Do you mind introducing yourself?
I'm Jon Doyle.
What sort of introduction?
So what are you doing right now?
I'm pursuing both how to represent
decision [INAUDIBLE] preference information,
which economists typically have not
had good representations for, but we'll
need good representations if you want
to have proxies or intelligent agents that do what you want.
And I am also working on mechanics,
and I just published a book that extends
the modern axioms of mechanics to cover minds as well
as bodies.
If you want to think back to the time of DesCartes,
where there was both the physical body
and the mental substance.
You now can have a theory in which both parts of this
are satisfying the same set of mechanical laws.
So that's the other part of my--
Well, we'll get back to that.
How did you first get into artificial intelligence?
Well, the proximate reason was I had a teacher at University
of Houston who thought that understanding the mind
was going to be the next big thing in the next 20 years.
And that got me very interested in it.
I went into the field.
Cool, so what other kind of research was-- when was that?
That was '72 or '73, somewhere around that.
So where did you go from there?
Well, I went to grad school at MIT,
and conceivably, I'm the first person
to actually get a degree with specification.
My PhD is in artificial intelligence.
Wow.
Congratulations.
They offered it.
I don't know-- maybe somebody must have had a thesis in that
before, but I picked it, and I work with Jerry Sussman, who
was my advisor.
And after that, I worked at Stanford University for a year
with John McCarthy, and I was at CMU for seven or so years.
All the big places.
And I'm back at MIT for another 13 or so.
And now I'm at North Carolina State University.
Great.
That's cool.
So tell me about, since our project is originally
about some film reels we found at Tech Square.
Could you tell us a little bit about what was
going on when you got there?
And maybe you remember about Sussman?
Well, I know various stories he told me,
but I saw the thing on the list.
The two best stories I know, that I can remember immediately
from my grad school days, both actually
involved Guy Steele, who was a student there
at the same time as myself.
And in one of them, near the--
I forget whether it was on--
I guess it was while he was working on his master's
thesis, maybe his PhD.
But he had a bug which depended upon the phase of the moon,
literally, which was a joke up until that point.
But [INAUDIBLE] at that point had
a thing where you could print out the time and the date.
In fact, you could also print the phase of the moon,
and it turned out that, in his program,
it would compile things into LISP.
And it would have these comments up at the top
saying when it was done, and then there
was a sentence about when it was done, and then at the end,
there would be a period at the end of the sentence.
Well, it turns out there was word wrapping in the output
routines, and if the face of the moon
was just right, that period would wind up
isolated on a [INAUDIBLE] bug.
When this happened to him, and he realized what it was,
he took a screenshot, and for years, he
had that screenshot on his office door.
The other thing is that I had taken, I'd say,
quantum mechanics before I came to MIT, and Guy, I think,
was taking a quantum mechanics class.
And there was this funny article--
maybe not so funny article in the paper, or a joke,
at the time about an aircraft windshield manufacturer who
had been testing windshields.
They would shoot chickens at them at 800 miles an hour,
and the joke was that somebody finally
realized they didn't need to use live chickens to do it.
But Guy decided to exercise his skills,
and he computed the wavelength of an 800 mile an hour chicken
and then put up, on his wall, frequencies,
various things about the electromagnetic spectrum
and then x-rays and gamma rays and then 800--
a big separation mark and then the frequency
of an 800 mile an hour chicken.
That's fabulous.
Was that representative of the general culture at MIT--
People thought about all sorts of stuff
and talked about all sorts of stuff.
There was a gang of us, of varying composition, that
would walk over to a sub shop near the labs,
and there would always be some discussion.
During my time, there were a lot of discussions of philosophy
and logic and all sorts of things,
and those were some of the most interesting discussions
of the day.
That's cool.
Were you part of the Chinese Food Sect?
I used to hear that was a very popular option.
Yeah, [INAUDIBLE] small eating place was down the street,
and they had very good food.
They had a hot and sour wonton soup which was extremely spicy,
and if you had a cold or something,
it was always a good idea to go there,
and your entire respiratory system
would be cleaned out in short order
by eating some hot and sour wonton soup.
I'm wondering how similar it was to now.
What were people's hours like at the lab?
A lot of people worked late.
I've always been a morning person,
so I would, a lot of the time, get there 4:00 or 5:00
in the morning, and I'd have--
some people would just be leaving at that point,
and I would have a lot of time.
And well, this was also--
I mean, I probably wouldn't have gotten there quite that early,
except this was the time of shared mainframes.
Oh, absolutely.
And also, not every--
there was a terminal in each office or so,
but not everybody--
So if you're much faster--
So if you wanted time to spend on a computer,
either you had to stay late, or later than everybody else,
for have to get there earlier than everybody else.
Interesting.
I got there earlier.
So what do you think some of the most challenging problems
in artificial intelligence were and remain today in the field?
Or what intrigues you?
Maybe that's too general a question.
Well, there's still-- we still don't
know how to represent lots of information,
and part of the reason is that everything
that we know how to do, we basically
have to filter through individual human minds.
Somebody has to understand how to do that.
And intelligence is so complicated,
and the mind is so complicated, that it's--
we can only understand little bits at a time,
and there's a finite number of people in the field.
And it's not clear that you can get something of the complexity
and size that would look like a regular person
without spending a very long time on it.
If I look at mathematics or some other fields
and see how long it took to develop
what are thought of as basic everyday tools today,
it was hundreds of years.
If you look at physics--
I mean, Newton's famous for these three postulates.
F equals MA was not one of those.
F equals MA is an equation published by Euler
50 years later.
There was a long period.
Now, there weren't very many people
working on this compared to today,
but there was a long period to get from initial ideas of how
something would work to the sort of things
you could use every day to calculate more things
or to do stuff.
And I think we're in that same sort of position in AI,
that we have all sorts of good ideas,
and it takes a long time to set the table,
if you want to think of it that way, for all the tools you need
and collecting all the information and trying it out.
And since it's a long time, invariably, somewhere, you're
part of the way through, and you have a better idea
about some part, and so you don't quite
finish what you were doing, and you try to keep adjusting.
And the time keeps moving off a little bit.
I mean, there's a lot of progress being made,
but it's a very difficult plotline.
So if they were a subarea or paradigm
of artificial intelligence in the future that
was developed and had a law named after you,
what area would you like that to be?
Well, aw, no.
I don't know anything about that.
What area do you find most fascinating
within artificial intelligence?
I don't know how-- well, I--
What do you think the biggest potentials are?
Well, one of my favorite activities
is trying to formulate the ideas that we don't understand well
yet in mathematical terms, not because the formalization
always gives you the--
that you can always succeed, but just
making that attempt raises a lot of problems
that you hadn't quite put your finger on before.
And I think one of the exciting things about the field
is that we have figured out how to formalize
various things that nobody has figured out
how to say precisely before, and I
think that's an exciting thing for me.
It's not to everybody's taste, but that's exciting to me,
and that's going to continue for a long while.
I think if we're going to make really big progress,
there has to be a lot of additional work on formalizing
that.
Can you give us some examples of things that
have been formalized already?
Well, the one that I've been involved with
is non-monotonic logics, which is
a real contribution to logic, as far as I'm concerned.
And it's the first theory anybody
had about how to talk about assumptions that you might want
to make and rules for making assumptions
when you want to have them fit well
or cohere with other information that you have.
It's not probabilistic.
There are probabilistic theories of it,
but in fact, I think those are not
the right way of thinking about it.
It's a theory that's combining both preferences about what you
think you'd rather conclude in a state of ignorance
with some sort of inference about, well, what do I know
and how does that constrain what I can
do to fulfill my preferences?
So it's a quite interesting logic.
It's the basis for a huge literature right now
on logic programming.
It still has not worked its way through most AI representations
or mechanisms partly because of some complexity issues
that we're still working out, but it's a new idea,
as far as new formalized idea, and it didn't exist before.
I think we're going to have more of those,
but we don't really have them.
The work I'm doing on preferences
right now is just trying to say, well,
if you talk about generic preference information, what
does that mean?
If I say I like red cars better than blue cars, to economists,
that doesn't necessarily make any sense because they
want to talk about decision preferences
that order the specific alternatives you had,
not properties of those.
And there are a lot of possible interpretations for what
it could mean to say, I like red cars better than blue cars,
and we still are just exploring the first couple
simplest of those.
And we have no idea whether, when
you or I talk about what we like or dislike,
what's involved in the meaning of this.
So have you been--
how do you feel about the development of the field?
What have been some of the biggest
disappointments, surprises, what you're particularly pleased
about?
The most surprising thing, I guess, to me,
is that there's been an enormous amount of progress
both in the computers that underlie what we can do
and in what people have actually done with it.
What's really surprising about that
isn't that all this progress has happened?
It's that almost all of it's invisible to most of the world,
because right now, huge sections of society
are dependent on various sorts of expert systems
or knowledge based systems or systems that were synthesized
by knowledge based systems that control
all sorts of interesting things, and these are right out of AI.
They were so used to the--
Well, most of them are hidden.
So in fact, if you look at AI in the popular world,
you see novels and whatnot and that--
I should mention that some of those
were things that got me interested in,
or at least set the stage for me.
But you see commercials where people--
or ads where people are claiming to have
AI in their toaster or whatever, or in their rice cooker.
Why are you looking at me?
It's not untrue, but it's not--
it makes people think that, well, that's what AI is.
I mean, from that point of view, my rice cooker knowing
to turn itself off is intelligence,
and that's not really a very good notion.
What we're really interested in is
people who know a lot about something,
or people who know something about everything.
We're in the process of formalizing the entire world's
knowledge, and this has not been done,
not even the expert knowledge that you find in any university
curriculum.
It's going to ripple through all the disciplines,
and eventually, we'll be able to make use of that.
But this is a long process because trying to figure out
how to say things formally in a reasonable way
is something that--
we know how to get started on it.
We have no idea whether the tools we have right now are
capable of completing the job.
Building on that, what kind of collaboration
do you see with other fields in the near future?
Which fields are they?
Oh, well, you can pick any field and just
try to say, how do we express the knowledge in that field
in formal terms that can be used by a machine to reason
or set up problems in that field, or to do analysis
or so forth.
Right now, this is all manual activity.
It's some person sitting down and worrying about it.
In some cases, you can have some data sets
from which you can extract some bits of the information,
but generally not.
I mean, you can't extract from data
sets the knowledge involved, and if I'm a chemical engineer,
how do I form--
look at this power plant that somebody
wants me to come consult on, and/or chemical plant,
and figure out which parts are relevant to the task
that they're setting me, keeping it from blowing up
or getting the efficiency higher on something?
I have to know what data to look at,
how to think about the problem, how to decompose it,
and this is not something that you can get out of a data set.
And the only way they can get it right now
is by reading textbooks and talking to experts
and doing a lot of manual work, and it'd be nice
if we could do that quicker.
But that's something which has not
been done for virtually any area for most
areas of human activity, and it's not just
sciences and engineering.
It's how to write well, how to speak well, how to learn--
do self-help, how to manage your own life.
So what do you think the most important skills
a new researcher in the field should have?
One of them is to always keep trying to understand something.
Don't ever think that just because you've
read a paper or two that you can then sit back,
and you know what you need to know.
Always be working on a problem, because if you have a problem,
that gives you something to think about.
And if you are just thinking of this
as a task of understanding and literature,
you're not going to be as motivated.
I think the more mathematics that you know, the better.
And by that, I don't mean just calculus or something
like that, but there's a lot of advanced mathematics
that is about the structural relations of things.
And I think all that's helpful in different ways
to different people, but I'm also trained--
I have medical training so that may be just a prejudice,
but I think it helps a lot of people in a lot of ways.
And so you mentioned before that there were some science fiction
authors that had inspired you at the beginning to get
into artificial intelligence?
Or think about artificial intelligence?
Who were they?
The main ones were the Asimov books on robots.
Maybe even more so were Heinlein's The Moon
is a Harsh Mistress was one of my favorite--
still is one of my favorite books.
Those are the main ones I can think of, but I mean,
I've always been interested in thinking,
and I was lucky that, when I was in college,
thinking was a topic that looked like you could actually
go and study.
Cool.
Well, thank you so much.
Oh, you're welcome.