I guess we'll start having you introduce yourself.
My name is Reid Simmons.
I'm a research professor at Carnegie Mellon University
in robotics and computer science.
And I came to the AI Lab at MIT in 1979 to do my graduate work.
As an undergraduate, I was very much interested
in computer graphics and that's basically
where I thought I was going to end up.
And I took a course from Stuart Shapiro, who is also
a AAAI fellow now, and just kind of was blown away by the fact
that you could--
there actually was a field there.
People weren't just playing, making machines
to be intelligent.
But there are actually people out there doing research,
and you can actually have a career
doing that type of thing.
So I say, wow, that's a lot more interesting
than doing computer graphics.
Those were the days when computer graphics were
like Pong, not what it is now.
But I think I picked the field that didn't accelerate
as much as the other one, but it's
been a lot more interesting I think.
So I guess we ended up at the MIT AI
Lab, what kinds of things were going
on there that you remember?
Any cool projects?
People?
Yeah.
There is a bit.
I started out working with--
for the first year basically, I was
working with Chuck Rich and Dick Waters
and the Programmer's Apprentice.
And what they were trying to do is get a software system
to generate software automatically.
But after the first year, I switched over
to working with Randy Davis.
He just started putting together a project
to do model-based reasoning.
So the big rage up until that time
had really been expert systems, role based systems.
And there was the common understanding
that these systems were very brittle,
because they didn't really capture didn't really
capture knowledge.
They just captured rules of thumb.
And so what we tried to do is develop
systems that had a deeper understanding of whatever
problem they were trying to solve and more general ways
of solving those problems.
And most of his group was working
on analysis of circuits.
Because of my background in graphics,
I was interested more in spatial reasoning.
And he had some contacts with Schlumberger,
who was doing oil exploration.
And they were very much interested
in spatial reasoning.
They were trying to understand what's
happening underneath the ground by looking at the data that
comes back, and trying to figure out what was happening there,
and what processes must have taken place millions of years
ago.
Because they were interested in trying to figure out
where oil was, so it was a nice combination--
it was a nice combination, my interest
in both model-based reasoning and also spatial reasoning.
So I started working with them on that problem of doing
geological interpretation.
And trying to understand from a picture of the geologic region
what actually occurred.
And this was related to work in planning, basically
interpretation.
Trying to figure out what sequence of events happened
is the opposite.
The inverse of the problem of planning
was just to generate a sequence of events
to make something happen.
So I got involved in the planning committee,
at which this was a time when you a lot of things
were going on there.
Drew McDermott was very much involved
in doing work and planning.
And he was at Yale at the time but had done his work at MIT.
And he was a big influence on the work I've done.
Gerald Sussman, his early work on planning and debugging
was influential when I was working on it.
And then some of the fellow students,
Phil Adrian and Dave Chapman, came along
and turned the whole thing on its ear
by saying, well, intelligence wasn't really planning
any more.
It was just more reacting to the moment.
So it was a time when there was a lot of big arguments
in the AI Lab about model-based reasoning
processes versus more reactive embedded type situations.
For a long time, the two camps were pretty separate.
You're either in one camp or the other.
And now after 20 years have passed,
there's been a lot more blending.
And most people, I think, will agree that neither camp
is right or wrong.
There's aspects of both of them.
And the trick has been to see how we can all put it together.
But in those days, people had very strong opinions of me.
And they really tried to stake out very rigid positions
and in order to make points.
OK.
That make sense.
So I guess where did you head after that,
the next stage in your career?
I had every expectation of continuing on in the area
that I've done my thesis work.
And I was looking for position, and, by chance,
I got a call from Tom Mitchell, who was
at Carnegie Mellon at the time.
And he said that he had just--
he was involved in a robotics project in which there
was Red Whitaker, who was in charge of building
the robots, Takeo Kanade, who was in charge of doing machine
perception, the vision, and Tom was basically
in charge of making the robot smart.
And he needed someone to come and basically work
as postdoc to do that.
I'd never done any work in robotics.
There was a lot of work in the AI Lab being done there.
Rod Brooks was there, Tomas Lozano-Perez,
and a lot of Brooks's students.
Ian Horswell and John Cannell were doing interesting work
there.
But I had never done any of that.
And in fact, the opposite, I was in the camp
where you do a lot of-- it's a lot of reasoning,
and you think about things, and that's it.
So now it's like you're reasoning and thinking
about things, and you have to actually connect it
to the real world.
And that was a real eye opener.
So that basically completely changed
the nature of my research by actually
having to make robots work.
Rather than just sticking with a very high level reasoning
aspects we had to look at how to actually deal with uncertainty,
how to deal with the fact that the world is really messy.
And as I said, it's helped--
it brought those two competing factions
closer and closer together.
Because it's clear that to actually do
things in the world, you have to react to the moment
and what's there.
But on the other hand, to be intelligent,
you have to take a step back and look back
at the higher level reasoning.
OK.
That makes a lot of sense.
So I guess I'll ask what some of the most challenging things
you're facing right now are.
So one of the things that I've been interested
in for the last couple of years, and it's kind of consuming
is human robot social interaction.
And the interesting thing is that as I start getting into,
there's more and more evidence that intelligence
and being intelligent and being social are inextricably linked.
So a lot of what we have what we consider intelligence,
like language and communication, are
there more for social reasons than anything else.
And we're intrinsically social creatures.
And you can't separate-- you really
can't separate the social aspects from the intelligence
aspects in many ways.
And it's fascinating because people are very, very good
at both generating and reading social cues,
from gaze, from gesture, from tone of voice, even posture.
If I want to become assertive, I can lean in like this.
And that tells you a lot about what I'm thinking about
and how you should react to that.
And when I back off, It's a lot less threatening
because I'm relaxed.
And our robot systems can't do any of that, in general.
There's some work that's going on in that,
but it's still fairly rudimentary I think.
So the interesting thing is if we
make these systems, these robots in particular, more social,
are they going to be more intelligent,
just by virtue of the fact that they're more social?
That's a really challenging problem.
Something that we're hoping is actually
going to bear a lot of fruit.
That makes sense.
OK.
Let's see.
Do you read science fiction?
Yeah.
I do read science fiction.
What are some of your favorite things you read recently?
Oh.
Let's see.
One of my favorite authors is Orson Scott Card,
the Ender's series.
And more recently, I've become enamored with Philip Dick.
And I've never really had much of an interest in reading,
in general, reading science fiction
that had to do with robots.
But I started reading some of his stuff,
and it's just very engrossing.
Yeah I wouldn't-- a lot of people I talked to say that
either reading science fiction about robots or AI,
or seeing movies like 2001 where the things that kind of drove
them into the field.
And I can honestly say that I don't
think that had anything to do with the fact
that I got involved in this.
It really was just this notion back in the mid '70s that you
could have-- and these days, the computers--
we had a computer at our university,
which I remember them.
They were going to cost $5 million and had 100K of memory.
It was probably less powerful than the computer in you
cell phone.
And this was what the whole university was using.
And it was incredible to think at that time
that something so relatively slow, compared to today,
was going to be able to emulate the types of capabilities
that units of humans exhibit.
And you really had to take it on faith.
Because a lot of the algorithms that people
were developing would run in minutes rather than in seconds
to make certain decisions.
And you say, this can't be right.
How could this be the way that--
how could this be a good model for how
humans work if it takes minutes to do
what humans can do in seconds?
And now of course, with computers 1,000 times faster
than 25 years, 30 years ago when I started,
the things that used to take minutes now take milliseconds.
And it's all feasible now.
So it's interesting how the technology
has evolved to the point where a lot of the ideas that people
had years and years ago that other people thought were just
completely infeasible have become
feasible because of technology.
So where do you think we're headed?
What's AI going to do?
Or robotics?
So they've been in academic institutions for a while now.
Is Homer Simpson ever going run into a situation where
he says, I'm talking to a computer
or computers are directly affecting my life as opposed
to--
for example, expert systems have been in industry and planning
and stuff for a long time.
But they're not really directly interacting--
With people.
Yeah.
I think so.
This is one of the reasons that drove me into social robots.
I'll give an anecdote from my time at Carnegie Mellon.
We had a robot named Xavier.
We were interested in robust navigation.
So in order to demonstrate robust navigation,
you wanted to navigate for many hours
over long periods of time.
And so what we did was rather than just have
it wander the halls, we had it to be
able to go from room to room.
You could, over the internet, tell-- this was the early '90s.
Over the internet, you could tell
it to go to different places.
And so the robot would basically wander the hall
for four or five hours a day.
And we kept this going for quite a number of months.
And it validated the algorithms that we worked on
but what was also interesting was the social aspects.
You could see how people reacted to the robots.
And these were people who were in a technology University.
They were in a place that did robotics,
but, by and large, they'd never seen a robot.
And they didn't know how to react to the robot.
And you could see that there were
kind of three types of people.
There were the wall huggers.
The robot would come down, and they'd just
stand against the wall like this and wait for it
to pass and let it go through.
And those are the most prevalent actually.
And then there were people who were kind of nonchalant,
who would walk by it normally.
And that was prob-- that was a small number.
And then actually, the smallest number were the antagonist.
They would jump in front of a robot
and harass it and say, if you think
you deserve to be in this corridor with me,
you know you need to get out of my way
or be able to deal with the fact that I'm in your face.
And those are graduate students.
But what was interesting is that even with the people
who are nonchalant about it, there was always--
you had the situation where you're
trying to go through a doorway and someone else is
coming through the doorway and does the little dance.
And so you go back and forth a few times before you finally
figure out how to pass.
Well this happened all the time.
And it was because the robot didn't know the social rule.
The social rule is you're coming down the corridor,
you go to the right.
The other person goes to the right.
You pass.
And the robot-- because it didn't have any inkling of that
social rule and it was just trying to find the best free
path to go through--
caused a lot of consternation.
It was fine for the robot.
The robot didn't care.
It didn't have feelings.
It didn't have to get any place any particular time.
But the people were always-- and so a lot of times the way
they would react to it was, rather than trying to out think
the robot, what it was going to do,
they would just stand to this side, let the robot pass,
and then go on.
And that's how they dealt with it.
And it was interesting because they were basically
adapting their behavior to the robot, which is completely
the wrong thing.
If we want to put robots in society,
then the robots need to adapt to humans rather than having
humans adapt to robots.
And I think until we get to that point, where the robots can,
both from a conversational point of view,
use cues like gaze and gesture like we do,
and also from a spatial point of view,
keep personal space, knowing social rules,
knowing things like, well, if you want the robot to ride
an elevator, it needs to wait until people get off,
just like a normal person would.
These days robots that ride elevators
have no social graces.
Basically, the elevator door opens and they go right on.
And it causes consternation in people.
So I think until we can solve that problem of having robots
behave more socially, then there's really
no hope for Homer Simpson to be interacting with the robot
and actually liking it.
Sounds good.
Unfortunately, we're going to have to wrap up.
But thanks for talking.
OK.
Great.
Sure.
My pleasure.