Thanks for visiting us today.
[INAUDIBLE] introduce yourself?
I'm Manuela Veloso a computer science faculty
of Carnegie Mellon University.
Cool.
So I guess I want to start off with asking you
when you got into the field of AI, and how are got into it?
OK, so I got into the field of the AI
when I became more of a grad student.
So my undergraduate degree is in electrical engineer.
And then I got interested on the information processing.
And I did a master's thesis in databases.
And then my PhD thesis starting in 1986
was on this problem of analogy, so planning by analogy.
And I got very interested on the problem
of actually problem solving in general, and in particular
through analogy.
So I've been working in AI since 1986, I would say.
Good deal.
And you just like the problem solving aspect?
So I was always fascinated by the problem solving aspect.
So even now as of today, I am a person
that likes to research in terms of the integration
of perception, cognition, and action, so
the actual understanding of the world,
but all the way to really solving problems,
to actually doing.
So not just labeling where you are, but actually take action.
So I think that I still have that in my background.
It's very interesting.
So when you--
I guess you did your thesis at Carnegie Mellon.
Yes.
And you still work there.
That's right.
I guess when you first entered in the field as a PhD student,
what else was going on at Carnegie Mellon?
Well, it was a very, very interesting and rich period
of the field of AI.
So at Carnegie Mellon in the late '80s,
there were like these giants of AI, Herb Simon, Allen Newell,
[INAUDIBLE],, Tom Mitchell, Jeff Hinton, Mark [INAUDIBLE],, Scott
[INAUDIBLE].
And there were many people that were all very enthusiastically
pushing the field of AI forward.
I find myself to be a real product
of those days in which problem solving was really underlying
a lot of the research there.
And I learned a lot from Herb Simon and Alan on this idea
that indeed systems could learn from experience.
But they could organize their problem-solving
through this concept of what's the state of the world, what
are all the possible actions you can take?
And you faced this decision making
challenge of making choices.
So you are going always to face this problem of, OK, I
could do one out of n things, and how do you choose?
And whatever you do choosing, you
may find sooner or later in your research, in your choosing,
that it was not the right thing.
And so the question becomes in AI,
if you are doing the choice management
and when you find it's not the right thing, what
do you do about that?
And one of the things that Allen Newell and Herb
Simon were pushing in those days was this concept
of chunking, this concept of, OK, get that impasse
and save that, and we learn that up there,
when you had to do that choice, that
doesn't work in this situation.
And I tried to build upon that trying to use that by analogy.
So you would save some experience
of the choice you've made, and you
tried to transfer it to new problems
and reproduce the same successful choices.
That's-- wow, OK.
So I guess, this might be too technical,
but what were the most challenging parts of not just
your work, but the general work in problem solving, that part
of the field?
So just to reiterate a little bit, so in those days,
there was this problem solving fever going on at CMU.
And there was a little bit of the beginning
of the neural net paradigms at CMU, all the non-symbolic kind
of reasoning, all the statistical,
the speech recognisers being highly based on statistics.
So there has been from those days these, well
how much do you put a symbolic knowledge into a system,
or how much you just look at a large amount of data
and gather statistical information and just use that.
So it's interesting that I don't think
we reached a solution to these two ways of approaching.
But those started in those days, that discussion.
And all of that is from the mid-80s, late '80s.
And we are still trying to resolve
how to put this all together.
And, for example, I've been doing
a lot of robot work, a lot of robot soccer work.
And from a technical point of view, indeed.
Because these robots need to solve a concrete problem,
like these scoring goals into a goal, working as a teammate.
There has always to be some symbolic knowledge,
but over the task.
On the other hand, they do have to intersect moving balls
and to process pixels of images, and to actuate their motors.
And, therefore, they have a lot, a lot,
of low level processing needed to be done.
So from a technical point of view,
the challenges become how-- in my problem solving view--
how do you combine all that low level processing
that needs to be done at a perceptual level,
at the atuator level with, indeed, a lot of the task
knowledge that is highly symbolic or highly abstract,
even to model what the others are doing
to come up with some teamwork.
So though it's not maybe--
that's the technical challenges of problem
solving from my point of view.
This problem solving is not done by a single creature,
it's a teamwork aspect.
Problem solving really involves going all the way down
to an execute, therefore the low level challenges of processing
and actuating very low level information with actually
solving the problem, which involves the high level aspect.
That makes sense.
OK.
Nice.
Do you have any--
I guess some people have talked to us
of a lot of favorite stories or anecdotes
that took place when they were--
--I can tell you one that was very striking to me.
So in 1998 we were doing robot soccer in Paris.
And with the first AIBO robots, which
were these four legged robots built by Sony.
And the robots had all on board, like a camera, a computer,
and they actually actuate their legs.
And so they were playing in a field that had landmarks so
that they knew where they were.
And so we solved the localization problem
by actually using a Bayesian model of the map and then
their sensors to predict where they were on the field,
it doesn't matter.
But I remember all the map that had been done til then
to the localization assuming that the robot either moved--
so only moved by itself and sensed the wall.
We were in Paris, and in the middle of a game
the robots started getting all entangled on each other.
And the referees picked up a robot
and put it somewhere else.
And at that moment, the robot was all lost because it was
believing that it was somewhere else, seeing something,
its map would say, I cannot see a yellow marker from down there
in the field.
So these yellow marker needs to be noise.
So his model was completely destroyed
because there was this pick up.
And I remember looking at the game
with my student [INAUDIBLE],, screaming and shouting,
do not touch our robots, because in some sense,
we knew that that lift up and put somewhere else
was not modeled, was absolutely not
part of the assumptions that were underlying
the whole problem solving.
So it was a panic, because reality
was so obviously defeating the model that we were using.
But that was-- I mean, that was a big lesson for me.
Because from then on, I will always
push this idea that no matter what model you come up with,
it might be defeated by reality.
So an executor, a problem solver has always
to hypothesize not only that information gathered is noisy
and you might not know how to solve,
but that it's model might be incorrect.
And that led in several pieces, and so forth.
Because this is still something I am very--
I still relate back to 1998 panic
when I saw this robot being picked up.
So many things came out of that, pick up the robot.
But anyway.
So switching gears a bit I guess given the real world computers,
they're related by get noisy data,
computer models are almost always more simple
than reality is, how can computers
help humans solve problems?
That is a very good one.
So I've been working a lot on autonomous,
like computers all by itself.
But a lot of the research now is, indeed,
towards more of these interaction, human robot
interaction, human computer interaction,
and have the computer or the robot be more of an assistant.
I think it's a much better way of looking at, eventually,
the future, when we have robots moving around.
Because there is a kind of a hope
that capabilities will be complementary.
So that like in humans, I mean, I might play squash better
than someone else.
But that person maybe plays basketball much better.
Or I might be a very good a cook and you might
be a very good poem writer.
So there is not a real human that's
omnipotent, in some sense.
So when we create the robot, we create a robot
or an AI creature that has its limitations
and can be complemented either by humans or by other AI
creatures.
I really think that that's a very nice way to go.
So indeed computers and robots can
assist people, both on their physical limitations
but also on their known limitations
or their execution limitations.
So it's a very, very healthy way of looking at AI
as more creatures in the world that will have limitations
like humans do.
So they're not-- they don't necessarily
need to be the ones that beat everyone
and be the best chess player, the best robot soccer
player, or this or that.
But they can be more of like a co-- they just
coexist with humans in the same way
that we all coexist with each other.
Look at my accent.
I mean, it's a limitation that other people don't have.
But I speak many languages, too, which other people might not.
So it's like we all complement each other.
And that's very nice.
So sort of give a robot or computer
a specific context within which it
can assist in problem solving.
Exactly.
Yeah, that makes sense.
And it will-- again, the system will grow.
I mean, we have this feeling that we all
learn from each other.
And these robots or these computers
need also to have that ability to not
be static in terms of what they've done,
but evolve with their interaction with either humans
and with the environment.
So they should be able to change their parameters,
their weights, their knowledge, whatever
they have inside, their databases, their access
functions.
So that the whole thing has to be flexible.
Not as we would have thought that it was going to converge,
but as a function of the interaction with other people,
with the environment.
And they just have to be built with the ability
to have these things being changed
as a function of the interaction.
Very interesting.
So I guess people usually don't like it
when you ask these kinds of questions,
but where do you see us going, concretely?
Well, I am not very good at handwaving, that's true.
But on the other hand, I do believe that we are
going in the right direction.
And I tell you there are two directions
in which we are going.
One, which is like addressing more challenging problems.
Look at the DARPA challenge in the desert, the Urban
Challenge, the RoboCub challenge, a lot of challenges,
problems.
I mean, the understanding language technology,
it's a challenge.
And then the other direction is like the developing
more and more sophisticated tools to address this problem,
and understanding how the math of some control theory,
or how the statistics of some approach,
or how the psychology models of something.
So how do these other sciences contribute
for these very high level goal of creating these human level
intelligence?
So that we kind of understand that we
have to learn the lessons of physics from the math,
from here, from there, from there, from the psychology,
from who knows, from all the medicine.
So this is all these people that--
all these research on.
So we are going to pursue this research
on building the tools, the really core tools.
But then there's also the problem to challenges.
And, yes, so that's the directions I think will happen.
And you are always going to towards problems.
Tools to build tools.
To build to the-- yeah, exactly.
Interesting.
OK.
I guess that'd probably do it for now.
OK, thanks.
Thanks very much for talking with us.
You are welcome.
OK, thanks for putting this together very.
Nice good.