So why don't we start out just by introducing yourself.
OK.
So my name's Bill Swartout.
And I'm now at USC, University of Southern California.
I got my undergraduate degree at Stanford,
which is where I actually first got involved
with artificial intelligence.
This was-- I graduated in 1974 and worked with Cordell Green
out there, who was then on the faculty,
and worked on the problem of--
he was interested in automatic programming, basically.
Could you get it so that, you know,
computer from some nonprogrammatic description
like input-output pairs or something like that
could actually infer what the program would
be that would generate that output from that input
and generalize it.
And so I was an undergraduate.
And there was a program that had been written
by a graduate student that actually did infer list
programs from example input-output pairs.
And it worked on exactly one case.
And so my job--
I took this course, and you had to have a project.
And, of course, my job was basically
to get it to work on the other 15 examples
that it was supposedly originally designed to work on.
And so this turned out to be, actually,
quite a intense debugging effort.
And I remember I finally got it to work on about 12
or 13 of the cases it--
Impressive.
--it was supposed to work on.
And about 4 o'clock in the morning
I had an experience where I realized, you know,
how your memory can be impaired as you get sleepier
and sleepier and sleepier.
And what was happening was I was working on the final thing
that I was going to do, the final example that I
was going to do.
And there was some bug that was causing it not to work.
And I figured it out.
But by the time I had figured it out,
I realized that my memory--
I had lost all of the memory traces
back to where I was starting from on this.
So I said, OK, time to go to bed.
It'll be fine if it works on 13 cases.
So that was my sort of introduction to,
you know, working in a small way, at least,
to working on AI.
And later Cordell and I and a graduate student
who was involved published a paper, my first paper in an AI
conference on that work.
And that was kind of fun.
As an undergrad.
Not bad.
Yeah, It was kind of fun.
And then I went to MIT and worked
with Peter Solovich in the--
well, initially I worked with Bill Martin
and then later with Peter Solovich
on the clinical decision-making group there,
which was basically concerned with trying to help physicians
by writing programs that could give
advice about diagnoses and therapy recommendations
and all of that.
He was [INAUDIBLE].
Oh really?
OK.
So the program that I worked on was the Digitalis Therapy
Advisor, which was--
Digitalis is a drug that's actually still given today.
It was first prescribed or described
in the medical literature in like the 1700s.
So it goes way back.
It's derived from foxglove plant.
And it's a drug though that's very difficult
to give properly because the difference
between a toxic dose--
one that's going to make you sick--
and one that's going to be therapeutic
is only about a factor of two compared with, say, something
like aspirin where the error range is a factor of about 100.
And in fact, the variability on sensitivity between patients
is bigger than the therapeutic window.
So you have to, essentially, customize the dose
for each particular patient that you're
going to give this drug to.
So Howie Silverman, who was a master's student
at MIT, who was a year ahead of me,
had written an initial version of this program.
And the job Pete gave me was to basically, allow this program
to explain its reasoning to a physician,
the idea being that if you give a physician some advice
but you don't also explain why this is the right thing to do,
they won't accept the advice.
And so, basically, I had to go back in
and reprogram, develop, essentially, a new approach
to programming that allowed the computer
to take the routines and procedures that it had
and paraphrase them into a form--
into English in a reasonably understandable sort of way,
even for somebody who isn't a computer genius.
And so that became my master's thesis, essentially.
And I then went on on my PhD to look at the issue of not only
how can you get a program to explain what it's doing,
which is what looking at the procedures
does, but also look at how can you get the program to explain
why it's doing what it's doing?
In other words, give you the rationale
behind the recommendations that it's
making that was sort of the causal reasoning.
The problem with that, the reason
why that was particularly difficult
is that that information, the why,
the why the program is doing what it's doing,
doesn't have to be in the program for it
to work correctly.
You know, you can get a rule that says,
if you see this and that phenomenon,
you know, just reduce the dose by 20%.
But it doesn't tell you-- it does the right thing,
but it doesn't tell you why it's doing what it's doing.
And so I basically looked into developing some techniques
for getting it so that we could build a automatic programmer
that would start from much more abstract knowledge
of the domain and problem-solving principles
and then from that derive--
infer the working system, keeping behind, essentially,
the mental breadcrumbs of where this procedure came
from in this causal reasoning chain
so that then, you know, you were running the program and said,
OK, you know, why is it doing this?
It would go back and say, well, there's this causal interaction
that happens between the drug and what you're-- you know,
and this phenomenon.
And if you don't do this, you know, bad things will happen.
And here's what they are.
And so that was pretty cool.
That was pretty fun.
Could those kind of systems work to infer what--
I don't know-- why a certain diseases is caused?
They would have information like that in the program.
And the program-- the system that's
either diagnosing-- building the diagnostic system
or the therapeutic system would use that causal information
together with some strategies for how to reason in the domain
to produce, then, a system that would do the diagnosis.
So yes, basically.
Cool.
And what do you think the most exciting potentials in AI
are right now?
Right now?
Well, the area I'm working on is kind of actually--
if you look at the Dartmouth conference
and the goals for that, which was really
to produce a integrated artificial intelligence that
really exhibited the whole gamut of human behavior, one
of the things I'm working on now is virtual humans, which
are computer-generated characters that appear
in a simulation or a game.
And the goal is basically to have
them behave much like people.
So you talk to them in natural language.
They talk back to you.
But they're not just talking.
It's not just verbal communication.
They're also using gestures where appropriate.
They're using eye gaze as appropriate, so
engaging the listener.
And they're exhibiting emotions.
So they're-- which is actually, interestingly,
an area of research that AI really hasn't paid much
attention to, I would argue, over the last 50 years.
It's just starting to become something
that we're looking at.
And one of the things that I think is--
that we found in our work is that emotion--
we initially went into this we thought emotion would just sort
of be an overlay, that, you know,
wit would kind of be a modifier of behavior.
So if you're speaking and you're angry, you kind of frown.
But actually, what we're finding is
that emotion cuts much deeper than that then.
There are aspects of emotions that are pervasive.
So, for example, it can affect the way
that the system reasons.
So if you're angry, if we as humans are angry,
you know, we will tend to reach conclusions much more rapidly.
If we're depressed, we'll be more deliberative.
We'll think things through more.
And it's clear that emotions are functioning
as a kind of metacontrol on some of our reasoning
processes that we use.
Seems like, maybe, AI systems could
benefit from some of that.
It also seems like emotions provide another window,
essentially, into the status of what's going on--
could provide a status of what's going on with these characters.
Very often now if you talk in the military,
if you talk to generals, they say, you know,
we're getting reports from the field.
We would much rather get a report from the field
by radio rather than by some sort of email message.
The reason is because they could hear
in the tone of the voice of the soldier, you know, how good
or how bad things are going.
And so, it's clear that we, as people, use emotions
as a kind of shorthand for a lot of things.
And it's part of the communication.
And it seems that if we're really
going to build AI systems, you know,
they have to have that capability
for a lot of different reasons.
What are some of those reasons?
Well, one is there's this communication aspect.
The other is that, actually, people
expect if they're looking at something that
looks like a person up on the screen,
they, in fact, will ascribe emotions to that character
whether they've been modeled or not.
It's just because we're terribly anthropomorphic.
You know, we think our pets have emotions.
All sorts of things have emotions.
And so if you're going to avoid miscuing people, you know,
you've really got to get into the game
of modeling these emotions.
So that's kind of a very quick summary of why some
of these things are important.
Has there been a lot of cross-pollination
between simulations and artificial Intelligence
and the video game world?
That is a area that is already quite warm
and is really heating up.
What's been happening with video games, as you know,
is over the last 10 years or so, the big--
for a game company, the big differentiator for them
was the graphics.
So if next Christmas your game had the greatest graphics,
you know, you had a hit.
What's happening now with the degree
of the advancement in graphics cards
and also the underlying middleware
for producing graphics that our games have,
it's getting to the point where, first of all,
the graphics are getting pretty close to photo
real in these environments.
And pretty much all the games are kind of
at a similar level in terms of graphics.
Once you get to photo real, you can't go any further, really.
I mean, you're dealing with the limitations
now of the human eye.
So that means they're recognizing
that graphics isn't going to be the big differentiator for them
anymore.
And so what they're starting to turn to is behavior,
looking at how do these things, these characters behave?
The other thing that they're looking to is how can they
expand the market base beyond, kind of, the first-person
shooter--
typical male, younger male--
to a much broader audience?
And what they're finding is that games
that emphasize relationships tend to appeal more to--
are more appealing to, I should say, to women.
And in fact, right now, The Sims 2
is, I believe, the largest-selling computer
game out there.
And that's a basically nonviolent game
that is really all about the relationships
between these simulated characters.
Now, if you look at The Sims, you know,
the characters by AI standards are still pretty primitive.
And so we're in this really interesting position
from the standpoint of AI research
that there's a lot of technology that we
have in the lab that without, I think,
without too much trouble, we can start
to move out into these areas and really create some really
interesting game experiences.
I think there will be a real revolution in video games.
Are there any other places where you see
that sort of symbiosis growing?
I think another area where this kind of thing--
I would say education is one, education and training.
Basically, being able to construct simulations
of various-- and one of the things we've been working on
is a simulator that uses virtual humans for teaching people
how to negotiate.
So, basically, you're talking to a virtual human.
And you're trying to strike a deal
with this virtual human, who has his own internal goals
and beliefs and desires.
And as you talk to him, you know,
he's appraising what you say in terms of his own desires
and then reacting and responding.
And basically, the characters respond
using psychologically-motivated strategies.
So they do, you know, they have a variety
of different strategies that they
use in responding to people.
So this kind of thing-- you can use this,
you can imagine using this not only in educating people
about how negotiation takes place
but also as a way of doing sort of role-playing
for a variety of business situations,
for example, where you might want to give people practice
in how to close a deal.
You might also want to use it as a way for human resources
in how to deal with a problem employee.
These are things that all managers hate to deal with.
So giving them the ability to rehearse
this with a artificial character might be really appealing.
So have you encountered any really amusing instances
of versions of these programs or even just in your research
group over the years?
Well, let's see.
So I believe-- I mean, give me a second to think about that.
In some of the--
well, I'll tell you one thing that was actually
quite moving to me.
We've been working a lot with the US Army.
And we constructed a simulation of a scene in Bosnia.
And it was using virtual humans.
And the idea was we wanted to put a--
it was just a prototype system.
But the idea was we wanted to be able to put
somebody like a lieutenant into a typically--
a dilemma that might occur, typically,
in a operation but the kind of thing
that is not typically part of the standard training manual.
So this was a situation where you're
in Bosnia with your platoon.
You're going to reinforce another unit that's
dealing with some civil unrest.
And on the way, your group, your platoon
gets involved with a traffic accident.
And there's a small child on the ground seriously injured.
And a crowd starts to form.
TV camera crew shows up and starts to film things.
This is all done virtually.
And you know, now you, as the lieutenant,
have to figure out what to do.
Well, we showed this to a bunch of army people.
And actually, some of them were really
moved by this because it was--
while the graphics weren't perfect or anything like that,
it was evocative enough.
They'd actually been on duty in Bosnia
and it reminded them of things.
And it really made me feel like this is something
that I can see how this might actually work out.
So it was a nice experience.
Well, that's cool.
Well, thank you very much.
Thank you.
Great.
I love to bring so many different perspectives
to this field.
It's absolutely amazing.