All right.
So like you said, could you introduce yourself?
My name is Ron Brachman.
I've been in the field for quite a long time.
I got my PhD from Harvard in 1977.
And I have worked many interesting jobs since then
and pretty much been in AI for my entire technical career.
And most recently, I am the most immediate past president
of AAAI, and I've also worked very extensively
with IJCAI and SIGART, and a lot of the other organizations
involved in making AI as a field successful
and supporting researchers, teachers,
and students in the field.
Cool.
So what got you interested in artificial intelligence?
How did you get started with all this?
I'm sure, like many people-- when I was a kid,
I was very interested in science fiction.
And I remember, my dad was an electrical engineer.
And actually, now that I'm much older,
I found out some of the things he did.
He actually worked for the army and ended up
supporting technical research in certain places--
sort of, contract research, the way we know it now.
And he was doing things like supporting work
on pattern recognition and trying
to replicate the kind of capability
that a very good mechanic has, where he or she could
listen to the engine of a car and end up
with a pretty good idea of what's wrong just
by doing that kind of pattern recognition.
The other thing we did is, we had a lot of electronic stuff
in the house.
And I don't remember the year, but for one of the science
fairs, probably in high school, with my dad,
we took out a Popular Science or Popular Electronics article,
and built a little robot that would follow--
you put some white tape on the floor,
and use a little light bulb, which would reflect off
the floor, and then a sensor.
And it would determine which way the robot's wheels would
turn, based on whether or not it apparently
had a lot of reflection.
So it would turn one way until it hit the white tape,
and then when it detected a switch,
it would turn it would go this way.
So you'd follow a path all the way around the room.
And-- sort of, got interested in seeing
a little, very unintelligent robot doing what it might do.
And then, with my interest in science fiction--
just enjoying watching TV and reading science fiction books,
pretty much, my entire life--
it was a natural thing when I got the opportunity
to at least consider intelligent computation and, I guess,
what eventually became AI.
Now, I have to tell you.
When I graduated from high school, I went to college.
I went to Princeton.
There was no computer science department.
It was before the days where many institutions had
computer science departments.
So people like me would tend to major
in electrical engineering, and you'd
get a pretty heavy dose of analog circuitry
and a little bit about digital computers.
And it was back in the days, when
people would this [INAUDIBLE] programmed machines.
But there was an inkling on the horizon of AI-type stuff.
And I ended up going to graduate school
at Harvard, which was certainly, at that time, not
a hotbed of artificial intelligence,
although now, there's a lot of really good work going
on there in the field.
But I got very lucky in meeting up
with the person who became my thesis advisor--
somebody named Bill Woods.
He was a very well-known person in the field.
He has made some major contributions
to natural language processing.
And Bill taught courses in natural language
and information retrieval, and was, kind of,
the window I had into AI at the very beginning
of graduate school.
The great thing, though--
to continue here-- about going to graduate school at Harvard,
at least at the time, was that we
were allowed to freely cross-register at MIT.
And this was-- I started graduate school in 1971.
And this was a time where Marvin Minsky and Seymour Papert
were very active in my lab.
And there was a huge number of really excellent graduate
students and young faculty there,
and it was a very, very exciting time for the field.
So this was early '70s into the early '80s--
both getting my degree, and then afterwards.
So I remember going over there and taking courses
at MIT of a sort that weren't given at Harvard.
So I got to take the Introduction to AI course
from Marvin and Seymour, which was really outstanding,
and I have some fond memories of that.
I know I took a number of seminars.
I took a course from Bill Martin.
That was, sort of, early hints of knowledge representation,
which eventually became the field that I got into.
I can't remember whether I took a course from him
or just met and spent time with Terry Winograd.
This was right after his thesis was published,
and there was a great deal of excitement.
And then, there were a host of other really top-notch
students, as I mentioned, there, who eventually went on
to be very strong contributors to the field,
like Drew McDermott, Bob Moore, and others, who
are some of my contemporaries.
So in a way, I really lucked out.
Because I got a great education at Harvard
with a fantastic advisor, and yet, I
could take all the courses I could possibly
drink in over at MIT, and get, especially
from the masters, the real essence of AI,
as they were thinking of it at the time.
Very nice.
So I guess, after you got your PhD,
you stuck around for a while.
Where did you go then?
One thing, again, that was very fortunate for me--
in Cambridge, there was a company called,
Bolt Beranek and Newman.
Since, they typically just use the name, BBN.
But in any case, Bolt Beranek and Newman started in the '40s.
And originally, they were an architectural acoustics firm,
but they very quickly got into a significant amount
of electronics and computation.
And in fact, you may recall that they were a major, major player
in the development and deployment of the ARPANET,
and very famous people like Bob Kahn and others
were there during the heyday of the ARPANET.
They pre-dated me a bit, but it was also a very exciting place
to get involved with.
It turns out that I had two different connections with BBN.
One is that when Bill Woods was teaching these courses
at Harvard.
He was actually a lecturer and was employed full-time at BBN.
He was the head of the AI department there.
So I had a connection to the company through Bill.
And then, secondly, I actually took a computer graphics course
my first year as a graduate student,
and ended up befriending one of the people who
are-- in fact, several people who
worked at BBN on some extraordinarily
interesting graphics projects.
So I was very lucky and got to work part-time at BBN,
starting as early as 72.
And I wasn't doing any AI work in this part-time job,
but I was getting to meet people like Allan Collins, John Seely
Brown, Richard Burton, Wally Feurzeig, Rusty Bobrow,
and a host of other people who had very significant
interaction with AI.
And so I was in that milieu, and Bill was my advisor.
And so by the time I finished my degree,
Bill was very interested in the work.
And my thesis was about a fundamentally rethought view
of knowledge representation, trying
to understand how it could be very firmly based
in mathematics and not a lot of handwaving
English words floating around on network diagrams.
And some of the work that Bill started to do at BBN
was based on this.
And in fact, interestingly enough,
connection to Bob Kahn--
Bob was the program manager and subsequently
an office director at DARPA, who was the sponsor of Bill's
project at BBN.
Oh, OK.
And so I met Bob as a graduate student.
And then, eventually, it was just a very natural thing
to finish my degree and to go work
as a full-time employee at BBN.
And then, I could change over from this part-time work
on computer graphics to being a full-time professional AI
employee, and working on a somewhat elaborate
implemented knowledge representation system
that eventually came to be called KL-ONE, which we used
in various applications for natural language processing
and other types of reasoning.
And it was a really phenomenal time to be there.
First of all, you could keep up your connections with MIT,
because it was all in Cambridge.
And secondly, there was this wonderful group
of people at BBN--
Candy Sidner and Phil Cohen were there.
Hector Levesque came and visited.
Brian Smith, who was at MIT at the time, would be over there.
Mitch Marcus, who was a very important natural language
person, was there.
So it was a very, very invigorating, very exciting
time to be at BBN.
And so I stayed there, getting to work on my first love, which
is basically the follow-on from my thesis
and the implementation of this KL-ONE system,
for, about, four years, full-time.
And then, I left the Boston area after living there
for 10 years, getting recruited to go out to the West Coast
to work for a new AI lab that was grown under Schlumberger,
which is a French oil services company [INAUDIBLE] put
at Fairchild Camera and Instrument,
because Schlumberger had bought Fairchild.
And that became known as FLAIR, the Fairchild
Lab for AI Research.
And although we're moving away from Cambridge,
I'll tell you a little bit about that.
That was-- again, it was a very exciting time for AI.
This was the mid-'80s.
Some of the new AI companies--
Technologen and Telecorp were beginning.
Some money was coming in, not only from the government,
but from the commercial sector.
And there was just a great buzz about the field experts.
Systems were getting big, and new labs were springing up all
over the place.
So I got recruited to go work with--
there were four prominent people who started this lab from SRI--
Peter Hart, who used to be the head of the AI Center, Marty
Tenenbaum, Dick Duda and Harry Barrow--
wonderful people who had a vision
they picked up out of SRI and started a brand new lab.
And we had a blast there.
I was there for another four years,
working very closely with Hector Levesque, and also Phil Cohen.
We got to branch out to work with colleagues
across the street at Xerox PARC.
It was a very exciting time at Xerox PARC.
Brian Smith ended up there.
Richard Fikes and Danny Bobrow were there.
And we ended up doing more knowledge representation work.
And with Hector and Richard, I ended up
building a system that was called Krypton.
We called it that, by the way, because the atomic symbol
for Krypton is Kr, and knowledge representation is KR.
And then, we did a very tiny version of it
to see how fast we could make it run and that system we called,
Kandor, because we mixed our metaphors.
You may remember that Krypton is where Superman originated.
And in the old Superman comic books,
there was this city called, Kandor, that Brainiac
shrunk and put in a bottle.
So this was a little miniature version of Krypton,
although we were mixing this metaphors.
Nevertheless, over that period, we did an awful lot of work
on these semantically well-founded, frame-based
knowledge representation systems that we were starting
to use in applications.
And I'm very happy to say that they ended up
being relatively influential with the knowledge
representation community.
So up until the middle of the '80s,
I was in this very exciting milieu in California,
in Palo Alto, with great stuff happening everywhere,
including at Stanford.
And then, after that set of events,
I got recruited to come back to the East Coast,
because Bell Labs was interested in building an AI group.
And Bell Labs-- again, a phenomenal scientific research
institution--
up until the early '80s, was expressly not interested in AI.
In fact, there were many strong detractors
who were working at Bell Labs, who
thought that AI was completely full of hype
and was never going to achieve its vision,
and that it was something to be avoided at all costs.
And finally, as AI was making its mark
in the commercial world in the '80s,
they woke up and decided that it was really probably worth
pursuing inside a large company like AT&T.
And in fact, there had been some other interesting work there.
One early prominent expert system called, ACE,
for automated cable expertise dealing
with outside plant cables for the phone company,
came into being.
That was done by a guy named Greg Vesonder, whom I ended up
working with when I came to Bell Labs for many, many years.
We had a great collaboration.
So that started the next phase of my career,
which is a very, very substantial, long amount
of time at AT&T--
Bell Labs and AT&T labs, which I can tell you about.
But I might as well.
There, again, we were able to do some very significant
new knowledge representation work.
And at the same time, for me, personally, this
was an opportunity to start to get
into management and leadership and grow a group.
So this is the point in my career
where I started to gently move from being purely
a technical person in publishing and doing research,
to a combination of that and managing,
recruiting, and building large projects, which for me happened
to be a lot of fun.
And we then built another knowledge representation system
called, CLASSIC, which, again, got a pretty wide amount
of recognition.
There were some really, really interesting things about it.
And this was during an era that, I guess,
you could say that Hector and I started in, around, 1984,
when we published an AAAI paper on an extremely interesting
technical fact that we uncovered,
which involved a very striking trade-off in knowledge
representation and reasoning between the expressive
capabilities of a language-- how many different kinds of things
can you say in it--
and the computational complexity of computing
with that language.
And this is something you see in other parts of computer
science, but it had not at that point
been applied to knowledge representation.
And we ended up showing how two very simple languages--
and they were really simple--
differed in one operator.
And in one case, you could prove that you
could get a polynomial time computation, and in the other--
and it just looked like a tiny little difference, one
to the other-- you ended up in an NP-hard or NP-complete
situation.
And so what we called-- it was the computational cliff.
Basically, you add this one little syntactic operator,
which you couldn't really tell by looking made any difference,
and off you went.
And in fact, it radically changed the worst-case behavior
of the system.
That actually led to an explosion of papers
analyzing these simple, frame-based systems,
adding all kinds of interesting operators and subtracting them,
and looking at the competition complexity consequences.
Literally, hundreds of papers subsequently
followed on that, which was really quite interesting.
And the kind of language that CLASSIC was, which really,
very much followed this KL-ONE tradition--
at one point, it occurred to me that the essence
of what we're trying to do is represent descriptions--
usually, of objects in the world-- sometimes, actions.
And the descriptions were structured,
and they could be small or complex.
And we ended up calling this class of languages, description
logics.
And that, in some ways, grew a whole small niche
area of AI, in which there's still
an awful lot of ongoing work.
And ultimately, that track has had a pretty substantial
influence, for example, on the development
of languages for the semantic web, which is very gratifying.
So to complete the story there for you,
I spent more than 16 years at AT&T,
ended up getting promoted to the level of a lab director--
so I ended up running a lab of 80 to 100 people at various
times--
gave up my AI department to other people--
Henry Kautz, Michael Kearns, and really great people like that--
and grew the influence in the company,
in terms of what we were doing-- the breadth--
as well as learned a lot about managing larger groups.
Well, yeah, sure.
We ended up having just an absolutely extraordinary lab
by the time I left AT&T in 2002.
And that lab had a tremendous AI component to it.
At the beginning, I have to admit, most of us
were not particularly interested in or very confident
in the effect of computer learning.
We were largely focused on reasoning
of various sorts-- information retrieval, web interfaces,
and the like.
But we were, kind of, cynical about learning.
But we finally found a really excellent person--
in this case, William Cohen from Rutgers,
who is now at Carnegie Mellon--
who was a terrific learning guy, who
did both the practical and the theoretical side.
And we hired him, and that started a cascade of hiring,
where we ended up with--
I think some people would say-- probably, the world's best
machine learning group for quite a few years.
And I can tick off the names of all the great people who
were there.
Many of them are extremely well-known.
Unfortunately, in 2001, AT&T decided
to change its mind about the size of the budget it wanted
to use for research in general.
And to make a long story short, we
ended up having to have a lot of people leave.
And I left and took a retirement package,
and ended up, in a sense, coming full circle, working at DARPA.
And in fact, I got Bob Kahn's old job
as the director of the Information Processing
Technology Office.
And in fact, I ended up having Bob be my boss,
because I technically went to work
for his company, the Corporation for National Research
Initiative, and then I did a rotation from his company
into DARPA for several years as the director of the Office.
And that was just a huge thrill and an incredible opportunity,
because the director of the agency at this point
was extremely interested in how to get computers
to learn and, overall, to have them
be more intelligent, in support, in this case, of the Department
of Defense.
So I got to help put in place a pretty wide variety of very
significant programs and to help spend a lot of money
on AI research, which was a great deal of fun--
great deal of fun.
And so I finished my stint at DARPA, working for Bob,
and ended up taking a really interesting job
now at Yahoo, which is developing a new research lab.
So that's my story.
So I guess we're getting ready to wrap up.
I'm gonna roll way back to Tech Square again.
Please do.
I spend a lot of time in the original Tech Square,
up on the eighth floor.
OK, you do?
So I guess, what were some of the cool things going on
that you specifically remember?
What were you really interested in when you stopped by?
Well, it's going to be a little tough to remember them
in detail at this point.
But I do remember being very impressed.
At this point, people were doing a lot of talk
about the blocks world, using it for all kinds of AI challenge
problems.
And I think it was up on the ninth floor--
I finally got to see, there was a robot arm that Winograd
and others had used, where, if you remember,
Terry's thesis allowed you to speak
in fairly free-form natural language within the blocks
world context.
You know, find a big red block, and move it
to next to a green pyramid, and things like that.
And you could actually go up and see the robot arm taking
and moving things around.
And to be honest, even though robots were in the literature
and on TV and movies at that point, to see one at the MIT AI
lab--
to see the AI work in the universe with people
like Marvin, Seymour, Winograd, Carl Hewitt, and Gerry Sussman
all floating around--
that was very exciting.
Another thing that was really quite interesting
was, I met quite a few other people,
and there were lots of fun discussions in the center
hall of the eighth floor there, and seminars,
and lots of arguments, and a lot of Chomsky bashing at the time.
People were discovering agents, and a lot
of really exciting work was going on in natural language
processing at the time and what was
called, procedural semantics.
And I think it's hard to remember
too many specific anecdotes, but I
remember, the people [INAUDIBLE] the classes and lectures.
I do remember one thing quite vividly from the Intro
to AI course.
It was given in a large auditorium.
Seymour got up and did a balancing act
while juggling on the kind of a cylinder
where you put a board--
[INTERPOSING VOICES]
Yeah.
I can't remember why he did that.
But I certainly remember that quite vividly.
And I also have to say, I remember the style of lectures
that Marvin would give.
One other quick anecdote-- another very important event
in my AI career was in 1977, which
was exactly when I finished my thesis and graduated.
The IJCAI conference, the large international AI conference,
was held in Cambridge.
Big lectures were held Kresge Auditorium,
and the talks were given in the classroom.
So it was very exciting to be living there
and be there as the whole world came for the AI conference.
And I remember Marvin giving an invited lecture.
And basically, this was well before PowerPoint, right?
He got up there with the transparency
and started doodling, and basically,
gave his talk, doodling on transparency,
and ended up writing right on the projector
and didn't even realize it.
He still does that.
Right.
But it was great fun-- very, very memorable.
I also remember at that point--
Doug [INAUDIBLE],, who was very well-known in AI
gave the computers and thought lecture.
And that was a great piece of showmanship as well.
So that was very exciting to be there,
having finished my thesis exactly at that time.
It was a major, major conference and a big happening
in the States.
And it was a great place to be at that particular point
in time.
[INAUDIBLE]
Yeah.
Thanks for talking.
I wish we could talk some more.
My pleasure.
And just so you know, I don't know if you realize,
but we have videotapes of the AOF arm moving the blocks
and [INAUDIBLE] system.
That's part of what we're developing--
Yeah, I would hope.