Introductions-- I'm Heather, obviously.
And I'm Candace Sidner.
And you're working right now at--
At Mitsubishi Labs in Cambridge, across from Tech Square.
Awesome.
Across from Tech Square, great.
And how did you first get into artificial intelligence
or in robotics?
Well, I started off in--
as an undergraduate, I was interested in how people
communicate and use language.
And I did my master's degree at the University of Pittsburgh,
and while I was there I took courses with Herb Simon.
Cool.
And when I started doing the master's thesis,
Herb said, well, go see this former student of mine whose
name is [INAUDIBLE] who's somewhere
at the University of Pittsburgh, and talk to him.
And Harry Pople was at the [INAUDIBLE] business school.
Harry was doing what was called, the INTERNIST medical system,
which was a very complicated and sophisticated AI
program to mimic the common decision-making that doctors
did when they were diagnosing patients.
And so I did my master's thesis with him.
And then after my thesis was finished,
I was admitted to the doctoral program at MIT at the AI lab.
And when did you get into emotional--
what are you mainly researching?
So I don't missay.
So my work has largely been in the area of human language
and human conversation.
So over the years, I've looked a lot
at how we model the intentions of people
when they're communicating with one another,
as well as what they do with their language barrier.
I've done a lot of work on how we understand pronouns,
how we understand all kinds of referring expressions,
and how we embed our intentions in our utterances,
and then convey that over successive parts
of the conversation.
A few years ago, I got interested
in the problem of how we use gestures and language.
So obviously, one of them is what I do with my hands.
But equally important is the fact
that I'm looking at you right now,
and I'm looking at the camera, for example.
And that when I'm talking to you, I actually talk to you.
I don't talk to the wall like this, because if I did,
there's something very importantly
wrong with that interaction.
I call that the process of engagement.
That is how we come to perceive connection
through nonverbal means and interactions.
There's also a verbal means.
We do it, of course, as long as we're having the conversation.
Even if I'm doing something else,
you know that I intend to be connected
to you because I continue to play
my part in the conversation.
But if I violate certain aspects of our non-verbal connection,
that can be very odd.
Some of it's OK, however.
So for example, if I'm washing the dishes,
I have to pay attention to the dishes
while I'm talking to you.
I will pay attention to this, and I'm only occasionally
looking up as I have something to say.
And if you're talking, instead I'll be doing "mhmm",
and I'm doing when I was doing the "mm".
I may not look at you at that point in time.
I may be able to do this, but you'll understand that I'm now
multitasking as we are and--
So can you learn those things?
At this stage do they have to be preprogrammed?
No.
Those are the sorts of things we still have to program.
Just getting clear about what the [INAUDIBLE]..
And the way I've been testing a lot of those ideas
is to have a robot that can converse with the user
and can both produce proper behavior
and then interpret the behavior of the person.
So that means the robot is not so important that it's mobile.
Up until now, we haven't really done work on mobile robotics.
But more importantly,
it has to move in the right sorts of way,
and it also has to have the ability
to interpret people's movements, as much
as you can do that with current vision systems.
So is your main goal more to interpret human expression
or to have humanoid robots that can replicate them, or both?
Well, you have to do both.
Conversation is a two way thing.
You both have to produce it properly, and you have to--
I was just thinking of an intelligent computer that
was just trying to interpret your work
or something like that, where you're getting frustrated.
That might be useful.
I'm interested both in the interpretation phase and also
the ability to generate or produce that kind of behavior.
What do you think the biggest challenges in AI
historically have been, and how has that changed, now?
Well, one of the challenges that really hasn't changed
is something that I talked about yesterday,
which is the need for a deep model of semantics of language.
So we now are able to say things about how a verb might have
certain arguments, and you can fill them in,
and you can do some of that adequate language
understanding.
But the much deeper problems of how we know certain things
about how sentences go together in terms of their meaning,
how we make deeper inferences about what's
going on between utterances or even
between practical sentences and how that's connected onto what
we know in general is still relatively poorly understood.
And so I think that's a pretty important problem.
Obviously, the work I've started doing on gesture
is only in its beginning phases up until now.
Until very recently-- and I'm about the only person even
working with Siri-- but up until very recently,
like maybe five years ago, nobody thought to.
And it's not that nobody thought about it.
Nobody really had a good way to go about it,
and that's gradually changed.
What do you think of Cynthia Breazeal's work?
Oh!
I like Cynthia's work, obviously.
In fact, some of her students have been my summer interns.
So we've happily shared back and forth
and I hope learned from each other in the process.
So another area, of course, that I think is largely unexplored
is things that have to do with social and emotional
interactions.
A number of years ago, there were a few people
in the language community who explored emotional stuff, texts
and things like that.
But it kind of died off, and looking at this stuff
any further or any deeper has really not happened.
So the reason I think it's important
is that when we communicate together
in a human to human situation, our emotional content
is expressed both in our language
and in our gestural behavior-- mostly in our faces, I think.
Although if we're really upset, obviously it
comes up in that way.
And it's useful to be able to take
advantage of that information on the part of the machine.
It may also turn out to be useful to have the machine be
able to express emotions.
You can affect the [INAUDIBLE].
More subtle but equally important
is our social interactions.
And recent researchers have shown that when we have social
conversations-- not just about putting things together--
with machines, people have a markedly different sense
of what that entity is.
You can measure that on standard psychological measures,
which is pretty interesting.
So it's interesting, and I always
write it as a kind of practical thing
you want to do with a computer.
But from the point of view of artificial intelligence,
it's really kind of interesting and important [INAUDIBLE]..
What do you think the most important skills
for an artificial intelligence researcher to have are today?
Researchers?
Wow.
Really good observational skills.
So I think it's really important to look
at what the human-human situation is
and to be able to observe that carefully.
Very good
analytic skills in terms of being
able to quantify and think about things
and really analyze what's going on.
The ability to listen carefully and read other people's work.
And then, to be creative and kind of
be able to step off the edge.
You can't make progress if you're not
willing to take risks.
Well, we're sort of short on time.
But do you have any anecdotes or stories
that you remember from your times at MIT
or pathways along the way?
Oh, wow.
Well, a lot of my anecdotes have to do
with being a woman in a highly male profession.
And it's not always been easy, then, to say the least.
So I don't know if I want to convey
all them at this particular point in time,
but there's a certain challenge to being
a woman in an all male field.
It has certainly colored my professional life
and my experiences as a scientist.
Cool.
Well, a pleasure talking to you.
Let's go.