Thanks for visiting today.
OK.
Great.
So yes, good afternoon.
So my name is Chuck Rich.
I got interested in AI when I was
an undergraduate at the University of Toronto
around 19--
early 1970s.
I came to Tech Square as an incoming graduate
student in 1973 to the AI Lab.
And I left in 1991.
I only managed to kind of make--
I didn't make escape velocity.
I'm still sort of in orbit around Tech Square.
I'm currently working at Mitsubishi Electric Research
Labs at Cambridge and in Kendall Square, about a block
and a half from Tech Square.
I've been there 15 years.
I was one of the founding members of that laboratory.
Let me see.
The kind of work I did in the field was basically--
so the first 7 and 3/4 years, I was doing my master's and PhD.
Both of which were concerned with a project that
was called the Programmer's Apprentice project that
was started by initially myself and Howie Shrobe,
and later joined by Dick waters.
And that project was really overlap between AI
and software engineering.
So it was using software engineering
as a domain for doing basic research and AI,
so [INAUDIBLE], reasoning.
And also showing that AI could be applied to making better
software engineering tools.
So it's an early project in the sort of intersection between AI
and software engineering.
And so basically, a couple of little
anecdotes along the way about the time
and about Tech Square, about my experience there.
Because one of the first interesting claims to fame
that Howie Shrobe and I had is--
so our supervisor was Gerry Sussman.
And I started talking to Gerry for a master's thesis topic.
And I was talking about AI and software engineering.
I was interested in both.
And I was going to his office as graduate students do
from time to time and trying to find an advisor and so on.
And it turns out that Howie Shrobe, who's also here--
he's also a fellow--
had independently been talking to Gerry
about fairly similar things.
And so Jerry said to both of us--
he introduced us to each other.
He said, listen, guys.
You're both talking about the same sort of thing.
Why don't you do a master's thesis together?
Gerry's a very collaborative person.
And we said, fine.
And he's a professor, right.
Well it turns out it was a great idea for Howie.
We got along great, and we've worked together,
and have been friends for a very long time.
And it was very good from the standpoint of the research.
But it was a disaster from an administrative point of view.
It turns out that really one of the big purposes
of the master's program--
at least the time I was there.
And maybe it's still here.
You had to do a master's thesis before a PhD.
It was to see if your master's thesis was good enough
to get into the PhD program.
Because you really weren't admitted into the PhD program
until you finished your master's thesis.
And the department was very concerned
about if they had a joint master's thesis,
how are they going to know there wasn't a weak sister.
No feminist offense intended.
That was a term.
How to know who did what and they
wouldn't be able to decide who should go to the PhD program.
And so Gerry was very, very good.
He apparently stood on the desk.
I wasn't there.
But I'm told he actually stood on the desk of the department
chair's office.
And he would resign if they didn't let us
both in the PhD program.
That was an excellent thesis and so on.
And in fact, we did some research.
We were the only joint master's thesis
that had been done in several decades in the EECS Department.
And this was 1975, '76 that we actually had it in.
And I bet you a nickel that we're probably after--
all the other students saw what we went through.
And they said, oh, god.
You guys almost murdered by this experience.
I'm sure no one ever attempted a joint master's thesis again.
So an interesting recollection there or claim to fame.
I guess a couple other things to say
what people that inspired you.
So Gerry Sussman, who is my thesis advisor,
both for a master's and a PhD, certainly
is a tremendous inspiration.
And particularly the one thing that I find just really
admirable about Gerry, GJS, and his research approach,
which I confess I haven't been able to duplicate,
is he was, and probably is, a master at
embedding a really big idea in a really small program.
So when I was there--
things like maintenance system.
So you'd be there one afternoon and we'd
be talking about something, he and John Doyle
and some of the other students.
And he stayed up all night with one of the other students.
The next morning he'd have this program that did it.
And it would be a paper or a thesis.
It was just really a real knack for how
to-- and you see it in the scheme
book and the whole approach.
He has a way of distilling ideas down
into a really tight computational framework.
I, on the other hand--
well I hope I have big ideas.
But I'm lucky if I get them into medium size programs.
More often, I have big ideas.
They're huge programs.
And it's a lot more work and a lot less successful to do that.
So Gerry was just a terrific example in that regard.
I guess the other comment that I was thinking
about in this interview--
I wanted to make about those years, those 18 years
at the eighth floor in Tech Square.
It was a different social sense, particularly
in the early time, late '70s and early '80s.
Yeah.
At that point, I'm going to sound old fogey,
but there's something that was really special then.
And maybe it was just a part of being a small lab.
So first of all, at that point, each year
there were a handful of new graduate students, two
or three.
It was a big deal.
You never know who's going to show up.
I remember going into Marvin's secretary's office
and looking, peeking at her desk to see
the resumes of the incoming graduate students this year,
that kind of thing.
Every person seemed to really matter more.
You also had the feeling that each new PhD
thesis was like a whole new paradigm, which
is good and bad.
In many ways, in sceince, you need
to build on other people's work.
But there was a certain naivete at that point
that the next big thing was the next PhD thesis.
In fact, one thing I really remember strongly
is in the playroom, which was the open area
on the eighth floor, which was kind
of the heart of the AI Lab.
There were beanbag chairs, and director
chairs, and stuff like that.
There was a table, a big plant table.
And one thing that I have a really strong memory of,
particularly when I was a first year graduate student,
was that on the table-- and I would go in there at lunchtime.
We would each lunch in there, sandwiches or whatever.
And on the table would be a copy of Scott Fahlman's master's
thesis that he just finished working on.
He hadn't quite handed it in yet,
like the second, or third, or last draft.
And he put it there, and we would read it.
It was like an important thing.
And you'd pick up Scott's thesis and you'd
read it, and talk about it, and talk to him.
And it was a real sense of important things happening.
I remember reading Howard Goldstein's PhD thesis
when it first came out.
And there it was sitting on the table.
And we had the advanced copy.
And it was something we looked forward to.
We really felt part of a process and excitement.
And this was important to understand that--
later on, about the time I left in '91,
it was much more business as usual.
It was lab, and there were offices,
and people working with their advisors and so on.
But there wasn't that sense of focus and this feeling
that this was the next--
that every thesis was the next big thing.
And I sort of miss that.
And maybe it's just the way the field
itself has changed, for better and for worse.
Why do you think that is that the shift took place?
I guess, I think there are two things.
One is I think that the beginnings of things
are special.
You see that in a lot of ways.
And that was closer to the beginning,
and things are smaller, and that's just true.
In the lab I'm in now, I was one of the founders of the lab.
And the first three, or four, or five years,
there were five people, 10 people, and then 15 people.
It was really special.
And now we're like 75 people.
And it's not a bad place or anything.
It's just different.
Anybody who studies organizations
will tell you that.
Also, there's this whole issue of trying
to be more scientific.
And that is good.
The idea that each PhD should be a whole new paradigm is fun.
But it's not exactly--
it's not good.
It's got its bad side to it too, because you could never--
we aren't complaining-- you could never
build anybody else's code or anybody else's work.
And that was a problem.
Also another big effect, just at that time,
was around 1980 David Maher arrived at the AI Lab.
I was there.
If you're a researcher, his name looms
large in the history of the field.
He really had a big effect on the lab,
I think positive and negative.
Up to that point, vision research--
a little bit except for the work that Berthold Horn was doing,
which was always very mathematical and very rigorous.
But a lot of the vision research was really quick and dirty
and fairly ad hoc, like all AI research
was really, good old fashioned AI style.
And David showed how you could do it differently, how
you could really be scientific.
And there's a lot of talk that science was like a big hammer
that you were going to be banging people in the head
with.
And there's a lot, I think, of envy
of how much success and progress, clear progress,
David's students were making, because he was really
making them be very rigorous, and very mathematical
and son on.
They pushed the vision problems.
They were making progress by leaps and bounds.
And there's a certain sense of superiority.
They were more scientific than the rest of us scruffy people.
And there's an envy and desire to imitate, that kind of thing.
Marvin complained bitterly.
I think he regretted.
He said to me, well at least in secret,
that he brought David Maher in, because he thought very highly
of him.
I don't know what the context was exactly.
And I think he regretted it.
Because basically, everyone tried to become a David Maher.
And that wasn't maybe the best thing for the field.
You need a little bit of diversity.
And we shouldn't all try to do things that way.
That might have worked at that point in time
for that kind of a problem in that area.
But it was--
I think that was the period when there
began to be a shift towards trying to make
AI more of a normal science.
And I think David's presence and David's example,
positive example in most ways, in many ways, I think,
had a lot to do with that shift, as well
as just the inevitable developing
and the maturing of the field.
So shifting gears a little, what's
the most challenging thing you've worked on personally
since you've been here?
I guess the most challenging area I've worked on
is theorem proving.
I guess as part of the Programmer's Apprentice
project, we were doing how to represent knowledge,
basically what programmers know about programs, the kind
of architectures and so on.
And then we wanted to build a system, a tool, that
would help programmers analyze their programs
and synthesize them and so on.
And that involved automated deduction.
And I ended up writing a kind of an automated deduction
theory proving system.
And I must say it basically scared me off
of that for the rest of my career.
Because what I discovered was that--
and people just probably to do this better than I do.
But my attempts to do it was--
you write a bunch of rules, or axioms, or procedures
interacted.
And you'd be focused on the path that you
were hoping the system would take on some examples you're
working on.
And you know the system, the computer, whatever
you want to call it, was just so good
at finding another path which you could just count.
It would just count.
Oh, I got one.
Successor, how about this successor to the successor one?
How about this successor to the successor to the successor one?
And somewhere in your framework, there
were some axioms that could count essentially.
Then you system would spend all this time counting
and not do any useful work at all.
So there's this feeling that these systems, theorem proving
systems, could just run away from you really easily.
And so since then I sort of ended up
adopting various techniques for incomplete but efficient
bounded inference techniques.
And I put one more plug in for someone
who influenced me on that one actually, which is--
since he's here.
It's David McAllester, who had the office next to me
as a graduate student.
And I am still using code that David wrote in 1979, '78, '79,
originally in Lisp.
And I translated it to another version of Lisp.
And I've translated it into Java.
And I'm still using his code for the equality
system for Boolean TMS.
Actually, it's a combination of a Boolean TMS,
truth maintenance system and equality
system, which still is actually down
deep in the guts of the work I'm doing now,
which is more on interfaces and task-based, collaborative
interfaces.
But down deep in the system is that same algorithm and almost
the same code translated line by line--
I've made a few improvements-- that David McAllester
wrote in the late '70s.
So I'm a big fan of David's.
What does it do?
It's a Boolean truth mainteance system of equality.
So what it does is it does unit propositional resolution, which
is a sound but incomplete version
of propositional resolution combined
with a complete decision procedure for equality
reasoning.
So it's basically a little inference engine
that is a trade off between efficiency and completeness.
It's incomplete, but it's very efficient.
And it turns out for a lot of the things
that I use it for, subsequently, it seemed
to be just the right thing.
And the code has just been following me
through revisions, and through translations,
and through translation.
I tell David that.
He cringes.
I say, David, it's great.
The code still works.
It was good code from the beginning.
So that's kind of cool.
So I don't know if you work with it much,
but do you have any strong feelings about Prolog?
Yeah.
I do actually.
I think that the right approach is
to have a prolog interpretor written in some other language.
So that for those things for which
you want to use that backtracking logical algorithm,
you could do that.
But you could also do conventional programming
in the same framework.
Because you don't end up doing this ridiculous thing where
you're using little tags to make things sequentialize.
It's always odd, the things you're forced into.
So I think it's not a good general purpose programming
language.
And you know you need to use it when--
for example, there's a various a Lisp Prolog hybrid system.
Is it LispLog or ProLisp?
I forget what it's called.
ProLisp sounds familiar.
Yeah.
I'm not sure.
I think it's actually the other way around.
But to me, having--
just like you'd want to have a standard limitation of astar
search available in a library, you'd
want to have a Prolog engine there in your library,
whether it's in Java or in Lisp.
And you want to use it when it's appropriate.
But I don't think you want to just have it be like,
my whole system's written in the Prolog.
Often, you end up doing things that are much easier done
in some other way.
But because you want to keep it in Prolog,
you end up making the code obscure.
So I'm not a big fan in that regard.
OK.
Skipping back a little bit to the idea of the Tech Square
and AI becoming more scientific.
Besides the approach to problems and the mathematical rigor,
I guess, can you think of any maybe specific, maybe not,
overlaps we can incorporate from other fields?
What would we do with that overlap exactly?
Let's see how best to formulate this.
Ways we can apply concepts from a different scientific field.
That happening all the time, a lot
of stuff from utility theory, from economics.
A lot of people are doing that kind of work.
It's not been my own particular forte or initiative,
not particularly.
So I don't have any great insights on that one.
That's OK.
Let's see.
Besides your own work of course, which
is the coolest thing out there.
Because otherwise, why would you be here?
No, no, no, no.
What do you think is the coolest thing going on right now?
If I were--
Bill said, if I were young again.
I'm not as old as Neal, so I don't have to say that.
But if I was starting fresh out a brand new fresh area,
I guess I'm most interested, and would
be most interested in, the intersection of information
technology, primarily AI, nanotechnology, and biology.
It seems to me that if you look at where those are coming
together, I think that's where, in some sense,
science fiction is really going to happen.
So I just think that that's a very cool place to be.
It would require a lot tooling up on my part to get there.
And I don't know if I have the flexibility
or have the opportunity interest, career wise,
if someone want to give me year to support me, get up to speed.
But if I was starting out fresh, I
would jump into the middle of that intersection.
Can you elaborate?
Or can you explain specifically?
No.
It's not specific.
It's more a general that there's real things have to change.
And the potential, the good matter, is always change.
Fundamentally, how humans live in the world.
I really do.
So looking ahead, how do you think it will affect us?
I hate asking people because they
don't like to say something concrete, but--
You mean about AI in general, or about--
Yeah, or specific-- perhaps [INAUDIBLE]..
Yeah-- no, I mean.
You know, what gets me out of bed
in the morning is really I really
want to see if an artificial intelligence before I
leave the planet.
And I think I will.
Assuming I live healthy and so on, maybe another 50
years or something, I absolutely believe
that I will see a time when we'll
be arguing about whether artificial intelligence is
around.
So artificial intelligence in the form of say expert systems
is incorporated into a lot--
Oh sure.
You can go to the innovative applications of the AI
conference.
There's lots of great stuff.
But I'm not talking about that.
I'm talking about an artificial.
That way I talk to my students, and I
had students that had recently been in industry mostly, is--
basically a program, or an entity,
a physically embodied robot, or a body agent
on screen or something for which there's something there.
Sorry I was I should have made it clear.
I was switching gears.
I know that these sort of systems have been implemented.
And they're behind the scenes in a lot
of the things you've seen.
Are people, do you think, going to end up
interacting with something that specifically identify as AI,
like this computer I'm talking.
Yeah.
I think so.
Just look at avatar and so on, the face.
Yeah, absolutely.
People want to.
It's absolutely clear.
Someone stood up in the earlier session this morning
and talked about your books are getting
some huge amount of money in euros for building
companions for older people.
I think it's absolutely going to happen,
and sooner than we think.
I should get back to a session.
Thanks for talking to us today.
Yeah.
Thanks.
And I'll talk to you later.