I guess I'll have you introduce yourself.
Yeah, I'm Bart Selman, currently at the Department of Computer
Science at Cornell University.
Cool.
What first got you into the field of AI?
I guess I studied physics.
Actually, I did a masters in physics in Holland
in the early '80s, and I took a course
on artificial intelligence in the philosophy department.
So that got me familiar with AI, and I was always
interested in human thought processes and cognition.
And so after I finished my masters in physics in Holland,
I got a scholarship from the Canadian government,
and I studied--
I went to Toronto and did my masters and PhD
in artificial intelligence.
What kind of problems were you working on when you first
entered the field?
So when I first entered the field,
I did a masters on actually connections
to natural language understanding,
so neural networks.
And at the end of that, actually, Hector Levesque
came to Toronto, and he got me interested in reasoning
and complexity issues, conditional complexity issues
in artificial intelligence.
And that's what I started working on then.
What have you worked on since?
So my work started--
in the mid '80s, there was a movement to study--
people started to realize a lot of things
we were trying to do in artificial intelligence
were computationally intractable and complete,
basically complete.
And there was a whole effort in identifying what could
be done in a tractable manner.
So people defined restricted representation languages,
restricted emotionalization languages, and restricted forms
of default reasoning.
So this was fairly formal work, where
you would prove properties about languages and about inference
and try to find tractable classes.
And that was my PhD work.
And then I finished 1990, and I went to the laboratories
and actually started working with Henry Kautz.
Do you have any fun stories or anecdotes
you liked from Toronto?
From Toronto?
Fun stories.
No, it was just a very exciting time.
I think, with the Canadian government-- it was
a private foundation, I think.
They brought back a number of prominent researchers,
kind of an answer of a brain drain going to the US.
So they set up these chairs in artificial intelligence,
and so they brought back Hector Levesque.
Actually, Geoff Hinton came later on,
and he attracted some big names to Toronto.
So it was an exciting time, and they didn't have to teach much,
so we could spend all our time on research.
So it was a very--
these were the highlights, the high points
of artificial intelligence.
I remember, I think it was the '84 and '85 IJCAI
when Hector won the Computer Thought Award.
I think he gave a talk for 5,000 people,
and that was quite something in the hall.
This was in the same place where they had the Olympics,
I guess, the year before.
The whole UCLA campus was full, and this
was a lot of press and a lot of hype basically about AI.
So it was the high point.
Cool.
So I've seen all the ups and downs, I guess, on the field.
And it's interesting how the field now is in an upswing
again.
So arriving in Toronto when there were all
these other real artificial intelligence minds coming,
where did you guys see artificial intelligence going
in the next 20 years or so from then?
It was moving fast.
There was a strong belief at that point in sort of logical
and the reasoning-based approaches and people
and cognitive reasoning, and those kind of areas.
Geoff Hinton, of course, the neural network perspective
and the learning perspective.
I think sort of what people realized in the next decade,
roughly from '85 to '95, is the limitations of some
of these approaches and that it wasn't as easy as we thought.
And so people went back to the basics,
again, trying to fix some of these problems.
So it's all very hard to predict.
I think one of the key things in artificial,
it's very hard to predict where the next breakthrough will be.
Sure.
And I'm sure we're actually going again at current times.
But those people always try.
And I think what brought us all together is
sort of we come to science with a desire
to figure out thinking, how people think,
and understanding mental images, how people manipulate
mental images to do thinking.
But at that point, it was a very optimistic period.
And I think that went away for a while.
And now, it's coming back.
I was going to ask if you've done any work
in computational neuroscience.
Also, for my master's, not literally neuroscience,
but we did build a parser that was actually using a Boltzmann
machine to disambiguate.
You give a little input sentence,
and then it would settle on one or two.
If it had multiple parses, it would settle
on possible parsing states.
I see.
And on small signs and basic examples, it worked.
So that was sort of fun.
OK.
That's cool.
What are you working on these days?
So basically, when I went to Bell Labs,
we were sort of in front of it.
That was actually the hey day for Bell Labs.
Ron Brachman started the artificial intelligence center
there.
And many, many very good people went there
in a short period of time, Bell Labs.
Arno Penzias.
I remember we presented that work to the Nobel Prize winner
in charge of Bell Labs, Arno Penzias.
And he liked AI and wanted to see sort of the latest things.
I remember him showing--
we had very stochastic search algorithms,
and we did sort of standard deterministic search of them
for logical inference.
We actually did stochastic local search management
for inference.
And he could solve very large problems.
And we would show it to him, and he would take a little video.
We had a little graphical demonstration of that.
And we would show the general audience to understand science.
And so that was an exciting time.
And I shifted sort of my emphasis
from looking at restricted formalisms and [INAUDIBLE]
and started working on [INAUDIBLE]
justafiability testing.
So it was propositional reasoning.
And that's what I have focused most on since then.
And there, we have made tremendous progress.
So from a few hundred variable problems
in the early '90s to now a million variable problems, five
million constraint problems.
And the area has nicely branched out.
So now, my interest is beyond.
I work with people in hardware verification, software
verification, because there these tools are really
used to support hardware and software design.
So I sort of see your reasoning now,
as sort of augmenting the human reasoning.
So human designers, engineers.
And I think that that's sort of where
I see the future, one part of the future of AI
going to be, I think, augmenting human intelligence
and actually strengthening it in many ways.
Where do you see your work specifically, your season, so
the next progression?
So that's sort of where I've always been interested.
For example, mathematical discoveries
and mathematical theorem proving.
And so I work with some people at Cornell
who have actually studied very sophisticated
theorem-proving systems.
The search space is too large for actually doing it
automatically.
So basically, they reformulate proofs by mathematicians
and then verify them automatically.
But what we now want to do is we actually
want to have these proofs automatically discovered.
So the one thing we're seeing is that I
think computers will do a slightly different kind of math
than humans do.
But that can be nicely complementary.
So you can conjecture some things
about mathematical objects, and a computer can be used
and hopefully will be used to verify these properties
in a reasonable amount of time.
And that's sort of an interactive setup
that could actually help the human scientist.
Can you go into a little more detail about that?
So basically--
The differences.
Oh, the differences in what they can do.
Well, I think it's clearest in something like chip
design or software design.
And chip design is already used.
So the chip designer has sort of a varying thing,
as a human, fairly good creative insights of what
a reasonably good design is.
However, when you use millions of gates,
millions of components, subtle interactions you may miss.
So you may want to know, can this register ever overflow?
And so what the chip designers do
is then they actually send that query.
It's actually going to be translated
by [INAUDIBLE] techniques, [INAUDIBLE] liability problem
and ask the computer, can you check whether this really
will work if I put it together?
And I sort of, what I would like to see
is an application like this, and that's
what we're starting to work on in, for example, mathematics.
So we can say, I think, that this observation
might have certain properties.
Can you check whether this property really whole or not?
Yes.
Wow.
And I think this is becoming possible.
So that's in the next 5 to 20 years.
What do you think about the new sort of emphasis
on tying artificial intelligence into sort
of biology, nanotechnology, stuff in that direction?
Yeah.
So those, I think, are interesting direction.
I mean, my own work, it has, for example, connections
to statistical physics.
So I think a big change in the last 10, 15 years
is the sophistication of techniques.
So we're looking at other fields,
like statistics, probability, biology,
where we reach out to other sciences.
And I think that can be very fruitful.
So I'm mostly really working as a physicist,
a physical physicist, who actually have come up
with new kinds of [INAUDIBLE] truth and dare techniques.
So I think that outreach to the community
is going to be very important, and that I
think will accelerate.
So I guess we have time for one final question?
Besides your own work, which--
because you're working on, is obviously
the coolest thing in the field, what
do you think the next coolest thing
is that's being worked on right now?
I think the whole integration with sensors and data.
So the fact that we have so much more data available
makes these statistical methods and the statistical approaches
more promising.
Similarly, we mine data on the web.
I think that might actually help at this moment.
Many years ago, sort of common-sense reasoning
may become more feasible by actually going back
to the web [INAUDIBLE].
Cool.
[INAUDIBLE] on the web.
Makes sense.
Unfortunately, we have to wrap things up.
But thanks a lot for talking to me.
Thanks.
I really enjoyed it.
It's good to meet you.
OK.