My name's Rodney Brooks.
I'm director of the Computer Science
and Artificial Intelligence Lab at MIT, and it's 2006.
So I'll start off with the start-up question.
What got you into the field of AI?
What got you interested?
When I was about eight years old in Australia,
I got this How and Why Wonder book
on giant electronic brains, and I
read that, it introduced the binary system
and had pictures of consoles with flashing lights,
and I knew I wanted to build them.
And then 2001, Space Odyssey came out,
and Marvin Minsky had worked on that
and was mentioned right at the start of the book,
has been inventor of AI.
And that made me want to come to MIT.
And so I spent my childhood in a shed in the back garden
building stuff, building computers,
and trying to build robots, and this disturbed my mother a lot.
She would come down and say, why can't you be like a normal kid
and come and watch TV?
Wow.
So childhood interest.
You came to MIT and--
I proudly have never been admitted to MIT.
I applied to grad school at MIT, and I did not get admitted.
I got to Stanford and CMU, so I went to the library
and got my atlas and found where Pittsburgh and Palo Alto were,
and Palo Alto seemed nearer to the ocean,
so I went to Stanford and did my PhD there.
Then I applied to MIT for a faculty position,
and I got turned down.
But eventually I got here.
Very nice.
When you first got to MIT, you worked AI Lab?
I first came to MIT in 1981 as a postdoc,
and I was working for Tomas Lozano-Perez with robots,
with robot arms, trying to get them to do assembly.
And we were approaching it very mathematically
and as we tried to put things together with the robots
and do planning, classical planning,
I realized I had to deal with the uncertainty,
because there was tolerances for all the parts,
and there was uncertainty in exactly where the robot was.
So I started trying to model that,
and I developed this whole theory of uncertainty analysis.
And I wrote the world's most boring paper
which was 30 or 40 pages, analyzing uncertainty,
and it just got worse and worse.
And that was trying to put a peg in a hole,
so I sort of realized that that wasn't going to work,
and I had to change things.
Do you mind if I'm not looking at you?
No, that's fine.
OK.
I assume you're going to edit this a little bit here
and there.
Probably, yeah.
Yeah.
It's fine.
Whatever you like.
So then what?
It got worse and worse.
I was out at Stanford on the faculty then,
and I went with my first wife to Thailand,
where she was from, and had spent
a month in this house on stilts in the river
while she talked Thai with all my relatives,
and I just sat there, not being able to communicate
with anyone, watching ants and mosquitoes, lots of mosquitoes.
And they were buzzing around and moving around
quite well in the world, and the ants
were picking things up and moving them.
And I was thinking about how many neurons could they have
and how fast could they compute?
And I figured they couldn't be as good as the mainframe
computers we were using then, so they
must be arranging their computation differently.
They couldn't be doing all this symbolic reasoning
that my planning program was trying to do.
And that got me thinking about how
I could make the system more like an animal system.
And so out of that came--
eventually, a few months after that,
I came back to MIT on the faculty--
out of that came what was called the subsumption architecture.
Very nice.
When you first came to MIT, what other kinds of research
were going on?
What else was happening?
When I was here in 1981, it was the year of the robot.
A lot of people came from outside
under some funding from the foundation,
and we really thought robotics was
going to take off in the classical sense of robotics.
And we had the various robot arms,
and a lot of computer vision, and we're
putting this on LISP machines.
And there were about, I don't know, 16 or 18 LISP machines.
And so the game was to get in early in the morning
and sit down at one before other people got here,
so you could hack all day.
But if you left the machine, someone would come and take it
from you.
So you had to sit there the whole day, hacking.
And they had 256K in memory, they were pretty big.
And we were trying to do vision and planning for robots.
We had this idea that we would go straight
from designing things to manufacturing
things with robots directly.
So someone would sit there with a CAD system, design the parts
and have them go together, and out
of that would generate programs that run the robots.
And that was a real force of things coming together
in AI Lab at that time.
At the same time, there was a connection machine
going on Danny Hillis, one of Marvin's students.
And Marvin was still around at that time,
but he was not as active as he had been, I would guess.
Got you.
So subsumption architecture, basically.
Is it on the questions?
Oh, sorry.
Did I miss something?
No, no.
Where did you go after you had this--
the need to sort of make the machines not
process like a math machine?
Our first robot, Alan, was able to navigate around
in real time with very little computation, actually,
and avoid obstacles and avoid even mobile obstacles,
and that was just maybe four or five years
after the cart at Stanford.
The cart at Stanford had gone 20 meters
in 6 hours in a static environment,
and when the environment wasn't static, it got very confused.
But my robot could go 20 meters in a minute,
so that was quite a big step up.
But it didn't build models of the world,
it just reacted to what was there.
This got a lot of people really angry.
You know, this isn't intelligence,
this is just reactivity, it's horrible.
It will never scale.
And so then rather than trying to make that robot better
and better, I fell into this game
of building more and more robots.
So I started just building robot after robot
and every new graduate student had their own robot.
And we started doing lots of things.
And eventually, in 1988, Colin Angle, who was then in Europe,
Grinnell More, who was the son of Trenchard More,
was at the original AI symposium in Dublin
and was a high school dropout who worked in my lab, he
and Colin and I built the robot Genghis, a six-legged walking
robot.
And whereas other robots had been very slow
and had worried about stability, I'd, again,
looked at insects and videos of insects,
seen how insects didn't maintain stability.
They fell down all the time.
So removing the constraint that the robot had
to stand up all the time just made it incredibly easier
to build a robot which could walk over rough terrain.
And so then when we offered a whole series of walking robots,
and again, people would say oh, well, that's not intelligence.
That's just Rob dealing with his little robots.
So Colin and I and another student, Helen Greiner,
decided well, we're just start a company and show them.
We did that in 1990.
And a mere 15 years later, we were
successful with real robots.
Good deal.
Yeah.
Or sorry.
The idea was 15 years, an overnight success.
Having talked to a bunch of different people in the field,
they say there seems to be a feeling of like,
we need to get common sense reasoning in order
to have a human level intelligence,
and this is the great goal of AI.
That would be Marvin?
That would be Professor Winston who had said that to me.
Marvin, yeah.
Yeah.
I think when we look back, when other people who
are alive in the future look back at what we were doing now,
just as with all sciences, they'll
see a lot of things we're doing and think, well, why were they
worried about that?
That's not the real issue.
So as an example when I started working with walking robots,
a lot of the previous work had been on the gate,
which legs go when?
And when we stopped worrying about that
and just made the robot walk, the gates
had an emerging property of the interactions of the legs
and the environment.
So I think a lot of things that people get upset about
in AI as being big, deep problems
will likely fall out as a side issues.
And I suspect non-monotic reasoning is one of them,
I suspect common sense is one of them.
That will come out of a bunch of other abilities of robots.
And that's my intellectual bit, I may be totally wrong.
But I think it's sort of putting the cart before the horse
to worry about either of those problems at this moment.
When you can't even get a robot to see that's a pen
and pick it up, well, how can I have
any common sense about someone that I
can't even perceive or touch?
That makes sense.
So you're sucker, like I sucked you in.
No, I mean it makes a lot of sense.
Sorry if I keep looking at the camera, but--
No, it's fine, wherever you want to look.
Lost my train of thought.
Oh, OK.
So human level intelligence, is this still a good goal?
Or is this still more or less the goal?
In some ways, I think we all suffer from what
I call "Star Trek syndrome."
If you watch Star Trek, all the aliens look humanoid.
Why do they look humanoid?
Because that's the actors you can get to play them,
Star Trek being made before computer graphics
was cheap enough to generate them.
And so Hollywood has this terrible lack of imagination
and only dreams up humanoid robots, and I think we,
and I've certainly been guilty of this for the last few years,
dream up humanoid robots, but that's
just one little piece of intelligence, one thing
that evolution stumbled across.
There could be lots of other sorts of intelligences.
We can't agree-- we don't know whether Dolphins
are intelligent or not.
So I can imagine us building all sorts of robots
that do all sorts of things.
And then we'll still be debating, well, they're not
really intelligent, are they?
Meanwhile, it might build some city and rockets
to the moon and stuff.
And we'll say, no, they're not intelligent,
they're just reacting.
So human level intelligence, yes, in principle,
but will actual humans--
that might be a sideshow.
I think we'll have, over the next 50 years,
more and more robots in our lives.
I can't imagine a future 50 years from now--
without some disaster having taken place--
where our everyday lives are not full of robots.
So whether they're humanoid or not,
I tend to think they're not going to be humanoid,
but there's going to be lots of robots in our lives,
just like there's computers everywhere in our lives now.
OK.
When you think of robots, you think of them as tools,
I guess.
[INAUDIBLE]
And eventuality that every home has robots just like today,
computers are everywhere, just tools
to help humans, coworkers?
Yeah.
Cynthia Breazeal, who was a PhD student of mine,
is now in the faculty of the Media Lab,
had this phrase "friend or appliance,"
and I think that sort of sums up the ambivalence towards what
these robots will be.
At one level, we just want appliances.
My fridge works 7 days a week, 24 hours a day,
I don't want to have to feel sorry for it working so hard.
So as I build robots, I've got a Roomba and a Scooba in my home
and they clean the floor.
And every day, my Scooba cleans up the dog poop,
and I don't feel bad about the robot cleaning up the dog poop,
and I wouldn't like to have to feel bad.
So maybe I don't want robots that are too intelligent,
because then I'll start identifying with them,
and I'll start feeling bad for them.
Certainly the experience that we've
had with our humanoid robots in the lab
and then my own experience with selling millions of robots
to the general public, a lot of people identify with them
and project onto them.
And some people say well, that's bad,
you're projecting too much.
You're anthropomorphizing the robots too much.
But I think we have antrhopomorphize people,
and that's how we make people people,
anthropomorphizing them.
I don't think it's anything particularly different.
That gets some people really upset.
But we're used to dealing with entities as though they have
free will, and that's what gives us
free will, because we attribute it.
That's interesting.
So I guess we'll step back a little bit.
We've heard a lot of sort of silly anecdotes and fun stories
from people we've talked to about this time at the AI lab
or at different places, I guess, largely
as a function of the unique atmosphere that was there.
Do you have any such recollections or fun stories?
We heard Professor Sussman and then Professor Minsky
we're talking about the same thing,
that apparently is sort of an internet legend about Professor
Sussman wiring a game to play tic-tac-toe and he made
a random net to play and--
I've heard them both tell the story.
OK.
Yeah, I don't know, just little things like that.
I think for me in Tech Square, the most fun
was just that we were always building robots.
There was the machine shop there up on the ninth floor,
and at all times of day or night--
you go in there 3:00 AM, 4:00 AM,
some people would be working on robots,
other people would be fixing their cars.
They'd bring half a car in there to fix it.
But it was just the atmosphere that nothing was forbidden,
anything could go, and you could do anything.
And that was a lot of fun.
There'd been a history, by the time I got there,
of building time-sharing systems and operating
systems and things like that.
But by the time I was there, it was all
about building robots and putting
embedded processors together and lots
of wires and connectors and batteries
and making the robots do stuff.
Actually, I think that probably the moment that
was the single most stunning moment to me
was with the first robot, how I had
come from this environment of trying
to do classical planning, and so we'd always
done things in simulation where we all simulated robots
and simulated graphics robots.
So when I started building my first mobile robot
at MIT in the AI Lab in late 1984 into 1985,
when I was a very junior assistant professor,
I, of course, built a 2D simulation on a LISP machine--
by that time we had a new generation of LISP machines,
and I got one of the old ones and had it in my office
all the time--
and I'd gotten the whole control system
to work on the simulation, the little circle robot
would move around in the graphics.
And then I had the physical robot there
and I was working late at night, night
after night, trying to get everything debugged,
get all the commands under the robot.
And time and after again, I'd get everything running,
switch it on and download and run, and nothing would happen.
And then I'd go poking the memory and find out, ah,
this thing's wrong, that thing's wrong.
It had been going on for days and days like that.
And then I switched it on, just like I had maybe 30 or 40 times
over the last few days, and this time, it moved.
The thing actually moved.
Instead of the simulation, the robot was actually working.
And that was just the greatest feeling.
It was alive.
And I think that's what, when I was eight years old
in Australia with the flashing lights, that's what I've
been after, making it alive.
And Alan was alive.
So that, to me, was the most fun.
I was all by myself at the time, but it was just the best
experience ever.
What did you have it do?
What was the first thing?
Was it just wandering around?
Alan was a layered architecture, and its lowest level layer
was to avoid stuff.
So when it finally moved--
it was right next to the bench, it
just ran away and went out in the middle of the room
and sat there, because that was away from everything.
Nice.
You talked a little about Star Trek syndrome.
I was just curious whether you read science fiction.
I devoured science fiction when I was a boy, Arthur C Clarke,
Isaac Asimov, both of whom knew Marvin well and further--
Marvin Minsky-- and Robert Heinlein, and those authors.
So I read a lot of that-- and then some British authors too,
Captain WE Johns, I think his name was.
And I really devoured that, but after I was in my 20s,
I didn't really read science fiction anymore,
because I was too busy building stuff and having fun.
That makes sense.
You don't read it now?
No.
Do you read fiction now?
No.
Fair enough.
No, I've heard this response a lot of times.
I guess that'd probably about do it.
Do you have anything else you wanted to talk about?
OK, tell one little thing.
Sure.
People often ask me about the future,
and they ask me about the future in two different ways.
One way they ask me, well, you know,
we saw such and such a movie, is that what's going to happen?
And I keep having to remind people
that movies are not predictors of the future,
because if it was, we'd all be fighting aliens on the streets
and there'd be ghosts everywhere.
And Hollywood movies about the future of robots
are normally pretty dumb, because what Hollywood
does in those cases is take a world just as it is today
and change one thing, put an emotional robot
or a humanoid robot amongst everything as it is today.
But the reality is everything changes all the time.
So our world is so much different
from what it was 10 or 15 years ago.
We now Google everything, we've got everything
at our fingertips.
We didn't have that 10 or 15 years ago.
The world has changed, the amount of information
we have has changed.
We can't fool people.
You can fool some people, but you can't fool a lot of people
as easily as we could.
So the world is not going to be like Hollywood.
But people ask me, are we going to have robots?
And so the game I like to play, I always choose 50 years.
They say, should we work on x, will we have x?
I think about 50 years from now, and can I
imagine x not being true?
So for instance, will we have teleportation?
50 years from now, will we have teleportation?
I can certainly imagine that we won't
have teleportation in 50 years.
So to me, that says it's not worth trying
to work on teleportation.
Because who knows?
Someone will have to invent some weird new physics and who
knows whether that will happen?
Will we have robots helping the elderly
in their homes in 50 years from now?
I can't imagine not.
I can't imagine that a vacuum cleaner in 50 years
won't be a robot.
Right now, 1% of the vacuum cleaners are robots.
In 50 years, every vacuum cleaner
will be a robot, just like every vacuum cleaner is now
electrified whereas they weren't 50 years ago.
So when I say, well, if it's going to be true in 50 years,
now it's just a matter of deciding well,
is it really only 20, 30 years, or 20 years, or 5 years?
And what do I have to work on to make that true?
And that tells me what the interesting questions are
to go after, because it's within people's lifetimes
that this can happen, and we just have to make it happen.
I lied.
I have one more question because you reminded me of it.
Say I'm a researcher who is just entering the field of AI.
Or, I don't know, just say I just got accepted at MIT
and I want to go do AI.
What do you say?
You got any advice?
Do you have any don't do this?
You have any I don't care?
I think there are four big issues
that if we make progress on, we will help robot-kind to exist.
Back 40 years ago, Gerry Sussman was assigned the task--
when he was a sophomore, I think--
of solving the object recognition problems.
The object recognition problem.
A two-year-old walking into this room, I gave him this,
they'll say that's a pen.
They'll say this is a sheet of paper.
They'll say that this is a table.
They can recognize all these objects never
having seen them before.
So the object recognition capabilities of a two-year-old,
an unsolved problem.
Gerry Sussman failed in 1966, my PhD in 1981 failed on that--
lots of failures, but that's a big issue.
Second issue, the language capabilities
of a four-year-old child.
A four-year-old child can talk with counterfactuals
and has linguistic capabilities that would enable,
if a robot had them, we could get the robot
to do almost anything and have the robot explain to us
almost anything.
Third, manual dexterity of a six-year-old.
A six-year-old child can do this sort of force insertion.
A six-year-old child can tie shoelaces,
they can do everything that manual workers really do,
but maybe with not as much strength.
And lastly-- so we got two-year-olds,
object recognition, four-year-old,
language capability, six-year-old,
manual dexterity--
the understanding that a 10-year-old
has people in social settings about their intents,
their goals, their subterfuges, if we get our robots
to understand that, they will understand what we really want
and why we're trying to do what we're doing.
So to me, they are four big problems.
Now, will you need to do non-monotic reasoning
or probabilistic reasoning or common sense to do those?
I'm somewhat agnostic about, but I think those capabilities,
I think it's worth going after.
Awesome.
[INAUDIBLE]
Yeah.