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I think I'll start out
and just talk a little bit

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about what exactly autism is.

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Autism is a very big continuum

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that goes from very severe --
the child remains nonverbal --

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all the way up to brilliant
scientists and engineers.

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And I actually feel at home here,

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because there's a lot
of autism genetics here.

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(Laughter)

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You wouldn't have any --

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(Applause)

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It's a continuum of traits.

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When does a nerd turn into Asperger,
which is just mild autism?

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I mean, Einstein and Mozart and Tesla
would all be probably diagnosed

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as autistic spectrum today.

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And one of the things
that is really going to concern me

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is getting these kids to be the ones

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that are going to invent
the next energy things

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that Bill Gates talked about this morning.

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OK, now, if you want
to understand autism: animals.

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I want to talk to you now
about different ways of thinking.

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You have to get away from verbal language.

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I think in pictures.
I don't think in language.

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Now, the thing about the autistic mind
is it attends to details.

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This is a test where you either
have to pick out the big letters

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or the little letters,

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and the autistic mind picks out
the little letters more quickly.

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And the thing is, the normal brain
ignores the details.

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Well, if you're building a bridge,
details are pretty important

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because it'll fall down
if you ignore the details.

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And one of my big concerns
with a lot of policy things today

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is things are getting too abstract.

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People are getting away
from doing hands-on stuff.

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I'm really concerned
that a lot of the schools

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have taken out the hands-on classes,
because art, and classes like that --

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those are the classes where I excelled.

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In my work with cattle,

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I noticed a lot of little things
that most people don't notice

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would make the cattle balk.

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For example, this flag waving
right in front of the veterinary facility.

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This feed yard was going to tear down
their whole veterinary facility;

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all they needed to do was move the flag.

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Rapid movement, contrast.

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In the early '70s when I started,
I got right down in the chutes

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to see what cattle were seeing.

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People thought that was crazy.

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A coat on a fence would make them balk,
shadows would make them balk,

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a hose on the floor --
people weren't noticing these things.

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A chain hanging down ...

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And that's shown
very, very nicely in the movie.

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In fact, I loved the movie,
how they duplicated all my projects.

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That's the geek side.

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My drawings got to star in the movie, too.

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And, actually, it's called
"Temple Grandin,"

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not "Thinking in Pictures."

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So what is thinking in pictures?

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It's literally movies in your head.

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My mind works like Google for images.

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When I was a young kid,
I didn't know my thinking was different.

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I thought everybody thought in pictures.

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Then when I did my book,
"Thinking in Pictures,"

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I started interviewing people
about how they think.

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And I was shocked to find out
that my thinking was quite different.

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Like if I say, "Think
about a church steeple,"

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most people get this sort
of generalized generic one.

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Now, maybe that's not true in this room,

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but it's going to be true
in a lot of different places.

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I see only specific pictures.

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They flash up into my memory,
just like Google for pictures.

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And in the movie,
they've got a great scene in there,

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where the word "shoe" is said,
and a whole bunch of '50s and '60s shoes

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pop into my imagination.

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OK, there's my childhood church;
that's specific.

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There's some more, Fort Collins.

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OK, how about famous ones?

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And they just kind of come up,
kind of like this.

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Just really quickly,
like Google for pictures.

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And they come up one at a time,

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and then I think, "OK, well,
maybe we can have it snow,

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or we can have a thunderstorm,"

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and I can hold it there
and turn them into videos.

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Now, visual thinking
was a tremendous asset

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in my work designing
cattle-handling facilities.

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And I've worked really hard
on improving how cattle are treated

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at the slaughter plant.

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I'm not going to go
into any gucky slaughter slides.

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I've got that stuff up on YouTube,
if you want to look at it.

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(Laughter)

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But one of the things
that I was able to do in my design work

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is I could test-run a piece
of equipment in my mind,

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just like a virtual reality
computer system.

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And this is an aerial view
of a recreation of one of my projects

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that was used in the movie.

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That was like just so super cool.

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And there were a lot of, kind of,
Asperger types and autism types

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working out there on the movie set, too.

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(Laughter)

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But one of the things
that really worries me is:

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Where's the younger version
of those kids going today?

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They're not ending up in Silicon Valley,

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where they belong.

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(Laughter)

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(Applause)

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One of the things I learned very early on
because I wasn't that social,

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is I had to sell my work, and not myself.

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And the way I sold livestock jobs
is I showed off my drawings,

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I showed off pictures of things.

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Another thing that helped me
as a little kid

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is, boy, in the '50s,
you were taught manners.

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You were taught you can't pull
the merchandise off the shelves

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in the store and throw it around.

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When kids get to be
in third or fourth grade,

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you might see that this kid's going
to be a visual thinker,

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drawing in perspective.

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Now, I want to emphasize

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that not every autistic kid
is going to be a visual thinker.

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Now, I had this brain scan
done several years ago,

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and I used to joke around about having
a gigantic Internet trunk line

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going deep into my visual cortex.

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This is tensor imaging.

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And my great big Internet trunk line
is twice as big as the control's.

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The red lines there are me,

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and the blue lines are the sex
and age-matched control.

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And there I got a gigantic one,

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and the control over there, the blue one,
has got a really small one.

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And some of the research now is showing

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that people on the spectrum actually
think with the primary visual cortex.

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Now, the thing is, the visual thinker
is just one kind of mind.

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You see, the autistic mind
tends to be a specialist mind --

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good at one thing, bad at something else.

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And where I was bad was algebra.

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And I was never allowed
to take geometry or trig.

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Gigantic mistake.

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I'm finding a lot of kids
who need to skip algebra,

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go right to geometry and trig.

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Now, another kind of mind
is the pattern thinker.

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More abstract.

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These are your engineers,
your computer programmers.

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This is pattern thinking.

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That praying mantis is made
from a single sheet of paper --

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no scotch tape, no cuts.

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And there in the background
is the pattern for folding it.

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Here are the types of thinking:

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photo-realistic visual thinkers, like me;

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pattern thinkers, music and math minds.

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Some of these oftentimes
have problems with reading.

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You also will see these kind of problems
with kids that are dyslexic.

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You'll see these different kinds of minds.

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And then there's a verbal mind,
they know every fact about everything.

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Now, another thing is the sensory issues.

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I was really concerned about having
to wear this gadget on my face.

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And I came in half an hour beforehand

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so I could have it put on
and kind of get used to it,

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and they got it bent
so it's not hitting my chin.

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But sensory is an issue.

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Some kids are bothered
by fluorescent lights;

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others have problems
with sound sensitivity.

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You know, it's going to be variable.

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Now, visual thinking gave me
a whole lot of insight

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into the animal mind.

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Because think about it:
an animal is a sensory-based thinker,

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not verbal -- thinks in pictures,
thinks in sounds, thinks in smells.

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Think about how much information
there is on the local fire hydrant.

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He knows who's been there --

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(Laughter)

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When they were there.

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Are they friend or foe?
Is there anybody he can go mate with?

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There's a ton of information
on that fire hydrant.

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It's all very detailed information.

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And looking at these kind of details
gave me a lot of insight into animals.

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Now, the animal mind, and also my mind,

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puts sensory-based information
into categories.

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Man on a horse,

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and a man on the ground --

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that is viewed as two
totally different things.

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You could have a horse
that's been abused by a rider.

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They'll be absolutely fine
with the veterinarian

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and with the horseshoer,
but you can't ride him.

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You have another horse,
where maybe the horseshoer beat him up,

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and he'll be terrible for anything
on the ground with the veterinarian,

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but a person can ride him.

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Cattle are the same way.

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Man on a horse, a man on foot --
they're two different things.

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You see, it's a different picture.

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See, I want you to think
about just how specific this is.

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Now, this ability to put
information into categories,

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I find a lot of people
are not very good at this.

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When I'm out troubleshooting equipment

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or problems with something in a plant,

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they don't seem to be able to figure out:

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"Do I have a training-people issue?

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Or do I have something wrong
with the equipment?"

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In other words, categorize
equipment problem from a people problem.

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I find a lot of people
have difficulty doing that.

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Now, let's say I figure out
it's an equipment problem.

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Is it a minor problem,
with something simple I can fix?

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Or is the whole design
of the system wrong?

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People have a hard time figuring that out.

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Let's just look at something
like, you know,

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solving problems with making
airlines safer.

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Yeah, I'm a million-mile flier.

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I do lots and lots of flying,

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and if I was at the FAA,

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what would I be doing a lot
of direct observation of?

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It would be their airplane tails.

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You know, five fatal wrecks
in the last 20 years,

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the tail either came off,

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or steering stuff inside the tail
broke in some way.

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It's tails, pure and simple.

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And when the pilots walk
around the plane, guess what?

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They can't see that stuff inside the tail.

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Now as I think about that,

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I'm pulling up all of that
specific information.

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It's specific.

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See, my thinking's bottom-up.

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I take all the little pieces and I put
the pieces together like a puzzle.

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Now, here is a horse that was deathly
afraid of black cowboy hats.

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He'd been abused by somebody
with a black cowboy hat.

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White cowboy hats,
that was absolutely fine.

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Now, the thing is,
the world is going to need

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all of the different kinds of minds
to work together.

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We've got to work on developing
all these different kinds of minds.

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And one of the things
that is driving me really crazy

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as I travel around
and I do autism meetings,

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is I'm seeing a lot of smart,
geeky, nerdy kids,

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and they just aren't very social,

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and nobody's working
on developing their interest

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in something like science.

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And this brings up the whole thing
of my science teacher.

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My science teacher is shown
absolutely beautifully in the movie.

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I was a goofball student
when I was in high school.

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I just didn't care at all about studying,

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until I had Mr. Carlock's science class.

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He was now Dr. Carlock in the movie.

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And he got me challenged
to figure out an optical illusion room.

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This brings up the whole thing
of you've got to show kids

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interesting stuff.

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You know, one of the things
that I think maybe TED ought to do

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is tell all the schools about all
the great lectures that are on TED,

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and there's all kinds
of great stuff on the Internet

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to get these kids turned on.

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Because I'm seeing a lot
of these geeky, nerdy kids,

242
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and the teachers out in the Midwest
and other parts of the country

243
00:10:50,615 --> 00:10:52,553
when you get away from these tech areas,

244
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they don't know what to do
with these kids.

245
00:10:54,613 --> 00:10:56,634
And they're not going down the right path.

246
00:10:56,658 --> 00:10:58,440
The thing is, you can make a mind

247
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to be more of a thinking
and cognitive mind,

248
00:11:01,213 --> 00:11:03,688
or your mind can be wired
to be more social.

249
00:11:03,712 --> 00:11:06,343
And what some of the research
now has shown in autism

250
00:11:06,367 --> 00:11:09,837
is there may by extra wiring back here
in the really brilliant mind,

251
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and we lose a few social circuits here.

252
00:11:11,798 --> 00:11:14,749
It's kind of a trade-off
between thinking and social.

253
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And then you can get to the point
where it's so severe,

254
00:11:17,389 --> 00:11:20,245
you're going to have a person
that's going to be non-verbal.

255
00:11:20,269 --> 00:11:21,976
In the normal human mind,

256
00:11:22,000 --> 00:11:25,622
language covers up the visual thinking
we share with animals.

257
00:11:25,646 --> 00:11:28,337
This is the work of Dr. Bruce Miller.

258
00:11:29,496 --> 00:11:33,228
He studied Alzheimer's patients
that had frontal temporal lobe dementia.

259
00:11:33,252 --> 00:11:36,275
And the dementia ate out
the language parts of the brain.

260
00:11:36,299 --> 00:11:38,570
And then this artwork came out of somebody

261
00:11:38,594 --> 00:11:40,562
who used to install stereos in cars.

262
00:11:41,567 --> 00:11:45,233
Now, Van Gogh doesn't know
anything about physics,

263
00:11:45,257 --> 00:11:48,578
but I think it's very interesting
that there was some work done

264
00:11:48,602 --> 00:11:51,325
to show that this eddy pattern
in this painting

265
00:11:51,349 --> 00:11:54,497
followed a statistical model
of turbulence,

266
00:11:54,521 --> 00:11:56,540
which brings up the whole interesting idea

267
00:11:56,564 --> 00:12:00,302
of maybe some of this mathematical
patterns is in our own head.

268
00:12:00,326 --> 00:12:01,879
And the Wolfram stuff --

269
00:12:01,903 --> 00:12:06,469
I was taking notes and writing down
all the search words I could use,

270
00:12:06,493 --> 00:12:09,820
because I think that's going to go on
in my autism lectures.

271
00:12:09,844 --> 00:12:12,271
We've got to show
these kids interesting stuff.

272
00:12:12,295 --> 00:12:14,562
And they've taken out the auto-shop class

273
00:12:14,586 --> 00:12:16,686
and the drafting class and the art class.

274
00:12:16,710 --> 00:12:18,976
I mean, art was my best subject in school.

275
00:12:19,000 --> 00:12:21,859
We've got to think about all
these different kinds of minds,

276
00:12:21,883 --> 00:12:24,663
and we've got to absolutely
work with these kind of minds,

277
00:12:24,687 --> 00:12:26,975
because we absolutely are going to need

278
00:12:26,999 --> 00:12:29,520
these kinds of people in the future.

279
00:12:30,000 --> 00:12:31,779
And let's talk about jobs.

280
00:12:32,264 --> 00:12:34,457
OK, my science teacher got me studying,

281
00:12:34,481 --> 00:12:36,895
because I was a goofball
that didn't want to study.

282
00:12:36,919 --> 00:12:39,264
But you know what?
I was getting work experience.

283
00:12:39,288 --> 00:12:42,752
I'm seeing too many of these smart kids
who haven't learned basic things,

284
00:12:42,776 --> 00:12:46,224
like how to be on time -- I was taught
that when I was eight years old.

285
00:12:46,248 --> 00:12:48,803
How to have table manners
at granny's Sunday party.

286
00:12:48,827 --> 00:12:51,012
I was taught that
when I was very, very young.

287
00:12:51,486 --> 00:12:56,463
And when I was 13, I had a job
at a dressmaker's shop sewing clothes.

288
00:12:56,487 --> 00:12:59,075
I did internships in college,

289
00:12:59,099 --> 00:13:02,302
I was building things,

290
00:13:02,326 --> 00:13:05,343
and I also had to learn
how to do assignments.

291
00:13:05,367 --> 00:13:08,957
You know, all I wanted to do was draw
pictures of horses when I was little.

292
00:13:08,981 --> 00:13:11,809
My mother said, "Well let's do
a picture of something else."

293
00:13:11,833 --> 00:13:13,997
They've got to learn
how to do something else.

294
00:13:14,021 --> 00:13:15,865
Let's say the kid is fixated on Legos.

295
00:13:15,889 --> 00:13:18,586
Let's get him working
on building different things.

296
00:13:18,610 --> 00:13:22,206
The thing about the autistic mind
is it tends to be fixated.

297
00:13:22,230 --> 00:13:26,108
Like if the kid loves race cars,
let's use race cars for math.

298
00:13:26,132 --> 00:13:29,572
Let's figure out how long it takes
a race car to go a certain distance.

299
00:13:29,596 --> 00:13:32,709
In other words, use that fixation

300
00:13:32,733 --> 00:13:36,589
in order to motivate that kid,
that's one of the things we need to do.

301
00:13:36,613 --> 00:13:39,701
I really get fed up when the teachers,

302
00:13:39,725 --> 00:13:42,527
especially when you get away
from this part of the country,

303
00:13:42,551 --> 00:13:44,931
they don't know what to do
with these smart kids.

304
00:13:44,955 --> 00:13:46,118
It just drives me crazy.

305
00:13:46,142 --> 00:13:48,384
What can visual thinkers
do when they grow up?

306
00:13:48,408 --> 00:13:51,357
They can do graphic design,
all kinds of stuff with computers,

307
00:13:51,381 --> 00:13:54,764
photography, industrial design.

308
00:13:56,383 --> 00:14:00,315
The pattern thinkers -- they're the ones
that are going to be your mathematicians,

309
00:14:00,339 --> 00:14:02,846
your software engineers,
your computer programmers,

310
00:14:02,870 --> 00:14:04,833
all of those kinds of jobs.

311
00:14:04,857 --> 00:14:08,429
And then you've got the word minds;
they make great journalists,

312
00:14:08,453 --> 00:14:10,976
and they also make really,
really good stage actors.

313
00:14:11,000 --> 00:14:13,392
Because the thing about being autistic is,

314
00:14:13,416 --> 00:14:16,247
I had to learn social skills
like being in a play.

315
00:14:16,271 --> 00:14:18,644
You just kind of ...
you just have to learn it.

316
00:14:19,000 --> 00:14:21,976
And we need to be working
with these students.

317
00:14:22,000 --> 00:14:24,108
And this brings up mentors.

318
00:14:24,132 --> 00:14:27,232
You know, my science teacher
was not an accredited teacher.

319
00:14:27,256 --> 00:14:28,976
He was a NASA space scientist.

320
00:14:29,000 --> 00:14:32,404
Some states now are getting it to where,
if you have a degree in biology

321
00:14:32,428 --> 00:14:33,699
or in chemistry,

322
00:14:33,723 --> 00:14:36,560
you can come into the school
and teach biology or chemistry.

323
00:14:36,584 --> 00:14:38,110
We need to be doing that.

324
00:14:38,540 --> 00:14:40,330
Because what I'm observing is,

325
00:14:40,354 --> 00:14:42,403
the good teachers,
for a lot of these kids,

326
00:14:42,427 --> 00:14:44,075
are out in the community colleges.

327
00:14:44,099 --> 00:14:46,635
But we need to be getting
some of these good teachers

328
00:14:46,659 --> 00:14:47,810
into the high schools.

329
00:14:47,834 --> 00:14:51,390
Another thing that can be very,
very, very successful is:

330
00:14:51,414 --> 00:14:53,522
there's a lot of people
that may have retired

331
00:14:53,546 --> 00:14:55,368
from working in the software industry,

332
00:14:55,392 --> 00:14:56,780
and they can teach your kid.

333
00:14:56,804 --> 00:14:59,613
And it doesn't matter
if what they teach them is old,

334
00:14:59,637 --> 00:15:02,364
because what you're doing
is you're lighting the spark.

335
00:15:02,388 --> 00:15:04,852
You're getting that kid turned on.

336
00:15:04,876 --> 00:15:08,291
And you get him turned on,
then you'll learn all the new stuff.

337
00:15:08,315 --> 00:15:10,579
Mentors are just essential.

338
00:15:10,603 --> 00:15:14,336
I cannot emphasize enough
what my science teacher did for me.

339
00:15:14,944 --> 00:15:17,976
And we've got to mentor them, hire them.

340
00:15:18,000 --> 00:15:20,808
And if you bring them in
for internships in your companies,

341
00:15:20,832 --> 00:15:23,305
the thing about the autism,
Asperger-y kind of mind,

342
00:15:23,329 --> 00:15:25,297
you've got to give them a specific task.

343
00:15:25,321 --> 00:15:27,163
Don't just say, "Design new software."

344
00:15:27,187 --> 00:15:29,454
You've got to tell them
something more specific:

345
00:15:29,478 --> 00:15:31,375
"We're designing software for a phone

346
00:15:31,399 --> 00:15:33,199
and it has to do some specific thing,

347
00:15:33,223 --> 00:15:34,976
and it can only use so much memory."

348
00:15:35,000 --> 00:15:37,427
That's the kind of specificity you need.

349
00:15:37,451 --> 00:15:39,310
Well, that's the end of my talk.

350
00:15:39,334 --> 00:15:41,577
And I just want to thank
everybody for coming.

351
00:15:41,601 --> 00:15:42,976
It was great to be here.

352
00:15:43,000 --> 00:15:50,000
(Applause)

353
00:15:54,526 --> 00:15:56,717
(Applause ends)

354
00:15:56,741 --> 00:15:58,566
Oh -- you have a question for me? OK.

355
00:15:58,590 --> 00:16:00,987
(Applause)

356
00:16:01,011 --> 00:16:03,039
Chris Anderson:
Thank you so much for that.

357
00:16:03,063 --> 00:16:05,389
You know, you once wrote --
I like this quote:

358
00:16:05,413 --> 00:16:09,974
"If by some magic, autism had been
eradicated from the face of the Earth,

359
00:16:09,998 --> 00:16:12,975
then men would still be socializing
in front of a wood fire

360
00:16:12,999 --> 00:16:14,340
at the entrance to a cave."

361
00:16:14,364 --> 00:16:15,392
(Laughter)

362
00:16:15,416 --> 00:16:18,652
Temple Grandin: Because who do you think
made the first stone spear?

363
00:16:18,676 --> 00:16:19,834
It was the Asperger guy,

364
00:16:19,858 --> 00:16:22,411
and if you were to get rid
of all the autism genetics,

365
00:16:22,435 --> 00:16:26,100
there'd be no more Silicon Valley,
and the energy crisis would not be solved.

366
00:16:26,124 --> 00:16:27,465
(Applause)

367
00:16:27,489 --> 00:16:29,719
CA: I want to ask you
a couple other questions,

368
00:16:29,743 --> 00:16:33,507
and if any of these feel inappropriate,
it's OK just to say, "Next question."

369
00:16:33,531 --> 00:16:37,145
But if there is someone here
who has an autistic child,

370
00:16:37,169 --> 00:16:41,910
or knows an autistic child and feels
kind of cut off from them,

371
00:16:42,533 --> 00:16:44,380
what advice would you give them?

372
00:16:44,404 --> 00:16:46,733
TG: Well, first of all,
we've got to look at age.

373
00:16:46,757 --> 00:16:50,706
If you have a two, three or four-year-old,
no speech, no social interaction,

374
00:16:50,730 --> 00:16:53,019
I can't emphasize enough: Don't wait.

375
00:16:53,043 --> 00:16:56,173
You need at least 20 hours a week
of one-to-one teaching.

376
00:16:56,352 --> 00:16:58,845
The thing is, autism comes
in different degrees.

377
00:16:58,869 --> 00:17:02,376
About half of the people on the spectrum
are not going to learn to talk,

378
00:17:02,400 --> 00:17:04,501
and they won't be working
in Silicon Valley.

379
00:17:04,525 --> 00:17:06,975
That would not be a reasonable
thing for them to do.

380
00:17:06,999 --> 00:17:10,076
But then you get these smart,
geeky kids with a touch of autism,

381
00:17:10,100 --> 00:17:12,580
and that's where you've got
to get them turned on

382
00:17:12,604 --> 00:17:14,093
with doing interesting things.

383
00:17:14,117 --> 00:17:17,132
I got social interaction
through shared interests --

384
00:17:17,156 --> 00:17:21,113
I rode horses with other kids,
I made model rockets with other kids,

385
00:17:21,137 --> 00:17:23,115
did electronics lab with other kids.

386
00:17:23,139 --> 00:17:27,390
And in the '60s, it was gluing mirrors
onto a rubber membrane on a speaker

387
00:17:27,414 --> 00:17:28,595
to make a light show.

388
00:17:28,619 --> 00:17:30,841
That was, like,
we considered that super cool.

389
00:17:30,865 --> 00:17:31,871
(Laughter)

390
00:17:31,895 --> 00:17:33,392
CA: Is it unrealistic for them

391
00:17:33,416 --> 00:17:38,156
to hope or think that that child
loves them, as some might, as most, wish?

392
00:17:38,180 --> 00:17:40,414
TG: Well, I tell you,
that child will be loyal,

393
00:17:40,438 --> 00:17:43,813
and if your house is burning down,
they're going to get you out of it.

394
00:17:43,837 --> 00:17:47,496
CA: Wow. So most people, if you ask them
what they're most passionate about,

395
00:17:47,520 --> 00:17:50,368
they'd say things like,
"My kids" or "My lover."

396
00:17:51,010 --> 00:17:52,762
What are you most passionate about?

397
00:17:52,786 --> 00:17:55,817
TG: I'm passionate
about that the things I do

398
00:17:55,841 --> 00:17:57,878
are going to make
the world a better place.

399
00:17:57,902 --> 00:18:00,124
When I have a mother
of an autistic child say,

400
00:18:00,148 --> 00:18:02,210
"My kid went to college
because of your book

401
00:18:02,234 --> 00:18:03,434
or one of your lectures,"

402
00:18:03,458 --> 00:18:04,609
that makes me happy.

403
00:18:04,633 --> 00:18:07,346
You know, the slaughter plants
I worked with in the '80s;

404
00:18:07,370 --> 00:18:08,687
they were absolutely awful.

405
00:18:08,711 --> 00:18:11,987
I developed a really simple
scoring system for slaughter plants,

406
00:18:12,011 --> 00:18:13,563
where you just measure outcomes:

407
00:18:13,587 --> 00:18:14,829
How many cattle fell down?

408
00:18:14,853 --> 00:18:16,601
How many got poked with the prodder?

409
00:18:16,625 --> 00:18:18,656
How many cattle
are mooing their heads off?

410
00:18:19,018 --> 00:18:20,352
And it's very, very simple.

411
00:18:20,376 --> 00:18:22,353
You directly observe a few simple things.

412
00:18:22,377 --> 00:18:23,541
It's worked really well.

413
00:18:23,565 --> 00:18:25,904
I get satisfaction out of seeing stuff

414
00:18:25,928 --> 00:18:28,648
that makes real change in the real world.

415
00:18:28,672 --> 00:18:31,421
We need a lot more of that,
and a lot less abstract stuff.

416
00:18:31,445 --> 00:18:32,596
CA: Totally.

417
00:18:32,620 --> 00:18:37,703
(Applause)

418
00:18:37,727 --> 00:18:40,826
CA: When we were talking on the phone,
one of the things you said

419
00:18:40,850 --> 00:18:42,052
that really astonished me

420
00:18:42,076 --> 00:18:45,237
was that one thing you were passionate
about was server farms.

421
00:18:45,843 --> 00:18:46,994
Tell me about that.

422
00:18:47,018 --> 00:18:50,395
TG: Well, the reason why I got really
excited when I read about that,

423
00:18:50,419 --> 00:18:52,075
it contains knowledge.

424
00:18:52,099 --> 00:18:53,757
It's libraries.

425
00:18:53,781 --> 00:18:57,300
And to me, knowledge is something
that is extremely valuable.

426
00:18:57,324 --> 00:19:00,440
So, maybe over 10 years ago
now, our library got flooded.

427
00:19:00,464 --> 00:19:02,515
This is before the Internet
got really big.

428
00:19:02,539 --> 00:19:05,220
And I was really upset
about all the books being wrecked,

429
00:19:05,244 --> 00:19:07,220
because it was knowledge being destroyed.

430
00:19:07,244 --> 00:19:11,657
And server farms, or data centers,
are great libraries of knowledge.

431
00:19:11,681 --> 00:19:12,972
CA: Temple, can I just say,

432
00:19:12,996 --> 00:19:15,082
it's an absolute delight
to have you at TED.

433
00:19:15,106 --> 00:19:16,257
Thank you so much.

434
00:19:16,281 --> 00:19:18,176
TG: Well, thank you so much. Thank you.

435
00:19:18,200 --> 00:19:23,436
(Applause)