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 cofounded iRobot, which is one of the most successful robotics companies ever.

Then he cofounded Rethink Robotics that created some amazing collaborative robots like Baxter

and Sawyer. Finally, he cofounded 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 Roos'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 Weber, 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 finger

hands, and face eyeballs. Not the eyeballs, but everything else, serious elastic actuators.

You can interact with it. Cable driven, all the motors are inside. And it's just gorgeous.

The eyeballs are actuated too or no? Oh yeah, the eyeballs are actuated with cameras.

And so it had a visual attention mechanism, looking when people came in and looking in their face

and talking with them. Why was it amazing? The beauty of it. You said what was the most beauty?

What is the most beautiful? It's just mechanically gorgeous. As everything Aaron builds has always

been mechanically gorgeous. It's just exquisite in the detail. We're talking about mechanically,

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 overpromise. Even with its essence,

like the thing it presents, it should not overpromise. Yeah, so the joke I make, which I think

you'll get, is if your robot looks like Albert Einstein, it should be as smart as Albert Einstein.

So the only thing in Domo's face is the eyeballs. And 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. And on both Baxter and Sawyer at Rethink Robotics, they had a screen

with, you know, graphic eyes. So it wasn't actually where the cameras were pointing,

but 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. I grew up in Adelaide, South Australia. And I have these

two books that are dated 1961. So I'm guessing my mother found them in a store in 62 or 63.

How and Why Wonder Books? How and Why Wonder Book of Electricity? And How and Why Wonder Book of

Giant Brains and Robots. And I learned how to build circuits, you know, when I was eight or nine,

simple circuits. And I read, you know, 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 look 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 ice cube tray was about my budget at that stage. Later,

I managed to buy a transistors. And then I could build gates and flip flops and stuff.

So one one of your first robots was an ice cube tray. Yeah.

And it was very cerebral because it went 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 we're just not smart enough to build. It's not 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? Yeah, I believe that it's very wrong. In fact,

I'm halfway through a, I think it'll be about a 480 page book titled, the working title is

Not Even Wrong. And if I may, I'll tell you a bit about that book. So there's two three thrusts

to it. One is the history of computation, what we call computation, goes all the way back to

some manuscripts in Latin from 1614 and 1620 by Napier and Kepler through Babbage and Lovelace.

And then Turing's 1936 paper is what we think of as the invention of modern computation.

And that paper, by the way, did not set out to invent computation. It set out to

negatively answer one of Hilbert's three later set of problems. He called it

as an effective way of getting answers. And Hilbert really worked with rewriting rules as did

a 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 Godel.

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, I was an undergrad at Cambridge who said, who turned it into, is there a mechanical

process? So he wanted to show a mechanical process that could calculate numbers because

that was a mechanical process that people used to generate tables. They were called computers,

the people at the time. And they followed a set of rules where they had paper,

and they would write numbers down. And based on the numbers, they'd keep writing other numbers.

And they would produce numbers for these tables, engineering tables, that the more iterations

they did, the more significant digits came out. And so Turing, in that paper, set out to define

what sort of machine could do that mechanical machine, where it could produce an arbitrary

number of digits in the same way a human computer did. And he came up with a very simple set of

constraints where there was an infinite supply of paper. This is the tape of the Turing machine.

And each Turing machine had a set, came with a set of instructions that, as a person could do

with pencil and paper, write down things on the tape and erase them and put new things there. And

he was able to show that that system was not able to do something that Hilbert hypothesized. So he

disproved it. But he had to show that this system was good enough to do whatever could be done,

but couldn't do this other thing. And there he said, and he says in the paper,

I don't have any real arguments for this, but based on intuition. So that's how he defined

computation. And then if you look over the next from 1936 up until really around 1975,

you see people struggling with, is this really what computation is? And so Marvin Minsky,

very well known in AI, but also a fantastic mathematician in his book, Finite and Infant

Machines from the mid 60s, which is a beautiful, beautiful mathematical book,

says at the start of the book, well, what is computation? Turing says this and,

yeah, I sort of think it's that. It doesn't really matter whether the stuff's made of wood

or plastic. It's just relatively cheap stuff can do this stuff. And so, yeah, seems like computation.

And Donald Knuth, in his first volume of his Art of Computer Programming in around 1968,

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

Oldman sort of said a similar thing later that year. And by 1975, in the Aho Hopcroft and Oldman

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 says 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. 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 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 a, Turing says in the 36 paper, one of the critical facts here is that a human

has a limited amount of memory. So that's what we're going to put onto our mechanical computers.

So, you know, I'm like mass, I'm like mass or charge or, you know, it's not, it's not given

by the universe. It was, this is what we're going to call computation. And then it has this really,

you know, it had this really good implementation, which has completely changed our technological

work. That's computation. Second part of the book, I, or argument in the book, I have this

two by two matrix with science in the top row, engineering the bottom row, left column is

intelligence, right column is life. So in the bottom row, the engineering, there's artificial

intelligence and there's artificial life. In the top row, there's neuroscience and

abiogenesis. How does living matter turn in, how does nonliving matter become living matter?

Yes. 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 ganglion and there's electrical charges,

but no one knows what to do with it. And furthermore, there are a lot of players who are common

across them. I've identified common players except for artificial intelligence and abiogenesis,

I don't have, but for any other pair, I can point to people who work in and a whole bunch of them

by the way. We're 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 that

anatomical components were doing and telling other anatomical components and, you know,

generating behavior in the frog. Would you put them as basically the fathers or one of the early

pioneers of what are now called artificial neural networks?

Yeah. I mean, McCulloch and Pitts. Pitts was much younger than him. In 1943,

had written a paper inspired by Bertrand Russell on a calculus for the ideas imminent in

neural systems, where they had tried to, without any real proof, they had tried to give a formalism

for neurons, basically in terms of logic and gates or gates and not gates, with no real evidence

that that was what was going on. But they talked about it. And that was picked up by Minsky for

his 1953 dissertation on, which was a neural network, we'll call it today. It was picked up by

John von Neumann when he was designing the EDVAC computer in 1945. He talked about its

components being neurons based on, I mean, references. He's only got three references

and one of them is the McCulloch Pets paper. So all these people and then the AI people and the

artificial life people, which was John von Neumann originally, is that overlap?

They're all going around 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.

Yeah. And, you know, from people who are computing stuff. And then another chapter,

wait, people are computers, which is about computational neuroscience. So there's this

whole circle here. And 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. Yeah. Okay. Well,

well, when Elon Musk's rocket goes up, is it computing? Is that how it gets into orbit?

By computing? But we've got this idea. If you want to build an AI system, you'll write a computer

program. Yeah. In a sense, so the word computation very quickly starts doing a lot of work that

was not initially intended to do. It's just like going to say, if you talk about the universe

as essentially performing a computation. Yeah, right. Wolfram does this. He turns it into

computation. You don't turn rockets into computation. Yeah. By the way, when you say

computation in our conversation, do you tend to think of computation narrowly in the way

touring thought of computation? It's gotten very, you know, squishy. Yeah. Squishy. But

computation in the way touring thinks about it and the way most people think about it actually

fits very well with thinking like a hunter gatherer. There are places and there can be stuff in

places and the stuff in places can change and it stays there until someone changes it. And it's

this metaphor of place and container, which, you know, is a combination of our place cells in our

hippocampus and 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 100 years because it's a particle. No, it's a wave. It's got to be something

we understand. And I say, no, it's some weird mathematical logic that's different from those.

But we want that metaphor. Well, you know, I suspect that, you know, 100 years or 200 years

from now, neither quantum mechanics nor dark matter will be talked about in the same terms,

you know, in the same way that Flodgerton's theory eventually went away because it just wasn't an

adequate explanatory metaphor. You know, 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 right about that. But I, you know, I give some ideas. But so, so

there's the three things. Computation is sort of a particular thing we use. Oh, can I tell you

one beautiful thing? One beautiful thing. So, you know, I used an example of a thing that's

different from computation. You hit a drum and it vibrates. And there are some, some stationary

points on the drum surface, you know, because the wave is 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 Adel

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, she's never got to run it. They

never built the analytical engine. She wrote the program without, without it, you know.

So, so computation. Computation is sort of, you know, a thing that's become dominant as a metaphor.

But yeah, is it the right metaphor? All three of these four fields adopted computation. And,

you know, the, a lot of it swirls around Warren McCulloch and his, all his students. And he

funded a lot of people. And, and our human metaphors, our limitations to human thinking

will play into this. 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, the, the machine that performs

computation and this machine that appears to have consciousness and intelligence. Yeah. Can we

that piece of meat in your head piece of meat, and maybe it's not just the meat in your head.

It's the rest of you too. I mean, you have, you have, you actually have a neural system in your

gut. I tend to also believe, not believe, but we're now dancing around things we don't know. But

I tend to believe other humans are important. Like, so we're almost like, I just don't think

we would ever have achieved the level of intelligence we have with other humans. I'm not

saying so confidently, but I have an intuition that some of the intelligence is in the interaction.

Yeah. And, and I think, you know, I think it seems to me very likely again, we, you know,

this speculation, but we, our species, and probably, probably in the end, those to some

extent, because you can find old bones where they seem to be counting on them by putting notches

that were near them in the anticles had 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, you know,

the single deep learning network, which was fed, you know, all of literature or something.

Yeah. The neural network can't step outside of itself. But is there, is there some,

can we explore this dark room a little bit and try to get at something? What is the magic? Where

does the magic come from in the human brain that creates the mind? What's your sense as scientists

that try to understand it and try to build it? What are the directions if followed might be

productive? Is it creative interactive robots? Is it creating large deep neural networks that

do like self supervised learning? And just like wolf will, will 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 you're most, if you had to bet all your money,

financial? I wouldn't. Okay. So every intelligence we know, who's, you know,

animal intelligence, dog intelligence, you know, 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 a, it's not a, it's not, the blueness is not a direct

function of the photons we're receiving. It's actually context, you know, which is why you can

turn, you know, maybe seen the examples where someone turns a stop sign into a, some other sort

of sign by just putting a couple of marks on them and the deep learning system gets it wrong.

And everyone says, but the stop sign's red. You know, why is it, why is it thinking it's the

other sort of sign? Because redness is not intrinsic in just the photons. It's actually

a construction of an understanding of the whole world and the relationship between objects

to get color constancy. But our tendency in order that we get an archive paper really quickly is

to just show a lot of data and give the labels and hope it figures it out. But it's not figuring

it out in the same way we do. We have a very complex perceptual understanding of the world.

Dogs have a very different perceptual understanding based on smell. They go smell, smell a post,

they can tell how many, you know, different dogs have visited it in the last 10 hours and how

long ago there's all sorts of stuff that we just don't perceive about the world. And just taking

a single snapshot is not perceiving about the world. It's not perceiving the registration between

us and the object. And registration is a philosophical concept. Brian Cantrell Smith

talks about a lot, very difficult, squirmy thing to understand. But I think none of our systems

do that. We've always talked in AI about the symbol grounding problem, how our symbols that we

talk about are grounded in the world. And when deep learning came along and started labeling

images, people said, ah, the grounding problem has been solved. No, the labeling problem was solved

with some percentage accuracy, which is different from the grounding problem. So you agree with Hans

Marwick and what's called the Marwick'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? That were great. Do you still kind of maybe you can elaborate and 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 or not? Well, let me give us sort of another story.

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

from the late 50s into the 60s, you know, and you go to the AI lab at MIT,

who was it that was doing that? Was it 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 that was hard stuff. But, you know, a baby could see stuff.

That wasn't that wasn't intelligent. I mean, anyone could do that. It's not intelligence.

And so it, you know, this, there was this intuition that the hard stuff is the things they were good

at. And the easy stuff was the stuff that everyone could do. Yeah. And maybe I'm overplaying it a

little bit. And I think there's an element of that. Yeah. I mean, there, I don't know how much

truth there is to like chess, for example, has was for the longest time seen as the highest

level of intellect, right? Until we got computers that were better at it than people. And then

we realized, you know, if you go back to the 90s, you'll see, you know, the stories and the press

around when, when Kasparov was beaten by Deep Blue. Oh, this is the end of all sorts of things.

Computers are going to be able to do anything from now on. And we saw exactly the same stories

with AlphaZero, the go playing program. Yeah. But still, to me, reasoning is a special thing.

And perhaps, no, we 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 stories. It is. It's the construction of the

metaphor and registering that something constant in our brains. Like, isn't that what we're doing

with with vision to and what 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. There was a little confusion that went on. Sure. As we were telling each other

stories. Yes, exactly. Trying to delude each other. No, I just think I'm not exactly, so I'm

trying to pull apart this Morvex paradox. I don't view it as a paradox. What did evolution,

what did evolution spend its time on? Yes, it spent its time on getting us to perceive and

move in the world. That was, you know, 600 million years as multi subtle creatures doing that. And

then it was, you know, relatively recent that we that we, you know, were able to hunt or gather

or, you know, even, even animals hunting. That's much more recent. And then, and then anything

that we, you know, speech, language, those things are, you know, a couple of hundred thousand years

probably, if that long, and then agriculture, 10,000 years, you know, all that stuff was built

on top of those earlier things, which took a long time to develop. So if you then look at the

engineering of these things, so building it into robots, what's the hardest part of robotics,

do you think, as the decades that you worked on robots, in the context of what we're talking

about vision, like a perception, the actual sort of the, the biomechanics of movement,

I'm kind of drawing parallels here between humans and machines always, like, 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, the location of all the

points in 3D, things would be great. You know, if only we had labels on the, on the images,

you know, things would be great. But, you know, as, as we see, that's not good enough. Some deeper

understanding. But if you, if I came to you and I could solve one category of problems in robotics

instantly, what would give you the greatest pleasure?

I mean, is it, you know, you look at robots that manipulate objects.

What's hard about that? You know, is it the perception? Is it the, the reasoning about the

world, like common sense reasoning? Is it the actual building a robot that's able to interact

with the world? Is it like human aspects of a robot that's interacting with humans and that,

that game theory of how they work well together?

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, right? First time in

this house. And he'd never been able to get to a window before, but this had some low windows.

And he goes up to this window with a handle on it that he's never seen before. And he's got one

hand pushing the window and the other hand turning the handle to open the window. He, he, he knew

two different hands, two different things he knew how to, how to put together. And he's 16 months

old. And there you are watching an awe.

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

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

That's why it's like, okay, like, you could see the leap of genius from using one hand to perform

a task to combining doing, I mean, first of all, in manipulation, that's really difficult. It's

like two hands, both necessary to complete the action and completely different. And he'd never

seen a window open before. But he inferred somehow a handle 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,

we'll give the robot plenty of time to try everything. Can I tell a little side story here?

So I'm in DeepMind in London, four years ago, where there's a big Google building,

and then you go inside and you go through this more security, and then you get to DeepMind,

where the other Google employees can't go. 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 Soyuz that we sold them. And they really like them because Soyuz are compliant

and can sense forces, so they don't break when they're bashing into walls. They stop and they

do stuff. So you just let the robot do stuff and eventually it figures stuff out.

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

Yeah, Soyuz is a robot arm. Just go, what's Soyuz?

Soyuz is a robot arm that my company, Rethink Robotics, built.

Thank you for the context.

Yeah, sorry. Okay, cool. So we're in DeepMind.

And it's in the next room, these robots are just bashing around to try and use reinforcement

learning to learn how to act. Can I go see them? Oh, no, they're secret. They're all

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 prefilter, which cut down the search space dramatically.

I wonder to what level we're able to introspect what's going on,

because what's also possible is you have something like reinforcement learning

going on in the mind in the space of imagination. So like you have a good model of the world you're

predicting, and you may be running those tens of thousands of like loops, but you're like as a human,

you're just looking at yourself trying to tell a story of what happened. And it might seem simple,

but maybe there's a lot of computation going on. Whatever it is, but there's also a mechanism

that's being built up. It's not just random search. That mechanism prunes it dramatically.

Yeah, that pruning, that pruning step, but it doesn't, it's possible that that's,

so you don't think that's akin to a neural network inside a reinforcement learning algorithm?

Is it possible?

Yeah, until it's possible. I'll be incredibly surprised if that happens. I'll also be incredibly

surprised that 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. Four or five years ago, I was saying, I don't think

we've got it yet. And everyone was saying, you don't understand how powerful AI is. I had people

tell me, you don't understand how powerful it is. I sort of had a track record of what the world

had done to think, well, this is no different from before. Well, we have bigger computers. We had

bigger computers in the 90s and we could do more stuff. But okay, so let me push back. I'm

generally sort of optimistic and tried to find the beauty in things. I think there's a lot of

surprising and beautiful things that neural networks, this new generation of deep learning

revolution has revealed to me, has continually been very surprising, the kind of things it's

able to do. Now, generalizing that over saying like this, we've solved intelligence, that's another

big leap. But is there something surprising and beautiful to you about neural networks that

where actually you said 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 in vision for robots. What about AlphaZero and self play mechanisms

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

was there, which introduced reinforcement learning. No, but come on. So you're talking

about the actual techniques. But isn't this surprising to you the level it's able to achieve

with no human supervision of chess play? Like, to me, there's a big, big difference in deep blue.

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

You know, we had that chess is easy. Yeah, I mean, I came across this 1946 report that,

and I'd seen this as a kid in one of those books that my mother had given me actually,

1946 report, which pitted someone with an abacus against an electronic calculator.

And he beat the electronic calculator. So there at that point was, well, humans are still better

than machines are calculating. Are you surprised today that a machine can do a billion floating

point operations a second and you're puzzling for minutes through one? So I don't know,

but I am certainly surprised. There's something to me different about learning.

So system that's able to learn learning. Now you see, now you're getting into one of the deadly

sins because of using terms overly broadly. Yeah, I mean, there's so many different forms of learning.

Yeah. And so many different forms. You know, I learned my way around the city. I learned to

play chess. I learned Latin. I learned to ride a bicycle. All of those are, you know,

a very different capabilities. Yeah. And if someone has a, you know, in the old days,

people would write a paper about learning something. Now the corporate press office

puts out a press release about how company X has leading the world because they have a system that

can. Yeah, but here's the thing. Okay. So what is learning? What I'm referring to learning is many

things, but I suitcase word. It's a suitcase word. But loosely, there's a dumb system. And over time,

it becomes smart. Well, it becomes less dumb at the thing that it's doing. Yeah. Smart is a

is a loaded word. Yes, less, less dumb at the thing. It gets better performance under some measure.

Yeah. And 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, right, 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, you know, well, would actually be able to play

a game. And actually, funny enough, if you look at DeepMind's work, since then, they are 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 system playing itself,

that can, it can achieve super human level performance through learning alone. So like.

Okay. So, so, you know, you didn't still fund them as you did in a search of that.

You didn't, you didn't like it when I referred to Donald Mickey's 1961 paper. There in the second

part of it, which came a year later, they had self play on an electronic computer at tic,

tac, toe. Okay. It's not as, but it learned to play tic, tac, toe through self play. That's

not what learned to play optimally. What I'm saying is I, okay, I have a little bit of a bias,

but I find ideas beautiful, but only when they actually realize the promise that's another level

of beauty. Like, for example, with Bezos and Elon Musk are doing with rockets, rockets for a

long time, but doing reusable, cheap rockets, it's very impressive. In the same way, I, okay,

yeah, I would have not predicted. First of all, when I was started and fell in love with AI,

the game of Go was seen to be impossible to solve. Okay. So I thought maybe, you know,

I, maybe it'd be possible to maybe have big leaps in a Moore's law style of way in computation,

I'll be able to solve it. But I would never have guessed that you could learn your way. However,

I mean, in the narrow sense of learning, learn your way to, to, to beat the best people in

the world at the game of Go without human supervision, not studying the game of experts.

Okay. So, so using a different learning technique, Arthur Samuel in the early 60s,

and he was the first person to use machine learning, got, had a program that could beat

the world champion at checkers. Now, so, and that 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 grad student 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

has been a vacuum tube engineer on 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 seek to get early failures. So here you had this whole farm

of computers running random code for hours and hours a week for each computer. He had a whole

bunch of them. So he ran his chess learning program with self play on IBM's production line.

He had more computation available to him than anyone else in the world. And then he was able

to produce a chess playing program. I mean, a checkers playing program that could beat the

world champion. So that's amazing. The question is, I mean, I'm surprised, I don't just mean

it's nice to have that accomplishment. Is there is a stepping towards something that feels

more intelligent than before? Yeah, but that's in your view of the world.

Okay, okay. Well, I mean, then it doesn't mean I'm wrong.

No, no, no. 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, you know, I'm putting my bet on

but still missing a whole lot a lot. And why would I say that? Well, in these games, they're all,

you know, 100% information games. But again, but each of these systems is a very short

description of the current state, which is different from registering and perception

in the world, which gets back to Maurevec'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 it's 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 in the game of chess. I think

the game of chess is, I love the Olympics. It's just another example of us humans picking a task

and then agreeing that a million humans will dedicate their whole life to that task. And that's

the cool thing that the human mind is able to focus on one task and then compete against each

other and achieve like weirdly incredible levels of performance. That's the aspect of chess that's

super cool. Not that chess in itself is really difficult. It's like the Fermat's last theorem

is not in itself to me that interesting. The fact that thousands of people have been struggling

to solve that particular problem is fascinating. So can I tell you my disease in this way? Sure.

Which actually is closer to what you're saying. So as a child, you know, I was building various,

I called them computers. They weren't general purpose computers. Ice cube tray. The ice cube

tray was one. But I built other machines. And what I liked to build was machines that could beat

adults at a game. And they couldn't, the adults couldn't beat my machine. Yeah. So that was,

you were like, that's powerful. Like that's a way to rebel. Yeah. By the way,

when was the first time you built something that outperformed you? Do you remember?

Well, I knew how it worked. I was probably nine years old. And I built a thing that

was a game where you take turns in taking matches from a pile. And either the one who

takes the last one or the one who doesn't take the last one wins, I forget. And so it was pretty

easy to build that out of wires and nails and little coils that were like plugging in the number

and a few light bulbs. The one that I was proud of, I was 12 when I built a thing out of old

telephone switchboard switches that could always win at tic tac toe. And that was a

much harder circuit to design. But again, it was just, it was no active components. It was just

three positions, which is empty x zero, and nine of them and light bulb one, which,

which move it wanted next. And then the human would go and move that.

See, there's magic in that creation. I tend to, I tend to see magic in robots that

like I also think that intelligence is a little bit overrated. I think we can have deep

connections with robots very soon. And we'll come back to connections. Sure. But, but I do want to

say I don't, I think too many people make the mistake of seeing that magic and thinking,

well, we'll just continue, you know, but each, 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

like this eternal optimism is it's not clear to me. So I don't do what obviously the journalists do

or like give into the hype, but it's not obvious to me how many steps away we are from,

from a truly transformational understanding of what it means to build intelligence systems,

like, or how to build intelligence systems. I'm also aware of the whole history of artificial

intelligence, which is where you're deep grounding of this is, is there has been an optimism for

decades. And that optimism, just like reading old optimism is absurd, because people were like,

this is, they were saying things are trivial for decades since the sixties. They're saying

everything is true computer vision is trivial. But I think my mind is working crisply enough to

where I mean, we can dig into if you want. I'm really surprised by the things deep mind has

done. I don't think they're so they're yet close to solving intelligence, but I'm not sure it's

not 10, 10 years away. What I'm referring to is interesting to see when the engineering

it takes that idea to scale and this and the idea works and no, it fools people.

Oh, okay. Honestly, Ronnie, if it was you, me and Demis inside a room, forget the press,

forget all those things. Just as a scientist as a roboticist, you know, that wasn't surprising to

you that at scale. So we're talking about a very large now. Okay, let's pick one that's the most

surprising to you. Okay, please don't yell at me. GPT three. Okay. Well, I was gonna bring that out.

Okay, Alpha zero, Alpha go Alpha go zero, Alpha zero, and then Alpha fold one and two. So aren't

any of do any of these kind of have this core of not forget usefulness or application and so on,

which you could argue for Alpha fold, like as a scientist was doors surprising to you that it

worked as well as it did. Okay, so if we're going to make the distinction between surprise and

usefulness, and I'll have to explain this, I would say Alpha fold. And one of the problems

at the moment with Alpha fold is, you know, it gets a lot of them right, which is a surprise to me

because they're a really complex thing. But you don't know which ones it gets right, which then

is a bit of a problem. Now they've come out with a reason. You mean the structure of the protein

gets a lot of those right? Yeah, it's a surprising number of them right. It's been a really hard

problem. So that was a surprise how many it gets right. So far, the usefulness is limited because

you don't know which ones are right or not. And now they've come out with a thing in the last few

weeks, which is trying to get a useful tool out of it. And they may well do it. In that sense,

the least Alpha fold is different, because your Alpha fold two is different. Because now it's

producing data sets that are actually, you know, potentially revolutionizing competition

biology, like they will actually help a lot of people. But you would say potentially

revolutionizing. We don't know yet. But yeah, that's true. Yeah, but they're, you know, but I

got your, I mean, this is 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 do you have built robotics companies, you're one of the

greatest roboticists in history. And that's not in space of 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 Tesla's. What are your thoughts about Tesla autopilot

or more generally vision based machine learning approach to semi autonomous driving?

These are robots that are 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, which there is, let's say

they're on par safety wise as humans currently, meaning human alone versus human plus robot.

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

a 2021 model Mercedes E 450. I am impressed by the machine vision. So now other things,

I'm impressed by what it can do. I'm really impressed with many aspects of it. And it's able

to stay in lane. It does the lane stuff. It's looking on either side of me. It's telling me

about nearby cars, blind spots and so on. Yeah. When I'm going in close to something in the park,

I get this beautiful, gorgeous, top down view of the world. I am impressed

up the wazoo of how registered and metrical that is. So it's like multiple cameras and

it's all very good together to produce a 360 view kind of 360 view, you know, synthesized

so 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 better.

So I'm not saying technologies are bad or not useful. But here's my point. Yes.

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

But when did the first car go over 55 miles an hour for over 10 miles

on a public freeway with other traffic around driving completely autonomously? When did that

happen? Was it in the 80s or something? It was a long time ago. It was actually in 1987

in Munich at the Bundeswehr. 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 US hands off the wheel, hands off the wheel, feet off the pedals,

coast to coast? As far as I know, a few people have claimed to do it. 1995. That was comedy.

I didn't know. But oh, that was the code. Yeah. They didn't claim, did they claim 100%

they claim? Not 100%. Not 100%. And then there's a few marketing people who have claimed 100%.

But my point is that what I see happening again is someone sees a demo and they over generalize

and say we must be almost there. But we've been 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. Because when I first started interacting with the

with the Mobileye implementation at Tesla Autopilot, I've driven a lot of cars. You know,

I've been in Google stuff driving cars since the beginning. I thought there was no way before I sat

and use Mobileye, I thought they're 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 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 Capati, what they were, what they're doing with the data

engine, which is converting into the driving tasks into these multiple tasks, and then doing this

edge case discovery when they're pulling back, like the level of engineering made me rethink

what's possible. I don't, I still, you know, I don't know to that intensity, but I always thought

it was very difficult to solve the time I was 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, you know,

just because I worked with a lot of car companies, they're, they're so a little,

a little bit old school to where I didn't think they could do this at scale, like AWS style,

data collection. So when Tesla was able to do that, I started to think, okay, so what are the limits

of this? I still believe that a driver like sensing and the interaction with a driver and like

studying the human factors psychology problem is essential. It's, it's always going to be there.

It's always going to be there, even with fully autonomous driving. But I've been surprised,

what is the limit, especially a 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 Alpha zero,

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

Pure vision, yeah. And, you know, they, and now no radar, which is, I suspect that can't go all the

way. And one reason is without, without new cameras that have a dynamic range closer to the human eye,

because human eye has incredible dynamic range. And we make use of that dynamic range in, in,

it's a, we have an autism magnitude or some crazy number like that. The cameras don't have that,

which is why you see the, the, the bad cases where the sun on a white thing and it blinds it

in a way, it wouldn't blind a person. I think there's a bunch of things to think about before

you say, this is so good, it's just going to 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, 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, so a few things, you know, 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 you know, there is no rationality. And that's part of the thing people have said,

engineers say to me, well, if we cut down the number of deaths by 10% by having autonomous

driving, that's going to be great. Everyone will love it. And my prediction is that if

autonomous vehicles kill more than 10 people a year, they'll be screaming and hollering,

even though 35,000 people a year have been killed by human drivers. It's not rational.

It's a different set of expectations. And that will probably continue.

So there's that 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, you know, into our daily lives, we completely rebuilt our cities and we changed

all the laws. J walking was not an offense. That was pushed by the car companies so that

people would stay off the road so there wouldn't be deaths from pedestrians getting hit.

We completely changed the structure of our cities and had these foul smelling things,

you know, everywhere around us. And now you see pushback in cities like Barcelona is 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 US?

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.

When is the first fully autonomous train system for mass transit expected to operate fully

autonomously with no driver in the US city? It's expected to operate in 2017 in Honolulu.

Oh, wow. It's delayed, but they will get there. But by the way,

it was originally going to be autonomous here in the Bay Area.

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

Yeah, but getting the closest to things. 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

that. And what do you see when you look at that? What do you see when you go to a fully autonomous

train in an airport? It's not like regular trains. At every station, there's a double

set of doors. So there's a door of the train and there's a door off the platform.

Yeah. And it's really visible in this Japanese one because it goes out in amongst buildings.

The whole track is built so that people can't climb onto it. Yeah.

So there's an engineering that then makes the system safe and makes them acceptable.

I think we'll see similar sorts of things happen in the US. What surprised me, I thought,

wrongly, that we would have special purpose lanes on 101 in the Bay Area, the leftmost lane,

so that it would be normal for Teslas or other cars to move into that lane and then say,

okay, now it's autonomous and have that dedicated lane. I was expecting movement to that. Five years

ago, I was expecting we'd have a lot more movement towards that. We haven't. And it may be because

Teslas has been overpromising by calling their system fully self driving. I think they may have

been gotten there quicker by collaborating to change the infrastructure. This is one of the

problems with long hold trucking being autonomous. I think it makes sense on freeways at night

for the trucks to go autonomously. But then how do you get on to and off of the freeway?

What sort of infrastructure do you need for that? Do you need to have the human in there to do that?

Can you get rid of the human? So I think there's ways to get there, but it's an infrastructure

argument because the long tail of cases is very long and the acceptance of it will not be at the

same level as human drivers. So I'm with you still and I was with you for a long time, but I am

surprised how well, how many edge cases of machine learning and vision based methods can cover.

This is what I'm trying to get at. I think there's something fundamentally different

with vision based methods and Tesla autopilot and any company that's trying to do the same.

I'm not going to argue with it because we're speculating. 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 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 just...

Well, in this country, in this country, and of course, there's many, many countries that are

actually much worse on infrastructure. Oh, yes, many of them are much worse in the

somewhat high speed rail that other countries have done much better.

I guess my question is at the core of what I was trying to think through here and ask is,

how hard is the driving problem as it currently stands? 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 it's,

with vision alone, it would be three decades, four decades.

Okay, so I don't know the answer to this thing I'm about to pose, but I do notice that on Highway 280

here in the Bay Area, which largely has concrete surface rather than blacktop surface,

the white lines that are painted there now have black boundaries around them.

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

Interesting. So I don't know whether they 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 lane.

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 always at it. But you wouldn't pay 10 times. The problem is there's not financial,

like it doesn't make sense to revamp the infrastructure to make lane keeping easier.

It does make sense to revamp the infrastructure. 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. Well, I mean, I rode in one before they were called Waymo at X.

So there's currently another surprisingly, but I didn't think it 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 a week, not thousands.

And there is no one in the car, but there's certainly safety people in the loop.

And it's not clear how many, you know, what the ratio of cars to safety people is.

It wasn't, obviously, they're not 100% transparent about this.

No, none of them are 100% transparent. They're very untransparent.

But at least the way they're, I don't want to make definitively, but they're saying

there's no teleoperation. 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. 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

was actually could happen. I don't care if that scales 100 cars. This is not a demo.

This is not, this is me as a regular. The argument I have is that people make

interpolations from that. Interpolations. That, you know, it's here, it's done.

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

Okay, so I'd like to go to, you keep a list of predictions on your amazing blog posts.

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 criticism of what is perceived as hype.

And so like, because people extrapolate, like you said, and they kind of buy into the hype,

and then they, they kind of start to think that the technology is way better than it is.

But let me ask you maybe a difficult question. 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, his, his, his, his, his 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. Barely get, 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, right? So like, not just possible soon, but like

ever. So, so, so, you know, how important is it to believe and be optimistic is what I guess?

Oh, yeah, it is important. It's when it goes crazy. When, when, when I, you know, you said,

what was the word you used for my bad harshness? Harshness. Yes. I just get so frustrated. Yes.

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

know, yeah. There's just from iRobot, which I was co founder of. Yeah. I don't know the exact

numbers now because I haven't, it's 10 years since I stepped off the board. But I believe it's well

over 30 million robots cleaning houses from that one company. And now there's lots of other companies.

Was that a crazy idea that we had to believe in 2002 when we released it? Yeah. That was, we,

we had, we had to, you know, believe that it could be done. Let me ask you about this. So iRobot,

one of the greatest robotics companies ever in terms of many, 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, 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 pet him on the back and say, well, you're doing is going to

fail, but go at it anyway. That's what I'm referring to with the harshness. You've accomplished an

incredible thing there. One of the several things we'll talk about. Well, like that's what I'm trying

to get at that line. No, it's, it's when my harshness is reserved for people who are not doing it,

who claim it's just, well, this shows that it's just going to happen. But here, here's the thing.

This show, but you have that harshness for Elon too. And no, no, it's a different harshness.

No, it's a different argument with Elon. You know, I, I think SpaceX is an amazing company.

On the other hand, you know, I, in one of my blog posts, I said, what's easy and what's hard? I said,

SpaceX vertical landing rockets, it had been done before grid fins had been done since the 60s.

Every Soyuz has them reusable space DCX reuse those rockets that landed vertically.

There's a whole insurance industry in place for rocket launches. So all sorts of infrastructure

that was doable. It took a great entrepreneur, a great personal expense. He almost drove himself,

you know, bankrupt doing it. A great belief to do it. Whereas Hyperloop has a whole bunch

more stuff that's never been thought about and never been demonstrated. So my estimation is

Hyperloop is a long, long, long further off. And if I've got a criticism of, of, 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, and mouth off about other things, which then people go and compete

about and try and do. And so this is where I, I understand what you're saying. I tend to draw

a different distinction. I, I have a similar kind of harshness towards people who are not telling the

truth, who are basically fabricating stuff to make money or to... Oh, he believes what he says.

I just think he's wrong. To me, that's a very important difference. Yeah, I'm not, 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, you know, 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, 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 overhyped. You know, if you focus yourself on the idea that

you shouldn't call a system full self driving when it's obviously not

autonomous, fully autonomous, you're going to miss the magic. Oh, yeah, you are going to miss the

magic. But at the same time, there are people who buy it, literally pay money for it and take those

words as given. So 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 like to push back on the very thing that you're frustrated

about, which is like journalists and general people buying all the hype and going 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 people use the way this is what this was this open 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 this.

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. The your perceptions of it are going to be way different.

And so my just tension was like, here's an innovator.

What is it? Sorry, super cruise from Cadillac was super interesting too. That's a really

interesting system. We should like be excited by those innovations.

Okay, so can I tell you something that's really annoyed me recently? It's really annoyed me

that the press and friends of mine on Facebook are going, these billionaires and their space games,

you know, why are they doing that? Yeah, that's been very frustrating.

It 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, you know, that I keep having to push back

against unrealistic expectations when these things can become real.

Yeah, I, this was interesting on the, because there's been a particular focus for me is

autonomous driving, Elon's prediction of when he's going to be able to do that.

When certain milestones will be hit.

There's several things to be said there that I always, I thought about because

whenever you said them, it was obvious that's not going to me as a person that kind of

not inside the system is obviously 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, I'm not talking about anyone here. I'm just saying.

So there's a line there, right? You have to have a line because you overextend and it's demoralizing.

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

And, 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.

There's an area here which is... I tend to be, maybe I'm naive, but I tend to believe

that engineers, innovators, people like that, they're not, they don't think like that,

like manipulating the stock price. But it's possible that I'm, I'm certain it's possible

that I'm wrong. It's a very cynical view of the world because I think most people that run companies

and build, like, 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.

Such a cool idea. I don't know how long ago you started, like three, four years ago. It was

January 1st, 2018. Yeah. And I made these predictions and I said that every January

1st, I was going to check back on how my predictions... That's such a great thought.

For 32 years. Oh, you said 32 years. I said 32 years because I thought that'll be January 1st,

2050. I'll be... I will just turn 95. 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. So I should hope myself to a higher standard. Yeah. But also just putting

years and date rangers on things, it's a good thought exercise and reasoning your thoughts out.

And so the topics are artificial intelligence, autonomous vehicles, and space. I was wondering

if we could just go through some that stand out. Maybe from memory, I can just mention to you some,

let's talk about self driving cars, some predictions that you're particularly proud of

or are particularly interesting from flying cars to the other element here is 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 like difficulties at different cities. Yeah. So 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. So 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. Relatively benign environment.

The streets are wide. The major problem is delivery trucks stopping everywhere,

which has made things more complicated. A taxi system there with somewhat designated pickup

and drop offs, unlike with Uber and Lyft, where you can sort of get to any place and the drivers

will figure out how to get in there. We're still a few years away. I live in that area. So I see

the, you know, the self driving car companies, cars, multiple, multiple ones every day out

there by the cruise. Zooks less often, Waymo all the time, different ones come and go.

And there's always a driver. There's always a driver at the moment, although I have noticed

that sometimes the driver does not have the authority to take over without talking to the

home office because they will sit there waiting for a long time. And clearly something's going on

where the home office is making a decision. So there, you know, and so you can see whether

they've got their hands on the wheel or not. And it's the incident resolution time that

tells you gives you some clues. So what year do you think? What's your intuition? What date range

are you currently thinking San Francisco would be autonomous taxi service from any point A to

any point B without a driver? Are you still 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 the market way longer, unless there's reengineering of roads.

By the way, what's the biggest challenge? You mentioned a few. Is it the delivery trucks?

Is it the edge cases, the computer perception? Well, it is a case that I saw outside my house

a few weeks ago, about 8 p.m. on a Friday night. It was getting dark 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. The 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. And now the

crosswalk is 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 this car, which was stopped there.

And no human driver would have stopped there for that length of time. They would have gotten out

of the way. And that's another one of my pet peeves, that safety is being compromised

for individuals who didn't sign up for having this happen in their neighborhood.

Yeah. But now you can say that's an edge case, but...

Yeah. Well, I'm in general not a fan of anecdotal evidence for stuff like this is

one of my biggest problems with the discussion of autonomous vehicles and in general,

people that criticize them or support them are using anecdotal evidence.

So let me... But I got you.

You know, your question is when is it going to happen in San Francisco? I say not soon,

but it's going to be one of the... 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, they've 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

current cars, they may be things like main mobility has those things and various companies have

these. Yeah, I wonder if that's a compelling experience. To me, it's not just about automations,

it's about creating a product that makes your... It's not just cheaper, but makes your... It's fun

to ride. One of the most... One of the least fun things is for a car that stops and 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 not 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 life. I define my lifetime as 2050. 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. 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. And I talk about it in one of the articles or blog posts that I wrote.

One of my coworkers has told me he does this. He tortures autonomously driven vehicles and

pedestrians. We'll torture them. Now, once they realize that putting one foot off the curb

makes the car think that they might walk into the road, kids, teenagers will be doing that

all the time. By the way, this is a whole other discussion because my main issue with robotics

is HRI, human and 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 eat them all the time. You cofounded iRobot, as we mentioned,

one of the most successful robotics companies ever. What are you most proud of with that company

and the approach you took to robotics? Well, there's something I'm quite proud of there,

which may be a surprise, but I was still on the board when this happened. It was March 2011,

and we sent robots to Japan and they were used to help shut down the Fukushima Daiichi nuclear

power plant, which was everything was up in there since I was there in 2014. Some of the

robots were still there. I was proud that we were able to do that. Why were we able to do that?

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, teleopped, but with intelligence,

dealing with roadside bombs. 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 goes back to what

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 Roomba, quote unquote, simple, that's deployed in tens of millions of homes?

What do you think about that? Well, I make the joke that I started out life as a pure

mathematician and turned into a vacuum cleaner salesman, so if you're going to be an entrepreneur,

be ready to do anything. But I was, you know, there was a wacky lawsuit that I got

opposed for not too many years ago, and I was the only one who had emailed from the 1990s,

and no one in the company had it, so I went and went through my email, and it reminded me of,

you know, the joy of what we were doing, and what was I doing? What was I doing at the time we were

building the Roomba. One of the things was we had this incredibly tight budget, 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 euros, and to us that was not going to be a

consumer product. So we had reason to believe that $200 was a thing that people would buy at,

that was our aim. But that meant we had, you know, that's on the shelf making profit.

That means the cost of goods has to be minimal. So I found all these emails of me going, you know,

I'd be in Taipei for a MIT meeting, and I'd stay a few extra days. I'd go down to Shinshu and talk to

these little tiny companies, lots of little tiny companies outside of TSMC, Taiwan Semiconductor,

Taiwan Semiconductor Manufacturing Corporation, which let all these little companies be fabulous.

They didn't have to have their own fab, so they could innovate. And they were building, their

innovations were to build stripped down 6802s. 6802 was what was in an Apple One. Get rid of

half the silicon and still have it be viable. And I'd previously got some of those for some

earlier failed products of iRobot. And then that was in Hong Kong, going to all these companies

that built, you know, they weren't gaming in the current sense. There were these handheld games

that you would play, or birthday cards, because we had about a 50 cent budget for computation.

And so I'm tracking from place to place, looking at their chips, looking at what they'd removed.

Oh, they're interrupt, they're interrupt handling is too weak for a general purpose. So I was going

deep technical detail. And then I found this one from a company called Winbond, which had,

and I'd forgotten that had this much RAM, it had 512 bytes of RAM. And it was in our budget,

and it had all the capabilities we needed. Yeah. So you're excited. Yeah. And I was reading all

these emails, Colin, I found this. So did you think, did you ever think that you guys could be

so successful? Like, eventually, this company would be so successful. Did you could you possibly

have imagined? And no, we never did think that we had 14 failed business models up to 2002.

And then we had two winners same year. No, and then, you know, we 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 18. And I was all sold by Christmas. So it was so we were gutsy. But, but yeah,

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

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 what can be delivered. Sure. Miss pricing. 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 and 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 so what would that company do? I can't, you know, because I'm still starting robot

companies. Yeah. 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 these are very hard to

to predict. But sorry to interrupt. But don't you because I kind of have a vision in a small way

and it's 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 trillion dollar. Right. And I think there's a

real market pull for them because of the demographic inversion, you know, who's who's

going to do the stuff for the older people? There's too many, you know, I'm leading here.

There's gonna be too many of us. And but we don't have capable enough robots to

to make that economic argument at this point. Do I expect that that will happen? Yes, I expect

it will happen. But I got to tell you, we introduced the Roomba in 2002, and I stayed another

nine years. We were always trying to find what the next home robot would be. And

still today, the primary product of 20 years late, almost 20 years later, 19 years later,

the primary product is still the Roomba. So I robot hasn't found the next one. Do you think it's

possible for one person in the garage to build it versus like Google launching Google self driving

car that turns into Waymo? Do you think 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 started

to see, you know, fair chunks of capital for some robotics companies, you know, Series Bs.

Because I saw one yesterday for $80 million, I think it was for covariant.

But it can take real money to get into these things, and you may fail along the way. I

certainly failed at Rethink Robotics. And we lost $150 million in capital there.

So okay, so Rethink Robotics is another amazing robotics company you co founded.

So what was the vision there? What was the dream? And what are you most proud of with

Rethink Robotics? I'm most proud of the fact that we got robots out of the cage in factories

that were safe, absolutely safe for people and robots to be next to each other.

So these are robotic arms? Robotic arms for me to pick up stuff and interact with humans.

Yeah, and that the humans could retask them without writing code. And now that's sort of

become an expectation for a lot of other little companies and big companies are advertising

they're doing. That's both an interface problem and also a safety problem. Yeah. 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've been stronger on, and I remember the day, I remember the exact meeting.

Can you take me through that meeting? Yeah. So I'd said that I'd set as a target for the company

that we were going to build $3,000 robots with force 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, you know, 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, we had a prototype

that worked reasonably well. But the control engineers were complaining about these plastic

gearboxes with a beautiful little planetary gearbox. But we could use something called

serious elastic actuators, we embedded them in there, we can measure forces, we knew when we

hit something, etc. The control engineers were saying, yeah, but this is 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

gear, the spur gearbox working. We cost reduced at 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 and factories. But they were used to

0.1 millimeter reproducibility of motion and certain velocities. And I kept thinking, but

that's not what we're giving you. You don't need position repeatability, 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 controls

you can grab the arm and you can move it? Yeah, 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 a fit here in this right angle.

Yeah. Under position control, you have fixed it where this is. You know where this is precisely

and you just move it and it goes there. If force control, you would do something like

slide it over here till we feel that and slide it in there. And that's how a human gets precision.

They use force feedback and get the things to mate rather than just go straight to it.

Yeah. Couldn't convince our customers who are in factories and were used to thinking about

things a certain way. And they 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 gearbox, as we ended up having to do,

the robot was now overpriced. And that was your intuition from the very beginning,

kind of that this is not... You're opening a door to solve a lot of problems that you're

eventually going to have to solve this problem anyway. Yeah. And also, I was aiming at a low

price to go into a different market that didn't have... $3,000 would be amazing.

Yeah. I think we could have done it for five. But 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,

SIFIUS. 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 SIFIUS

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 and pay more. Dropped out when we were out of cash and then

bought the assets at one 30th of the price they had offered a week before. That was a tough week.

Do you, does it hurt to think about like an amazing company that didn't, you know,

like iRobot didn't find a way? 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 screen with a face. What, that's a revolutionary idea for

a robot manipulation, a robotic arm. How much opposition did you get? Well, first the screen

was also used during codeless programming where you taught by demonstration that 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 saying, well this one doesn't

look as human as the old one. We like the human looking. So there was a mixed bag.

But do you think that's, I don't know, I'm kind of disappointed whenever I talk to

roboticists, like the best robotics people in the world, they seem to not want to do the eyes type

of thing. They seem to see it as a machine as opposed to a machine that can also have a human

connection. I'm not sure what to do with that. It seems like a lost opportunity. I think the

trillion dollar company will have to do the human connection very well no matter what it does.

Yeah, I agree. Can I ask you a ridiculous question? Sure. Can I give a ridiculous answer?

Do you think, well maybe by way of asking the question, let me first mention that

you're kind of critical of the idea of the Turing test as a test of intelligence.

Let me first ask this question. Do you think we'll be able to build an AI system that humans

fall in love with and it falls in love with the human, like romantic love?

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

It's a different love, right? I think there's a lifelong partnership where you can communicate

and grow. I think we're a long way from that. I think Blade Runner had the time scale totally

wrong. 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. But 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

is today, and you change one thing. But that's the same with the self driving car. You change

one thing. Everything changes. Everything grows together. Surprisingly, I might be surprising

to you or might not, I think the best movie about this stuff was by Centennial Man.

And what was happening there? It was schmaltzy. But what was happening there?

As the robot was trying to become more human, the humans were adopting the technology of the robot

and changing their bodies. So there was a convergence happening in a sense. So we will not

be the same. We're already talking about genetically modifying our babies. 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 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 touring test side,

do you think, so forget about love for a second. Let's talk about just the

elect surprise. Actually, I was invited to be an interviewer for the elect surprise 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 for to check the weather or to check music, but just to talk as friends.

Yeah. Well, we saw Weisenbaum back in the 60s with his programmer, Liza,

being shocked at how much people would talk to Eliza. And I remember,

in the 70s, typing stuff to Eliza to see what it would come back with.

I think right now, and this is a thing that

Amazon's been trying to improve with elect. So there is no continuity of

topic. 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, right? Changes. We share moments

together and they last in our memory together. Yeah. There's none of that. And there's no

sort of intention of these systems that they have any goal in life, even if it's to be happy.

You know, they don't even have a semblance of that. Now, I'm not saying this can't be done.

I'm just saying, I think this is why we don't feel that way about them.

I'm sort of a minimal requirement. If you want the sort of interaction you're talking about,

it's a minimal requirement. Whether it's going to be sufficient, I don't know.

We haven't seen it yet. We don't know what it feels like.

I tend to think it's not as difficult as solving intelligence, for example,

and I think it's achievable in the near term. But on the Turing test,

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

Oh, because again, the Turing, if you read the paper, Turing wasn't saying this is

a good test. He was using this as a rhetorical device to argue that if you can't tell the

difference between a computer and a person, you must say that the computer's thinking because

you can't tell the difference when it's thinking. You can't say something different.

What it has become as this sort of weird game of fooling people. So

back at the AI lab in the late 80s, we had this thing that still goes on called the AI Olympics.

And one of the events we had one year was the original imitation game as Turing talked about

because he starts by saying, can you tell whether it's a man or a woman? So we did that at the lab.

We had, you know, you'd go and type and the thing would come back and you had to tell whether it

was a man or a woman. And 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 do 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. 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 it's sort of devolved into this.

Nevertheless, conversation not formulated as a test is a pretty,

it's a fascinatingly challenging dance. That's a really hard problem. To me, conversation when

non poses a test is a more intuitive illustration how far away we are from solving intelligence

than my computer vision. It's hard. Computer vision is harder for me to pull apart. But with

language, with conversation, you could see... Because language is so human. We don't...

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, AI lab for a long time.

You're, I don't know, to me, when I came to MIT, you're like one of the greats at MIT. So, what

was that time like? And plus, you were friends with, but you knew Minsky and all the folks there,

all the legendary AI people of which you were one. So, what was that time like? What are memories

that send 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 a faculty lunch. And I didn't ask them 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. And I didn't think to ask them any of these

questions, 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? I would ask him, you know, he had the vision for humans and computers

working together. And he really founded that at DARPA. And he gave the money to MIT, which

started Project MAC in 1963. And I would have talked to him about what the successes were,

what the failures were, what he saw as progress, etc. I would have asked him more questions about

that. Because now I could use it in my book. But I think it's lost. It's lost forever. A lot of

the motivations are lost. I should have asked Marvin why he and Seymour Papert came down so hard

on neural networks in 1968 in their book Perceptrons. Because Marvin's PhD thesis was on

neural networks. How do you make sense of that? That book destroyed the field.

Do you think he knew the effect that book would have?

All the theorems are negative theorems. That's the way of life. But still,

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

Yeah. Is there other memory standouts from the robotics and the AI work at MIT?

Yeah, but you'll 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

fabulous salaries to some of the highest producers. And then just you never hear from them again.

They're not allowed to give public talks. It's sort of locked away. And it's sort of like collecting

Hollywood stars or something. And they're not allowed to make movies anymore. I heard them.

Yeah. That's tragic. I mean, there's an openness to the university setting where you do research

to both in the space of ideas and space like publication, all those kinds of things.

Yeah. And there's the publication and all that and often, although these places,

say they publish this pressure. But I think, for instance, net net, I think

Google buying those eight or nine robotics company was bad for the field because it locked

those people away. They didn't have to make the company succeed anymore. Locked them away for years

and then sort of all fiddled away. Yeah. So 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 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,

there's so much progress is made. Yeah. So I'm not saying they're actually leading. I'm not

saying that the universities are leading. But I don't think those companies are leading in general

because they're, you know, we saw this incredible spike in attendees at NeurIPS. And as I said,

in my January 1st review this year for 2020, 2020 will not be remembered as a watershed year

for machine learning or AI. You know, there was nothing surprising happen. But 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. And incremental work. Yeah.

Is there a particular memories you have with Minsky or somebody else at MIT that stand out?

Funny stories. I mean, unfortunately, he's another one that's passed away.

You've known some of the biggest minds in AI. Yeah. And, you know, they did amazing things.

And sometimes they were grumpy. Well, he was, he was interesting because he was very grumpy.

But that, that was, I remember him saying in an interview that the key to success

or being, to keep being productive is to hate everything you've ever done in the past.

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

But he, meaning like, just like, I mean, maybe that's the way to not treat yourself too seriously,

just always be moving forward. That was his idea. I mean, that crinkiness, I mean,

that's the character. So let me, let me, let me tell you what really,

you know, the joy memories are about having access to technology before anyone else has seen it.

And so, so, you know, I got to Stanford in 1977 and we had, you know, we had terminals that could

show live video on them, digital, digital sound system. We had a Xerox graphics printer. We could

print, it wasn't, you know, it wasn't like a typewriter ball hitting characters. It could print

arbitrary things, only in, you know, one bit, you know, black or white, but you could arbitrary

pictures. This was science fiction sort of stuff. At MIT, the list machines, which, you know, they

were the first personal computers and, you know, they were 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, you know, I've had email

every day since 1977. And, you know, the host field was only eight bits, you know, that many places,

but I could send email to other people at a few places. So that was, that was pretty exciting

to be in that world so different from what the rest of the world knew. And

let me ask you, I probably edited 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 at such

a different world than yours? He's a very kind of theoretical computer science, the puzzle of

computer science and mathematics. And you're so much about the magic of robotics, like the

practice of it. Did you mention him earlier for like, not, you know, about computation? Did your

worlds cross? They did in a, you know, I know him now, we talked, you know. But let me tell you

my Donald Knuth story. So, you know, besides, you know, analysis of algorithms, he's well

known for writing tech, which is in latex, which is the academic publishing system.

So he did that at the AI lab. And he would do it, he would work overnight at the AI lab.

And one, one day, one night, the mainframe computer went down. And a guy named Robert

Paul was there. He led his PhD at the Media Lab at MIT. And he was, you know, engineer. And so

he and I, you know, tracked down what were the problem was. It was one of this big refrigerator

size or washing machine size disk drives had failed. And that's what brought the whole system

down. So we got panels pulled off. And we're pulling, you know, circuit cards out. And Donald

Knuth, who's a really tall guy, walks in and he's looking down and says, when will it be fixed?

Because he wanted to get back to writing his tech system. Well, Donald Knuth. And so we figured

out, you know, it was a particular chip, 7400 series chip, which was socketed, we popped it out,

we put a replacement in, put it back in, smoke comes out, 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, those, all those figures are just fascinating.

All the people who've passed through MIT is really fascinating. Is there a, let me ask you to put on

your big wise man hat. Is there advice that you can give to young people today, whether in high

school or college, who are thinking about their career, or thinking about life? How to live

a life they're proud of, a successful life? Yeah, so, so many people ask me for advice and have

asked for, and I give, 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 was, maybe I come from an age where I

would, I could be a rebel against that and still succeed. Maybe it's harder today.

But I think it's important not to get too caught up with what everyone else is doing.

And if you, 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 make, keep doing it for a long time. And then one of

them will be work out. And so that, that will make something successful. Or not. Or not.

Yeah. Or you may, or you just may, you know, end up, you know, not having a, you know,

having a lousy career. I mean, it's certainly possible. Taking the risk is the thing.

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

really contribute. Do you think about your death, about your mortality?

I got to say, when COVID hit, I did, because we did, you know, in the early days, we didn't

know how bad it was going to be. And I, that, that made me work on my book harder for a while.

But then I'd started this company and now I'm doing full time, more than full time at the

company. So the book's on hold. But I do want to finish this book. When you think about it,

are you afraid of it? I'm afraid of dribbling.

Yeah. I'm, 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 and knowing. It's such a, yeah. And it really sucks.

It feels, it feels a lot closer. So my, in, in my, my blog with my predictions, my sort of push

back against that was that I said, I'm going to review these every year for 32 years. That puts

me into my mid 90s. So, you know, it's my, every, every time you write the blog posts,

you're getting closer and closer to your own prediction of your, of your death.

Yeah. What do you hope your legacy is?

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

Um, what I hope is that I actually finish writing this book and that there's one person

who reads it and sees something about changing the way they're thinking. And that leads to

the next big. And then there'll be on a podcast 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, the, the, the, all the hurried things we do on this

planet. What do you think is the meaning of it all?

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

Life or finding meaning or both.

Yeah. We get caught up in, in the, it's easier to get, easier to do the stuff that's immediate

and not through the stuff. It's not immediate. So the big picture or bad. Yeah. Yeah.

Do you have a sense of what that big picture is? Like why ever look up to the stars and

ask why the hell are we here?

You know, my, my, my, my atheism tells me it's just random, but, you know, I want to understand

the, the way random in the, in the, that's what I talk about in this book, how order comes from

disorder. Yeah. Um, but it kind of sprung up like most of the whole thing is random, but this little

pocket of complexity they will call earth that like, why the hell does that happen?

And, and what we don't know is how common that those pockets of complexity are or how often,

um, because they may not last forever, which is, uh, more exciting slash sad to you if we're alone

or if there's infinite number of, oh, I think, I think it's impossible for me to believe that

we're alone. Um, that was just too horrible, too cruel. Could be like the sad thing. It could be

like a graveyard of intelligent civilizations. Oh, everywhere. Yeah. That might be the most

likely outcome. And for us too. Yeah, exactly. Yeah. 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. Yeah. Right. Yeah. Most people are not remembered

beyond the generational too. Um, I mean, yeah. Well, not just on average,

basically very close to a hundred percent of people who've ever lived are forgotten.

Yeah. I mean, no long arc of time. I don't know anyone alive who remembers my great grandparents

because we didn't meet them. So still this fun, this, uh, this, uh, life is pretty fun somehow.

Yeah. Even the immense absurdity and, uh, at times, meaninglessness of it all. It's pretty fun.

And one of the, for me, one of the most fun things is robots. And I've looked up to your work. I've

looked up to you for a long time. That's right. Rod, it's an honor that you would spend your

valuable time with me today talking. It was an amazing conversation. Thank you so much for being

here. Well, thanks for, thanks for talking with me. I 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 human being to come to harm.

Two, a robot must obey the orders given to it by human beings, except when such orders

would conflict with the first law. And three, a robot must protect its own existence as long

as such protection does not conflict with the first or the second laws. Thank you for listening.

I hope to see you next time.