The following is a conversation with Rodney Brooks, one of the greatest roboticists in history.
He led the computer science and artificial intelligence laboratory at MIT, then co-founded
iRobot, which is one of the most successful robotics companies ever. Then he co-founded
Rethink Robotics that created some amazing collaborative robots like Baxter and Sawyer.
Finally, he co-founded robust.ai, whose mission is to teach robots common sense,
which is a lot harder than it sounds. The 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' 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-fingered
hands, and face eyeballs. Not the eyeballs, but everything else, series elastic actuators.
You can interact with it, cable-driven. All the motors are inside, and it's just gorgeous.
The eyeballs are actuated, too, or no? Oh, yeah, the eyeballs are actuated with cameras.
And so it had a visual attention mechanism, looking when people came in and looking in their face
and talking with them. Why was it amazing? The beauty of it. You said what was the most beauty?
What is the most beautiful? It's just mechanically gorgeous. As everything Aaron builds,
there's 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, 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, because that's all it can do. It can look at you and pay attention.
So 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 as part of the communication. 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. On both Baxter and Sawyer at Rethink Robotics, they had a screen with graphic eyes,
so it wasn't actually where the cameras were pointing, but the eyes would look in the direction
it was about to move its arm. So people in the factory nearby were not surprised by its motions,
because it gave that intent away. Before we talk about Baxter, which I think is a beautiful robot,
let's go back to the beginning. When did you first fall in love with robotics? We're talking
about beauty and love to open the conversation. This is great. I was born in
the end of 1954, and I grew up in Adelaide, South Australia. I have these two books that
are dated 1961. I'm guessing my mother found them in a store in 62 or 63. How and why wonderbooks?
How and why wonderbook of electricity? How and why wonderbook of giant brains and robots?
I learned how to build circuits when I was eight or nine, simple circuits,
and I learned the binary system and saw all these drawings mostly of robots,
and then I tried to build them for the rest of my childhood.
Wait, 61 you said? This was when the two books, I've still got them at home.
What does the robot mean in that context? Some of the robots that they had were
arms, big arms to move nuclear material around, but they had pictures of welding robots that
look like humans under the sea welding stuff underwater. They weren't real robots,
but they were what people were thinking about for robots.
What were you thinking about? Were you thinking about humano? 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. That was
mostly things were driven by my budget, and nails as electrodes and an ice cube tray
was about my budget at that stage. Later, I managed to buy a transistors and then I could
build gates and flip flops and stuff. So one of your first robots was an ice cube tray?
Yeah, and it was very cerebral because it learned 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 stuff like us. The kind of computer that Alan Turing was thinking about, do you think there
is something fundamentally or significantly different between the computer, between our
ears, the biological computer that humans use, and the computer that he was thinking about
from a sort of high level philosophical. Yeah, I believe that it's very wrong. In fact,
I'm halfway through a, I think it'll be about a 480 page book titled, the working title is
not even wrong. And if I may, I'll tell you a bit about that book. So there's two, well,
three thrusts to it. One is the history of computation, what we call computation. Goes
all the way back to some manuscripts in Latin from 1614 and 1620 by Napier and Kepler through
Babbage and Lovelace. And then Turing's 1936 paper is, you know, what we think of as the
invention of modern computation. And that paper, by the way, did not set out to, you know, invent
computation. It set out to negatively answer one of Hilbert's three later set of problems.
He called it an effective way of 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 had 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 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 just proved 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? This stuff, like Turing says, that a person
could do each step without too much trouble. And so one of his examples of what would be too much
trouble was a step which required knowing whether Fermat's last theorem was true or not, because
it was not known at the time. And that's too much trouble for a person to do as a step.
And Hopcroft and Allman sort of said a similar thing later that year. And by 1975,
in the Aho Hopcroft and Allman book, they're saying, well, you know, we don't really know
what computation is, but intuition says this is sort of about right. And this is what it is.
That's computation. It 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. I certainly wouldn't argue with that. The version we have of computation, incredibly
powerful. Can we just take a pause? So what we're talking about is there's an infinite tape with
some simple rules of how to write on that tape. And that's what we're kind of thinking about.
This is computation. Yeah. And it's modeled after humans, how humans do stuff. And I think it's 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,
unlike mass or charge or, you know, it's not given by the universe. It was, this is what we're
going to call computation. And then it has this really, you know, it had this really good
implementation, which has completely changed our technological work. That's computation.
Second part of the book, or argument in the book, I have this two by two matrix with
science in the top row, engineering in the bottom row, left column is intelligence,
right column is life. So in the bottom row, the engineering, there's artificial intelligence
and there's artificial life. In the top row, there's neuroscience and abiogenesis. How does
living matter turn and how does nonliving matter become living matter? Four disciplines.
These four disciplines all came into the current form in the period 1945 to 1965.
That's interesting. There was neuroscience before, but it wasn't effective neuroscience. It was,
you know, there was ganglia and there'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, were at the
research lab for electronics at MIT, where Warren McCulloch held forth. And in fact, McCulloch,
Pitts, Letvin and Maturana wrote the first paper on functional neuroscience called What the Frog's
Eye Tells the Frog's Brain, where instead of it just being a bunch of nerves, they sort of showed
what different anatomical components were doing and telling other anatomical components
and, you know, generating behavior in the frog. Would you put them as basically the fathers or
the one of the early pioneers of what are now called artificial neural networks?
Yeah. I mean, McCulloch and Pitts, Pitts was much younger than him. In 1943, had written a paper
inspired by Bertrand Russell on a calculus for the ideas eminent in neural systems where they
had tried to, without any real proof, they had tried to give a formalism for neurons
basically in terms of logic and gates or gates and not gates with no real evidence that that was
what was going on, but they talked about it. And that was picked up by Minsky for his 1953
dissertation on, which was a neural network, we 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 think, references. He's only got three references
and one of them is the McCulloch-Petz paper. So all these people and then the AI people
and the artificial life people, which was John von Neumann originally, is like overlap.
They're all going around at the same time. And three of these four disciplines
turn to computation as their primary metaphor. So I've got a couple of chapters in the book. One
is titled, wait, computers are people because that's where our computers came from and 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 I have talked to people about maybe it's not computation that goes on
in the head. Of course it is. Yeah. Okay. Well,
when Elon Musk's rocket goes up, is it computing? Is that how it gets into orbit? By computing?
But we've got this idea. If you 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 the second and same. If you talk about the universe,
it's 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 squishy. Yeah. Squishy. Okay. 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 is a combination of our place cells in our
hippocampus and our cortex. But this is how we use metaphors for mostly to think about. And when
we get outside of our metaphor range, we have to invent tools, which we can switch on to use. So
calculus is an example of a tool. It can do stuff that our raw reasoning can't do, and we've got
conventions of when you can use it or not. But sometimes people try to, all the time,
we always try to get physical metaphors for things, which is why quantum mechanics has been
such a problem for 100 years, 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, I suspect that 100 years or 200 years from now,
neither quantum mechanics nor dark matter will be talked about in the same terms,
in the same way that Flodgerton's theory eventually went away, because it just wasn't an
adequate explanatory metaphor. That metaphor was the stuff. There is stuff in the burning.
The burning is in the matter. It turns out the burning was outside the matter. It was the oxygen.
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 for a book about that, but I give some ideas. So there's three things.
Computation is sort of a particular thing we use. Can I tell you one beautiful thing?
I used an example of a thing that's different from computation. You hit a drum and it vibrates,
and there are some stationary points on the drum surface, 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 Ada Lovelace to compute Bernoulli numbers? And Bernoulli numbers are
exactly what you need to find those stable points in the drum surface. And there was a bug in the
program. The arguments to divide were reversed in one place. And it still worked. She never got
to run it. They never built the analytical engine. She wrote the program without it.
So computation is sort of a thing that's become dominant as a metaphor, but is it the right
metaphor? All three of these four fields adopted computation, and a lot of it swirls around Warren
McCulloch and all his students, and he funded a lot of people. And our human metaphors,
our limitations to human thinking will play into this. There are three themes in the book.
So I have a little to say about computation. So you're saying that there is a gap between the
computer or the machine that performs computation and this machine that appears to have consciousness
and intelligence. Yeah. That piece of meat in your head. 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 be very likely,
again, we, you know, this speculation, but we, our species, and probably, probably Neanderthals
to some extent, because you can find old bones where they seem to be counting on them by putting
notches that when Neanderthals 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 Lettuce or something. Yeah. The, the, the neural network
can't step outside of itself. But is there, is there some, can we explore this dark room
a little bit and try to get at something? What is the magic? Where does the magic come from
in the human brain that creates the mind? What's your sense as scientists that try to understand
it and try to build it? What are the directions it followed might be productive? Is it creative,
interactive robots? Is it creating large deep neural networks that do like self-supervised
learning? And just like, we'll, we'll, we'll discover that when you make something large enough,
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, and 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,
phenomenal, and surprising. You know, we tend to think, you know, that, that, that the box over
here between us, which is a sound box, I think, is a blue box. But a blueness is something that
we construct with, 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, you maybe seen the examples where someone turns a stop sign into a
some other sort of sign by just putting a couple of marks on them and the deep learning system
gets it wrong. And everyone says, but the stop sign's red. You know, why is it, why is it
thing it's the other sort of sign? Because redness is not intrinsic in just the photons. It's actually
a construction of an understanding of the whole world and the relationship between objects,
the color constancy. But our tendency in order that we get an archive paper really quickly
is 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 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 Cantwell Smith
talks about a lot, very difficult, squirmy thing to understand. But I think none of our systems
do that. We've always talked in AI about the symbol grounding problem, how our symbols that we
talk about are grounded in the world. And when deep learning came along and started labeling
images, people said, ah, the grounding problem has been solved. No, the labeling problem was solved
with some percentage accuracy, which is different from the grounding problem. So you agree with Hans
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. Anyone could do that. That's not intelligence.
And so it, you know, this, there was this intuition that the hard stuff is the things they were good
at. And the easy stuff was the stuff that everyone could do. Yeah. And maybe I'm overplaying it a
little bit. And I think there's an element of that. Yeah. I mean, I don't know how much truth
there is to like chess, for example, 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 Kasparov was beaten by Deep Blue. Oh, this is the end of all sorts of things.
Computers are going to be able to do anything from now on. And we saw exactly the same stories
with AlphaZero, the go-playing program. Yeah. But still, to me, reasoning is a special thing.
And perhaps... No, actually, we're really bad at reasoning. We just use these analogies based on
our hunter-gatherer intuitions. But why is that not, don't you think the ability to construct
metaphor is a really powerful thing? Oh, yeah, it is. It is. It 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 vision, too? And we're telling our stories. We're constructing good models of the world.
Yeah, yeah. But I think we jumped between what we're capable of and how we're doing it,
right? There was a little confusion that went on 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 Maravax 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 were able to hunt or gather or, you know, even animals hunting.
That's much more recent. And then anything that we, you know, speech, language, those things are,
you know, just 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 of the decades that you worked
on robots, in the context of what we're talking about, vision, perception, the actual sort of the
biomechanics of movement. I'm kind of drawing parallels here between humans and machines always.
Like, what do you think is the hardest part of robotics?
I sort of think all of them. There are no easy parts to do well. We sort of go reductionist and
we reduce it. If only we had all the location of all the points in 3D, things would be great.
You know, if only we had labels on the images, you know, things would be great. But, you know,
as we see, that's not good enough. Some deeper understanding. But if I came to you
and I could solve one category of problems in robotics instantly, what would give you the
greatest pleasure? I mean, is it, you know, you look at robots that manipulate objects,
what's hard about that? You know, is it the perception? Is it the reasoning about the world,
the 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 in 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. Yes. Just three or four miles from here last year, my 16-month-old grandson
was in his new house, first time, right? First time in this house. And he'd never been able to get
to a window before, but this had some low windows. And he goes up to this window with a handle on it
that he's never seen before. And he's got one hand pushing the window, and the other hand
turning the handle to open the window. He knew two different hands, two different things he knew
how to put together. Yeah. And he's 16 months old. And there you are watching an awe.
In an environment he'd never seen before, a mechanism. How did he do that?
Yes, that's a good question. How did he do that? That's why...
It's like, okay, you could see the leap of genius from using one hand to perform a task,
to combining, to doing... I mean, first of all, in manipulation, that's really difficult. There'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.
Yeah. There may have been a lot of slightly different failure cases that you didn't see.
And not with a window, but with other objects of turning and twisting and handles.
There's a great counter to reinforcement learning. We'll just give the robot plenty
of time to try everything. Can I tell a little side story here?
Yeah. So I'm in DeepMind in London. This is three, four years ago, where there's a big Google
building. And then you go inside and you go through 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 all this stuff. And so you just let the robot do stuff and eventually it figures
stuff out. By the way, Soyuz, we're talking about robot manipulation. So robot arms and so on.
Yeah. Soyuz is a robot. Yeah. 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's a secret. That's hilarious. Okay. Anyway, the point is, you know, this idea that you just
let reinforcement learning figure everything out is so counter to how a kid does stuff. So
again, story about my grandson, I gave him this box that had lots of different lock mechanisms.
He didn't randomly, you know, and he was 18 months old, he didn't randomly try to touch every
surface or push everything. He found, he could see where the mechanism was, and he started
exploring the mechanism for each of these different lock mechanisms. And there was reinforcement,
no doubt, of some sort going on there. But he applied a pre-filter which cut down the search
space dramatically. I wonder to what level we're able to introspect what's going on.
Because what's also possible is you have something like reinforcement learning
going on in the mind in the space of imagination. So like you have a good model of the world you're
predicting, and you may be running those tens of thousands of like loops, but you're like, as a human,
you're just looking at yourself trying to tell a story of what happened. And it might seem simple,
but maybe there's a lot of computation going on. Whatever it is, but there's also a mechanism that's
being built up. It's not just random search. That mechanism prunes it dramatically.
Yeah, that pruning step, but it doesn't, it's possible that that's, so you don't think that's
akin to a neural network inside a reinforcement learning algorithm. Is it possible?
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 let me push back. I'm generally sort of optimistic and try to find the beauty in
things. I think there's a lot of surprising and beautiful things that neural networks,
this new generation of deep learning revolution has revealed to me is 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 were actually you said back and said, I did not expect this?
Oh, I think, I think their performance, their performance on ImageNet was shocking.
So computer vision those early days was just very like, wow, okay.
That doesn't mean that they're solving everything in computer vision.
We need to solve or in vision for robots.
What about Alpha zero and self play mechanisms and reinforcement learning?
Isn't that? Yeah, that was all in Donald Mickey's 1961 paper.
Everything that was there, which introduced reinforcement learning.
No, but come on. So no, you're talking about the actual techniques, but isn't this surprising to
you the level it's able to achieve with no human supervision of chess play? To me, there's a big,
big difference. Maybe maybe blue and maybe what that's saying is how overblown our view of ourselves
is, you know, we the chess is easy. Yeah, I mean, I came across this 1946 report that,
and I'd seen this as a kid in one of those books that my mother had given me actually.
1946 report which pitted someone with an abacus against an electronic calculator
and he beat the electronic calculator. So there at that point was, well, humans are still better
than machines 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, you know, I am, I mean, 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, you know, are very different
capabilities. Yeah. And if someone, you know, has a, you know, in the old days, people would write
a paper about learning something. Now the corporate press office puts out a press release about how
company X has, has leading the world because they have a system that can. Yeah. But here's the thing.
Okay. So what is learning? When I refer to learning 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 loaded word. Yes. Less, less dumb at the
thing. It gets better performance under some measure. Yeah. Under some set of conditions
at that thing. And most of these learning algorithms, learning systems, fail when you
change the conditions just a little bit in a way that humans don't. So I was at DeepMind.
The AlphaGo had just come out. And I said, what would have happened if you'd given it a 21 by
21 board instead of a 19 by 19 board? They said fail totally. But a human player would actually,
you know, well, would actually be able to play. 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.
I think 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 superhuman level
performance through learning alone. So like, okay, so, so, you know, it's still fundamentally
doing 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, that's not us. 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,
we had Rockets for a long time, but doing reusable, cheap Rockets, it's very impressive.
In the same way, I, okay, yeah, I would have not predicted, first of all, when I was,
uh, started in fellow level 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 that would be able to solve it, but I would never have
guessed that you could learn your way. However, I mean, in the narrow sense of learning, learn your
way to, 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.
Yes. Arthur Samuel in the early sixties, and he was the first person to use machine learning,
got, had a program that could beat the world champion at checkers now. Yes. 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 had 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 he had this whole farm of computers running random code
for hours and hours a week for each computer. He had a whole bunch of them. So he ran his
chess learning program with self-play on IBM's production line. He had more computation available
to him than anyone else in the world. And then he was able to produce a chess playing program,
I mean, a checkers playing program that could beat the world champion.
So that's amazing. The question is, I mean, surprise, I don't just mean it's nice to have
that accomplishment. Is there is a stepping towards something that feels more intelligent
than before? Yeah, but that's in your view of the world. Okay, well, let me then,
doesn't mean I'm wrong. 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 even any kind of robotics in the
physical world, I definitely think is way harder than the game of chess. So I was always much more
impressed by the workings of the human mind that is incredible. The human mind is incredible. I
believe that from the very beginning. I want 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. 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 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? Like, 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 and 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 position switches, empty, X, zero,
and nine of them and a light bulb on which move it wanted next. And then the human would go and
move that. See, there's magic in that creation. I tend to see magic in robots that,
I also think that intelligence is a little bit overrated. I think we can have deep connections
with robots very soon. And we'll come back to connections with robots. Sure. But I do want to
say, I think too many people make the mistake of seeing that magic and thinking, well, we'll just
continue. But each one of those is a hard fought battle for the next step, the next step.
Yes. The open question here is, and this is why I'm playing devil's advocate, but I often do
when I read your blog post in my mind, because I have 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 your deep grounding of this is, is there has been an optimism for decades.
And that optimism, just like reading old optimism is absurd, because people were like,
this is, they were saying things are trivial for decades, since the 60s, they'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 DeepMind has done. I don't think
they're yet close to solving intelligence, but I'm not sure it's not 10 years away.
What I'm referring to is interesting to see when the engineering,
it takes that idea to scale and the idea works.
And no, it fools people.
Okay, honestly, 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 don't, that wasn't surprising to
you that at scale. So we're talking about a very large number. Okay, let's pick one that's the most
surprising to you. Okay, please don't yell at me. GPT three. Okay, hold on. I was gonna bring that out.
Okay, alpha zero, alpha go, alpha go zero, alpha zero, and then alpha fold one and two.
So do any of these kind of have this core of forget usefulness or application and so on,
which you could argue for alpha fold? As a scientist, was those 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, 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 potentially revolutionizing
competition biology, like they will actually help a lot of people.
But you would say potentially revolutionizing. We don't know yet.
That's true. Yeah. But I got you. 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 have built robotics companies.
You're one of the greatest roboticists in history, and that's not just in the space of ideas.
We'll also probably talk about that. But in the actual building and execution of businesses
that make robots that are useful for people and that actually work in the real world and make money.
You also sometimes are critical of Mr. Elon Musk, or let's more specifically focus on this
particular technology, which is autopilot inside 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 let's say they're on
par safety-wise as humans currently, meaning human alone versus human plus robot. Okay. So first,
let me say I really like the car I came in here today, which is a 2021 model Mercedes E450.
I am impressed by the machine vision, so are other things. I'm impressed by what it can do.
I'm really impressed with many aspects of it. It's able to stay in lane, is it?
Oh yeah, it does the lane stuff. It's looking on either side of me. It's telling me about
nearby cars. From blind spots and so on. Yeah. When I'm going in close to something in the park,
I get this beautiful, gorgeous, top-down view of the world. I am impressed
up the wazoo of how registered and metrical that is. Oh, so it's like multiple cameras and
it's all ready to go to produce the 360 view kind of thing? 360 view. It's 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 are better.
So I'm not saying technology is 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 Carnegie
now. Oh, that was the code. Did they claim 100%? Not 100%, not 100%.
And then there's a few marketing people who have claimed 100% since then.
But my point is that 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.
Okay. I'll just say what I am because I thought, okay, when I first started interacting 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 used Mobileye, I thought just knowing computer vision,
I thought there's no way it could work as well as 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 started by Amnon Schescher 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, though, was involved. That was impressive. And the recent progress, especially
under, well, with the involvement under Kapathi, what they were, what they're doing with the data
engine, which is converting into the driving task into these multiple tasks, and then doing this
edge case discovery when they're pulling back, like the level of engineering made me rethink
what's possible. I don't, I still, you know, I don't know to that intensity, but I always thought
it was very difficult to solve 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 factor psychology problem is essential. It's always going to be there.
It's always going to be there even with fully autonomous driving. But I've been surprised
what is the limit, especially 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 it. 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, in it's, in leaven
orders of magnitude or some crazy number like that. The cameras don't have that, which is why you see
the, the, the bad cases where the sun on a white thing and it blinds it in a way it wouldn't blind
a person. 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, 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, you know, 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.
Yeah. 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 over-promising by calling their system fully self-driving.
I think they may have gotten there quicker by collaborating to change the infrastructure.
This is one of the problems with long-haul trucking being autonomous. I think it makes
sense on freeways at night for the trucks to go autonomously. But then there's that,
how do you get onto and off of the freeway? What sort of infrastructure do you need for that?
Do you need to have the human in there to do that? Or can you get rid of the human?
So I think there's ways to get there, but it's an infrastructure argument because
the long tail of cases is very long and the acceptance of it will not be at the same level
as human drivers. So I'm with you still and I was with you for a long time, but I am surprised
how well, how many edge cases of machine learning and vision-based methods can cover.
This is what I'm trying to get at is,
I think there's something fundamentally different with vision-based methods
and Tesla autopilot and any company that's trying to do the same.
Okay, well, I'm not 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'm a bit, I don't know about you, but I'm a bit cynical that
infrastructure, which relies on government to help out in these cases.
If you just look at infrastructure in all domains, it's just government always drags
behind on infrastructure. There's like, 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 than the some of them,
like high-speed rail, the other countries are done much better.
I guess my question is like, which is at the core of what I was trying to think through here
and ask is like, how hard is the driving problem as it currently stands?
So you mentioned like, we don't want to just take the human out and duplicate
whatever the human was doing, but if we were to try to do that,
what, 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 black
top 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. Yeah, although 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 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. So 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 wasn't 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 it's here, it's done.
It's just... We've solved it. No, we haven't yet. And that's my argument.
Okay. So I'd like to go to... You keep a list of predictions on your amazing blog post. It'd be
fun to go through them. But before that, let me ask you about this. You have 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 kind of start to think that the technology is way better than it is.
But let me ask you maybe a difficult question. Sure. Do you think if you look at history of progress,
don't you think to achieve the quote impossible, you have to believe that it's possible?
Oh, absolutely. Yeah. Look, his two great runs. Great. Unbelievable. 1903,
the first human power, human heavier than air flight. 1969, we land on the moon. That's 60,
60 years. I'm 60, 60 years old. In my lifetime, that span of my lifetime, barely 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
not just possible soon, but like ever. Yeah. So how important is it to believe and be optimistic
is what I guess... Oh, yeah, it is important. It's when it goes crazy. You said it. What was
the word you used for my bad harshness? Harshness. Yes. I just get so frustrated when people make
these leaps and tell me that I don't understand. Right. Yeah. Just from iRobot, which I was co-founder
of, 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. Yes. Was that a crazy idea that we had to believe
in 2002 when we released it? Yeah. We had to believe that it could be done.
Let me ask you about this. So iRobot, one of the greatest robotics companies ever,
in terms of creating a robot that actually works in the real world is probably the greatest
robotics company ever. You were the co-founder of it. If the Rodney Brooks of today talked to
the Rodney of back then, what would you tell him? Because I have a sense that would you pet him on
the back and say, well, your doing is going to fail, but go at it anyway. That's what I'm
referring to with the harshness. You've accomplished an incredible thing there. One of
several things we'll talk about. Well, like that's what I'm trying to get at that line.
No, it's when my harshness is reserved for people who are not doing it, who claim it's just, well,
this shows that it's just going to happen. But here's the thing. This shows. But you have that
harshness for Elon too. No, it's a different harshness. No, it's a different argument with
Elon. I think SpaceX is an amazing company. On the other hand, in one of my blog posts,
I said, what's easy and what's hard? I said, SpaceX, Vertical Landing Rockets, it had been
done before. Grid fins had been done since the 60s. Every Soyuz has them. Reusable space,
DCX, reused those rockets that landed vertically. There's a whole insurance industry
in place for rocket launches. There are all sorts of infrastructure. That was doable. It took a
great entrepreneur, a great personal expense. He almost drove himself bankrupt doing it.
A great belief to do it. Whereas Hyperloop, there's a whole bunch more stuff that's never
been thought about and never been demonstrated. So my estimation is Hyperloop is a long,
long, long further off. If I've got a criticism of Elon, it's that he doesn't make distinctions between
when the technology's coming along and ready. Then he'll go off and mouth off about other
things, which then people go and compete about and try and do. I understand what you're saying.
I tend to draw a different distinction. I have a similar kind of harshness towards people who
are not telling the truth, who are basically fabricating stuff to make money. He believes
what he says. To me, that's a very important difference. 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. That's the same thing I have with Tesla
autopilot. I think that's an interesting one. Especially when I was at MIT and just the entire
human factors in the robotics community were very negative towards Elon. It was very interesting
for me to observe colleagues at MIT. I wasn't sure what to make of that. That was very upsetting to
me because I understood where that's coming from. I agreed with them and I almost felt the same
thing in the beginning until I opened my eyes and realized there's a lot of interesting ideas here.
There might be overhype. If you focus yourself on the idea that you shouldn't call a system full
self-driving when it's obviously not fully autonomous, you're going to miss the magic
but at the same time, there are people who buy it, literally pay money for it and take those
words as given. Take words as given is one thing. I haven't actually seen people that use autopilot
that believe that. The behavior is really important. The actual action. 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
opened my eyes, actually. The way people use a product is very different than the way they talk
about it. This is true with robotics, with everything. Everybody has dreams of how a
particular product might be used or so on. When it meets reality, there's a lot of
fear of robotics, for example, that robots are somehow dangerous and all those kinds of things.
But when you actually have robots in your life, whether it's in the factory or in the home,
making your life better, that's going to be... That's way different. Your perceptions of it
are going to be way different. My tension was, here's an innovator.
What is it? Sorry, Super Cruise from Cadillac was super interesting, too. That's a really
interesting system. We should be excited by those innovations.
Okay. 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. Why are they doing that? That really pisses me off. I applaud that.
It's the taking and not necessarily the people who are doing the things, but that I keep having
to push back against unrealistic expectations when these things can become real.
This was interesting because there's been a particular focus for me is autonomous driving.
Elon's prediction of when certain milestones will be hit.
There's several things to be said there that I always thought about because whenever you said
them, it was obvious that's not going to me as a person that's not inside the system. It was
obvious it's unlikely to hit those. There's two comments I want to make. One, he legitimately
believes it. Two, much more importantly, I think that having ambitious deadlines drives people to
do the best work of their life, even when the odds of those deadlines are very low.
To a point, and I'm not talking about 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.
Yeah. But I will say that there's an additional thing here that those words also drive the
stock market. And we have, because of the way that rich people in the past have manipulated
the rubes through investment, we have developed laws about what you're allowed to say,
and 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 don't think like that, manipulating the stock price, but it's possible
that I'm wrong. It's a very cynical view of the world because I
think most people that run companies, especially original founders, they...
Yeah. I'm not saying that's the intent. I'm saying it's a...
Eventually, you fall into that kind of behavior pattern. I don't know. I tend to...
I wasn't saying it's falling into that intent. It's just you also have to protect investors
in this market. Yeah. Okay. So you have... First of all, you have an amazing blog that people
should check out, but you also have this in that blog, a set of predictions. It's such a cool idea.
I don't know how long ago you started, like three, four years ago. It was January 1st, 2018.
And I made these predictions and I said that every January 1st, I was going to check back
on how my predictions are... 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.
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 said, I should hold 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,
like some predictions that you're particularly proud of or are particularly interesting,
from flying cars to the other element here is like how widespread the location,
where the deployment of the autonomous vehicles is. And there's also just a few fun ones. Is
there something that jumps to mind that you remember from the predictions?
Well, I think I did put in there that there would be a dedicated self-driving
lane on 101 by some year. And I think I was over optimistic on that one.
Yeah. Actually, yeah. I actually do remember that. But I think you were mentioning
like difficulties at different cities. Yeah. 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? I've gotten a little more pessimistic since I made these internally
on some of these things. So what... Can you put a year to a major milestone of deployment of a
taxi service in a few major cities? Like something where you feel like autonomous vehicles are here.
So let's take the grid streets of San Francisco north of market. Okay. Okay.
Relatively benign environment. The streets are wide. The major problem is
delivery trucks stopping everywhere, which has made things more complicated.
And a taxi system there with somewhat designated pickup and drop offs,
unlike with Uber and Lyft, where you can get to any place and the drivers will figure out how to
get in there. We're still a few years away. I live in that area. So I see the self-driving
car companies, cars, multiple, multiple ones every day out there by the crews.
Zooks less often, Waymo all the time, different and different ones come and go.
And there's always a driver. There's always a driver at the moment, although I have noticed
that sometimes the driver does not have the authority to take over without talking to the
home office because they will sit there waiting for a long time. And clearly something's going on
where the home office is making a decision. That's fascinating. So you can see whether they've
got their hands on the wheel or not. And it's the incident resolution time that gives you some clues.
So what year do you think? What's your intuition? What date range are you currently thinking
that San Francisco would be a 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
market way longer, unless there's re-engineering of roads. By the way, what's the biggest challenge?
You mentioned a few. Is it the delivery trucks? Is it the edge cases, the computer perception?
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. It was before the solstice. It was a cruise vehicle come down the hill,
turned right and stopped dead, covering the crosswalk. Why did it stop dead? Because there was a
human just two feet from it. Now, I just glanced. I knew what was happening. The human was a woman,
was at the door of her car, trying to unlock it with one of those things that you don't have a
key. 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 it. She's lost her keys. She's trying
to get in the car. It stayed there until I got bored. The human driver in there did not take
over, but here's the kicker to me. A guy comes down the hill with a stroller. I assume there's
a baby in there. Now, the crosswalk 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. He had to go behind it. He had to
get off the crosswalk out into the intersection to push his baby around this car, which was stopped
there and no human driver would have stopped there for that length of time. They would have got out
of the way. 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. You can say that's an edge case, but
yeah, well, I'm in general not a fan of anecdotal evidence for stuff. This is one of my biggest
problems with the discussion of autonomous vehicles in general, people that criticize them
or support them are using anecdotal evidence. Your question is when is it going to happen
in San Francisco? I say not soon, but it's going to be one of them. 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 cannot do that on the freeway.
That, I think, we're going to start to see. 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 it makes this fun to ride.
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.
Think about not you as the customer, but someone who's in their 80s in a retirement village
whose kids have said, you're not driving anymore, and this gives you the freedom to go to the market.
That's a hugely beneficial thing, but it's a very few orders of magnitude less impact on the world.
It's just a few people in a small community using cars as opposed to the entirety of the world.
I like that the first time that a car equipped with some version of a solution to the trolley
problem is, what's NIML stand for? Not in my 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 your brake is out as you can, 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. I talk about it in one of the articles or
blog posts that I wrote. People have told me, one of my co-workers has told me he does this.
He tortures autonomously driven vehicles and pedestrians will torture them. Once they realize
that putting one foot off the curb makes the car think that they might walk into the road,
kids, teenagers will be doing that over time. By the way, this is a whole other discussion
because my main issue with robotics is HRI, human-robot interaction. I believe that robots that
interact with humans will have to push back. They can't just be bullied because that creates a
very uncompelling experience for the humans. Waymo, before it was called Waymo, discovered that.
They had to do that at four-way intersections. They had to nudge forward to give the cue that
they were going to go because otherwise the other drivers would just beat them all the time.
You co-founded iRobot, as we mentioned, one of the most successful robotics companies ever.
What are you most proud of with that company and the approach you took to robotics?
Well, there's something I'm quite proud of there, which may be a surprise,
but I was still on the board when this happened. It was March 2011 and we sent robots to Japan
and they were used to help shut down the Fukushima Daiichi nuclear power plant,
which was everything. I've been there since I was there in 2014 and some of the robots were still
there. I was proud that we were able to do that. Why were we able to do that? People said Japan
is so good at robotics. It was because we had had about 6,500 robots deployed in Iraq and Afghanistan
and 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 deposed 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,
so 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, you know, 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 strip down
6802s, 6802 was what was in an Apple One, get rid of half the silicon, still have it be viable,
and I'd previously got some of those for some earlier failed products of iRobot, and that was
in Hong Kong, going to all these companies that built, you know, they weren't gaming in
the current sense, there were these handheld games that you would play, or birthday cards,
because we had about a 50 cent budget for computation, so I'm tracking from place to place,
looking at their chips, looking at what they'd removed, ah, the interrupt handling is too weak
for a general purpose, so I was going deep technical detail, and then I found this one from
a company called Winbond, which had, and I'd forgotten, it had this much RAM, it had 512
bytes of RAM, and it was in our budget, and it had all the capabilities we needed, so.
And you were excited?
Yeah, and I was reading all these emails, Colin, I found this, well, so.
Did you think, did you ever think that you guys could be so successful?
Like, eventually this company would be so successful, could you possibly have imagined?
And, no, we never did think that, we'd 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 18th, and they were all
sold by Christmas, so it was, so we were gutsy.
But yeah, you didn't think this would take over the world?
Well, this is, so a lot of amazing robotics companies have gone under over the past few
decades. Why do you think it's so damn hard to run a successful robotics company?
There's a few things. One is expectations of capabilities by the founders that are off base.
The founders, not the consumer, the founders.
Yeah, expectations of what can be delivered, sure.
Mispricing, and what a customer thinks is a valid price, is not rational necessarily,
and expectations of customers, and just the sheer hardness of getting people to adopt a new
technology. And I've suffered from all three of these. I've had more failures 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, because I'm still starting robot companies. I'm not making any such predictions
in my own mind. I'm not thinking about a trillion dollar company. And by the way,
I don't think in the 90s anyone was thinking that Apple would ever be a trillion dollar company.
So these are very hard to predict. 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. Who's going to do all the stuff for the older people?
I'm leading here. It's going to be too many of us. But we don't have capable enough robots to
make that economic argument at this point. Do I expect that that will happen? Yes,
I expect it will happen. But I got to tell you, we introduced the Roomba in 2002, and I stayed
another nine years. We were always trying to find what the next home robot would be. And
still today, the primary product of 20 years, almost 20 years later, 19 years later,
the primary product is still the Roomba. So iRobot hasn't found the next one.
Do you think it's possible for one person in the garage to build it versus
this Google launching, Google self-driving car that turns into Waymo? Do you think it's
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?
Yes. So it's very hard to get there without a lot of capital. And we're starting to see
fair chunks of capital for some robotics companies, Series Bs. I just saw one yesterday for $80
million, I think it was, for covariant. But it can take real money to get into these things,
and you may fail along the way. I certainly failed to 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. Robotics arms for me. They're able to pick up stuff and interact with
humans. Yeah. And that humans could retask them without writing code. And now that's sort of
become an expectation for a lot of other little companies and big companies are advertising
they're doing. That's both an interface problem and also a safety problem. Yeah. 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 another $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
in our lifetime. It's going to be so cheap that it's OPEX, not CAPEX. And so we had a prototype
that worked reasonably well. But the control engineers were complaining about these plastic
gearboxes with a beautiful little planetary gearbox. But we could use something called
serious elastic actuators. We embedded them in there. We could measure forces. We knew when we
hit something, etc. The control engineers were saying, yeah, but 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 in factories. But they were used to 0.1 millimeter reproducibility
of motion and certain velocities. And I kept thinking, but that's not what we're giving you.
You don't need position repeatability. Use force control like a human does. No,
no, but we want that repeatability. We want that repeatability. As 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? Well, you can move it around. But suppose you, can you
see that? Yes. Suppose you want to... Yes. Suppose this thing is a precise thing that's got to fit
here in this right angle. Under position control, you have fixtured 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. Couldn't convince our customers who were 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? Certain sort of gearbox made by a company whose name I
can't remember right now, but it's the name of the gearbox. But it's got torque ripple in it.
So now there was an extra two years of solving the problem of doing the force with the torque
ripple. So we had to do the thing we had avoided. And for the plastic gearboxes, we ended up having
to do the robot was now overpriced. And that was your intuition from the very beginning,
kind of that this is not... You're opening a door to solve a lot of problems there. 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.
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. It was a tough week.
Do you, does it hurt to think about like an amazing company that didn't, you know,
like iRobot didn't find a way? 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? Well, I think there's a lifelong partnership where you
can communicate and grow. I think we're a long way from that. I think we're a long way. I think
Blade Runner was at the timescale totally wrong.
Yeah, but to me, honestly, the most difficult part is the thing that you said with the Marbex
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.
No, everything changes. Everything grows together. So, 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 program, Eliza, 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? It 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. 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. It's a sort of a minimal requirement. If you want the sort of
interaction you're talking about, it's a minimal requirement. Whether it's going to be sufficient,
I don't know. We haven't seen it yet. We don't know what it feels like.
I tend to think it's not as difficult as solving intelligence, for example,
and I think it's achievable in the near term.
But on the Turing test, why don't you think the Turing test is a good test of intelligence?
Oh, because 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 is 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. Back at the AI lab in the late 80s, we had this
thing that still goes on called the AI Olympics. One of the events we had one year was the original
imitation game as Turing talked about because he starts by saying, can you tell whether it's a man
or a woman? So we did that at the lab. You'd go and type and the thing would come back and you
had to tell whether it was a man or a woman. 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 the green plastic toy soldiers as a kid? Yeah. What do you do with
them? 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 that's what the Turing test with computers has become. What's the trick question?
That's why I say it's sort of devolved into this weirdness. 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. Computer vision is harder
for me to pull apart. But with language, with conversation, you could see... Because language
is so human. We can so clearly see it. Shit, you mentioned something I was going to go off on.
I mean, I have to ask you, because you were the head of CSAIL, ALF for a long time.
To me, when I came to MIT, you're one of the greats at MIT. So what was that time like?
I don't know, friends with, but you knew Minsky and all the folks there, all the legendary AI
people of which you're one. So what was that time like? What are memories that
stand out to you from that time? From your time at MIT, from the AI lab, from the dreams
that the AI lab represented to the actual revolutionary work? Let me tell you first,
a disappointment in myself. As I've been researching this book and so many of the players
were active in the 50s and 60s, I knew many of them when they were older. 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
on our 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.
Right. Even as I saw them and said hello to them on a daily basis. So maybe also another question
I want to ask, if you could talk to them today, what question would you ask? What questions would
you ask? Well, Rick Leiter, I would ask him, 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 forever. A lot of the
motivations are lost. I should have asked Marvin why he and Seymour Pappett came down so hard on
neural networks in 1968 in their book Perceptrons. Because Marvin's PhD thesis was on neural networks.
Yeah. How do you make sense of that? That book destroyed the field.
Do you think he knew the effect that book would have?
All the theorems are negative theorems. Yeah. So, yeah, that's the way of life.
Yeah. But still, it's kind of tragic that he was both the proponent and the destroyer of neural
networks. 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, there's pressure. But I think, for instance, net net, I think
Google buying those eight or nine robotics company was bad for the field because it locked
those people away. They didn't have to make the company succeed anymore. Locked them away for years
and then sort of all 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 happened
anyway, unlike when deep learning hit ImageNet. That was a shake.
And there's a lot more people writing papers, but the papers are fundamentally boring.
Yeah.
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 interesting because he was very grumpy. But that was, I remember him saying
in an interview that the key to success or to keep being productive is to hate everything
you've ever done in the past.
Maybe that explains the Perceptron book.
And there it was. He told you exactly what. 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, there's a, 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. 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
the, 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, couldn't stand up and 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, probably edit this out, but just in case you have a story. I'm
hanging out with Don Knuth for a while tomorrow. Did you ever get a chance 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.
You mentioned him earlier for like, not, you know, about computation. Did your worlds cross?
They did in a, you know, I know him now, we talk, 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 an engineer. And so he and I, you know,
tracked down what 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. 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?
That, 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, who are 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 when 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 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 to 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 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. That's, that's true.
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. Uh, what do you think is the meaning of life?
This whole thing, the existence, the, the, the, all the hurried things we do on this planet.
What do you think is the meaning of it all?
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. Um, so the big picture or bad. Yeah. Yeah.
Do you have a sense of what that big picture is? Like why?
You ever look up to the stars and ask why the hell are we here?
You know, my, my, my, my atheism tells me it's just random, but you know, I want to understand 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 may 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 generation or two.
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. 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, uh, 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.