I'll just ask you introduce yourself.
Hi, I'm Danny Bobrow.
I work at the Palo Alto Research Center,
formerly called Xerox PARC, but now I just called PARC.
And I first started working artificial intelligence in 1957
when Marvin first came to MIT, and John--
just started the MIT AI project.
And I took Marvin's first course in artificial intelligence,
which was--
which spent a lot of time on Turing machines,
because that was the model.
What was he like then?
What was that?
If I can jump in, what was he like then?
Marvin was a 30-year-old--
if you can imagine--
full of energy, young professor who could do anything.
He would program, he would--
he's an incredible piano player.
I don't know if you know that.
He's a-- and composer, and inventor.
He invented the scanning electron microscope, one
of the first versions of that.
No way.
Really?
Yeah.
And I think one of his first inventions was something--
I forget what it was to detect metal in the eye,
because his father was a physician.
And when I joined him, I found that I
wasn't joining kind of of a faculty club,
it was kind of a family.
Marvin always opens his house to graduate students,
and people could just--
were over there all the time.
And I was there with him when he had his--
when he had his first kids, and then when he had the twins.
There was always interesting people around.
He had a living room in his house over there,
which has room for two grand pianos,
plus electronic instruments.
And he would play these things, among other things.
And other people would come, play these things.
He moved into this house and what he had
there was more like a play pen.
In his living room, since Cynthia was only
a year older so, is he had all the boxes of books
since he hadn't found a place to put them yet.
And he arranged them around like a giant playpen
in the middle of the floor, and she played in the middle.
And everybody else was around the outside.
The room was about three times the size of this room.
And it was an old house, and he had
put a bolt in one of the ceiling beams, and then a rope that you
could go swinging back and forth across the playpen area.
And then he had all sorts of toys and things
that you could make things with.
So if we step back to you before you joined the lab,
what got you interested in AI before hand?
There was-- it was just a dream of what AI was.
And I loved the idea, from the a little bit--
all I read was the proposal for the project.
I was offered a job as research assistant on the project.
And it was Marvin Minsky, John McCarthy,
and Claude Shannon were the three principals.
But then Marvin had all sorts of other friends
like Norbert Wiener, who he knew,
or McCulloch and Pitts, who were people
who did the neural models.
And then McCulloch was a philosopher
and wrote this wonderful paper called,
"What is The Number that Man May Know It,
And What Is a Man that He May Know a Number?"
So we had all these different kinds of people around.
And it was really an intellectual community
where what you did, you read things
and then you have discussions.
We talk about things.
I came up with an idea for my thesis,
and I talked to him about it.
He said, well, that's a good idea.
I don't know if you heard my talk this morning,
I mentioned it, anyways.
And so it was doing algebra story problems.
And Marvin thought that was a good idea,
and he let me go on with it.
And if I ever came to him, he would answer questions.
But he always believed that he was just
providing the atmosphere for students to explore themselves.
So I recently read that Eugene Charniak's thesis
on [INAUDIBLE].
I don't know whether you're familiar.
I'm quite familiar with that.
He followed on-- his thesis on [INAUDIBLE] followed
on to my student thesis.
That's what I was going to ask.
So he was he was in the next generation.
So we were in the--
you might be interested in where the AI lab started.
It started in the basement of building 26.
Really?
Yes.
So we had a big room, which we had our own punch card
machine so we could actually type in our things.
And it was really quite amazing then.
I think Tech Square opened in '61 or '62.
1967.
--like that.
And to have all that space to be able to be outside
was really quite amazing.
What kind of stuff did you--
I guess, what sort of computer did you have in your--
Well, originally it was a 704, which was a tube machine.
And then it got to be a 709.
And then they finally got a 7094,
which was the transistor version of this thing.
And as I mentioned before, it had 32,036 bit words.
That's where the-- and that was the size of the memory,
and that was as good as it was going to get.
That was the big machine around.
And we had Lisp.
And Lisp was not an interactive language.
Lisp, you just put in a file in.
A day or two later, you get back.
We'll punch your printout, whatever you sent
to print out from your thing.
That's how you do things.
I've heard of--
I've read a paper outlining Lisp as a mathematical construct
before it was even brought into computers.
Do you know much about this transition?
Well, I was there.
I watched McCarthy do this.
McCarthy was-- McCarthy, it was a whole seminar he was running.
It was a whole issue about what to do.
It was really based on--
you should talk to John.
John is here.
Yeah, we're hoping to talk to him.
My impression was he was very impressed
by [INAUDIBLE] mathematics.
And [INAUDIBLE] had basically some mathematics
which allowed the representation of various kinds
of expressions.
And John translated that.
And in his paper, in his original paper
on symbolic computation he talked about how
those things fit together.
So how long were you at MIT [INAUDIBLE]??
Let's see.
The first year, I was actually at Harvard
and got my master's degree from there.
But since I was taking some courses in MIT,
I was coming down.
I went to Harvard because I was offered a position
both at Harvard and MIT.
And I chose Harvard because they said, oh, we'll
give you research--
we'll give you a fellowship.
At MIT, they were going to give me a research assistantship.
Well, a fellowship must be better, but it was boring.
Harvard applied mathematics did not have anything very exciting
going on.
There were a few interesting people coming
out and [INAUDIBLE] few others.
But at MIT, you had Marvin, and John,
and all those other people that I mentioned.
And so I came down.
And things were loose enough at that time,
I had gotten the offer from the mathematics department.
And I just went down and talked to the secretary down there
and said look, I think I made a mistake.
I'd like to come here next year.
She said, yeah, we still have your file.
I think that would work.
So I-- they made it work, like that.
So I was there, well '57 or '58.
In 1964 I got my degree.
And then I was for postdoctoral fellow assistant professor
for another year.
So I was there for eight years at MIT.
So this was the years--
we were just four or five or six graduate students working
with Marvin and John, up to having the whole AI
laboratory over in Tech Square.
So you got to see the beginnings of a lot of projects
that were going on here.
That's right.
Do you remember any particular-- if you
had anything to say about anything in particular?
Oh, let's see what kind of--
If you found anything specifically cool, or.
For a new scholar of artificial intelligence,
or for people not super familiar with the field,
what would you say were some of the most exciting things?
Well, let's see.
I still think some of the most interesting early work,
one of the interesting pieces of early work
was Tom Evan's thesis on analogy.
I don't know whether that's still done,
but that was a really marvelous piece of work
where he took standard college board analogy problems.
He had two programs, one of which
transformed the pictures into descriptions,
and the other which did the analogy work, actually
do very well on the analogy tests.
So he did that.
The other thing was that whole development of what Lisp was
and what it meant to be able to have this.
And the idea that inside of this,
you could build other languages.
I mean, a great insight for me was
looking at the interpreter for Lisp written in Lisp, realizing
that if you build an interpreter Lisp,
you could build an interpreter for other kinds of languages.
And, in fact, that's what I did for my thesis,
is I built another language which
made it easy to transform those natural language statements
into algebra story problems.
And I built another language which
was good at solving equations.
So I built these languages.
So the heart of my thesis was only
like six or seven pages of text, which
just had those transformations of the language part.
But it was 30 pages for the interpreter before that.
So that was what that was about.
And then there was the entire stuff about building,
it was time sharing system work that was going on
at MIT at that time.
And this was another revolution, because as I
was saying, when I was doing my thesis, I had a deck of cards.
This was this big.
And I had to bring that in and carefully edit the deck,
put the cards in the right place so it would
load the program correctly.
And then I'd submit the deck, and they
would give me back the deck.
And they would give me back the printout day or day
and a half later.
But when we were building the sharing system,
we could, when we weren't actually testing
the sharing system, get to use the machine ourselves.
I had the entire $5 million machine that I could work with.
I can get rapid turnaround, meaning putting in a run
and getting it back 10 minutes later.
What kind of machine was it?
This was a 7094.
And then what they did was, when they put a sharing in,
I could sit there and I could actually get results out
incrementally.
And it just changed the way you thought about the work.
Imagine if you had to today do your programming
and you have to go away for an hour to get a compilation done,
or you have to do something.
It just change this thing.
This was another insight of John McCarthy's who
did this whole thing.
And, let's see, what were the kind of--?
Oh, I guess there was work that was Richard Greenblatt's
work on chess, which I always thought he was really fun.
I met him [INAUDIBLE].
Were you involved at all in the [INAUDIBLE] group?
I was not.
I visited there.
It was nice to see.
But I never got--
I had a lot of other kinds of interests.
I actually enjoy going out on dates, and things like that.
[LAUGHTER]
Yeah, well I-- anyway, especially toward the end
I got married and had a child.
So one thing is, I had this dream that I could really
carry on having done language processing
for this first algebra story problems.
I said, well now, I would be able to do better.
And I had my first child, my daughter, my oldest daughter,
just a week after I turned in my thesis.
Using parallel processing here.
Fortunately, the heavy work was being done by my wife.
Well, it was quite good of her to wait that week,
because she was typing up my thesis,
and getting further and further from the typewriter.
But she had typing skills at that point I had not developed.
But then I had this dream I said, oh look,
I have a brand new child.
Now I ought to be able to keep up with language learning
my child is doing.
How proud-- I'll be able to program and do things that way.
And after about three years, I realized
I was just not keeping up.
So I had another child, another daughter.
And still, the problem was harder than I thought.
But I thought, this I should learn, one shot learning.
It's not going to work to just keep having
the children to keep up with.
So do you have any favorite anecdotes or fun stories
from the AI lab, either [INAUDIBLE]??
Let's see.
I don't know whether anybody ever told you
that they actually hacked into the elevator control system
so that if you wanted the elevator to come up,
at one point they actually hacked into it so that you
could, from your terminal, you could get the thing
to get up to the ninth floor.
There were people who decided they didn't like that.
It was some military group, I forget on which floor.
And the fact that they were being called up
to the ninth floor when they thought they were going down,
they decided this was not fun.
Anyway, so they were hacking into the [INAUDIBLE]..
I guess having been basically in the field of AI
and seeing the whole thing go from nothing to what
it is today, I guess what are some
of the problems you see that have been solved,
and what are some of the challenges that have been
with us from the beginning?
Well, let's see.
The challenge, I think the continuing challenge has always
been how to transform and represent the knowledge
that we have, and that way use it--
not just declarative knowledge, but the procedural knowledge--
into a form that makes it possible to manipulate
in the machine.
As I mentioned, I spent a lot of time on building new languages.
Because that turns out, I've always
believed that the right way to solve a problem
and really solve a class of problems
is to build a language in which the solution to the problem
is expressed very easily.
So if that takes building a language in which you
can express that, [INAUDIBLE] is there.
So I think we've seen more and more of these things, more
languages, more ways of expressing these things, more
ways of doing the manipulation.
And that's, I think, one of the continuing challenges
that we see today is, how do you do
that in a way that makes it be coherent,
and makes it be livable?
The other challenge that I see as going on is what--
I call it my two baby problem.
It's really hard to scale.
It's really hard to make things get past the first three
examples.
And I think one of the reasons why
we see a lot of machine learning today
is because people say, oh, well now I will be
able to deal with lots of data.
And they do it on the average.
But what have we done?
We've done expert systems were success in that,
it was a language that was--
a set of languages that were designed to make
it easy to express knowledge.
And they're now in use today all over.
There are a lot of vision programs.
There's ways that language, natural language is being
used in very limited domains.
But I still think the general problem is still wide open.
Lots of problems to be solved, lots of interesting.
Makes sense.
So I guess, so when you, not necessarily
at the inception in the field, but I guess during your time
when you were at MIT during the really early years
of the field, where did you see it going?
Where did you think you were going to be in 20 years?
Where did you think we'd find ourselves in [INAUDIBLE],,
in terms of AI?
One of the things that--
one of the problems I thought we faced
was a version of my natural language story.
Which is every time we were able to solve
some simple version of a problem,
we thought the next step would be easy.
And it's basically-- so language, we
would have in 20 years and pattern recognition.
We thought it would be easy to do all these things.
And none of these things turned out to be simple.
The world turns out to be much more complicated.
There's far more interaction among things,
as in this recent panel somebody was
talking about the brain is a 400,000 year-old legacy
application.
[LAUGHTER]
It's one continuous add on from a basic fish brain.
And so it's not that it's simple in structure.
And there's a-- somebody else was referring to, I guess,
a recent paper by Jerry Sussman about why it is that we want--
how do we build robust systems?
And to build robust systems, you don't
want to get minimal engineering models.
We want to get things which have lots
of redundancy and alternative ways of doing things.
And I think we haven't yet figured
out an architecture which allows us to do that sort of thing.
We keep trying to make the problems simpler.
One of the problems today AI is faced is math envy.
Namely, mathematics gets reduced to a few forms,
a few equations.
And we can extrapolate from those,
and everything follows from those.
But I don't think that's going to be
true for AI, or intelligence.
We have two minutes.
We have about a minute.
Oh, shoot.
OK, I better be quick.
One more question.
If you were--
I guess, besides your own work, which obviously
is coolest thing out there, or you wouldn't be working on it.
What else do you find really, really neat
going on in the field right now?
What makes you go wow?
Well, let's see.
What makes me go wow.
Well, there are two things that I
think were discussed in this last panel, one of which
is some of the robotics work.
I think actually being able to get in there
and affect and interact with the world is
really fun and exciting.
And it really challenges you, because, as I said,
the world is not simple.
the combinator-- you can't solve everything in advance.
You have to be able to use feedback.
And the other intriguing one is work
that Tom Mitchell was just talking about,
which is how do we use brain instruments to teach us
things about intelligence?
How do we use our--