Professor at MIT.
That's correct.
So start with the starting question.
What got you into the field of artificial intelligence?
I've been interested in how I think since I was a small kid.
And for many years I was searching for that field which
would provide some answers to the questions I was asking.
So I tried neurobiology.
I tried information theory.
And then one day I attended a lecture by Marvin Minsky,
and that was the end of the story.
I knew where I was.
I guess, what was not there in the other disciplines
you investigated?
A computational understanding of the nature of intelligence.
Not just a catalog of behavior, but an explanation of it.
Something that reaches all the way down
from the highest levels of reflective
thought to the kind of thinking that's closely aligned with
and touching of our perceptions.
OK, that makes sense.
So you took a course with Marvin Minsky?
Or you just attended a lecture?
What course?
Oh, I've forgotten the name, but it
was the graduate artificial intelligence course,
which I subsequently translated into an undergraduate version
and have been teaching ever since.
Cool, so I guess you ended up working in the AI lab?
Yes, on the basis of the term paper I wrote for that--
something that Marvin was teaching,
he invited me to spend the summer
in the artificial Intelligence lab,
and I was of course thrilled beyond description
and never left.
What sort of things were going on when you first
arrived at the AI lab?
Building robots, making computers see,
building descriptions of things, trying
to understand how computers can learn
in ways that were different from the way rats
learn from repeated trials.
All that sort of thing was tremendously exciting,
and especially so because the kind of hardware
that people like Tom Knight and Richard Greenblatt
were building and programming was unique in the world,
and we felt that nobody else had anything
like the kinds of tools we had to work on the problems.
List machines?
Even before list machines, yeah.
List machines came along.
A chess machine was there.
But even the beginning, those guys knew how to put together.
Didn't have displays before anybody
had heard of bit map displays.
Ethernet-like connectivity and email systems, all of which
were pioneered and championed by the early days
of the artificial Intelligence lab.
OK, sweet.
I suppose I should add, especially the time
sharing system.
It was a fabulous time sharing system
that made it possible for a lot of people
to do good work on a computer that by today's standards,
of course, would probably be about the sort of thing
you would think to put in a watch.
Fair enough.
So I guess you--
what was your first project when you went to the AI lab?
Oh, I did some things on computer vision.
I did an analysis of what it would look like,
how a computer could recognize that the objects had
a hole in it.
But pretty quickly I was on to my PhD thesis
topic, which was on the subject of how a computer can learn.
Right.
I suppose you probably haven't seen our project page,
but the first film in the collection we have
is Professor Minsky talking about his thesis
with the [INAUDIBLE].
Often is the case that someone else
can explain it better you can.
Yeah.
Whenever we explain anything, we're
hallucinating that the other person already
knows what we're about to tell them,
so it's not necessarily the best person
to get the explanation from.
You're right about that.
So I guess, where did your work go from there?
Well, it's always been a focus on--
to some extent, accidentally, I suppose-- always
been focused on problems having to do with learning.
And to back up a little bit and add something to that earlier
discussion-- you might want to add it in--
I think that the main characteristic
of those early days and in the Artificial Intelligence
Laboratory was a tremendous excitement that
was derived partly from the monster brains who
are working there, and partly from the general mission.
We really felt that we were going
to be able to develop a computational theory
of intelligence, and do some very deep scientific work
that no one would ever be able to do again
because once you discovered it, you discovered it.
Makes sense.
I guess we've heard a lot about the atmosphere of the AI lab
in the early days of its existence.
Do you have any particular anecdotes
or cool stories you remember from when you were there?
No.
No?
Just general work.
All of them are--
all of them are good.
Oh, OK.
Fair enough.
So after the arch thesis, you still
work on sort of machine learning.
Still do, yes.
Of course, the field has changed a great deal
since those early days.
Partly a natural consequence of a field
growing up and maturing, people working in it,
standards changing.
And to some extent, I think that the field
has suffered a bit from the disappointments
of the early days.
The problems proved to be much harder than anyone anticipated.
Early progress was so rapid that there was a general sense
that we'd be able to solve all the problems
within a decade or two, but we weren't able to do that.
And as a consequence, I think there
was a kind of general and glacial drift
away from addressing scientific questions,
and toward developing leading edge applications in computer
science.
And that glacial drift over a long period of time, of course,
has a big impact on who goes into the field and what kinds
of things that they do.
I'm pleased to say, though, that I think that these days there
is emerging a new sense of enthusiasm,
or a new sense of expectation, a new belief that we can grapple
with the scientific questions and develop what we all
originally set out to develop, which
is a computational theory of human level intelligence
and beyond.
So what do you think of--
I mean, so at the Triple AI conference
there was sort of a fair amount of complaining
about everything's statistical these days.
There are all sorts of fads that have appeared and disappeared
in artificial intelligence over the past 50 years,
and that will continue to happen, I think,
until we have a much better sense of what
the answers really look like.
Certainly those statistical approaches have merit.
They're especially valuable in applications.
They shed light on some scientific questions,
but like all the other popular approaches,
they're a tool, not the answer.
So OK, makes sense.
The real answer lies in the observation
that intelligence is the product of thinking
with our perceptions, and thinking with our descriptions,
and a blurry line that separates the two.
But thinking that is backed in all cases up and down the line
by tying a couple of loops, by an incredible capability
for accumulating, analyzing, and making use of regularities
in what we experience, and not all of that
is going to be susceptible to attack
by any one particular method.
So professor McCarthy said his opinion was
that within the hardware of the machines
we had in the 70s, human level intelligence is possible.
He said that it's entirely not hardware dependent.
All the problems remaining to be solved
are software breakthroughs.
What do you think?
I get-- I think that we will need some new hardware,
but we don't know what it will look like.
But we do know that whatever it will look like,
Tom Knight can build it.
Fair enough.
I think our thinking about the problem
has not been severely handicapped
by the hardware of the day, and I pick my words carefully
because in the early days, I used to do a little computer
vision research.
And in those early days, it was an expensive operation
to drive a 3 by 3 filter across a television image.
And so you didn't think about driving a 30 by 30 filter
across a much larger range.
That's easy with the kind of hardware we have today,
but we didn't think about it then because it was so far
beyond what we could anticipate having
within our graduate students' lifetime.
That is to say within a time a student will spend [INAUDIBLE]..
So it did influence our thinking a bit,
but aside from that small footnote,
I think that our integrated artificial intelligence has
never been severely handicapped by computational limitations
because we know that if we need massive amounts
of parallel look-up, we can get it.
After all, one of the great reasons for optimism
these days is that computing has become free,
as Tom Knight likes to say.
In the computer I have on my side,
I have a gigabyte of memory.
And when I came into the field of artificial intelligence,
memory was a dollar of bytes.
So there's a billion dollars, unadjusted for inflation,
over there in my laptop.
And years from now when this videotape
is seen, having a gigabyte of memory only in a laptop
will seem quaint, just as thinking of memory
as costing $1 a byte seems quaint today.
So computing in massive quantities
is no longer a cost obstacle, so that's
one of the great reasons why we can
be optimistic about the future.
It no longer requires a big laboratory, a giant contract,
and vast teams of people to do good work in the field.
So people tend to hate questions that are specific,
but I guess, where do you think we're headed,
and when do you think we're going to be?
I'm very optimistic.
I think if you look at where the field
of artificial intelligence is today,
you can say it's at the point where molecular biology was
in about 1950.
So we're about a half a century behind.
But what I mean by that is that we've been in business now
for 50 years.
We've done some fabulous things in the world of applications,
just as molecular biology had, or as biology had in 1950.
They had antibiotics, and we have
various kinds of applications that
populate all bit systems today.
But we're still waiting for analog
to the discovery of structured DNA.
And I think we have accumulated enough knowledge in our field
and in the [INAUDIBLE] fields that that kind of breakthrough
can now be expected.
Whether it will come tomorrow or a decade from now,
I can't say for sure, but I have every confidence
that we are ready for that kind of a great discovery,
after which the world will never be able to look back.
What do you see a human level intelligence
doing for us as either individuals or as a society?
We don't want to get hysterical about those possibilities.
That kind of hysteria comes and goes
in the field of artificial intelligence,
and what we generally find is that the commercial value
of new technology, especially artificial intelligence
technology, is not in displacing people,
but making it possible to do something
you couldn't do before.
Companies that go into business to replace people using
artificial intelligence generally
go broke within a short period of time
because people are pretty hard to replace.
On the other hand, if you can combine people and computers,
and put them together in combinations
that can do something that couldn't be done before,
then you've got a serious business.
And that's the way I think artificial intelligence will
be making itself felt in the years to come--
many years to come.
And by the time computers are really as smart as people,
we will have had many generations to adjust.
Fair enough.
Do you read science fiction?
No, I make it.
Very good answer.
What do you read?
What do I do?
Do you read?
I like to read history.
History?
Yeah.
What kind of history?
American Civil War history in particular, but just
about any history from--
histories focused on Roman emperors
to more modern histories of Peter the Great.
Anything that is similar to that.
Fair enough.
Just curious.
Sometimes people have very crazy tastes.
Oh, artificial intelligences.
That's all they read about.
A lot of people in artificial intelligence
are addicted to science fiction.
Fair enough, OK.
Well, I think that should probably do it.
OK, let me think of anything else I want to touch on.
Can I just say a few things you might want
to edit in if you feel like it?
Everyone is always curious about how long it was going to take,
and JCR Licklider always said that we
tend to overestimate what we can do in a year,
and underestimate what we can do in a decade.
And I suppose if we take this problem
of artificial intelligence, we can usefully
paraphrase what Licklider said, and argue
that we tend to overestimate what we can do in a decade
and underestimate what we can do in a century.
We ought to have a great deal of optimism
now because of all the knowledge has been accumulated.
We're a little frustrated as a field
because we haven't solved a big problem yet.
On the other hand, I think we have a much clearer picture
of the way forward.
I think many of us believe that to understand
the nature of human intelligence is
going to require a great deal of capability
for understanding intelligence at every level,
from perceptions, to descriptions,
from the interactions of those, from how we hallucinate images
to help us answer questions.
How, if I were to say to you, John loves Mary.
Did John kiss Mary?
If I were to say to you, John kissed Mary.
Did John touch Mary?
You would, I think, imagine the scene
and read the answer off of it.
So we're beginning to understand now that intelligence is not
the product of a single faculty, but it's
the product of many faculties in our brains
all working together.
We think with our eyes.
We think with our hands.
We think with language.
We think with all parts of our brains
working together, and working in the light of all the experience
that they accumulate.
So when we get the answers, I think
what we'll find is that there's a fabulous engineering
in the brain, and that that engineering has a--
rests on a foundation of fabulous scientific principle.
When we understand the whole story,
we'll see very sophisticated mechanisms
that are involved in there.
And how long will it take?
I have a great deal of confidence
now because I think that we have such a much clearer idea
of what needs to be done than we used to,
that we can expect in my lifetime to substantially solve
the key problems.
Excellent.
OK.
Well, that's encouraging to hear.