Knowledge Fight Interactive Search Tool

All Episodes

Previous Next

Sept. 3, 2021 - Lex Fridman Podcast

02:24:39

Rodney Brooks: Robotics | Lex Fridman Podcast #217

Large

Audio Only |

Scroll

Time	Text
	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 going to do.
	He just started spending money.
	He spent a lot of money.
	He and Jeff Webber, who was 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 fingers?
	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, so it had a visual attention mechanism, looking when people came in and looking in their face and talking with them.
	Wow, was it amazing. The beauty of it.
	You said what was the most beautiful?
	What is the most beautiful? It's just mechanically gorgeous.
	As everything Aaron builds, it's always been mechanically gorgeous.
	It's just exquisite in the detail.
	We're talking about mechanically, like literally the amount of actuators...
	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 over-promise.
	Even with its essence, the thing it presents, it should not over-promise.
	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, because that's all it can do.
	It can look at you and pay attention.
	And so there is no...
	It's not like one of those 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.
	you know, graphic eyes, so it wasn't actually where the cameras were pointing, but it, the eyes would
	look in the direction it was about to move its arm, so people in the factory nearby were not
	surprised by its motions because it gave that intent away.
	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 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 a How and Why Wonder Book of giant brains and robots.
	I learned how to build circuits when I was eight or nine, simple circuits, and I 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, you know, 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 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. And it was very cerebral, because it wasn't to add.
	Very nice. Well, just a decade or so before, in 1950, Alan Turing wrote the paper that formulated the Turing test, and he opened that paper with the question, can machines think?
	So let me ask you this question.
	Can machines think?
	Can your ice cube tray one day think?
	Certainly machines can think because I believe you're a machine and I'm a machine and I believe we both think.
	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, you know, 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 the 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 perspective?
	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.
	The working title is Not Even Wrong.
	And if I may, I'll tell you a bit about that book.
	So there's three thrusts to it.
	One is the history of computation, what we call computation.
	It goes all the way back to...
	Some manuscripts in Latin from 1614 and 1620 by Napier and Kepler through Babbage and Lovelace.
	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...
	It's 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, because 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, who's 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.
	they would produce numbers for these tables, engineering tables, that the more
	the more iterations they did the more significant digits came out. And so
	Turing in that paper set out to define what sort of machine could do that
	mechanical machine.
	Where it can 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 came with a set of instructions that...
	as a person could do with pencil and paper, write down things on the tape and erase them and put
	new things there, and he was able to show that that system was not able to do something that
	Hilbert had hypothesized, so he disproved it, but he had to show that this 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 Infant Machines from the mid-60s, which is a beautiful, beautiful mathematical book.
	It says at the start of the book, well, what is computation?
	Schering says it's this, and yeah, I sort of think it's that.
	It doesn't really matter whether 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, He says, well, what's computation?
	It's the 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. 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 36th paper, one of the critical facts here is that a human has a limited amount of memory.
	So that's what we're going to put onto our mechanical computers.
	So... So, you know, I'm like mass or charge or, you know, it's not given by the universe.
	It was, this is what we're going to 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 non-living matter become living matter?
	Four disciplines.
	These four disciplines all...
	Came into the current form in the period 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 was electrical charges, but no one knows what to do with it.
	And furthermore, there are a lot of players who are common across them.
	I've identified common players except for artificial intelligence and abiogenesis.
	I don't have, but for any other pair, I can point to people who work.
	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, you know, generating behavior in the front.
	Would you put them as basically the fathers or one of the early pioneers of what are now called artificial neural networks?
	Yeah, 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 and 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 EDBAC computer in 1945, he talked about its components being neurons based on 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's like overlap between all.
	They're all going around at the same time.
	And three of these four disciplines turn to computation as their primary metaphor.
	So I've got a couple of chapters in the book.
	One is titled, Wait...
	Computers are people, because that's where our computers came from, you know, 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, you know, I have talked to people about, well, maybe it's not computation that goes on in the head.
	Of course it is!
	Okay, well, So when Elon Musk's rocket goes up, is it computing?
	Is that how it gets into orbit?
	By computing? But we've got this idea, if you want to build an AI system, you write a computer program.
	Yeah, so the word computation very quickly starts doing a lot of work that it was not initially intended to do.
	It's the second and second, 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.
	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 squishy.
	But computation and 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.
	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, you know, people try to, all the time, we always try to get physical metaphors for things, Which is why quantum mechanics has been such a problem for a hundred years.
	Because it's a particle. No, it's a wave.
	It's got to be something we understand.
	And I say, no, it's some weird mathematical object that's different from those.
	But we want that metaphor.
	Well, you know, I suspect that, you know, a hundred years or two hundred years from now, neither quantum mechanics nor dark matter will be talked about in the same terms.
	You know, in the same way that Flogiston's theory eventually went away because of It just wasn't an adequate explanatory metaphor.
	That metaphor was the stuff.
	There is stuff in the burning.
	The burning is in the matter.
	It turns out the burning was outside the matter.
	It was the oxygen. 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.
	I tried to talk about that. But, you know, I give some ideas.
	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've done?
	So, you know, I used an example of a thing that's different from computation.
	You hit a drum and it vibrates.
	And there are some stationary points on the drum surface, you know, because the waves are going up and down the stationary points.
	Now, you could compute them to arbitrary precision, But the drum just knows them.
	The drum doesn't have to compute.
	What was the very first computer program ever written by Ada Lovelace?
	To compute Bernoulli numbers.
	And Bernoulli numbers are exactly what you need to find those stable points in the drum 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?
	Yeah. 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.
	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. 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.
	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 were Neanderthals done.
	We're 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.
	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 angle, 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 advice?
	I wouldn't. Okay.
	So every intelligence we know, 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, which is why you can turn...
	You know, you maybe seen the examples where someone turns a stop sign into a,
	some other sort of sign by just putting a couple of marks on them and the deep learning system
	gets it wrong and everyone says, but the stop sign's red.
	You know, why is it, why is it 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 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 perceiving the registration between us and the object.
	And registration is a philosophical concept.
	Brian Cantwell-Smith talks about it 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 Marowak and what's called the Marowak'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. 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.
	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?
	It 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 That wasn't intelligence.
	Anyone could do that.
	That's not intelligence. And so, you know, 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 who were better at it than people, and then we realized, you know, if you go back to the 90s, you'll see, you know, 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 going to 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! It is!
	It's constructing the metaphor and registering that, something constant in our brains.
	Isn't that what we're doing with vision too?
	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 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 Marvac's paradox.
	I don't view it as a paradox.
	Yeah. 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-cell creatures doing that.
	And then it was relatively recent that we were able to...
	gather or you know even even animals hunting that's much more recent and then
	and then anything that we you know speech language those things are you
	know a couple of hundred thousand years probably if that long and then
	agriculture ten thousand years you know 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?
	Through the decades that you worked on robots, In the context of what we're talking about, perception, the actual sort of the biomechanics of movement.
	I'm kind of drawing parallels here between humans and machines always.
	What do you think is the hardest part of robotics?
	I sort of think all of them.
	There are no easy parts 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?
	I mean, is it, you know, 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, because 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.
	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.
	In an environment he'd never seen before.
	How did he do that?
	Yes, that's a good question.
	How did he do that? That's why...
	It's like, okay, you could see the leap of genius from using one hand to perform a task to combining, doing...
	I mean, first of all, in manipulation, that's really difficult.
	It's like two hands, both necessary to complete the action.
	Completely different, and he'd never seen a window open before.
	But he inferred somehow, handle, open, something.
	There may have been a lot of...
	Slightly different failure cases that you didn't see.
	Not with a window, but with other objects of turning and twisting and handles.
	There's a great counter to reinforcement learning.
	We'll just give the robot plenty of time to try everything.
	Can I tell a little side story here?
	So I'm in DeepMind in London, this was three, four years ago, where, you know, 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.
	And I'm in a conference room, bare 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 Soyers. We sold them.
	And they really like them because Soyers are compliant and can sense forces, so they don't break when they're bashing into walls.
	They stop and they do all this stuff.
	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 arm.
	What's Sawyer? Sawyer's a robot arm that my company, Rethink Robotics, built.
	Thank you for the context. Sorry.
	Okay, cool. So we're in deep mind.
	And it's in the next room.
	These robots are just bashing around to try and use reinforcement learning to learn how to act.
	Can I go see them? Oh, no, they're secret.
	They were my robots that were secret.
	That's hilarious. Okay.
	Anyway, the point is, you know, this idea that you just let reinforcement learning figure everything out is so counter to how a kid does stuff.
	So, again, story about my grandson.
	I gave him this 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 But 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 you don't think that's akin to a neural network inside a reinforcement learning algorithm.
	Is it possible?
	Yeah, until 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.
	Or we have bigger computers.
	We had bigger computers in the 90s and we could do more shit stuff.
	Yeah. But okay, so let me push back because 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, it 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, where actually you sat back and said, I did not expect this?
	Oh, I think their performance on ImageNet was shocking.
	So computer vision in those early days was just very, like, wow, okay.
	That doesn't mean that they're solving everything in computer vision we need to solve, or in What about AlphaZero and self-play mechanisms and reinforcement learning?
	Yeah, that was all in Donald Mickey's 1961 paper.
	Everything was there, which introduced reinforcement learning.
	No, but come on. You're talking about the actual techniques, but isn't it surprising to you, the level it's able to achieve with no human supervision of chess play?
	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... 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.
	You know, so there, at that point, was, well, Humans are still better than machines are calculating.
	Are you surprised today that a machine can do a billion floating-point operations a second and you're puzzling for minutes to do one?
	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! See, now you get into one of the deadly sins.
	Because of using terms overly broadly.
	Yeah, I mean, there's so many different forms of learning.
	Yeah. There's 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...
	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 is leading the world because they have a system that can...
	Yeah, but here's the thing.
	Okay, so what is learning?
	Learning is many things.
	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.
	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 be able to play a 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, it can achieve superhuman-level performance through learning alone.
	It's so fundamentally different in search of different people.
	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. But it learned to play tic-tac-toe through self-play.
	And it learned to play optimally.
	What I'm saying is...
	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.
	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 would have not predicted...
	First of all, when I started and fell in love with AI, the game of Go was seen to be impossible to solve.
	So I thought 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, Arthur Samuel, in the early 60s, and he was the first person to use machine learning, had a program that could beat the world champion at Checkers.
	Yes. 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 a graduate from there.
	He wore a tie and a jacket every day.
	The rest of us didn't. He's a 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 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.
	That's amazing. The question is, what I mean surprise, 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?
	Yeah, but that's in your view of the world.
	Okay, well, let me then. It doesn't mean I'm wrong.
	No, no, it doesn't.
	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.
	And why would I say that?
	Well, in these games, they're all 100% information games.
	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 Marovec's paradox.
	I'm definitely not saying that chess is somehow harder than perception.
	Or any kind of robotics in the physical world, I definitely think is way harder than the game of chess.
	So I was always much more impressed by the workings of the human mind.
	It's incredible. The human mind is incredible.
	I believe that from the very beginning.
	I wanted to be a psychiatrist for the longest time.
	I always thought that's way more incredible than the game of chess.
	I think the game of chess 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 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, you know, 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 adults couldn't beat my machine.
	Yeah. So you were like, that's powerful.
	That's a way to rebel.
	Yeah. By the way, when was the first time you built something that outperformed you?
	Do you remember? Yeah.
	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.
	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. I tend to see magic in robots that...
	I also think that intelligence is a little bit overrated.
	I think we can have deep connections with robots very soon.
	Well, we'll come back to connections with robots.
	Sure. But I do want to say, I think too many people make the mistake of seeing that magic and thinking, well, we'll just continue.
	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 in to the hype, but it's not obvious to me how many steps away we are from 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, they were saying things are trivial for decades, since the 60s.
	They were saying everything is trivial.
	Computer vision is trivial.
	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 takes that idea to scale and the idea works.
	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 numbers.
	Okay, let's pick one that's the most surprising to you.
	Okay, please don't yell at me.
	GPT-3. Okay.
	Hold on a second. I was going to 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 or so on, which you could argue for alpha fold.
	Like, as a scientist...
	Was it 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.
	One of the problems at the moment with alpha-fold is it gets a lot of them right, which is a surprise to me because they're a really complex thing.
	But you don't know which ones it gets right, which then is a bit of a problem.
	You mean the structure of the protein that 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 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 AlphaFold is different because your AlphaFold tool is different because now it's producing data sets that are actually potentially revolutionizing competition in biology.
	They will actually help a lot of people.
	You'd say potentially revolutionizing.
	We don't know yet. That's true.
	I got you.
	Okay, so you know what?
	This is going to 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. 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 want to go there, I can go there, but that's not too much.
	Let's say they're on par safety-wise as humans currently, meaning human alone versus human plus robot.
	So, first let me say, I really like the car I came in here today.
	Which is? A 2021 model Mercedes E450. I am impressed by the machine vision, sonar, other things.
	I'm impressed by what it can do.
	I'm really impressed with...
	Many aspects of it.
	It's able to stay in lane?
	Oh yeah, it does the lane stuff.
	It's looking on either side of me.
	It's telling me about nearby cars.
	For 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, you know, registered and metrical that is.
	Oh, so it's like multiple cameras and it's all registered together to produce a 360 view kind of thing?
	360 view, you know, it's synthesized as though it's above the car.
	And 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.
	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...
	Over 10 miles on a public freeway with other traffic around driving completely autonomously.
	When did that happen? Was it CMU in the 80s or something?
	It was a long time ago.
	It was actually in 1987 in Munich at the Bundesfahrt.
	So they had it running in 1987.
	When do you think, and Elon has said he's going to do this, when do you think we'll have the first car drive coast-to-coast in the U.S., 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%. 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.
	But we've been working on it for 35 years.
	So that's demos. But this is going to take us back to the same conversation with AlphaZero.
	Are you not...
	Okay, I'll just say what I am.
	When I first started interacting with the Mobileye implementation of Tesla Autopilot, I've driven a lot of cars.
	I've been in the Google self-driving car since the beginning.
	I thought there was no way, before I sat and used Mobileye, 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.
	I was 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 Shresher 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'll do to make a successful product, right?
	At scale, 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 under Karpathy, 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.
	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 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, because I thought 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 sensing and the interaction with the driver and studying the human factor 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 of vision-based alone, how far that can take us.
	So that's my levels of surprise.
	Now... Okay.
	Can you explain, in the same way you said, like, AlphaZero, that's a homework problem that's scaled large in his chest.
	Like, who cares? Go with...
	Here's actual people using an actual car and driving.
	Many of them drive more than half their miles using this system.
	Right. 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, because the human eye has incredible dynamic range, and we make use of that dynamic range in 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 it blinds it 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 gonna work.
	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 gonna be great.
	Everyone will love it. And my prediction is that if autonomous vehicles kill more than 10 people a year, they'll be screaming and hollering, even though 35,000 people a year have been killed by human drivers.
	It's not rational.
	It's a different set of expectations.
	And that will probably continue.
	So there's that 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 offence.
	That was pushed by the car companies so that people would stay off the road so there wouldn't be deaths from pedestrians getting hit.
	We completely changed the structure of our cities and had these foul-smelling things, you know, everywhere around us.
	And now you see pushback in cities like Barcelona that's really trying to exclude cars, etc.
	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 case is too many.
	Here's an interesting question.
	How many fully autonomous train systems do we have in the U.S.? I mean, do you count them as fully autonomous?
	I don't know. Because they're usually as a driver, but they're kind of autonomous, right?
	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.
	Okay. 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 a U.S. city.
	It's expected to operate in 2017 in Honolulu.
	Oh, wow. It's delayed, but they will get there.
	Bart, by the way, was originally going to be autonomous here in the Bay Area.
	I mean, they're all very close to fully autonomous.
	Yeah, but getting the closest thing.
	And I have 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.
	There's At every station, there's a double set of doors.
	So there's a door of the train and there's a door off the platform.
	And this is 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 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 calling their system fully self-driving.
	I think they may have gotten there quicker by Tesla.
	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 how to 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 many edge cases of machine learning and vision-based methods can cover.
	This is what I'm trying to get at.
	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 going to argue with you because, you know, we're speculating.
	Yes. You know, my gut feeling tells me it's going to 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, government always drags behind on infrastructure.
	There's so many...
	Well, in this country.
	Sure. Sorry. Yes.
	In this country. And of course, there's many, many countries that are actually much worse on infrastructure.
	Oh, yes. Many are much worse.
	And there's some that, you know, like high-speed rail.
	Other countries have done much better.
	I guess my question is like, which is at the core of what I was trying to think through here and ask you is like, how hard is the driving problem?
	As it currently stands, so you mentioned 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.
	I used to think 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...
	It's orders of magnitude more valuable to have a fully autonomous system.
	But for me, lane keeping is really helpful because I'm lousy at it.
	But you wouldn't pay 10 times.
	The problem is there's not financial, it doesn't make sense to revamp the infrastructure to make lane keeping easier.
	It does make sense to revamp the infrastructure.
	If you have a large fleet of autonomous vehicles, now you change what it means to own cars.
	You change the nature of transportation.
	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.
	I don't know if you'd call them self-driving, but- Well, I mean, I rode in one before.
	They were called Waymo. So there's currently 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 expanding.
	Although, you know, I do an annual checkup on this.
	So as of late last year, they were aiming for hundreds of rides.
	Yeah. Not thousands.
	And there is no one in the car, but there's certainly safety people in the loop.
	And it's not clear what the ratio of cars to safety people is.
	Obviously, they're not 100% transparent about this.
	No, none of them are 100% transparent.
	They're very untransparent.
	At least the way they're...
	I don't want to 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, It was incredible to me that this actually could happen.
	I don't care if that scale is 100 cars.
	This is not a demo. This is me as a regular human.
	The argument I have is that people make interpolations from that.
	Interpolations. It's here.
	It's done. 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 on your amazing blog post.
	It'd be fun to go through them. But before that, let me ask you about this.
	You have... You have a harshness to you sometimes in your criticisms of what is perceived as hype.
	Because 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.
	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, you know, flying, I don't know what it was, 50 feet or the length of the first flight or something, to landing on the moon.
	Unbelievable. Fantastic.
	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.
	Not just possible soon, but ever.
	How important is it to believe and be optimistic is what I guess.
	Oh yeah, it is important.
	It's when it goes crazy.
	You know, you said, what was the word you used for my bad?
	Harshness? Harshness, yes.
	I just get so frustrated when people make these leaps and tell me that I don't understand.
	Right. Just from iRobot, which I was co-founder of, I don't know the exact numbers now because 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.
	Now there's lots of other companies.
	Was that a crazy idea that we had to believe in 2002 when we released it?
	Yeah, we had to believe that it could be done.
	Let me ask you about this.
	So iRobot, one of the greatest robotics companies ever.
	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?
	Because 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 several things we'll talk about.
	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 going to happen.
	But here's the thing. This shows.
	But you have that harshness.
	For Elon, too.
	Or no, it's a different harshness.
	No, it's a different argument with Elon.
	I think SpaceX is an amazing company.
	On the other hand, 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 Soyuz 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 lot further off.
	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 and 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...
	Well, 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 overhype.
	If you focus yourself on the idea that You shouldn't call a system full self-driving when it's obviously not fully autonomous.
	You're going to miss the magic.
	You are gonna miss the magic but at the same time there are people who buy it
	Literally pay money for it. Yeah and take those words as given so it's that's
	But I haven't so that I take words as given as one thing I haven't actually seen people that use autopilot that
	believe that the behavior is really important like the actual action
	So like this is I did to push back And the very thing that you're frustrated about which is
	like journalists and general people Buying all the hype and going on
	In the same way, I think there's a lot of hype about the negatives of this, too, that people are buying without using.
	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 going to be way different.
	And so my just tension was like, here's an innovator Super Cruise from Cadillac was super interesting too.
	That's a really interesting system.
	We should be excited by those innovations.
	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 These billionaires and their space games, why are they doing that?
	And that 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, because there's been a particular focus for me on 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 a line 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.
	Yeah. And, you know, 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 promise.
	And, you know, there's an area here which is...
	Maybe I'm naive, but I tend to believe that engineers, innovators, people like that, they don't think like that, 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, 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 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 going to check back on how my predictions were.
	That's such a great thought experiment.
	For 32 years.
	Oh, so you said 32 years.
	I said 32 years because that'll be January 1st, 2050.
	I'll be, I will just turn 90.
	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 going to make new predictions.
	I was just going to measure this set of predictions that I made.
	Because I was sort of annoyed that everyone could make predictions.
	They didn't come true and everyone forgot.
	Yeah, I forgot. I should hold myself to a high standard.
	Yeah, but also just putting years and date rangers on things.
	It's a good thought exercise.
	Yeah. And reasoning your thoughts out.
	And so the topics are artificial intelligence, autonomous vehicles, and space.
	Yep. I was wondering if we could just go through some that stand out.
	Maybe from memory, I can just mention to you some, let's talk about self-driving cars, like some predictions that you're particularly proud of or are particularly interesting from flying cars to the other element here is Like how widespread the location where the deployment of the autonomous vehicles is.
	And there's also just a few fun ones.
	Is there something that jumps to mind that you remember from the predictions?
	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, I actually do remember that.
	But I think you were mentioning difficulties in different cities.
	Yeah, yeah. Cambridge, Massachusetts, I think, was an example.
	Yeah, like in Cambridgeport.
	I lived in Cambridgeport 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.
	It's just...
	Your prediction was driverless taxi services operating on all streets in Cambridgeport, 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.
	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, you know, I live in that area.
	So I see, you know, the self-driving car companies, cars, multiple, multiple ones every day.
	Now, if they drive, cruise, Zook's less often, Waymo all the time, and 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.
	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 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.
	Unless it'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, computer vision problems?
	Here's a case that I saw outside my house a few weeks ago.
	About 8pm 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? Because 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. Yes. That car
	thought, oh she could jump out in front of me any second.
	As a human, I could tell, no, she's not going to 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 until I got bored.
	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.
	Now the crosswalk's blocked by this cruise vehicle.
	What's he going to do? Cleverly, I think, he decided not to go in front of the car.
	But he had to go behind it.
	He had to get off the crosswalk out into the intersection to push his baby around his 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...
	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 anecdotal evidence.
	But I got you.
	Your question is when is it going to happen in San Francisco?
	I say not soon, but it's going to be one of them.
	But where it is going to 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.
	It's been warned about them.
	And at velocities where it's always safe to stop dead.
	You can't do that on the freeway.
	That, I think, we're going to start to see.
	And they may not be shaped like, you know, current cars.
	They may be, you know, things like, you know, 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, like, that makes your...
	It's not just cheaper, but it makes your...
	It's fun to ride. One of the most...
	One of the least fun things is for a car that stops...
	And, like, waits. There's something deeply frustrating for us humans for the rest of the world to take advantage of us as we wait.
	But... Think about not you as the customer, but someone who's in their 80s in a retirement village whose kids have said, you're 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 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?
	Not in my lifetime. Not in my lifetime.
	I define my lifetime as...
	As 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.
	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.
	And people have told me, one of my co-workers has told me he does this.
	He tortures autonomously driven vehicles, and pedestrians will torture them.
	Once they realize that putting one foot off the curb makes the car think that they might walk into the road, teenagers will be doing that all the time.
	By the way, this is a whole other discussion, because my main interest with robotics is HRI, human-robot interaction.
	I believe that robots that interact with humans will have to push back.
	They can't just be bullied, because that creates a very uncompelling experience for the humans.
	Waymo, before it was called Waymo, discovered that they had to do that at four-way intersections.
	They had to nudge forward to give the cue that they were going to go, because otherwise the other drivers would just beat them all the time.
	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.
	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.
	I've been there since.
	I was there in 2014, and 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 people have said, well, Japan is so good at robotics.
	It was because we had had about 6,500 robots deployed in Iraq and Afghanistan, teleopt, but with intelligence, dealing with roadside bombs.
	So we had, I think it was at that time, nine years of in-field experience with the robots in harsh conditions.
	Whereas the Japanese robots, which were, you know, getting, you know, this goes back to what?
	It annoys me so much. Getting all the hype.
	Look at that. Look at that Honda robot.
	It can walk. Well, 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 Roombus, 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 email 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 because we wanted to put it on the shelves at $200.
	There was another home cleaning robot at the time.
	It was the Electrolux Trilobite.
	Which sold for €2,000.
	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...
	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...
	I'd be in... Yeah.
	Yeah. Let all these little companies be fabulous.
	They didn't have to have their own fab so they could innovate.
	And their innovations were to strip 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 that was in Hong Kong, going to all these companies that built, you know, they weren't gaming in the current sense.
	They were these handheld games that you would play or birthday cards.
	Because we had about a 50 cent budget for computation.
	So I'm tracking from place to place.
	Looking at their chips, looking at what they'd removed, ah, their 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 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.
	And you were excited.
	Yeah! And I was reading all these emails.
	Colin, I found this! Did you ever think that you guys could be so successful?
	Like eventually this company would be so successful.
	Could you possibly have imagined?
	No, we never did think that.
	We'd had 14 failed business models up until 2002, and then we had two winners the same year.
	No, and then, you know, I remember the board, because 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...
	Because we released on September 18th and they were all sold by Christmas.
	So it was...
	So we were gutsy.
	But yeah, you didn't think this will take over the world.
	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?
	Well, 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.
	You know, 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.
	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...
	Sorry to interrupt, but don't you, because I kind of have a vision in a small way, a big vision in a small way, that I see that there would be robots in the home at scale, like Roomba, but more.
	And that's a trillion dollar.
	Right. And I think there's a real market pull for them because of the demographic inversion.
	Who's going to do all the stuff for the older people?
	I'm leading here.
	There's going to 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 got to 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 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.
	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.
	Yeah. But it can take real money to get into these things, and you may fail along the way.
	I certainly failed at Rethink Robotics, and we lost $150 million in capital there.
	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. They're able to pick up stuff and interact with humans.
	Yeah, and that humans could retask them without writing code.
	Right. And now that's sort of become an expectation for a lot of other little companies and big companies that 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've 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 as a target for the company that we were going to build $3,000 robots with forced feedback that were 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 gearboxes. And at a $3,000 lifetime, or $3,000, I was saying we're going to go after not the people who already have robot arms in factories, the people who would never have a robot arm.
	We're going to go after a different market.
	So we don't have to meet their expectations.
	And so we're going to build it out of plastic.
	It doesn't have to have a 35,000-hour lifetime.
	It's going to be so cheap that it's OPEX, not CAPEX. And so we had a prototype that worked reasonably well.
	The control engineers were complaining about these plastic gearboxes.
	It was a beautiful little planetary gearbox.
	We could use something called series elastic actuators.
	We embedded them in there. We could measure forces.
	We knew when we hit something, etc.
	The control engineers were saying, yeah, but there's this torque ripple, because these plastic gears, they're not great gears, and there's this ripple, and trying to do force control around this ripple is so hard.
	And I'm not going to name names, but I remember one of the mechanical engineers saying, 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 it 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 cost $25,000.
	And the only people who were going to look at that were people who had arms in factories because that was somewhat cheaper for two arms than arms in factories.
	But they were used to 0.1 millimeter reproducibility of motion and certain velocities.
	And I kept saying, but that's not what we're giving you.
	You don't need position repeatability.
	You use force control like a human does.
	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 got to fit here in this right angle.
	Yeah. 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 until we feel that, and slide it in there.
	And that's how a human gets precision.
	They use force feedback and get the things to mate rather than just go straight to it.
	Couldn't convince... 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 going to 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.
	And 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.
	You're eventually going to 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 know, you talked about setting the goal a little too far for the engineers.
	Yeah, exactly. So 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 going to 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 going to get past CFIUS. And the investors would go away.
	And then we had two buyers.
	We were about to run out of money.
	Two buyers. And one used heavy-handed legal stuff with the other one.
	Said they were going to take it, 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.
	Does it hurt to think about an amazing company that didn't, like iRobot, didn't find a way?
	Yeah, it was tough. I said I was never going to 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 has a screening with a face.
	That's the revolutionary idea for a robotic arm.
	How much opposition did you get?
	Well, first the screen was also used during codeless programming.
	We 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 like the human looking.
	So there was a mixed bag there.
	But you think that's...
	I don't know. I'm kind of disappointed whenever I talk to roboticists.
	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 might 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.
	But we've had that with humans falling in love with cars, even back in the 50s.
	It's a different love, right?
	Well, yeah. 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 way.
	I think Blade Runner had the time scale totally wrong.
	Yeah, but to me, honestly, the most difficult part is the thing that you said with the Marvel X 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.
	You know, as humans, as we think about the future, we always try, 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.
	But that's not how, and it's the same with a 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.
	Yeah, so there was a convergence happening in a sense.
	And we will not be the same, you know, we're already talking about
	genetically modifying our babies, you know, there's a There's more and more stuff happening around that.
	We will want to modify ourselves even more for all sorts of things.
	We put all sorts of technology in our bodies to improve it.
	I've got I've got things in my ears so that I can sort of hear you.
	So we're always modifying our bodies.
	So 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 the Alexa Prize.
	Actually, I was invited to be a...
	What is it? The interviewer for the Alexa prize or whatever that's in two days.
	Their idea is success looks like a person wanting to talk to an AI system for a prolonged period of time, like 20 minutes.
	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?
	Not to check the weather or to check music, but just to talk as friends.
	Yeah, well, you know, we saw Weizenbaum back in the 60s with his program, Eliza, being shocked at how much people would talk to Eliza.
	I remember, you know, in the 70s typing, you know, stuff to Eliza to see what it would come back with.
	You know, I think right now, and this is a thing that 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 intention of these systems that they have any goal in life, even if it's to be happy.
	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.
	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... 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, Turing, if you read the paper, Turing wasn't saying this is a good test.
	He was using a rhetorical device to argue that if you can't tell the difference between a computer and a person, you must say that the computer's thinking because...
	Can't tell the difference, you know, when it's thinking.
	You can't say something different.
	What it has become is this sort of weird game of fooling people.
	So back at the AI lab in the late 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, because he starts by saying, can you tell whether it's a man or a woman?
	So we did that at the lab.
	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 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.
	He would ask them, did you have green plastic toy soldiers as a kid?
	Yeah. What did 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. So, you know, that's what the Turing test with computers has become.
	What's the trick question?
	That's why I say it. That's right.
	It sort of devolved into this weirdness.
	Yeah. Nevertheless, conversation, non-formulated as a test, is a fascinatingly challenging dance.
	It's a really hard problem.
	To me, conversation, when non-poses a test, is a more intuitive illustration of how far away we are from solving intelligence than computer vision.
	Computer vision is harder for me to pull apart.
	But with language, with conversation, you can see...
	No, because language is so human.
	It's so human. We can so clearly see it.
	Shit, you mentioned something I was going to go off on.
	Okay. I mean, I have to ask you, because you were the head of CSAIL and I left for a long time.
	I don't know. To me, when I came to MIT, you're one of the greats at MIT. So what was that time like?
	And plus 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 revolutionary work.
	Let me tell you first a disappointment in myself.
	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 him so many questions that now I wish I had.
	Can I ask you that question? Because you wrote that.
	You wrote that you were fortunate to know and rub shoulders with many of the greats, those who founded AI, robotics, and computer science, and the World Wide Web.
	And you wrote that your big regret nowadays is that often I have questions for those who have passed on.
	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 want to ask.
	If you could talk to them today, what question would you ask?
	What questions would you ask? Rick Leiter.
	I would ask him.
	He had the vision for humans and humans.
	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, etc.
	I would have asked him more questions about that, because now I could use it in my book.
	You know what I mean? 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 Pappett 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.
	Do you think he knew the effect that book would have?
	All the theorems are negative theorems.
	Yeah. That's just 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've got to be more specific.
	Well, I mean, it's such a magical place.
	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 Yeah, that's tragic.
	There's an openness to the university setting where you do research, both in the space of ideas and publication, all those kinds of things.
	Yeah, you know, there's the publication and all that, and often, you know, although these places say they publish, there's pressure.
	But I think, for instance, you know, NetNet, I think Google buying those 809 robotics companies was bad for the field.
	Because it locked those people away.
	They didn't have to make the company succeed anymore.
	It locked them away for years and then sort of all fritted 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, a different answer.
	I'll come back to Facebook in a minute.
	But I think those other places are following a dream of He's 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.
	We saw this incredible spike in attendees at NeurIPS.
	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. You know, 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 who'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 because 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 what.
	Meaning, maybe that's the way to not treat yourself too seriously, just always be moving forward.
	Oh, that was the idea. I mean, that crankiness, I mean, there's a...
	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 like a typewriter ball hitting characters.
	It could print arbitrary things, only in one bit black or white, but arbitrary pictures.
	This was science fiction sort of stuff at MIT. The list 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. So having that, like, direct glimpse into the future.
	Yeah, and I've had email every day since 1977, and the host field was only 8 bits in 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 to 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.
	You mentioned him earlier about computation.
	Did your worlds cross?
	I know him now, we talk, but let me tell you my Donald Knuth story.
	Besides 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 work overnight at the AI lab.
	And one night, the mainframe computer went down.
	A guy named Robert Paul was there.
	He did his PhD at the Media Lab at MIT. And he was an engineer.
	And so he and I tracked down what the problem was.
	It was one of these big refrigerator-sized or washing machine-sized disk drives had failed.
	And that's what brought the whole system down.
	So we've got panels pulled off and we're pulling, you know, circuit cards out.
	And Donald Knuth, who's a really tall guy, walks in and he's looking down and says, when will it be fixed?
	You know, because he wanted to get back to writing his tech system.
	Well, it's Donald Knuth.
	And so we figured out, you know, it was a particular chip, 7400 series chip, which was socketed.
	We popped... We put a replacement in, put it back in, smoke comes out because we put it in backwards because we were so nervous that Donald Knuth was standing over us.
	Anyway, we eventually got it fixed and got the mainframe running again.
	So that was your little, when was that again?
	Well, that must have been before October 79 because we moved out of that building then.
	So sometime, probably 78 sometime or early 79.
	Yeah, all those figures are just fascinating.
	All the people who have passed through MIT is really fascinating.
	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 I talk to a lot of people all the time.
	And there is no one way.
	You know, 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.
	Well, it depends on what you want of life.
	If you want to have real impact, you have to be ready to fail a lot of times.
	So you have to make a lot of unsafe decisions.
	And the only way to make that work is to 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.
	It's certainly possible.
	Taking the risk is the thing.
	But there's no way to make all safe decisions and actually really contribute.
	Do you think about your death, about your mortality?
	I've got to say, when COVID hit, I did.
	Because in the early days, we didn't know how bad it was going to 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 want to finish this book.
	When you think about it, are you afraid of it?
	I'm afraid of dribbling.
	You know, I'm 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 in knowing.
	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 going to review these every year for 32 years.
	That puts me into my mid-90s.
	Every time you write the blog posts, you're getting closer and closer to your own prediction of your death.
	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 they'll be on a podcast a hundred 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, you know, 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? Do 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 want to 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 would 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, because they may not last forever.
	Which is more exciting slash 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.
	It 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. And all of this will be forgotten.
	Yeah. Including all the robots you build, everything forgotten.
	Well, on average, everyone has been forgotten in history, right?
	Yeah. Most people are not remembered beyond a generation or two.
	Yeah. I mean, yeah, 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...
	I don't know anyone alive who remembers my great-grandparents because we didn't meet them.
	So... Still, this life is pretty fun, somehow.
	Yeah. 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, God. 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. 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.

▲