[INTERPOSING VOICES]
What do I say?
Well, I think I'll ask you to introduce yourself first.
I'm Gerry Sussman, Gerald Jay Sussman.
I've been at MIT since 1964 when I was a freshman.
Very nice.
With two years off, for good behavior.
So how did you get into the field back in '64 or shortly
after?
Well, actually, came a little earlier.
As I explained in this morning's session, which probably-- well,
you didn't have the camera on before.
At Erasmus Hall High School in Brooklyn,
New York, a huge high school, I got
involved with some Columbia program for high school
students.
And one of the teachers in that program
was Joel Moses, who was an undergraduate at Columbia
at the time.
And he taught a class computer programming
for high school students, which I took,
and so that's how I got hooked on computers.
The most interesting thing, of course,
was I was also a ham radio operator.
And there were these--
this machine with lots of electron tubes in it, and that
was very interesting to me, because I've never
seen something with 5,000 electron tubes.
So that was sort of fun to understand those things.
So I then got hooked on computation.
Went off to look at various things,
and I'd got pretty good at programming,
and I suppose in the Columbia Library--
actually, the Library of library studies--
there was a book called The IPL-V Manual,
and I read this thing and then said, "Oh, man.
I could write an interpreter for this.
It could be--"
Well, I didn't know the word interpreter.
"I could write a program that could really understand
this language," and I did.
So that was fun, and I was probably a junior
in high school at that time.
And then somewhere I read a book called Autonomous Studies,
with Shannon and people like that, with Minsky's papers
in it, McCarthy.
And that said, MIT was the right place,
so I showed up at MIT as a freshman in 1964.
I didn't really know I was going to have anything
to do with artificial intelligence training,
I was just interested in playing with computers,
but I was a math major.
That was pretty clear to me, math and physics
were what I liked a lot.
And so somewhere in my beginning of my freshman
year, I hung out with people who lived in Baker House
at MIT, who were mostly physicists at that time.
Alan Guth was an undergraduate, for example.
Wow.
There was a fellow named David Cressey who
played with computers a lot, he was a physics undergraduate.
He said, "There's this interesting computer
over at Tech Square on the ninth floor
that nobody seems to be using very much.
Let's go play with it."
So I went over there with him and there
was a fellow who seemed to be in charge of it
by the name of Greenblatt.
He seemed to be an upperclassman and he didn't mind
me playing with the computer.
So I learned how to program this,
and it was pretty painful.
And although I'd programmed other computers before,
this was sort of strangely documented.
Although, it was a very nice machine.
And so I made a little neural net that played tic-tac-toe.
And one day this bald-headed fellow showed up,
and it was obvious he was going to throw me out
because he was the boss.
Turned out it was Minsky.
And he came over and asked me, "Gee, what are you up to?"
And I explained to him what I was doing.
I said, "How do I get paid for that sort of work?"
So that was fun.
And then he explained to me the most important thing
I'd ever heard in my life, but I was a freshman,
so I got hooked.
He said, "Why do you make your network random?"
I said, "I didn't want to have preconceived misconceptions."
He said, "It has them, it's just,
you don't know what they are."
So that's how I got hooked into AI,
because this was the smartest thing I'd ever
heard anybody say in my life.
So I did all sorts of things.
At some point, Minsky put me as an undergraduate
in charge of the summer vision project Minsky and Papert did.
And that didn't work very well, but it was supposed
to solve vision in one summer.
[INTERPOSING VOICES]
They [INAUDIBLE] didn't understand.
Actually, that's the right thing to do.
The right thing to do is always to take a hard problem
that you don't know much about and find an undergraduate
to work on it.
I do that too, and the reason is because undergraduates
don't know enough to know that it's hard
and therefore you're going to see them try something,
whereas somebody who knows that it's hard
is not going to make any progress at all.
Fair enough.
So we found out what things were hard.
A lot of things were hard.
That's probably what Marvin wanted,
he wanted to find out what was hard.
So I think that was very, very smart.
I've been working on lots of things
ever since, in both artificial intelligence and related
fields.
And so, for example, my doctoral thesis-- well,
actually, my bachelor's thesis was a little theorem prover.
[INAUDIBLE]
I learned about resolution theorem proving
and I thought it was a great thing.
And around 1968 when I was a senior
I wrote a little theorem prover that
was sort of nice although, of course,
it didn't do anything more than other people's theorem
provers did.
I suppose I grew out of logic after a while.
I wrote a PhD thesis about programs
that debug programs, which was sort of fun.
And as Marvin pointed out, nobody
has really followed up on that very much
and that's sad, because it was a good idea.
I think it still is.
I didn't follow up on it because I got attracted to lots
of other things, you see?
So I got involved in understanding how people--
I got into teaching.
As soon as I got a doctor's degree,
I became an assistant professor.
It probably was a bad idea, but I did it anyway.
And so the thing is I got into teaching
electrical engineering, which is fun,
since I liked electronics and was a ham radio
operator and all that.
My main concerns started to be concerns
about how to think about teaching better,
how to understand how to explain to people the things that
go on in the minds of good engineers and scientists.
And so I started modeling those things.
And so most of the work I've done
in artificial intelligence has been understanding
the techniques that are used by scientists and engineers
and they're doing their work for the purpose of--
partly, for automating something, or partly
because a formal precise description--
for example, in a computer program--
is something that can be used as an explanation.
So if I get you the program and you
know how to read the programming language,
then you understand how to do the job
that the program explains.
So that was sort of a teaching idea.
Most of the work I've done is basically in that direction,
and that's what I'm very proud of.
Could you tell me about LISP and SCHEME?
Well, that's not very important actually,
but I'll tell you about it.
Go ahead.
Well, people always ask about that,
because somehow it seems to be very associated
with me and my friend Guy Steele who
was an undergraduate student.
But we never thought it was important.
Let's see.
The reason I ask--
LISP is a wonderful language.
First of all, when I was a freshman, I was taught LISP.
I was taught LISP by very, very smart people.
Like Gosper, for example, put a lot of effort into that.
And he had wonderful, wonderful ways to think about programs.
He was a upperclassman, also.
And I learned huge amount of programming from Gosper.
I'm a mathematician type person, so the thing
that's attractive about something like LISP
is that it's a language which, among other things,
it's possible to express statements about the statements
and language in the language.
You could quote, and that you could talk about the quoted
sentences, and that you can take them apart and put them
together with [INAUDIBLE] and so on.
The language itself is manipulable,
and that's why you have things like
the eval-apply interpreter.
That's a beautiful, beautiful idea.
It had some bad properties, the LISP that we had.
It was dynamically scoped, for example, which
I thought was not that great.
I don't remember why I learned that.
I suppose it's because I-- again, a mathematician
type logician person, I care about reference transparency.
But I didn't think much about that.
Every so often I had to invent a language to push my AI ideas.
So I had to invent something.
The first one was I had to make an implementation
for a very small subset of Carl Hewitt's PLANNER language
to help Terry Winograd finish his PhD thesis.
I'm explaining that because it was a programming thing
and I like programming and I understand
how to write interpreters.
I loved that idea.
MicroPLANNER.
So that was microPLANNER.
And then the next thing was for my own stuff
I wanted to make a little bit more elaborate thing, which
turned out to be a flop, called CONNIVER.
And it would have been called SCHEMER
if it were-- it didn't take six letters.
Because the size of a filename in PDP-6 or PDP-10
ITS system was six letters, and so that becomes SCHEME.
But the way this occurred was because it's basically a LISP.
There's no question, it's nothing special or new,
it's a LISP.
Guy was a Harvard undergraduate who then became
a graduate student at MIT.
We were sitting next to each other, I think, in a talk
being given by Carl Hewitt about control
as being patterns of passing messages.
It was a very hard talk when you're having trouble
understanding it.
Well, I said to Guy or he said to me,
"Let's try to figure this out."
So we curl up in my office and he had spent all night.
I called my wife up and said, "We're very busy,"
and she would complain that I left her in the lurch.
But we stayed up all night and wrote a few interpreters.
And the actor invocation mechanism
that we thought we were inventing
to fit what Carl's talked about, it
turned out to be exactly the same as Lambda
application in a less mixed up language.
So we threw away the extra copy of that, and that was SCHEME.
SCHEME was an attempt to understand Carl Hewitt.
That's how it--
Makes sense.
Arose.
Interesting.
We didn't think much of it, other than the fact
that because we had produced it, it
was easily manipulable by us.
And so Guy Steele wrote a master's thesis
about compiling, which was very important,
which invented the continuation-passing style
and things like that.
But that was the reason why he used it,
it was because he understood the thing very well, the language.
We invented this thing, this very nice interpreter
that we can manipulate.
And we never actually published papers about it,
we wrote monthly AI memos.
But we never thought it was sufficiently important.
We were mostly AI people and it got
[INAUDIBLE] got constraints, not about SCHEME.
In fact the reason why SCHEME became more interesting
to the world was, for the most part,
due to Dan Friedman from University of Indiana
who picked it up and said, "This is a good thing."
It wasn't our deal of doing it all.
How's that?
That's good, thanks.
So you were at the AI lab pretty soon after it was created,
[INAUDIBLE]--
Well, originally it wasn't called the AI lab,
it was the AI Group of Project MAC.
Project MAC was a rather impressive but small community
of people who did all sorts of things with computers.
Do you have any favorite anecdotes or cool stories
from back then?
And I've heard some talking to Rick Greenblatt,
he had some pretty--
Yeah, well, probably one of--
Fun tales.
The most interesting to me was there
used to be a thing called TPSS there,
they had a time sharing system.
And there were these people called the hackers, of which I
was sort of a marginal member.
Tom Knight was a high school student
when I was a freshman so he was, I think,
one year younger than me.
So Stewart Nelson and Tom Knight, I think, at one point
decide to invade and take over CTSS.
And they had some plan, but they didn't know how to get the--
for some reason, what you had to do
is overcome a particular operator.
The operator who runs the big machine,
there was an operator guy, he-- you
have to be able to deposit some numbers and some memory
locations.
So I became the, what's the right word?
In the spy kind of novels and things, it's somebody who--
the guy who actually does it.
So I have to be friends with the operator.
And then at one point at the appropriate time
while Mr. Nelson was going to deposit 177 in the restriction
code 4T2012881, which is Seymour Papert's login,
I deposited the 177 in the right thing
by taking over the data channel on the code 7909
when the operator wasn't looking.
Well, actually, they didn't know this happened to me
but Tom Knight got banished for a while from the building.
But it was [LAUGHTER]---- it all worked out OK.
[INTERPOSING VOICES]
Right.
So this was sort of one of the early computer
unpleasantness-es.
Right, fair enough.
That was fun, actually.
Let's see.
I guess when you were there at Tech Square--
Well, I still am.
MIT.
Tech Square's gone, that's the problem.
Early years of Tech Square, in terms
of communicating with other labs,
was there really any kind of collaboration going on?
Other labs?
Other AI labs.
Oh, AI labs?
Well--
The impression I have gotten is that there are
sort of different enclaves of--
Well, it is true that from time to time,
John McCarthy would drop in because he
was friends with Marvin.
And I met him when I was an undergraduate.
But remember, I was an undergraduate,
so it's hard for me to estimate that.
But I did have a lot of connection
with a fellow by the name of Jerry Lettvin,
a rather famous neurophysiologist
and an electrical engineer who invented
some of the early amplifiers for recording from nerve cells.
And he was friends with Marvin, and he
had some people he was with called McCulloch and Pitts,
I think.
I never met Pitts, but I used to get McCulloch's mail
and bring it to him.
And I got to be friends with Jerry Lettvin, who also taught
classes in the humanities department,
besides being a professor of biology
and electrical engineering.
So he taught a class with Giorgio de Santillana
on some history, specifically The Crime of Galileo,
which was a book written by Santillana explaining
how-- why Galileo got himself into trouble.
And so there was a lot of connection
at that level inside MIT.
But for an undergraduate, it's very hard for me
to report on the connections between laboratories.
Yeah, that's a great question.
As far as AI goes involved in the more theoretical
[INAUDIBLE] constraints and [INAUDIBLE]----
I don't know.
Theoretically, I did a lot of things that work.
Yeah.
Sorry.
No, you don't have to apologize.
I'm saying I'm a programmer.
Right.
Do you have any experience with robotics or use thereof?
Well, sure.
At the very beginning when I first arrived,
people were building certain kinds of robot arms.
And in particular, there was a arm made by AMF--
American Machine and Foundry--
which was a large hydraulic machine, quite dangerous,
and I nearly got killed by it.
Really?
Oh, yes.
How'd that happen?
In fact, I think after I almost got killed by it,
they put in a safety push off switch.
So now, you got to have some sort of deadman control
to use it.
This was a very, very big machine
used for automobile factories for putting the sheet metal
into the press or something.
It would go, "Womp."
And I suppose when we got it, it had a tape-driven controller
where it would do the same thing over and over again
and you could train it to do that.
And to connect it to the PDP-6 computer
required building a transistorized controller
that would interface with it in a funny way.
There were certain valves that had only eight positions
of openness or something, and I remember
there were several valves that had to be manipulated.
And the various other things, like the mechanisms
for measuring where it was, the various position controls.
When it came, they were optical encoders
and they were replaced for the moment
by 10-turn potentiometers, which were glued into place
with a minute cure epoxy.
Oh, man.
And now unfortunately, this is a very dangerous thing,
and I didn't realize that.
And it was very hard to control because it
had such crude control.
So one day when I was measuring its motion,
Guy is writing programs to try and get it
to stop and move to places, and it
was sort of wobbling back and forth
because it was being unstable.
And one day I was measuring it with a ruler, seeing
how much it was moving, and one of the pots fell out
and the minute cure broke or something,
and the thing went, "Womp."
Back and forth like this, because its limits stop
and then it reversed.
Because my program says if it hits limits, time to reverse.
You see?
And it did this bouncing back and forth,
sort of walking along and I started yelling and screaming,
and Richard being like, "Hey, man,
hit the stop switch of the computer,"
which helped me a lot because I thought
I was going to be killed by this thing.
Wow.
Thing was swinging around wildly.
Oh, boy.
I could've been the first casualty of a robotics
experiment, but it turned out it wasn't.
Do you have any thoughts on robotics today
and how it relates to AI?
I didn't get involved in robotics after that.
Not because I was afraid of them,
but because it seemed to me there were other things that--
where you had more--
I decided that there was more intellectual bang per unit
effort in other things.
That makes sense.
In fact, that's the way Marvin Minsky feels that way now,
apparently.
And I think I agree with him.
If you want to have the maximum intellectual impact
for the unit of amount of work you
put in, then building physical robots is not
the place where it happens.
I can see that, definitely.
So I guess you're more of a CS person,
but how do you see AI impacting societies that feel--
we've heard that, for example, expert systems
have branched out to a lot of industry
and are used all over--
Sure, that's one application.
The place.
And there's lots of other things like that, too.
Sure.
I think that it's very unlikely that much
of the technology you see right now
would be working if it weren't for AI.
There's a real question of whether we're
talking about the core AI question of making
intelligent machines, which clearly has not
happened very much.
There is not very much common sense in the mind of a machine,
probably worse than I would have expected at the time.
But there is a huge number of spin-off results
that have become standard practice in the industries.
So for example, you now may use a language called Java.
It has a garbage collector, the garbage collector
was invented by McCarthy and Minsky.
The first paper on the garbage collector, I think,
was the 1962 paper by Minsky.
AI, now on number eight, I think.
I'm not sure.
It was for the PDP-1, but it took a long time for that.
It took until 1990, approximately,
for people to even realize that this
was a good idea for commercial thing.
And when it does it, it gets rid of all memory allocation
errors.
That's an example.
There's lots of technology like that.
The ways which operating systems work, part of that
came from AI type things and part of it
came from other work.
So a lot of the infrastructural things
that go on your computer, I think, are AI lab outlets.
So I guess back then when you were
involved in AI lab activity, where
did you see it going then?
I don't know how to think of that.
Again, you're talking about being an undergraduate.
It was just fun.
These were smart people.
I'd go to dinner with very smart people
and learn a lot of stuff.
That was one of the things, some of them
were mathematician types and some of them
were people who were interested in just computing,
and things like that.
It was very clear to me that there
was a great mystery of how things think,
or how to make things that could think.
How it could be the way I could work.
And that's a great mystery that's
about the same level of mystery as to how the universe is
put together as well.
Physics is about that mystery.
And I'm interested in both of them.
the very large mystery about the universe
and the very small mystery about how I work.
And it seemed to me that the method of investigating
the universe as a whole is more of this reductionist thing
that physicists do and I like that,
where you try to understand how the little pieces work
and how little pieces are put together to make bigger pieces.
So you get fundamental particles of atoms and things like that,
and the forces that you have to deal with
and fields and that sort of thing.
And that's a wonderful thing to understand.
But then there's another way to think about the world
when you get very complicated things,
like biological systems, or information processing systems.
Like thinking that it's more synthetic
will help you rather than analytic.
You say, "If I had to make something
that had this behavior, how would I make it?"
Well, there might be only a few ways to do that.
It would be nice if it may be true, may be false,
that the way it actually works in-- say, a person--
is one of those ways.
If I'm looking at it, and it is, then that
would be a great thing.
But in any case, I could think about how it might be to do it.
Makes sense.
I guess one last question, I'm going to throw you a curveball.
Do you read science fiction?
I have.
Do I read it a lot?
No.
There are lots of people in the AI world
who spend a lot of time on science fiction,
and who like it a lot.
As for me, I don't much like fiction at all,
so I don't read much fiction.
And the reason why is the world is interesting enough
without having to make up made-up worlds.
The real world is very interesting.
Makes sense.
What's your favorite thing you've read?
Recently?
Sure.
I'm working on a book by Carlo Rovelli called Quantum Gravity.
It is very hard.
It's taken me about a day per page.
Wow.
Got you.
All right, thanks very much for talking.
Sure.