You or should I look at the camera.
However you want.
Oh, it's a conversation, so you can consult the camera.
So why don't you start out by introducing yourself?
I'm Jim Hendler.
I'm at the University of Maryland,
and in January, we'll be starting a new research group
at Rensselaer Polytechnic Institute looking at what's
called the semantic web.
I've been doing AI since mid '70s,
and a lot of the work I've done comes more out
of the scruffy part of the deal.
So making it work rather than the theoretical.
I also spent a few years of DARPA funding AI from 1998
to 2001.
Great, cool.
So how did you get into the field?
So there's a generation of AI researchers--
I guess, now, we're all in their late 40s or about to turn 50--
who were all young teenagers when the film 2001 came out.
And even though Hal was not the--
the supercomputer was not the hero of the movie,
it was, in fact, that villain, none of us saw it that way.
We were just blown away by this concept of--
most of us had already either had our hands
on computers or calculators or were thinking about some
of this stuff.
But they were still big machines that were somewhere
you had to go, and you can--
and just the notion of this thing you talked to
and you interacted with, and it could fly a spaceship
and could kill people and stuff, it was just great.
Had it's own personality of sorts?
That's right.
The personality stuff was very good.
That's great.
So taking it from movie to reality,
and what was your path?
So I ended up at Yale as an undergraduate and then
as a research assistant in the Yale AI project,
which I guess around '74 or '75, Roger Schank,
who had been a student at Stanford,
moved to Yale to start his own AI group--
actually, he'd been a postdoc at Stanford--
to really get looking at natural language.
And he had a very interesting perspective on language,
which is that there would be a small number
of primitive representational objects
that could represent great amounts of language,
and that you could use these to create what were
called scripts in those days.
So what Minsky was doing for the physical world in frames,
Schank said we could do similar sorts of things
for stories, things like that.
So a story, the famous story, was John went to a restaurant.
He ordered lobster.
He ate and left.
And the question answering system asked,
what did John eat?
Should say lobster, but the story doesn't say that.
That's an inference.
And so this was that sort of simple storytelling inference
thing that was very popular in the mid to late '70s.
How far did he get?
It actually went very far.
It started growing in many different ways.
The project I was involved in, Jerry D Young,
who's now at the University of Illinois--
he couldn't be here--
was a project called From Fast Reading, Understanding Memory
Program.
And its idea was to summarize news stories as they came by,
and we actually had it reading a news feed.
And you'd get an article, and it would say there was a hurricane
or there was a flood, and it would
try to report there was a flood in such and such a place,
and it had so many casualties.
And it made some very amusing mistakes
and a lot of getting it right.
We were being interviewed one time,
and as Roger was being interviewed,
his screen flashed, and it said there was a flood in Missouri,
one person died.
And he said see, and later we realized
the story was about Congressman Flood, who had died--
Congressman Flood of Missouri, who had died.
So you get those kinds of things going on,
but mostly, it was really an effort
to pull these things together.
Then the success of students started increasing it,
so Wendy Leonard did a lot on how you could use this approach
to answer questions.
Mallory Selfridge did how we could
use it to do some language learning,
and how it might model how children learned.
It eventually led into what became
known as case based reasoning with people
like [INAUDIBLE] and Chris Hammond, who were both fellows.
So a large number of the fellows--
who, unfortunately, many of them aren't here today--
grew from this very different tradition than the formal MIT
language tradition, from this much more AI-ish--
more influenced by the AI lab than the linguistics department
kind of approached the language.
Interesting.
So tell me more about the atmosphere, the Yale AI lab.
Oh, gosh.
What other things were going on?
What was--
Yes, sure.
Were there bean bags on the floor?
No.
Well, it was more formal than some of the others,
and less formal than anything today.
I mean, it really was mostly just office type space,
but there was a conference room, and there were things.
What I think Roger Schank was famous for
was starting what became known as the Friday Afternoon
Fights, which was the research-- so it wasn't always traditional
that all the grad students working on something
got together.
And Roger felt it was very important to have a once a week
meeting where everybody would get together,
and they went from--
was a rough and tumble attitude, let's say.
So if you got past about the second sentence
of your first slide, you were considered
to have succeeded greatly.
And the idea was to provoke ideas, provoke discussion.
It could be your own work.
It could be reporting on other people's work.
So it took the notion of the journal club, which
is a traditional scientific notion of everybody
reading a paper and coming and discussing it.
This was much more of a let's take some kind of research
ideas and really explore and tumble them,
and refutations would come back a few weeks later saying, no,
look, you really can do this.
That's very embarrassing.
[CELL PHONE RINGING]
Let's just turn off this part of the--
I'm so sorry.
It's OK.
That's really embarrassing.
Just have to rewind.
Film editing.
Computers are wonderful tools.
All right.
My mother called me during the panel yesterday,
and I sat there going, not mine--
Yeah, people always joked about the funniest place
to pick up your phone and answer it would be
in the middle of a final exam.
I have learned now, one is to turn off
my IM whenever I'm at a panel.
So where were we?
Talking about the Friday Afternoon Fights.
I was saying how these clubs had turned
into much more of a rough and tumble,
but what happened was you'd get an idea.
It would be explored.
People would have counterexamples.
Those would be worked on, and so it actually
made for a very active research area.
And in fact, many of Schank's former students
have gone off elsewhere and kept that notion
of the weekly meeting, a lot of interaction,
that sort of thing.
That's very cool.
So you said you're a much more hands-on,
building things kind of person.
Why do you say that?
Can you give me some examples of research
along the way, or just interests?
Well, so the AI community-- in fact, at the first, I guess,
it was probably the first AAAI conference.
If not, it was the first [INAUDIBLE] Science conference.
Bob Abelson, who was a colleague of Schank's, got up
and gave a talk where he introduced the notion
that different AI people had these two
different approaches-- the neat approach, who
wanted to make everything formal and theoretical,
and the scruffy approach, which is people
who wanted to build and test.
The person who probably had most influence in my thinking
about this was Herb Simon, who said
the coin of the realm in AI is an implemented system.
So you give me a great theory of how something can understand
language, that's not going to be nearly as compelling as when
I'm sitting in front of a system that's talking to me.
Absolutely.
And furthermore, he felt we could make best progress
by defining--
things we wanted to do and then get it to do that.
And the more different things it could do,
or the better it could do things,
the more you would say it was approaching
some kind of intelligence.
I wish the field had kept more to that in some ways.
So I've always done more coming from that kind of spec
that we've always built systems.
We've always had demos.
We always tried to show our concept
in something that works because making it work
exposes the flaws in your argument.
If we're publishing it as a theoretical piece of work,
really, it's very hard to probe what's
going on around the edges.
Absolutely.
So you mentioned you were at ARPA for a period of time?
Yes, so I was there--
so ARPA and DARPA change names periodically.
DARPA, yeah.
They're the same place.
Later on.
More recent.
Last time it was ARPA was, I think, early '90s.
So by the time I was there, it was DARPA and SSA.
So I was there from '99 through 2001, and what I was doing
there was primarily creating web languages for AI.
So what's now called semantic web,
I did a lot of the funding of that,
and the goal was to try to take some of these ideas
that the AI community had, that had never gotten out
into the real world as it were, and look
at what had worked on the web and try
to see if we could do it.
And in fact, the MIT team-up now comes back
because one of the people who had been very involved
in this early on was Tim Berners-Lee who had invented
the web and was now in CSAIL.
So what are the main goals of the semantic web?
Get this AI knowledge stuff all over the place.
Get it linked together so that what--
on the web, if I have a document,
and you have a document, my document can point at yours.
So I don't have to necessarily say all the things that
are in your document.
I just point.
I can't do that with my knowledge base.
I can't do that with my database.
So we want to get these other kinds of forms
of data and knowledge linked together, so now, my data
pace can be described by some kind of AI knowledge
representation of ontology, which
can link it, in turn, to other databases, to their ontology.
Like that, so you can start to really view
the world as linking the knowledge together
rather than just the text.
Instead of having to discover that knowledge by reverse
engineering the text in some way,
we can make it much more explicit.
Interesting.
[INAUDIBLE]
No, I was just thinking about the possibilities for that.
It's great, too.
So what do you think the biggest applications
for AI can be in the near future?
Biggest applications for AI?
I think we're going to start seeing a little bit of AI
across very broad swaths of things.
So my slogan for the semantic web work,
before it was even named that, was a little semantics
goes a long way.
And most of the work in the field
is still been trying to build big structured complex
ontologies, but we're starting to find things
like a little frame system for people,
something called Friend of a Friend,
which let you define a few concepts about a person--
their name, their email, address, things like that--
and who else they know.
So create a social network.
There are now about six million people with FUD files
known to the world, so here, it's
become the most used AI thing in a simple way,
just by being very simple and spreading virally
rather than our traditional build a big killer app.
And so I think it's the combination
of some of those things.
I think it's the much simpler AI across a much, much wider--
From batch processing to the micro-computer.
No, from centralized processing to the web.
That's what I meant.
To the web, and just think of the web of knowledge
and knowledge ripe.
That's an idea AI has never played with before--
linking things together.
My knowledge base about cat can point at your concept about cat
and say, these are the same thing.
All of a sudden, all the stuff you've built
and all the stuff I've built can start
getting that network effect that decentralizes, opens,
et cetera.
But you lose a lot of things.
You can't keep this thing consistent.
What happens if I'm pointing at your knowledge,
and the server's down?
So a lot of things we haven't worked on in AI,
because our knowledge base has always
been clean and central and carefully designed,
become big problems.
But big challenges and opportunities,
much like on the web, a 404 error
makes it possible to scale.
You can't not have links to things that go away
and still be scalable.
And so what do you think the most important skills
for someone, a new researcher in the field, are?
I think they're still the skills that we've
had for a long time-- a strong background across the field,
a understanding of some depth of some or all of the areas
that they're interested in, and personally, I think,
creativity.
I think we keep trying to stamp that out of AI and failing,
and I think that's great because no matter how mathematical you
get, there's these big hard problems that take somebody
really just saying, let's go after it.
And that just makes for great, fun work,
and I wish we had more of that going sometimes.
Well, thank you very much.
Thank you.
Cool, so do we have.