So thank you for coming.
Why don't we start out by just, why don't you
tell me a little bit about yourself, what your background
is, and what you're doing now?
What I'm doing now?
Well, I've been at Yale University
since I graduated from MIT in 1976.
I've been teaching computer science ever since
and doing AI research, first in areas having to do with logic.
So I did a lot in the foundations
of nonmonotonic logic in the '80s, things
like truth maintenance systems.
And then starting in the '90s, I began to get back to the--
well, I guess I did this in the '80s, as well--
started to get back to work on automated planning,
which was things I'd--
that was the topic of my thesis.
And like many people, I was sick of the topic when
I finished my thesis, so it took me a while to get back to it.
But I've been doing a lot of work in that area since.
And I began to get disillusioned with logic in the late '80s
and decided that robotics would be the foundation of AI,
and I still think that's probably true.
So I made a shift in that area for about 10 years.
But around 2000, I stopped work in robotics, partly because
of health problems, partly because the infrastructure
requires too much effort to keep going,
and it's much easier to do purely software work.
So I've been working on things like knowledge representation
for the semantic web for the last few years,
in addition to the automated planning work.
Cool, very cool.
And what were you doing at MIT, originally?
You mean how did I get there?
Yeah, what attracted you to the institution,
what did you study?
I was there as an undergraduate, and I
was interested in artificial intelligence
long before I got to MIT.
Ever since I was a little kid, I was
interested in artificial intelligence.
I don't know why, exactly, but I've just
been fascinated by the idea that everything about the human mind
can be duplicated in a machine.
And so pretty much knew that's what I was
going to do in graduate school.
And I started hanging around the AI lab when I was a senior,
and I went into a program where I got a simultaneous master's
and bachelor's degree after five or six years,
or something like the.
Great, yeah.
I think it still exists.
And I was interested-- so I got there and was hanging around
with Sussman.
This was around '73?
No, 1970, really, I think.
And I was hanging around with people like Sussman and Terry
Winograd, and they were working on micro-planner,
which was a small version of Carl Hewitt's planner language.
And for some reason, people thought
this would be an important vehicle for AI work,
and it was loosely derivative from predicate calculus.
It was really prologue, and really written
in prologue, if you think of it like that.
So we hacked on that, and Sussman
was finishing up his thesis.
What was he like?
Gerry?
Mhm.
Exactly the way he is now.
He was very energetic.
And he became my advisor for my PhD thesis.
And I decided to do it in the area of electronic circuit
design.
I don't know why I did this.
I was really interested in planning and in organization
of behavior by the computer.
And on top of that, I was interested
in meta-level reasoning.
So I thought that the computer would attack a problem,
and it would use, think of it as something
where it had to make a plan, so it would break it down
into substeps.
And if it got into trouble, it would
ascend to a higher level and reason
about the problem it was tackling using meta rules.
Randy Davis from Stanford had finished his dissertation,
I think, around 1974, or something.
And he had made heavy use of meta rules
for the MYCIN system.
OK, yeah.
So we were all really--
That was for medical information?
--intoxicated with this kind of meta stuff.
Yes, the MYCIN system was an expert system
for prescribing antibiotics.
So it decided what antibiotic to prescribe for a given patient.
And he added meta rules to the system.
And when you say meta rules, what do you mean by that?
Rules for reasoning about an application's rules.
For example?
I can't think of an example, off hand.
But that's the problem.
I really decided that the whole thing had been misguided,
that what I really got out of my experience
on working on my dissertation was that what's really
important is an algorithm.
That is, m the problem with the work,
the early work in AI in that era was, we really all--
we were all--
we had GPS envy.
We really wanted to invent GPS, the general problem solver.
OK, yes.
Which had already been invented, but we thought, well,
we could do better.
We could do better than that.
It reflected Newell and Simon's assumptions
about problem, spaces, and things
like this that we didn't necessarily think
were the right approach.
But that's kind of what we wanted to do.
So there was this idea that meta reasoning
is, that somehow this would magically make things general.
[INAUDIBLE]
What?
Oh, it's OK.
But in other words, here I had a circuit design system.
That was what it was supposed to do.
But I was really focusing on all of the machinery, the plan
library, and the meta reasoning, and the deductive system,
and the unification algorithm, and all this stuff.
I really didn't focus at all on the problem of circuit,
electronic circuit design.
And so that didn't really work very well.
Some of the ideas that came out of it
were later quite influential.
I think the phrase hierarchical task network,
I didn't use hierarchical, but I believe
I called it a task network.
I mean, it was probably the, or one of the first programs
that ever had a circuit diagram, or a circuit design system
on a computer, right?
I really don't know.
OK.
I mean, circuit designers everywhere use those now.
Yes.
Yes, but it wasn't, as I said, circuit design
wasn't really what it was about.
So it was really about HDM planning and organizing,
planning information, planning knowledge.
But well, it wasn't, you couldn't point to an algorithm
and say, the algorithm in this, this algorithm
does the following.
That would have been not general enough.
Generality was what we were obsessed with.
I think it's sort of interesting when
you say how you didn't want to just,
you all had GPS envy a little bit,
because it speaks a little bit about the evolution
of the field.
Because the field was so young at that point.
So if you have any-- do you have any insights about how
the field has been developing?
Do you think it's still in its infancy,
or where are we in our artificial intelligence
research?
No, I don't think we're in the infancy at all.
I think we're in the--
I think the field is really mature,
in the sense that I know a lot of people
might be disappointed by this, but I really
swung to the opposite extreme.
That is, I tend to think that the human mind is
a collection of modules, each of which
solves a fairly small problem.
People often are apologetic about the fact that
our algorithms, each of them will solve some
narrowly-defined problem, but they don't--
this is something that Ray Kurzweil,
he makes this distinction in his most recent book,
The Singularity is Near, between when
he calls narrow AI and strong AI, perhaps an unfortunate
phrase.
Because [INAUDIBLE] used strong AI
to mean something completely different.
But narrow AI, in Kurzweil's view,
is these little algorithms for solving
things like speech recognition, or stereo vision,
or classical planning, or something,
these algorithms that make no pretense to solving
all of intelligence.
And so he imagines that at some point,
this other thing will come into being, although he doesn't
discuss how that will happen.
And I think everybody in the field sort of
is disappointed that this other thing hasn't come into being.
So a lot of the discussions here at this session, this meeting
of the Fellows a lot of these, every time
I go to a Fellows meeting, people talk about,
how are we going to get this general-purpose intelligence?
What's going to cause us to make this transition
to a broad intelligence that can solve a wide range of problems,
like a person?
And I think it's going to happen gradually,
without anybody really noticing it.
That is, one module after another is going to plug in.
And as they do this--
They'll all just be integrated together.
Well, we'll gradually be thinking--
we'll be thinking of machines more and more as being
intelligent, not the way, initially, not the way
people are, but not the way animals
are, either, but sort of like a weird artificial animal.
It's like, you don't expect your dog
to be aware of everything you do,
and you won't expect your computer
to be aware of everything that's going on.
But you'll expect it to be aware of a lot of things.
You walk into a room, and it will see you,
and it will recognize you, which may be a little spooky.
Probably.
Well, you want to be able to turn this off, obviously.
But the point is that speech recognition has
been coming along gradually.
And the other day, I had a conversation
with an automated system that was gathering information
on a Sears dishwasher repair call.
I was very impressed with the fact
that it would say, what appliance are you
calling about?
And I said, dishwasher.
And it said, your dishwasher is giving you a problem.
I thought that's quite interesting.
Obviously, this is something I'm going to want in my computer,
that I can carry on a conversation with it,
and not about some arbitrary topic.
But I can tell it something about what's going on,
and it'll supplement the usual mouse-touchpad interface.
And as these sensory channels come online,
we'll just gradually take for granted
that computers have more and more abilities, even
though we're not talking about passing the Turing test,
or something of that sort.
And so at some point, I think the computer
will pass the Turing test, some version of it.
But we'll realize that in some ways, the Turing test is not,
it's not really a test of general-purpose intelligence.
That is, if you listen to human conversations,
I think there's probably a lot of people
who couldn't pass Turing's test, in the sense
that they couldn't pretend to be an academic.
You know what I mean?
One problem with this field is that everybody in it
is an academic, or has a PhD, or whatever,
and they tend to have an odd idea of what it
is that human intelligence is.
And so if you're really trying to duplicate
human intelligence, you might--
I mean, for instance, an example I've used recently
is, supposed you're trying to carry
on a conversation about sports with a man on a bus.
I've listened to, occasionally, two men talking about sports
on busses, or places like that.
And the conversations are really quite shallow.
If you could do speech recognition and have
access to a database about sports statistics.
A robot that could go to a baseball game
and be indistinguishable from--
Well, going to the game and observing is a different thing,
but carrying on conversation.
The people in the bleachers.
What?
We could carry on conversation with the--
No, it's important that they be on a bus.
OK.
This is the thing.
It's important.
You've got to define the problem narrowly.
Because you're sitting down, and they
don't know that [INAUDIBLE].
They're sitting down.
The context is-- you have to pay attention
to the facial expressions.
That's important.
You have to catch certain nuances, and things like this.
You have to be aware of emotions.
People get very emotional about certain topics.
And it's not an easy problem, but on the other hand,
you can keep the conversation focused.
That is, if you try to change the topic--
well, you could talk about the weather or something,
I suppose.
But there's certain topics you just couldn't raise.
People would get annoyed with you,
or they'd look baffled, or something.
You couldn't start talking about cold fusion.
You couldn't say, what do you think about cold fusion?
Do you think it's going to actually happen?
They're going to-- they would get
uncomfortable, or something.
[INAUDIBLE]
The point is that that's what they want to talk about,
is sports, or something like that.
And so it's socially important.
Now, I think it's much harder to pass,
to do the equivalent thing with women, I think,
because women talk about much deeper topics
having to do with human relationships.
So that would, I mean, I don't think it's impossible.
On a bus?
In any context, I think you'd have
to have more than just access to a database about sports
statistics, or something.
It's just a different set of social conventions.
And maybe I believe it would be easier to do it with men,
because I'm a man, so I think I have more insight into it.
I don't know.
But what I'm saying is that I don't
think there's ever going to be some magic moment where people
say, oh, we've passed some threshold,
and all of a sudden computers are intelligent,
or we've finally figured out this notion
of general-purpose intelligence, strong AI,
whatever you want to call it.
I think even the smartest people are surprisingly narrow,
in a lot of ways.
It's true they can make a lot of connections.
We tend to think of somebody as being really creative if they
can do things that we can't do.
So you know, if you're wrestling with a problem,
and you go to talk to somebody, and that person
can make connections you couldn't make, you say,
oh, that's a creative person.
I really admire that person.
He's very smart.
Whereas if somebody comes to you and asks you to do something,
and you come up with a connection, and you just think,
well, this is just a mental trick I always use.
I don't know what it is, but I just, I
tend to do these things.
It's like Feynman in one of his books
says, well, when somebody--
I have a little trick I use, which
is when somebody comes to me and starts talking about a theory,
I try to think of an example.
And as they elaborate it, I elaborate my example
to fit their case.
So they're talking about some general-purpose, general laws
of physics, I'll be thinking about
a particular physical situation where those laws would apply.
So he's talking about the theory of billiard balls.
I'm thinking about two particular balls
and how they would bounce off of each other, or something
like that.
And then when they ask a question,
I'll think about my example.
Now, for Feynman, it's just a simple trick.
For somebody else, thinking of the right physical situation
and being able to do this in their heads
while the person is talking, it's
something they think of as magic, perhaps.
But if you actually could open up Feynman's brain
and see how he did it, you'd see, ah, yes, he happens--
his brain happened to develop in a certain way
so that it tended to make these inferences
in a certain situation that other people wouldn't make.
So if we want to duplicate Feynman, we can do that.
We just add this module.
But still, that was focused--
it's a particular kind of knowledge that he had.
And he was very, very good at it,
and everybody admired him, justifiably.
But it doesn't mean that he had some set
of general-purpose techniques that
transcended the sorts of things we're doing now.
I think that the idea is that people are exploring now,
and the statistical methods people
are using, the discovery that if you
are willing to optimize your search algorithms,
you can often attack exponential problems
and have a surprising degree of success.
These ideas, which aren't really that earthshaking,
are being applied to a wide variety of problems,
and they're solving them one after another.
And I think that's where the field is
going to continue to go.
So that's why I think it's mature.
I don't think that we're still waiting for the big, the really
good idea to come along.
Cool.
That sound good to me.
So switching gears back to my MIT,
because we want to just hear about what the atmosphere was
like and capture some of what was going on in the past,
cause we are doing a history project.
Yes, right.
Well, the atmosphere was terrific.
This was from '71 to '76.
It turned completely sour when I actually
had to start working on my dissertation.
That spoiled it.
I know what you mean, yeah.
But up to that point, though, it was great.
We would sit around in the playroom.
I Mean, every day we would--
I just learned about the playroom.
I had never heard of it before.
It was this open area.
The offices were all around it.
And this is the fifth floor of Tech Square, I guess.
The offices were around it.
There were beanbag chairs.
So we'd come out of our offices and sit around talking
about technical topics.
And the people that I worked most closely with
were Bob Moore and Scott Fomin, and we were all thinking
about inference and planning.
And so we just bounced ideas off each other.
Did people have similar sources of inspiration about
why they were interested in artificial intelligence,
or what drove people?
What drove us?
Well, as I said we, were looking for--
we thought we were going to come up
with some algorithms that would explain all of intelligence,
really.
I believe we thought that sensory perception wasn't
so important, that you could get the data in and convert it
to a formal notation.
Then after that, you just did some reasoning.
You would come to some conclusions,
and those conclusions, then, would be shipped out
to the motors, into to the end defectors,
and that would be that.
A similar story underlay the Shakey project,
the robot at Stanford.
So even on the West Coast, they had some similar ideas.
We were actually, Bob Moore and I especially,
were opposed to the ideology at MIT
which was sort of anti-logic.
There was a big controversy about procedural
versus declarative knowledge representation.
Everybody thought this was of earthshaking importance.
And so I was a convert to the idea
that logical representations were important, declarative
representations.
Bob Moore, I guess, converted me to that idea.
Because they just had a lot of cool properties.
You could study them, whereas with
procedural representations, it's just programs.
What can you say about programs?
You can organize them in various ways,
but you actually have to write them.
Whereas with logic, you could just think about,
inference algorithms, you didn't actually
have to sit down and write the axioms.
So one of us, Bob Moore or I, invented the bomb
in the toilet problem.
I guess you could say we did it together.
But I thought he invented it, and he thought I invented it.
And so when we compared notes years later,
we realized we had no idea who invented it.
But the bomb in the toilet problem
is this problem that you're given two bomb-shaped objects,
and one of them is a bomb.
And you have, as your only method for neutralizing bombs,
to put it in the toilet.
So what do you do?
Well, the solution is to put both in the toilet.
The reason this was important is because it's
a counterexample to the idea that solving the problem
requires merely proving that there
exists a course of action.
So if you think of this as a prologue problem,
then you might think of it as, you have a free variable, which
is a course of action.
You want to solve, find a course of action
such that if you carry it out, you'll be safe.
And yes, you can prove, there is a course of action.
It's either put bomb A in the toilet,
or it's put bomb B in the toilet.
But all you've done is prove that there
exists a course of action.
You haven't found it, done the one that
will actually protect you.
So it's a counterexample to that simple idea.
And so that's the sort of thing we would think about.
Throughout my career, I've been very good at coming up
with examples.
Important skill.
Huh?
That's an important skill.
I guess.
But so the bottom of the toilet problem, the little mel
problem.
People come up with proposals, and I come up
with examples that show that won't work, which is, I guess,
a small contribution to the progress of the field.
But so in the late '80s, I came up
with what got named the Yale Shooting Problem.
I'm not sure why.
For some reason, I tend to name things after Yale.
I wish I called it the McDermott,
or the McDermott-Hanks shooting problem.
Steve Hanks was my graduate student,
and he and I wrote a paper about this in the late '80s.
And this problem derailed nonmonotonic logic
for a few years.
So anyway, so that's the sort of thing
we thought we should toss around back in the '70s,
in the AI lab.
And it was just a tremendous amount of fun.
I mean, I always tell incoming grad students
that this is going to be the happiest time of your life,
until you have to write your thesis.
And after that, you have to get a job,
and you'll be doing a lot of distracting chores
for the rest of your life.
So enjoy graduate school, because it's really the peak.
Not everybody feels that way.
Do you have any other advice?
Do you have any other advice for new researchers in the field?
Any other advice?
Or aspiring?
In AI?
Well, one piece of advice I always
give students is, pick a very simple problem,
and then make it even simpler, because it will still
be too hard, usually.
So AI, really, to my mind is, in many cases,
is a methodology for attacking things,
or that it is a set of techniques.
One big difference between then and now
is that we thought we were discovering new techniques
for solving problems.
And really, a lot of those things are still around,
but they're very weak.
For example, if you take something
like Kalman filtering, we would say, oh, my God.
How uninteresting is that?
It's a statistical technique.
It only works if things are linear.
It's for signal processing.
But nowadays, AI is often--
we often partner with other people.
We're studying signal.
We borrow signal processing techniques.
We're doing things in the life sciences.
A lot of people are doing game theory with e-commerce.
People are working in e-commerce.
So now we constantly borrow techniques, problems,
algorithms from other areas.
What distinguishes AI is that we study
problems that are harder than other people are
willing to study.
We're not afraid of the fact that these problems are
intractable, and our method is to try something and see
if it works.
And if it does, then we explore that space for a while.
So if you look at things like SAT solvers,
who would have tried to solve SAT except an AI person?
It's the intractable problem.
Theorists would say, that's it.
I proved it's intractable.
I'm not interested anymore.
But an AI person says, I'm going to try it.
So they optimized the hell out of these things.
And then Moore's law comes along and makes
it possible to really look at the big problems.
And all of a sudden, you discover
there's this incredibly intricate space
of hard problems, easy problems.
You discover phase transitions in space.
You discover that there are lots and lots of special cases.
So AI people are discovering these things
and spitting out problems for theorists to explore for years.
And it's often hard for us to justify what we've done.
We have to prove, empirically, that it's useful.
It's often not easy.
So it's this willingness to tackle very hard problems
with empirical methods, often with [INAUDIBLE]
in other areas.
It's not techniques.
It's not subject matter.
Great.
Well, thank you so much.
You're welcome.