sartorial quotient of Google, and it hasn't worked at all.

On the other hand--

well, I noticed you have a coat on, that's true.

Greg Chesson gets two points for showing up with a coat.

It's a real pleasure to introduce Bruce Schatz to you.

I've known Bruce for rather a long time.

My first introduction to him came as we both began getting

excited about digital libraries and the possibility

of accumulating enormous amounts of information in

digital form that could be worked on, manipulated by,

processed through software that we hope would augment our

brain power.

So Bruce has been in the information game for longer

than he's actually willing to admit I suspect.

He's currently at the University of Illinois,

Champaign-Urbana.

As you will remember, that's also the area where the

National Center for Supercomputer

Applications is located.

Bruce was around at the time when Mark and Jason was doing

work on the first browsers, the mosaic versions of the

browsers derived from Tim BernersLee's work.

Actually, the one thing that Bruce may not realize he gets

credit for is teaching me how to pronounce

caenorhabditis elegans.

I looked at it before and I couldn't figure out, and maybe

I didn't even say it right this time.

But this is a tiny little worm that consists of 50 cells.

It was the first living organism that we actually

completely sequenced the genome for.

Then we got interested in understanding how does the

genome actually reflect itself as this little worm develops

from a single fertilized cell.

So Bruce introduced me to the idea of collecting everything

that was known about that particular organism, and to

turn it into a database that one could manipulate and use

in order to carry out research.

Well, let me just explain a little bit more about his

background and then turn this over to him, because you're

here not to listen to his bio, but to listen to

what he has to say.

He's currently director of something called CANIS--

C-A-N-I-S. I thought it had to do with dogs

until I re-read it.

It says Community Architecture is for Network Information

Systems.

BRUCE SCHATZ: That's why they let me in the building.

MALE SPEAKER: I'm sorry.

BRUCE SCHATZ: That's why they let me in the building.

MALE SPEAKER: Because along with the other

canines that are here.

It's at the University of Illinois, Champaign-Urbana,

and he's been working on federated all the world's

knowledge, just like we are, by building pioneer research

systems in industrial and academic settings.

He's really done a lot of work over a period of 25 or 30

years in this domain.

The title of the talk uses the term telesophy, which he

introduced as a project at Belcorp in the 1980s.

Later on, he worked at UIUC on something called DeLIver

D-E-L-I-V-E-R, and now more recently on semantics.

That's the reason that I asked him to come here.

He's working on something called BeeSpace, which is

spelled B-E-E, as in the little buzzing organism.

This is an attempt as I understand it, but I'm going

to learn more, an attempt to take a concept space and

organize it in such a way that we can assist people thinking

through and understanding more deeply what we know about that

particular organism.

So this is a deep dive into a semantic problem.

So I'm not going to bore you with any more biographical

material, except to say that Bruce has about nine million

slides to go through, so please set your modems at 50

gigabits per second because he's going to have to go that

fast to get through all of it.

I've asked him to leave some time at the end for questions.

I already have one queued up.

So Bruce, with that rather quick introduction, let me

thank you for coming out to join us at Google and turn

this over to you to teach us about semantics.

BRUCE SCHATZ: Thank you.

I have one here, so you can just turn yours off.

Thank you.

I was asked to give a talk about semantics, which I

supposedly know something about.

So this is going to be both a talk that's broad and deep at

the same time, and it's going to try to do something big and

grand, and also try to do something deep that you can

take away with it.

So that may mean that it fails completely and does none of

those, or maybe it does all of those.

I've actually been giving this talk for 25 years and--

now, of course, it doesn't work.

Am I not pointing it in the right place?

I'm pushing it but it's not going.

Oh, there it goes.

OK, sorry.

Can you flip it back there?

Sorry about that.

Small technical difficulty, but the man behind the curtain

is fixing it.

So I gave this talk first more than 20 years ago in the hot

Silicon Valley research lab that all the grad students

wanted to go to, which was called Xerox PARC.

I think a few people actually have heard of Xerox PARC.

It sort of still exists now.

We went down completely?

There we go.

Thank you very much.

I was pushing this idea that you could federate and search

through all the world's knowledge, and the uniform

reaction that was, boy, that would be great,

but it's not possible.

And I said, no, you're wrong.

Here, I'll show you a system that searches across multiple

sources and goes across networks, and does pictures

and text and follows links, and I'll explain each piece

about how it works.

Then they said, that's great, but not in our lifetime.

Well, 10 years later was mosaic and the web.

And 20 years later I'm delighted to be here, and all

of you have actually done it.

You've done all the world's knowledge to some degree.

What I want to talk about is how far are you and what you

need to do before you take over the rest of the world and

I die, which is another 20 years.

So what's going to happen in the next 20 years.

The main thing I'm going to say is a lot's happened on

tele, but not too much on sophy.

So you're halfway to the hive mine, and since I'm working on

honey bees, at the end you will see a picture of honey

bees and hear something about hive mines, but it will be

very short.

Basically, if you look at Google's mission, the mission

is doing a lot about access and organization of all the

world's knowledge.

Actually, to a degree that's possible, you do an excellent

job about that.

However, you do almost nothing about the next stages, which

are usually called analysis and synthesis.

Solving actual problems, looking at things in different

places, combining stuff and sharing it.

And that's because if you look at the graph of research over

the years, we're sort of here, and you're doing commercially

what was done in the research area about 10 years ago, but

you're not doing this stuff yet.

So the telesophy system was about here.

Mosaic was about to here.

Those are the things that

searching across many sources--

like what I showed, we're really working pretty well in

research labs with 1,000 people.

They weren't working with 100 million.

But if Google's going to survive 10 more years, you're

going to have to do whatever research systems do here.

So pay attention.

This doesn't work with students.

With students I have to say I'm going to

fail you at the end.

But you have a real reason, a monetary reason, and a moral

reason to actually pay attention.

So back to the outline.

I'm going to talk about what are different ways to think

about doing all the world's knowledge, and how to go

through all the levels.

I'm going to do all the levels and sort of say you are here,

and then I'm going to concentrate on the next set of

things that you haven't quite got to.

The two particular things I'm going to talk about our

scalable semantics and concept navigation, which probably

don't mean anything to you now, but if I do my job right,

45 minutes, actually now 10 of them are up, so 35 minutes

from now they will mean something.

At the end I'm going to talk about suppose you cared about

this enough to do something, what kind of big thing would

you actually do?

I sort of do these big, one of the kind pioneering projects

with stuff that doesn't quite work just to

show it's really possible.

So the overall goal is you probably all grew up on

reading cyberspace novels is sort of plugging your head and

being one with all the world's knowledge.

Trying to sort of get the concepts in your head to match

whatever is actually out there in a way that you can

get what you want.

The problem is over time what the

network can do has increased.

So in the--

I can't say the old days, man--

in the good days, people worked on packets and tried to

do data transmission.

The era that I sort of worked mostly in was an object era

where we try and give the information to people to do,

[UNINTELLIGIBLE] to do pictures.

All the action in big research labs now is on concepts, is on

trying to do deeper things, but still it

worked like these too.

They work everywhere.

So you don't have a specialized AI program that

only works for income taxes.

That's not good enough.

No Google person would ever do something that only works in

one case, unless there was a huge amount of

money behind it.

I'll stop making money comments, but the food is

great here.

So this is one common layout, and there's four or five

others, which in the absence of time, I will omit.

But if you want to talk to me afterwards, there's lots of

points of view about how to get from here to there, where

there is always all the world's knowledge, and here is

whatever you can do now.

Depending on what point of view you take, it's possible

to go to the next step differently because you have a

different orientation.

So the one that I'm going to do in this talk is the

linguistic one, which usually goes syntax, structure,

semantics, pragmatics.

So syntax is what's actually there, like an actual set of

bits in a file, a set of words in a document.

Structure is the parts, it's not the holes.

So if you part something in structure, you can tell that

this particular thing is a person's name, this is the

introduction to a paper, this is the methods part.

You can tell what the parts are and you can search those

differentially.

Semantics is when you go inside and you try to get

something about the meaning, and as you'll see, people have

pretty much given up on doing real meaning, and they pretty

much try to do, rather than meaning,

they try to do context.

What's around it in a way that helps you understand it.

Actually, when Google was a research project, and the

people that started it were actually on the Stanford

Digital Library Project, I was running the Illinois Digital

Library Project at the same time, they said there's enough

context in web links to be able to really do something.

There were a lot of people that said no, web links are