, here's two great runs. Great, unbelievable. 1903, first human power, human, you know, heavier than air flight.

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

- Yeah. - Fantastic. - 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. - So, you know. - How important is it to believe and be optimistic is what I guess.

- Oh yeah, it is important. It's when it goes crazy. When, you know, you said that, what was the word you used for my bad? - Harshness? - Harshness, yes. (both laughing) I just get so frustrated. - Yes. - When people make these leaps and tell me that I don't understand.

- Right. - Yeah. Just from iRobot, which I was co-founder of, I don't know the exact numbers now 'cause 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.

- Yes. - Was that a crazy idea that we had to believe in 2002 when we released it? Yeah, that was, we had to, you know, believe that it could be done. - Let me ask you about this. So iRobot, one of the greatest robotics companies ever in terms of manufacturing, creating a robot that actually works in the real world is probably the greatest robotics company ever.

You were the co-founder of it. 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. Like that's what I'm trying to get at that line. - No, 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 gonna happen.

- But here's the thing. - This shows- - But you have that harshness for Elon too. - And no- - Or no, it's a different harshness. - No, it's a different argument with Elon. You know, I think SpaceX is an amazing company. On the other hand, you know, in one of my blog posts, I said, what's easy and what's hard.

I said, SpaceX, vertical landing rockets, it had been done before. Grid fins had been done since the '60s. Every Sawyer's has them. Reusable space, DCX reused those rockets that landed vertically. There was a whole insurance industry in place for rocket launches. There were all sorts of infrastructure. That was doable.

It took a great entrepreneur, a great personal expense. He almost drove himself bankrupt doing it. A great belief to do it. Whereas Hyperloop, 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, a 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 and try and do. - This is where I understand what you're saying.

I tend to draw a different distinction. 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-- - Oh, he believes what he says. I just think he's wrong sometimes. - To me, that's a very important difference.

- Yeah, I'm not. - Because I think in order to fly, in order to get to the moon, you have to believe even when 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 think that's an interesting one. I was, especially when I was 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. I wasn't sure what to make of that. That was very upsetting to me because I understood where that's coming from.

And I agreed with them. And I kind of almost felt the same thing in the beginning until I kind of opened my eyes and realized there's a lot of interesting ideas here that might be over hype. If you focus yourself on the idea that you shouldn't call a system full self-driving when it's obviously not autonomous, fully autonomous, you're going to miss the magic of proxy.

- You are gonna miss the magic, but at the same time, there are people who buy it, literally pay money for it and take those words as given. - But I haven't, so 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 to push back on the very thing that you're frustrated about, which is like journalists and general people 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 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 has dreams of how a particular product might be used or so on. 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 gonna be way different. And so my just tension was, was like, here's an innovator, what is it?

Sorry, Super Cruise from Cadillac was super interesting too. That's a really interesting system. We should like be excited by those innovations. - Okay, so let me, 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?

- Yeah, that's been very frustrating. - Really pisses me off. I must say, I applaud that. I applaud it. It's the taking and not necessarily the people who are doing the things, but that I keep having to push back against on realistic expectations of when these things can become real.

- Yeah, this was interesting, Ana, because there's been a particular focus for me is autonomous driving. Elon's prediction of when certain milestones will be hit. There's several things to be said there that 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, it was obvious 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. - To a point, and I'm not talking about Elon here.

I'm just saying. - So there's a line there, right? - You have to have a line, because you overextend and it's demoralizing. But I will say that there's an additional thing here, that those words also drive the stock market. And we have, because of the way that rich people in the past have manipulated the rubes through investment, we have developed laws about what you're allowed to say and have a promise.

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

- Yeah, I'm not saying that's the intent. I'm saying it's a... - Eventually it's kind of, you fall into that kind of a behavior pattern. I don't know. I tend to... - I wasn't saying it's falling into that intent. It's just, you also have to protect investors in this market.

- 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. It's such a cool idea. I don't know how long ago you started, like three, four years ago? - It was January 1st, 2018.

- 18, yeah. - And I made these predictions, and I said that every January 1st, I was gonna check back on how my predictions had... - That's such a great thought experiment. - For 32 years. - Oh, so you said 32 years. - I said 32 years, 'cause I thought that'll be January 1st, 2050.

I'll be, I will just turn 95. (both laughing) - Nice. And so people know that your predictions, at least for now, are in the space of artificial intelligence. - Yeah, I didn't say I was gonna make new predictions. I was just gonna measure this set of predictions that I made, 'cause I was sort of annoyed that everyone could make predictions that didn't come true and everyone forgot.

So I said, "I should hold myself to a high standard." - Yeah, but also just putting years and date ranges on things, it's a good thought exercise. And reasoning your thoughts out. And so the topics are artificial intelligence, autonomous vehicles, and space. 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, some predictions that you're particularly proud of or are particularly interesting, from flying cars to, the other element here is 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? - Well, 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.

- Yeah, actually, yeah, I actually do remember that. But I think you were mentioning difficulties in different cities. - Yeah, yeah. - So Cambridge, Massachusetts, I think was an example. - Yeah, like in Cambridge Port. I lived in Cambridge Port for a number of years, and the roads are narrow, and getting anywhere as a human driver is incredibly frustrating when you start to put, and people drive the wrong way on one-way streets there.

- So your prediction was driverless taxi services operating on all streets in Cambridge Port, Massachusetts, in 2035. - Yeah, and that may have been too optimistic. - You think, so- - You know, I've gotten a little more pessimistic since I made these internally on some of these things. - So what, can you put a year to a major milestone of deployment of a taxi service in a few major cities?

Like something where you feel like autonomous vehicles are here. - So let's take the grid streets of San Francisco north of Market. - Okay. - Okay. 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.

We're still a few years away. I live in that area. So I see the self-driving car companies, cars, multiple ones every day. Now if they drive a cruise, Zoox less often, Waymo all the time, different ones come and go. - And there's always a driver. - There's always a driver at the moment.

Although I have noticed that 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. And clearly something's going on where the home office is making a decision. - That's fascinating. - So they're, you know, and so you can see whether they've got their hands on the wheel or not.

And it's the incident resolution time that tells you, gives you some clues. - So what year do you think, what's your intuition? What date range are you currently thinking San Francisco would be autonomous taxi service from any point A to any point B without a driver? Are you still, are you thinking 10 years from now, 20 years from now, 30 years from now?

- Certainly not 10 years from now. It's going to be longer. If you're allowed to go South of market, way longer. And unless there's re-engineering of roads. - By the way, what's the biggest challenge? You mentioned a few. Is it the delivery trucks? Is it the edge cases, the computer perception?

- Well, here's a case that I saw outside my house a few weeks ago, about 8 p.m. on a Friday night. It was getting dark. It was before the solstice. It was a cruise vehicle come down the hill, turned right and stopped dead covering the crosswalk. Why did it stop dead?

'Cause there was a human just two feet from it. Now I just glanced, I knew what was happening. The human 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. That car thought, oh, she could jump out in front of me any second.

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 stayed there for, until I got bored. - Yeah. - And so the human driver in there did not take over.

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. What's he gonna do? Cleverly, I think he decided not to go in front of the car. (laughing) He went, but he had to go behind it.

He had to get off the crosswalk, out into the intersection to push his baby around this car, which was stopped there. And no human driver would have stopped there for that length of time. 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.

- Yeah, but-- - Now you can say that's an edge case, but-- - Yeah, well, I'm in general not a fan of anecdotal evidence for stuff. 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 cases.

- Okay. - Are using anecdotal evidence. - So let me-- - But I got you. - Your question is when is it gonna happen in San Francisco? I say not soon, but it's gonna be one of them. But where it is gonna happen is in limited domains, campuses of various sorts, gated communities, where the other drivers are not arbitrary people.

They're people who know about these things. They, you know, it's been warned about them. And at velocities where it's always safe to stop dead. - Yeah. - You can't do that on the freeway. That I think we're gonna start to see. And they may not be shaped like current cars.

They may be things like May Mobility has those things and various companies have these. - 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 makes your, it's not just cheaper, but it makes your, that's fun to ride.

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. - But think about, you know, not you as the customer, but someone who's in their 80s in a retirement village whose kids have said, "You are not driving anymore." And this gives you the freedom to go to the market.

- That's a hugely beneficial thing, but it's a very 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. I like that the first time that a car equipped with some version of a solution to the trolley problem is what's NIML stand for?

- None in my life. - None in my life. - I define my lifetime as-- - Up to 2050. - 2050. Yeah. - You know, I ask you, 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.

I mean, you're not making that decision. - 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-- - Yeah, well, so here's, and I talk about it in one of the articles or blog posts that I wrote.

Here's, and people have told me, one of my coworkers has told me he does this. He tortures autonomously driven vehicles and pedestrians will torture them. Now, 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. - I, by the way, one of my, and this is a whole nother discussion, 'cause 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.

- Yeah, well, you know, Waymo, before it was called Waymo, discovered that, you know, they had to do that at four-way intersections. They had to nudge forward to get the queue that they were gonna go, 'cause otherwise the other drivers would just beat them all the time. - 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? - Well, there's something I'm 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 help shut down the Fukushima Daiichi nuclear power plant, which was, everything was, I've been there since.

I was there in 2014, and the robots, some of the robots were still there. I was proud that we were able to do that. Why were we able to do that? And, you know, people have said, well, you know, Japan is so good at robotics. It was because we had had about 6,500 robots deployed in Iraq and Afghanistan, tele-opt, 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. And so we're able to do something very positive in a very bad situation. - 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.

What about the simple robot that is the Roomba, quote unquote simple, that's deployed in tens of millions of homes? What do you think about that? - 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 to do anything.

But I was, you know, there was a wacky lawsuit that I got posed for not too many years ago. And I was the only one who had emailed from the 1990s and no one in the company had it. So I went and went through my email and it reminded me of, you know, the joy of what we were doing.

And what was I doing? What was I doing at the time we were building the Roomba? One of the things was we had this incredibly tight budget 'cause we wanted to put it on the shelves at $200. There was another home cleaning robot at the time. It was the Electrolux Trilobyte, which sold for 2000 euros.

And to us, that was not going to be a consumer product. So we had reason to believe that $200 was a thing that people would buy at. That was our aim. But that meant we had, you know, that's on the shelf making profit. That means the cost of goods has to be minimal.

So I find all these emails of me going, you know, I'd be in Taipei for a MIT meeting and I'd stay a few extra days. I'd go down to Hsinchu and talk to these little tiny companies, lots of little tiny companies outside of TSMC, 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 they were building, their innovations were to build stripped down 6802s. 6802 was what was in an Apple One. Get rid of half the silicon and still have it be viable. And I'd previously got some of those for some earlier failed products of iRobot.

And then that was in Hong Kong, going to all these companies that built, you know, they weren't gaming in the current sense. There were these handheld games that you would play or birthday cards. 'Cause we had about a 50 cent budget for computation. So I'm trekking from place to place, looking at their chips, looking at what they'd removed.

Oh, the interrupt, the interrupt handling is too weak for a general purpose. So I was going deep technical detail. And then I found this one from a company called Winbond, which had, and I'd forgotten that it had this much RAM. It had 512 bytes of RAM and it was in our budget and it had all the capabilities we needed.

- Yeah. - So. - And you were excited. - Yeah, and I was reading all these emails, "Colin, I found this." (laughs) So. - 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?

- No, we never did think that. We'd had 14 failed business models up to 2002. And then we had two winners the same year. No, and then, you know, we, I remember the board, 'cause by this time we had some venture capital in. The board went along with us building some robots for, you know, aiming at the Christmas 2002 market.

And we went three times over what they authorized and built 70,000 of them and sold them all in that first, 'cause we released on September 18th and they were all sold by Christmas. So it was, so we were gutsy, but. (laughs) - But yeah, you didn't think this will take over the world.

Well, this is, 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 run a successful robotics company? - There's a few things. One is expectations of capabilities by the founders that are off base. - The founders, not the consumer, the founders.

- Yeah, expectations of what can be delivered, sure. Mispricing, and what a customer thinks is a valid price is not rational necessarily. - Yeah. - And expectations of customers. And just the sheer hardness of getting people to adopt a new technology. And I've suffered from all three of these.

I've had more failures than successes in terms of companies. I've suffered from all three. So. - Do you think 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? - I can't, you know, because I'm still starting robot companies. - Yeah. (both laughing) - 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 '90s anyone was thinking that Apple would ever be a trillion dollar company.

So these are very hard to predict. - But, sorry to interrupt, but don't you, 'cause I kind of have a vision in a small way, a big vision in a small way, that I see that there will be robots in the home at scale, like Roomba, but more. And that's trillion dollar.

- Right. And I think there's a real market pull for them because of the demographic inversion. You know, who's gonna do all the stuff for the older people? There's too many, you know, I'm leading here. (both laughing) There's gonna be too many of us. But we don't have capable enough robots to make that economic argument at this point.

Do I expect that that will happen? Yes, I expect it will happen. But I gotta tell you, we introduced the Roomba in 2002 and I stayed another nine years. We were always trying to find what the next home robot would be. And still today, the primary product of 20 years, almost 20 years later, 19 years later, the primary product is still the Roomba.

So iRobot hasn't found the next one. - 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? 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? - 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 Covariant. 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 lost $150 million in capital there. - 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? - 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.

- So these are robotic arms. - Robotic arms for people to pick up stuff and interact with humans. - Yeah, and that humans could retask 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.

- That's both an interface problem and also a safety problem. - Yeah, yeah. So I'm most proud of that. I completely, I let myself be talked out of what I wanted to do. And you know, you always got, you know, I can't replay the tape. You know, I can't replay it.

Maybe, maybe, 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. So I'd said that, I'd set as a target for the company that we were gonna build $3,000 robots with force feedback that was safe for people to be around.

- Wow. - That was my goal. And we built, so we started in 2008 and we had prototypes built of plastic, plastic gear boxes and at a $3,000, you know, lifetime, oh, $3,000, I was saying, we're gonna go after not the people who already have robot arms in factories, the people who would never have a robot arm.

We're gonna go after a different market. So we don't have to meet their expectations. And so we're gonna build it out of plastic. It doesn't have to have a 35,000 hour lifetime. It's gonna be so cheap that it's OPEX, not CAPEX. And so we had a prototype that worked reasonably well, but the control engineers were complaining about these plastic gear boxes with a beautiful little planetary gearbox, but we could use something called serious 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 is torque ripple 'cause 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 gonna name names, but I remember one of the mechanical engineers saying, we'll just build a metal gearbox with spur gears and it'll take six weeks, we'll be done, problem solved. Two years later, we got the spur gearbox working. We cost reduced in every possible way we could, 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. And the only people who were gonna look at that were people who had arms in factories 'cause that was somewhat cheaper for two arms than arms in factories, but they were used to 0.1 millimeter reproducibility of motion and certain velocities.

And 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 that repeatability. We want that repeatability. All the other robots have that repeatability. Why don't you have that repeatability? - So can you clarify, force control is you can grab the arm and you can move it.

- Yeah, well, you can move it around, but suppose you, can you see that? - Yes. - Suppose you want to, - Yes. - Suppose this thing is a precise thing that's gotta fit here in this right angle. Under position control, you have fixtured where this is. You know where this is precisely, and you just move it, and it goes there.

In 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 that precision. - Yeah. - They use force feedback. - Yes. - And get the things to mate, rather than just go straight to it.

- Yeah. - Couldn't convince our customers who were in factories and were used to thinking about things a certain way, and they wanted it, wanted it, wanted it. So then we said, okay, we're gonna build an arm that gives you that. So now we ended up building a $35,000 robot with one arm with, oh, what are they called?

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. But it's got torque ripple in it. So now there was an extra two years of solving the problem of doing the force with the torque ripple. So we had to do the thing we had avoided for the plastic gearboxes.

We ended up having to do, the robot was now overpriced. - And that was your intuition from the very beginning, kind of that this is not, you're opening a door to solve a lot of problems that you're eventually gonna have to solve this problem anyway. - Yeah, and also I was aiming at a low price to go into a different market.

- Low price. - That didn't have robots. - $3,000 would be amazing. - Yeah, I think we could have done it for five. But you said, talked about setting the goal a little too far for the engineers. - Exactly. Why would you say that company not failed, but went under?

- We had buyers and there's this thing called the Committee on Foreign Investment in the US, CFIUS. And that had previously been invoked twice around where the government could stop foreign money coming into a US company based on defense requirements. We went through due diligence multiple times. We were gonna get acquired, but every consortium had Chinese money in it.

And all the bankers would say at the last minute, you know, this isn't gonna get past CFIUS. And the investors would go away. And then we had two buyers, when we were about to run out of money, two buyers. And one used heavy-handed legal stuff with the other one, said they were gonna take it and pay more, dropped out when we were out of cash, and then bought the assets at 1/30th of the price they had offered a week before.

It was a tough week. - Do you, does it hurt to think about? Like an amazing company that didn't, you know, like iRobot didn't find a way. - Yeah, it was tough. I said I was never gonna start another company. I was pleased that everyone liked what we did so much that the team was hired by three companies within a week.

Everyone had a job in one of these three companies. Some stayed in their same desks because another company came in and rented the space. So I felt good about people not being out on the street. - So Baxter's a screen with a face. That's a revolutionary idea for a robot manipulation, like for a robotic arm.

How much opposition did you get? - Well, first, the screen was also used during codeless programming, where you taught by demonstration, it showed you what its understanding of the task was. So it had two roles. 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 people, and some of them surprised me who they were, were saying, "Well, this one doesn't look as human "as the old one. "We liked the human looking." So there was a mixed bag there. - But do you think that's, I don't know. I'm kind of disappointed whenever I talk to roboticists, like the best robotics people in the world, they seem to not want to do the eyes type of thing.

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.

- Yeah, I agree. - Can I ask you a ridiculous question? - Sure. - I give a ridiculous answer. - Do you think, well, 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.

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

- It's a different love, right? I think there's a lifelong partnership where you can communicate and grow like... - 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. - Yeah, but do you, so to me, honestly, the most difficult part is the thing that you said with the Marvax 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. - Well, for some people, maybe not, but I, you know, I, 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 it is today and you change one thing, right?

But that's not how, and it's the same with the self-driving car. You change one thing. No, everything changes. Everything grows together. So surprisingly, it might be surprising to you, it 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. So there was a convergence happening in a sense. So we will not be the same, but, you know, we're already talking about genetically modifying our babies. You know, there's a, you know, there's a, more and more stuff happening around that.

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. - Yeah. (laughs) - So we're always modifying our bodies.

So, you know, I think it's hard to imagine exactly what it will be like in the future. - 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 turn on the Alexa and talk to an AI system for a prolonged period of time, like 20 minutes. 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 talk as friends. - Yeah, well, you know, we saw, we saw Weizenbaum back in the sixties with his program, Eliza, being shocked at how much people would talk to Eliza. And I remember, you know, in the seventies 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 Amazon's been trying to improve with Alexa, there is no continuity 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, which changes.

- We share moments together and they last in our memory together. - 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 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 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 it feels like. - I tend to think it's not as difficult as solving intelligence, for example. And I think it's achievable in the near term. But on the Turing test, why don't you think the Turing test is a good test of intelligence?

- Oh, because, you know, again, the Turing, if you read the paper, Turing wasn't saying this is a good test. He was using it 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 you can't tell the difference when it's thinking.

You can't say something different. What it has become as this sort of weird game of fooling people. So back at the AI lab in the late '80s, we had this thing that still goes on called the AI Olympics. 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 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, one of the, 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, what he would ask them, did you have green plastic toy soldiers as a kid? Yeah, what'd you do with them? And 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 would say, I stomped on them, I burned them.

(laughing) So, you know, 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. - 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-posed as a test 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-- - No, 'cause language is so human.

- It's so human. We can so clearly see it. Shit, you mentioned something I was gonna go off on. Okay. I mean, I have to ask you, 'cause you were the head of CSAIL, 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? And plus you, you're, I don't know, friends with, but you knew Minsky and all the folks there, all the legendary AI people of which you're one. So what was that time 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?

- Let me tell you first a disappointment in myself. You know, as I've been researching this book and so many of the players 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 at faculty lunch. And I didn't ask them so many questions that now I wish I had. - Can I ask you that question? 'Cause 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. - Yeah. - And I didn't think to ask them any of these questions. - Right. - Even as I saw them and said hello to them on a daily basis.

So maybe also another question I wanna ask, if you could talk to them today, what question would you ask? What questions would you ask? - Oh, well, Rick Leiter. I would ask him, you know, he had the vision for humans and computers working together. And he really founded that at DARPA.

And he gave the money to MIT, which started Project MAC in 1963. And I would have talked to him about what the successes were, what the failures were, what he saw as progress, et cetera. I would have asked him more questions about that. 'Cause now I could use it in my book.

(laughing) But I think it's lost, it's lost forever. A lot of the motivations are lost. I should have asked Marvin why 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. - How do you make sense of that?

- That book destroyed the field. - He probably, do you think he knew the effect that book would have? - All the theorems are negative theorems. - Yeah. - So yeah. - That's the way of life. But still, it's kind of tragic that he was both the proponent and the destroyer of neural networks.

Is there other memories stand out from the robotics and the AI work at MIT? - Well, yeah, but you gotta be more specific. - Well, I mean, it's such a magical place. I mean, to me, it's a little bit also heartbreaking that with Google and Facebook, like DeepMind and so on, so much of the talent doesn't stay necessarily for prolonged periods of time in these universities.

- 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 Hollywood stars or something, and they're not allowed to make movies anymore.

I own them. - Yeah, that's tragic, 'cause I mean, there's an openness to the university setting where you do research, both in the space of ideas and the space, like publication, all those kinds of things. - Yeah, and there's the publication and all that, and often, although these places say they publish, there's pressure.

But I think, for instance, net-net, I think 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, locked them away for years, and then sort of all threaded away. - Do you have hope for MIT?

For MIT? - Yeah, why shouldn't I? - Well, I could be harsh and say that, I'm not sure I would say MIT is leading the world in AI, or even Stanford or Berkeley. I would say DeepMind, Google AI, Facebook AI. I would take a slightly different approach, or a different answer.

I'll come back to Facebook in a minute. But I think those other places are following a dream of one of the founders, 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. - You're talking about Facebook and Google and so on.

- I'm talking about Google. - Google. But the thing is, those research labs aren't, there's the big dream, and I'm usually a fan of, no matter what the dream is, a big dream is a unifier, because what happens is you have a lot of bright minds working together on a dream.

What results is a lot of adjacent ideas. I mean, this is how so much progress is made. - Yeah, so I'm not saying they're actually leading. I'm not saying that the universities are leading, but I don't think those companies are leading in general, because they're, we saw this incredible spike in attendees at NeurIPS.

And as I said in my January 1st review this year for 2020, 2020 will not be remembered as a watershed year for machine learning or AI. There was nothing surprising happened anyway, unlike when deep learning hit ImageNet. That was a shake. And there's a lot more people writing papers, but the papers are fundamentally boring and uninteresting.

- Incremental work. - Yeah. - 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. You've known some of the biggest minds in AI. - Yeah, and they did amazing things, and sometimes they were grumpy.

- Well, he was interesting, 'cause he was very grumpy, but that was, I remember him saying in an interview that the key to success or to keep being productive is to hate everything you've ever done in the past. - Maybe that explains the Perceptron book. There it was. He told you exactly.

- 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... - Yeah, so let me tell you what really, you know, the joy memories are about having access to technology before anyone else has seen it.

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 sound system. We had a Xerox graphics printer. We could print, it wasn't, you know, it wasn't like a typewriter ball hitting characters. It could print arbitrary things, only in, you know, one bit, you know, black or white, but you could, arbitrary pictures.

This was science fiction sort of stuff. At MIT, the Lisp machines, which, you know, they were the first personal computers, and, you know, they cost $100,000 each, and I could, you know, I got there early enough in the day, I got one for the day. Couldn't stand up, had to keep working.

(both laughing) - So having that like direct glimpse into the future. - Yeah, and, you know, I've had email every day since 1977, and, you know, the host field was only eight bits, you know, there weren't that many places, but I could send email to other people at a few places.

So that was pretty exciting, to be in that world so different from what the rest of the world knew. - Let me ask you, I'll probably edit this out, but just in case you have a story. 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? - They did in a, you know, I know him now, we talk, 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 day, one night, the mainframe computer went down, and a guy named Robert Paul was there.

He later did his PhD at the Media Lab at MIT, and he was an engineer. And so he and I, you know, tracked down what were the problem was. It was one of this big refrigerator size or washing machine size 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, 'cause he wanted to get back to writing his tech system. We're like, "It's Donald Knuth." And so we figured out, you know, it was a particular chip, 7400 series chip, which was socketed.

We popped it out. We put a replacement in, put it back in. Smoke comes out, 'cause we put it in backwards, 'cause we were so nervous that Donald Knuth was standing over us. Anyway, we eventually got it fixed and got the mainframe running again. - So that was your little, when was that again?

- Well, that must have been before October '79, 'cause we moved out of that building then. So sometime, probably '78, sometime early '79. - Yeah, all those figures are just fascinating. All the people who have passed through MIT is really fascinating. Is there, 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, how to live a life they're proud of, a successful life? - Yeah, so many people ask me for advice and have asked, and I talk to a lot of people all the time.

And there is no one way. There's a lot of pressure to produce papers that will be acceptable and be published. Maybe I come from an age where I could be a rebel against that 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 well, it depends on what you want in life. If you wanna 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 keep doing it for a long time.

And then one of them will be work out. And so that will make something successful. - Or not. That's the whole point. - Or you just may end up not having a lousy career. I mean, it's certainly possible. - Taking the risk is the thing. - Yeah. But there's no way to make all safe decisions and actually really contribute.

- Do you think about your death, about your mortality? - I gotta say when COVID hit, I did. 'Cause in the early days, we didn't know how bad it was gonna be. That made me work on my book harder for a while. But then I'd started this company and now I'm doing more than full-time at the company so the book's on hold.

But I do wanna finish this book. - When you think about it, are you afraid of it? - I'm afraid of dribbling. Of losing it. - The details of, okay. - Yeah, yeah. - But the fact that the ride ends? - I've known that for a long time. So it's...

- Yeah, but there's knowing and knowing. It's such a... - Yeah, and it- - It really sucks. - It feels a lot closer. So in my blog with my predictions, my sort of pushback against that was I said, I'm gonna review these every year for 32 years. And that puts me into my mid-90s.

So it was my- - That puts a whole, every time you write the blog posts, you're getting closer and closer to your own prediction. - That's true. - Of your death. - Yeah, yeah. - What do you hope your legacy is? You're one of the greatest roboticist, AI researchers of all time.

- What I hope is that I actually finish writing this book and that there's one person who reads it and sees something about changing the way they're thinking. And that leads to the next big. And then there'll be on a podcast 100 years from now saying I once read that book.

And that changed everything. What do you think is the meaning of life? This whole thing, the existence, all the hurried things we do on this planet? What do you think is the meaning of it all? - Well, I think we're all really bad at it. - Life or finding meaning or both?

- Yeah, we get caught up in the, it's easier to do the stuff that's immediate and not do the stuff that's not immediate. - The big picture, we're bad at. - Yeah, yeah. - 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?

- You know, my atheism tells me it's just random, but I wanna understand the way random, and that's what I talk about in this book, how order comes from disorder. - But it kind of sprung up, like most of the whole thing is random, but this little pocket of complexity they will call earth, why the hell does that happen?

- And what we don't know is how common those pockets of complexity are or how often, 'cause they may not last forever. - Which is more exciting/sad to you, if we're alone or if there's infinite number of-- - Oh, I think it's impossible for me to believe that we're alone.

That would just be too horrible, too cruel. - Could be like the sad thing, it could be like a graveyard of intelligent civilizations. - Oh, everywhere, yeah. That may be the most likely outcome. - And for us, too. - Yeah, exactly, yeah. - And all of this will be forgotten, including all the robots you build, everything forgotten.

- Well, on average, everyone has been forgotten in history. - Yeah. - Right? - Yeah. - Most people are not remembered, beyond a generation or two. - I mean, yeah, well, not just on average, basically. Very close to 100% of people who have ever lived are forgotten. - Yeah, I mean-- - In a long arc of time.

- I don't know anyone alive who remembers my great-grandparents, 'cause we didn't meet them. - Still, this life is pretty fun, somehow. - Yeah. (laughing) - Even the immense absurdity and at times meaninglessness of it all, it's pretty fun. And 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.

- Oh, that's right, Rod. 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. - Well, thanks for talking with me. I've enjoyed it. - 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. (upbeat music) (upbeat music)