0:00:00.444,0:00:02.890
I think I'll start out[br]and just talk a little bit

0:00:02.914,0:00:04.405
about what exactly autism is.

0:00:04.429,0:00:07.064
Autism is a very big continuum

0:00:07.088,0:00:10.262
that goes from very severe --[br]the child remains nonverbal --

0:00:10.286,0:00:12.976
all the way up to brilliant[br]scientists and engineers.

0:00:13.000,0:00:14.977
And I actually feel at home here,

0:00:15.001,0:00:17.547
because there's a lot[br]of autism genetics here.

0:00:17.571,0:00:18.603
(Laughter)

0:00:18.627,0:00:19.785
You wouldn't have any --

0:00:19.809,0:00:22.778
(Applause)

0:00:22.802,0:00:24.838
It's a continuum of traits.

0:00:24.862,0:00:30.519
When does a nerd turn into Asperger,[br]which is just mild autism?

0:00:30.543,0:00:35.701
I mean, Einstein and Mozart and Tesla[br]would all be probably diagnosed

0:00:35.725,0:00:37.617
as autistic spectrum today.

0:00:37.641,0:00:40.932
And one of the things[br]that is really going to concern me

0:00:40.956,0:00:43.191
is getting these kids to be the ones

0:00:43.215,0:00:46.037
that are going to invent[br]the next energy things

0:00:46.061,0:00:48.311
that Bill Gates talked about this morning.

0:00:48.798,0:00:52.711
OK, now, if you want[br]to understand autism: animals.

0:00:52.735,0:00:55.625
I want to talk to you now[br]about different ways of thinking.

0:00:55.649,0:00:58.499
You have to get away from verbal language.

0:00:58.523,0:01:02.124
I think in pictures.[br]I don't think in language.

0:01:02.822,0:01:08.092
Now, the thing about the autistic mind[br]is it attends to details.

0:01:08.116,0:01:11.171
This is a test where you either[br]have to pick out the big letters

0:01:11.195,0:01:12.418
or the little letters,

0:01:12.442,0:01:16.335
and the autistic mind picks out[br]the little letters more quickly.

0:01:16.687,0:01:20.438
And the thing is, the normal brain[br]ignores the details.

0:01:20.462,0:01:23.472
Well, if you're building a bridge,[br]details are pretty important

0:01:23.496,0:01:25.894
because it'll fall down[br]if you ignore the details.

0:01:25.918,0:01:28.785
And one of my big concerns[br]with a lot of policy things today

0:01:28.809,0:01:30.487
is things are getting too abstract.

0:01:30.511,0:01:33.738
People are getting away[br]from doing hands-on stuff.

0:01:33.762,0:01:35.922
I'm really concerned[br]that a lot of the schools

0:01:35.946,0:01:39.914
have taken out the hands-on classes,[br]because art, and classes like that --

0:01:39.938,0:01:42.251
those are the classes where I excelled.

0:01:42.275,0:01:44.429
In my work with cattle,

0:01:44.453,0:01:47.431
I noticed a lot of little things[br]that most people don't notice

0:01:47.455,0:01:48.743
would make the cattle balk.

0:01:48.767,0:01:52.314
For example, this flag waving[br]right in front of the veterinary facility.

0:01:52.338,0:01:55.665
This feed yard was going to tear down[br]their whole veterinary facility;

0:01:55.689,0:01:57.603
all they needed to do was move the flag.

0:01:57.627,0:01:59.695
Rapid movement, contrast.

0:01:59.719,0:02:02.753
In the early '70s when I started,[br]I got right down in the chutes

0:02:02.777,0:02:04.276
to see what cattle were seeing.

0:02:04.300,0:02:05.762
People thought that was crazy.

0:02:05.786,0:02:09.068
A coat on a fence would make them balk,[br]shadows would make them balk,

0:02:09.092,0:02:12.156
a hose on the floor --[br]people weren't noticing these things.

0:02:12.180,0:02:13.696
A chain hanging down ...

0:02:13.720,0:02:15.976
And that's shown[br]very, very nicely in the movie.

0:02:16.000,0:02:19.455
In fact, I loved the movie,[br]how they duplicated all my projects.

0:02:19.479,0:02:20.820
That's the geek side.

0:02:20.844,0:02:23.264
My drawings got to star in the movie, too.

0:02:23.288,0:02:25.398
And, actually, it's called[br]"Temple Grandin,"

0:02:25.422,0:02:26.746
not "Thinking in Pictures."

0:02:26.770,0:02:28.335
So what is thinking in pictures?

0:02:28.359,0:02:31.038
It's literally movies in your head.

0:02:31.062,0:02:33.944
My mind works like Google for images.

0:02:33.968,0:02:37.012
When I was a young kid,[br]I didn't know my thinking was different.

0:02:37.036,0:02:38.955
I thought everybody thought in pictures.

0:02:38.979,0:02:41.234
Then when I did my book,[br]"Thinking in Pictures,"

0:02:41.258,0:02:43.670
I started interviewing people[br]about how they think.

0:02:43.694,0:02:46.849
And I was shocked to find out[br]that my thinking was quite different.

0:02:46.873,0:02:49.049
Like if I say, "Think[br]about a church steeple,"

0:02:49.073,0:02:51.586
most people get this sort[br]of generalized generic one.

0:02:51.610,0:02:53.522
Now, maybe that's not true in this room,

0:02:53.546,0:02:56.812
but it's going to be true[br]in a lot of different places.

0:02:56.836,0:02:59.339
I see only specific pictures.

0:02:59.363,0:03:03.172
They flash up into my memory,[br]just like Google for pictures.

0:03:03.196,0:03:05.739
And in the movie,[br]they've got a great scene in there,

0:03:05.763,0:03:09.563
where the word "shoe" is said,[br]and a whole bunch of '50s and '60s shoes

0:03:09.587,0:03:11.253
pop into my imagination.

0:03:11.648,0:03:14.159
OK, there's my childhood church;[br]that's specific.

0:03:14.547,0:03:16.415
There's some more, Fort Collins.

0:03:16.439,0:03:18.486
OK, how about famous ones?

0:03:18.510,0:03:21.187
And they just kind of come up,[br]kind of like this.

0:03:21.211,0:03:24.122
Just really quickly,[br]like Google for pictures.

0:03:24.146,0:03:25.705
And they come up one at a time,

0:03:25.729,0:03:28.680
and then I think, "OK, well,[br]maybe we can have it snow,

0:03:28.704,0:03:30.328
or we can have a thunderstorm,"

0:03:30.352,0:03:32.976
and I can hold it there[br]and turn them into videos.

0:03:33.000,0:03:36.213
Now, visual thinking[br]was a tremendous asset

0:03:36.237,0:03:39.039
in my work designing[br]cattle-handling facilities.

0:03:39.539,0:03:42.739
And I've worked really hard[br]on improving how cattle are treated

0:03:42.763,0:03:43.915
at the slaughter plant.

0:03:43.939,0:03:46.435
I'm not going to go[br]into any gucky slaughter slides.

0:03:46.459,0:03:49.347
I've got that stuff up on YouTube,[br]if you want to look at it.

0:03:49.371,0:03:50.412
(Laughter)

0:03:50.436,0:03:53.329
But one of the things that I was able[br]to do in my design work

0:03:53.353,0:03:55.976
is I could test-run a piece[br]of equipment in my mind,

0:03:56.000,0:03:58.432
just like a virtual reality[br]computer system.

0:03:59.448,0:04:03.089
And this is an aerial view[br]of a recreation of one of my projects

0:04:03.113,0:04:04.475
that was used in the movie.

0:04:04.499,0:04:06.510
That was like just so super cool.

0:04:06.534,0:04:10.012
And there were a lot of, kind of,[br]Asperger types and autism types

0:04:10.036,0:04:11.984
working out there on the movie set, too.

0:04:12.008,0:04:13.184
(Laughter)

0:04:13.208,0:04:15.999
But one of the things[br]that really worries me is:

0:04:16.023,0:04:19.027
Where's the younger version[br]of those kids going today?

0:04:19.560,0:04:22.015
They're not ending up in Silicon Valley,

0:04:22.039,0:04:23.279
where they belong.

0:04:23.303,0:04:25.123
(Laughter)

0:04:25.147,0:04:30.055
(Applause)

0:04:30.079,0:04:33.660
One of the things I learned very early on[br]because I wasn't that social,

0:04:33.684,0:04:37.095
is I had to sell my work, and not myself.

0:04:37.446,0:04:40.406
And the way I sold livestock jobs[br]is I showed off my drawings,

0:04:40.430,0:04:42.162
I showed off pictures of things.

0:04:42.186,0:04:44.425
Another thing that helped me[br]as a little kid

0:04:44.449,0:04:46.678
is, boy, in the '50s,[br]you were taught manners.

0:04:46.702,0:04:49.741
You were taught you can't pull[br]the merchandise off the shelves

0:04:49.765,0:04:51.399
in the store and throw it around.

0:04:51.423,0:04:53.575
When kids get to be[br]in third or fourth grade,

0:04:53.599,0:04:56.427
you might see that this kid's going[br]to be a visual thinker,

0:04:56.451,0:04:57.716
drawing in perspective.

0:04:57.740,0:04:58.938
Now, I want to emphasize

0:04:58.962,0:05:01.885
that not every autistic kid[br]is going to be a visual thinker.

0:05:02.452,0:05:06.430
Now, I had this brain scan[br]done several years ago,

0:05:06.454,0:05:10.387
and I used to joke around about having[br]a gigantic Internet trunk line

0:05:10.411,0:05:12.338
going deep into my visual cortex.

0:05:12.362,0:05:14.237
This is tensor imaging.

0:05:14.261,0:05:18.113
And my great big Internet trunk line[br]is twice as big as the control's.

0:05:18.137,0:05:19.741
The red lines there are me,

0:05:19.765,0:05:23.674
and the blue lines are the sex[br]and age-matched control.

0:05:24.590,0:05:26.612
And there I got a gigantic one,

0:05:26.636,0:05:30.644
and the control over there, the blue one,[br]has got a really small one.

0:05:31.841,0:05:33.976
And some of the research now is showing

0:05:34.000,0:05:38.031
that people on the spectrum actually[br]think with the primary visual cortex.

0:05:38.055,0:05:41.096
Now, the thing is, the visual thinker[br]is just one kind of mind.

0:05:41.120,0:05:44.705
You see, the autistic mind[br]tends to be a specialist mind --

0:05:44.729,0:05:47.342
good at one thing, bad at something else.

0:05:47.842,0:05:49.596
And where I was bad was algebra.

0:05:49.620,0:05:51.910
And I was never allowed[br]to take geometry or trig.

0:05:51.934,0:05:53.097
Gigantic mistake.

0:05:53.121,0:05:55.556
I'm finding a lot of kids[br]who need to skip algebra,

0:05:55.580,0:05:57.229
go right to geometry and trig.

0:05:57.587,0:06:00.187
Now, another kind of mind[br]is the pattern thinker.

0:06:00.211,0:06:01.450
More abstract.

0:06:01.474,0:06:04.037
These are your engineers,[br]your computer programmers.

0:06:04.061,0:06:05.455
This is pattern thinking.

0:06:05.479,0:06:08.364
That praying mantis is made[br]from a single sheet of paper --

0:06:08.388,0:06:09.805
no scotch tape, no cuts.

0:06:09.829,0:06:13.364
And there in the background[br]is the pattern for folding it.

0:06:13.388,0:06:15.033
Here are the types of thinking:

0:06:15.471,0:06:18.023
photo-realistic visual thinkers, like me;

0:06:18.596,0:06:21.976
pattern thinkers, music and math minds.

0:06:22.000,0:06:24.588
Some of these oftentimes[br]have problems with reading.

0:06:24.612,0:06:29.093
You also will see these kind of problems[br]with kids that are dyslexic.

0:06:29.117,0:06:31.141
You'll see these different kinds of minds.

0:06:31.165,0:06:34.739
And then there's a verbal mind,[br]they know every fact about everything.

0:06:34.763,0:06:36.729
Now, another thing is the sensory issues.

0:06:36.753,0:06:40.045
I was really concerned about having[br]to wear this gadget on my face.

0:06:40.432,0:06:42.586
And I came in half an hour beforehand

0:06:42.610,0:06:45.593
so I could have it put on[br]and kind of get used to it,

0:06:45.617,0:06:48.355
and they got it bent[br]so it's not hitting my chin.

0:06:48.379,0:06:49.531
But sensory is an issue.

0:06:49.555,0:06:51.704
Some kids are bothered[br]by fluorescent lights;

0:06:51.728,0:06:53.885
others have problems[br]with sound sensitivity.

0:06:54.237,0:06:56.410
You know, it's going to be variable.

0:06:57.392,0:07:00.812
Now, visual thinking gave me[br]a whole lot of insight

0:07:00.836,0:07:03.096
into the animal mind.

0:07:03.120,0:07:06.553
Because think about it:[br]an animal is a sensory-based thinker,

0:07:06.577,0:07:12.774
not verbal -- thinks in pictures,[br]thinks in sounds, thinks in smells.

0:07:12.798,0:07:16.142
Think about how much information[br]there is on the local fire hydrant.

0:07:16.166,0:07:17.528
He knows who's been there --

0:07:17.552,0:07:18.583
(Laughter)

0:07:18.607,0:07:19.764
When they were there.

0:07:19.788,0:07:22.683
Are they friend or foe?[br]Is there anybody he can go mate with?

0:07:22.707,0:07:25.589
There's a ton of information[br]on that fire hydrant.

0:07:25.613,0:07:28.399
It's all very detailed information.

0:07:28.736,0:07:32.975
And looking at these kind of details[br]gave me a lot of insight into animals.

0:07:33.428,0:07:36.976
Now, the animal mind, and also my mind,

0:07:37.000,0:07:41.569
puts sensory-based information[br]into categories.

0:07:41.593,0:07:43.296
Man on a horse,

0:07:43.320,0:07:44.875
and a man on the ground --

0:07:44.899,0:07:47.684
that is viewed as two[br]totally different things.

0:07:47.708,0:07:50.207
You could have a horse[br]that's been abused by a rider.

0:07:50.231,0:07:52.501
They'll be absolutely fine[br]with the veterinarian

0:07:52.525,0:07:54.976
and with the horseshoer,[br]but you can't ride him.

0:07:55.000,0:07:58.175
You have another horse,[br]where maybe the horseshoer beat him up,

0:07:58.199,0:08:02.194
and he'll be terrible for anything[br]on the ground with the veterinarian,

0:08:02.218,0:08:04.233
but a person can ride him.

0:08:04.257,0:08:05.564
Cattle are the same way.

0:08:05.588,0:08:09.513
Man on a horse, a man on foot --[br]they're two different things.

0:08:09.537,0:08:11.493
You see, it's a different picture.

0:08:11.517,0:08:14.638
See, I want you to think[br]about just how specific this is.

0:08:14.662,0:08:18.049
Now, this ability to put[br]information into categories,

0:08:18.073,0:08:21.425
I find a lot of people[br]are not very good at this.

0:08:21.449,0:08:23.617
When I'm out troubleshooting equipment

0:08:23.641,0:08:25.469
or problems with something in a plant,

0:08:25.493,0:08:27.498
they don't seem to be able to figure out:

0:08:27.522,0:08:29.222
"Do I have a training-people issue?

0:08:29.246,0:08:31.681
Or do I have something wrong[br]with the equipment?"

0:08:31.705,0:08:35.399
In other words, categorize[br]equipment problem from a people problem.

0:08:35.423,0:08:38.519
I find a lot of people[br]have difficulty doing that.

0:08:38.543,0:08:41.107
Now, let's say I figure out[br]it's an equipment problem.

0:08:41.131,0:08:43.907
Is it a minor problem,[br]with something simple I can fix?

0:08:43.931,0:08:46.246
Or is the whole design[br]of the system wrong?

0:08:46.270,0:08:48.976
People have a hard time figuring that out.

0:08:49.000,0:08:51.096
Let's just look at something[br]like, you know,

0:08:51.120,0:08:53.366
solving problems with making[br]airlines safer.

0:08:53.390,0:08:54.976
Yeah, I'm a million-mile flier.

0:08:55.000,0:08:56.826
I do lots and lots of flying,

0:08:56.850,0:09:00.082
and if I was at the FAA,

0:09:00.106,0:09:03.628
what would I be doing a lot[br]of direct observation of?

0:09:04.197,0:09:06.501
It would be their airplane tails.

0:09:06.525,0:09:09.075
You know, five fatal wrecks[br]in the last 20 years,

0:09:09.099,0:09:10.874
the tail either came off,

0:09:10.898,0:09:14.332
or steering stuff inside the tail[br]broke in some way.

0:09:14.747,0:09:16.800
It's tails, pure and simple.

0:09:16.824,0:09:19.357
And when the pilots walk[br]around the plane, guess what?

0:09:19.381,0:09:21.432
They can't see that stuff inside the tail.

0:09:21.456,0:09:22.893
Now as I think about that,

0:09:22.917,0:09:26.376
I'm pulling up all of that[br]specific information.

0:09:26.400,0:09:27.573
It's specific.

0:09:27.597,0:09:29.092
See, my thinking's bottom-up.

0:09:29.116,0:09:32.813
I take all the little pieces and I put[br]the pieces together like a puzzle.

0:09:32.837,0:09:36.604
Now, here is a horse that was deathly[br]afraid of black cowboy hats.

0:09:36.628,0:09:39.188
He'd been abused by somebody[br]with a black cowboy hat.

0:09:39.212,0:09:42.101
White cowboy hats,[br]that was absolutely fine.

0:09:42.596,0:09:45.146
Now, the thing is,[br]the world is going to need

0:09:45.170,0:09:49.259
all of the different kinds of minds[br]to work together.

0:09:49.283,0:09:52.474
We've got to work on developing[br]all these different kinds of minds.

0:09:52.498,0:09:55.040
And one of the things[br]that is driving me really crazy

0:09:55.064,0:09:57.411
as I travel around[br]and I do autism meetings,

0:09:57.435,0:10:00.576
is I'm seeing a lot of smart,[br]geeky, nerdy kids,

0:10:00.600,0:10:02.775
and they just aren't very social,

0:10:02.799,0:10:05.890
and nobody's working[br]on developing their interest

0:10:05.914,0:10:07.289
in something like science.

0:10:07.666,0:10:10.353
And this brings up the whole thing[br]of my science teacher.

0:10:10.377,0:10:13.485
My science teacher is shown[br]absolutely beautifully in the movie.

0:10:13.509,0:10:15.995
I was a goofball student[br]when I was in high school.

0:10:16.019,0:10:18.047
I just didn't care at all about studying,

0:10:18.071,0:10:21.300
until I had Mr. Carlock's science class.

0:10:21.324,0:10:23.696
He was now Dr. Carlock in the movie.

0:10:24.097,0:10:29.976
And he got me challenged[br]to figure out an optical illusion room.

0:10:30.000,0:10:32.715
This brings up the whole thing[br]of you've got to show kids

0:10:32.739,0:10:33.896
interesting stuff.

0:10:34.314,0:10:37.627
You know, one of the things[br]that I think maybe TED ought to do

0:10:37.651,0:10:40.966
is tell all the schools about all[br]the great lectures that are on TED,

0:10:40.990,0:10:43.477
and there's all kinds[br]of great stuff on the Internet

0:10:43.501,0:10:44.873
to get these kids turned on.

0:10:44.897,0:10:47.383
Because I'm seeing a lot[br]of these geeky, nerdy kids,

0:10:47.407,0:10:50.591
and the teachers out in the Midwest[br]and other parts of the country

0:10:50.615,0:10:52.553
when you get away from these tech areas,

0:10:52.577,0:10:54.589
they don't know what to do[br]with these kids.

0:10:54.613,0:10:56.634
And they're not going down the right path.

0:10:56.658,0:10:58.440
The thing is, you can make a mind

0:10:58.464,0:11:01.189
to be more of a thinking[br]and cognitive mind,

0:11:01.213,0:11:03.688
or your mind can be wired[br]to be more social.

0:11:03.712,0:11:06.343
And what some of the research[br]now has shown in autism

0:11:06.367,0:11:09.837
is there may by extra wiring back here[br]in the really brilliant mind,

0:11:09.861,0:11:11.774
and we lose a few social circuits here.

0:11:11.798,0:11:14.749
It's kind of a trade-off[br]between thinking and social.

0:11:14.773,0:11:17.365
And then you can get to the point[br]where it's so severe,

0:11:17.389,0:11:20.245
you're going to have a person[br]that's going to be non-verbal.

0:11:20.269,0:11:21.976
In the normal human mind,

0:11:22.000,0:11:25.622
language covers up the visual thinking[br]we share with animals.

0:11:25.646,0:11:28.337
This is the work of Dr. Bruce Miller.

0:11:29.496,0:11:33.228
He studied Alzheimer's patients[br]that had frontal temporal lobe dementia.

0:11:33.252,0:11:36.275
And the dementia ate out[br]the language parts of the brain.

0:11:36.299,0:11:38.570
And then this artwork came out of somebody

0:11:38.594,0:11:40.562
who used to install stereos in cars.

0:11:41.567,0:11:45.233
Now, Van Gogh doesn't know[br]anything about physics,

0:11:45.257,0:11:48.578
but I think it's very interesting[br]that there was some work done

0:11:48.602,0:11:51.325
to show that this eddy pattern[br]in this painting

0:11:51.349,0:11:54.497
followed a statistical model[br]of turbulence,

0:11:54.521,0:11:56.540
which brings up the whole interesting idea

0:11:56.564,0:12:00.302
of maybe some of this mathematical[br]patterns is in our own head.

0:12:00.326,0:12:01.879
And the Wolfram stuff --

0:12:01.903,0:12:06.469
I was taking notes and writing down[br]all the search words I could use,

0:12:06.493,0:12:09.820
because I think that's going to go on[br]in my autism lectures.

0:12:09.844,0:12:12.271
We've got to show[br]these kids interesting stuff.

0:12:12.295,0:12:14.562
And they've taken out the auto-shop class

0:12:14.586,0:12:16.686
and the drafting class and the art class.

0:12:16.710,0:12:18.976
I mean, art was my best subject in school.

0:12:19.000,0:12:21.859
We've got to think about all[br]these different kinds of minds,

0:12:21.883,0:12:24.663
and we've got to absolutely[br]work with these kind of minds,

0:12:24.687,0:12:26.975
because we absolutely are going to need

0:12:26.999,0:12:29.520
these kinds of people in the future.

0:12:30.000,0:12:31.779
And let's talk about jobs.

0:12:32.264,0:12:34.457
OK, my science teacher got me studying,

0:12:34.481,0:12:36.895
because I was a goofball[br]that didn't want to study.

0:12:36.919,0:12:39.264
But you know what?[br]I was getting work experience.

0:12:39.288,0:12:42.752
I'm seeing too many of these smart kids[br]who haven't learned basic things,

0:12:42.776,0:12:46.224
like how to be on time -- I was taught[br]that when I was eight years old.

0:12:46.248,0:12:48.803
How to have table manners[br]at granny's Sunday party.

0:12:48.827,0:12:51.012
I was taught that[br]when I was very, very young.

0:12:51.486,0:12:56.463
And when I was 13, I had a job[br]at a dressmaker's shop sewing clothes.

0:12:56.487,0:12:59.075
I did internships in college,

0:12:59.099,0:13:02.302
I was building things,

0:13:02.326,0:13:05.343
and I also had to learn[br]how to do assignments.

0:13:05.367,0:13:08.957
You know, all I wanted to do was draw[br]pictures of horses when I was little.

0:13:08.981,0:13:11.809
My mother said, "Well let's do[br]a picture of something else."

0:13:11.833,0:13:13.997
They've got to learn[br]how to do something else.

0:13:14.021,0:13:15.865
Let's say the kid is fixated on Legos.

0:13:15.889,0:13:18.586
Let's get him working[br]on building different things.

0:13:18.610,0:13:22.206
The thing about the autistic mind[br]is it tends to be fixated.

0:13:22.230,0:13:26.108
Like if the kid loves race cars,[br]let's use race cars for math.

0:13:26.132,0:13:29.572
Let's figure out how long it takes[br]a race car to go a certain distance.

0:13:29.596,0:13:32.709
In other words, use that fixation

0:13:32.733,0:13:36.589
in order to motivate that kid,[br]that's one of the things we need to do.

0:13:36.613,0:13:39.701
I really get fed up when the teachers,

0:13:39.725,0:13:42.527
especially when you get away[br]from this part of the country,

0:13:42.551,0:13:44.931
they don't know what to do[br]with these smart kids.

0:13:44.955,0:13:46.118
It just drives me crazy.

0:13:46.142,0:13:48.384
What can visual thinkers[br]do when they grow up?

0:13:48.408,0:13:51.357
They can do graphic design,[br]all kinds of stuff with computers,

0:13:51.381,0:13:54.764
photography, industrial design.

0:13:56.383,0:14:00.315
The pattern thinkers -- they're the ones[br]that are going to be your mathematicians,

0:14:00.339,0:14:02.846
your software engineers,[br]your computer programmers,

0:14:02.870,0:14:04.833
all of those kinds of jobs.

0:14:04.857,0:14:08.429
And then you've got the word minds;[br]they make great journalists,

0:14:08.453,0:14:10.976
and they also make really,[br]really good stage actors.

0:14:11.000,0:14:13.392
Because the thing about being autistic is,

0:14:13.416,0:14:16.247
I had to learn social skills[br]like being in a play.

0:14:16.271,0:14:18.644
You just kind of ...[br]you just have to learn it.

0:14:19.000,0:14:21.976
And we need to be working[br]with these students.

0:14:22.000,0:14:24.108
And this brings up mentors.

0:14:24.132,0:14:27.232
You know, my science teacher[br]was not an accredited teacher.

0:14:27.256,0:14:28.976
He was a NASA space scientist.

0:14:29.000,0:14:32.404
Some states now are getting it to where,[br]if you have a degree in biology

0:14:32.428,0:14:33.699
or in chemistry,

0:14:33.723,0:14:36.560
you can come into the school[br]and teach biology or chemistry.

0:14:36.584,0:14:38.110
We need to be doing that.

0:14:38.540,0:14:40.330
Because what I'm observing is,

0:14:40.354,0:14:42.403
the good teachers,[br]for a lot of these kids,

0:14:42.427,0:14:44.075
are out in the community colleges.

0:14:44.099,0:14:46.635
But we need to be getting[br]some of these good teachers

0:14:46.659,0:14:47.810
into the high schools.

0:14:47.834,0:14:51.390
Another thing that can be very,[br]very, very successful is:

0:14:51.414,0:14:53.522
there's a lot of people[br]that may have retired

0:14:53.546,0:14:55.368
from working in the software industry,

0:14:55.392,0:14:56.780
and they can teach your kid.

0:14:56.804,0:14:59.613
And it doesn't matter[br]if what they teach them is old,

0:14:59.637,0:15:02.364
because what you're doing[br]is you're lighting the spark.

0:15:02.388,0:15:04.852
You're getting that kid turned on.

0:15:04.876,0:15:08.291
And you get him turned on,[br]then you'll learn all the new stuff.

0:15:08.315,0:15:10.579
Mentors are just essential.

0:15:10.603,0:15:14.336
I cannot emphasize enough[br]what my science teacher did for me.

0:15:14.944,0:15:17.976
And we've got to mentor them, hire them.

0:15:18.000,0:15:20.808
And if you bring them in[br]for internships in your companies,

0:15:20.832,0:15:23.305
the thing about the autism,[br]Asperger-y kind of mind,

0:15:23.329,0:15:25.297
you've got to give them a specific task.

0:15:25.321,0:15:27.163
Don't just say, "Design new software."

0:15:27.187,0:15:29.454
You've got to tell them[br]something more specific:

0:15:29.478,0:15:31.375
"We're designing software for a phone

0:15:31.399,0:15:33.199
and it has to do some specific thing,

0:15:33.223,0:15:34.976
and it can only use so much memory."

0:15:35.000,0:15:37.427
That's the kind of specificity you need.

0:15:37.451,0:15:39.310
Well, that's the end of my talk.

0:15:39.334,0:15:41.577
And I just want to thank[br]everybody for coming.

0:15:41.601,0:15:42.976
It was great to be here.

0:15:43.000,0:15:50.000
(Applause)

0:15:54.526,0:15:56.717
(Applause ends)

0:15:56.741,0:15:58.566
Oh -- you have a question for me? OK.

0:15:58.590,0:16:00.987
(Applause)

0:16:01.011,0:16:03.039
Chris Anderson:[br]Thank you so much for that.

0:16:03.063,0:16:05.389
You know, you once wrote --[br]I like this quote:

0:16:05.413,0:16:09.974
"If by some magic, autism had been[br]eradicated from the face of the Earth,

0:16:09.998,0:16:12.975
then men would still be socializing[br]in front of a wood fire

0:16:12.999,0:16:14.340
at the entrance to a cave."

0:16:14.364,0:16:15.392
(Laughter)

0:16:15.416,0:16:18.652
Temple Grandin: Because who do you think[br]made the first stone spear?

0:16:18.676,0:16:19.834
It was the Asperger guy,

0:16:19.858,0:16:22.411
and if you were to get rid[br]of all the autism genetics,

0:16:22.435,0:16:26.100
there'd be no more Silicon Valley,[br]and the energy crisis would not be solved.

0:16:26.124,0:16:27.465
(Applause)

0:16:27.489,0:16:29.719
CA: I want to ask you[br]a couple other questions,

0:16:29.743,0:16:33.507
and if any of these feel inappropriate,[br]it's OK just to say, "Next question."

0:16:33.531,0:16:37.145
But if there is someone here[br]who has an autistic child,

0:16:37.169,0:16:41.910
or knows an autistic child and feels[br]kind of cut off from them,

0:16:42.533,0:16:44.380
what advice would you give them?

0:16:44.404,0:16:46.733
TG: Well, first of all,[br]we've got to look at age.

0:16:46.757,0:16:50.706
If you have a two, three or four-year-old,[br]no speech, no social interaction,

0:16:50.730,0:16:53.019
I can't emphasize enough: Don't wait.

0:16:53.043,0:16:56.173
You need at least 20 hours a week[br]of one-to-one teaching.

0:16:56.352,0:16:58.845
The thing is, autism comes[br]in different degrees.

0:16:58.869,0:17:02.376
About half of the people on the spectrum[br]are not going to learn to talk,

0:17:02.400,0:17:04.501
and they won't be working[br]in Silicon Valley.

0:17:04.525,0:17:06.975
That would not be a reasonable[br]thing for them to do.

0:17:06.999,0:17:10.076
But then you get these smart,[br]geeky kids with a touch of autism,

0:17:10.100,0:17:12.580
and that's where you've got[br]to get them turned on

0:17:12.604,0:17:14.093
with doing interesting things.

0:17:14.117,0:17:17.132
I got social interaction[br]through shared interests --

0:17:17.156,0:17:21.113
I rode horses with other kids,[br]I made model rockets with other kids,

0:17:21.137,0:17:23.115
did electronics lab with other kids.

0:17:23.139,0:17:27.390
And in the '60s, it was gluing mirrors[br]onto a rubber membrane on a speaker

0:17:27.414,0:17:28.595
to make a light show.

0:17:28.619,0:17:30.841
That was, like,[br]we considered that super cool.

0:17:30.865,0:17:31.871
(Laughter)

0:17:31.895,0:17:33.392
CA: Is it unrealistic for them

0:17:33.416,0:17:38.156
to hope or think that that child[br]loves them, as some might, as most, wish?

0:17:38.180,0:17:40.414
TG: Well, I tell you,[br]that child will be loyal,

0:17:40.438,0:17:43.813
and if your house is burning down,[br]they're going to get you out of it.

0:17:43.837,0:17:47.496
CA: Wow. So most people, if you ask them[br]what they're most passionate about,

0:17:47.520,0:17:50.368
they'd say things like,[br]"My kids" or "My lover."

0:17:51.010,0:17:52.762
What are you most passionate about?

0:17:52.786,0:17:55.817
TG: I'm passionate[br]about that the things I do

0:17:55.841,0:17:57.878
are going to make[br]the world a better place.

0:17:57.902,0:18:00.124
When I have a mother[br]of an autistic child say,

0:18:00.148,0:18:02.210
"My kid went to college[br]because of your book

0:18:02.234,0:18:03.434
or one of your lectures,"

0:18:03.458,0:18:04.609
that makes me happy.

0:18:04.633,0:18:07.346
You know, the slaughter plants[br]I worked with in the '80s;

0:18:07.370,0:18:08.687
they were absolutely awful.

0:18:08.711,0:18:11.987
I developed a really simple[br]scoring system for slaughter plants,

0:18:12.011,0:18:13.563
where you just measure outcomes:

0:18:13.587,0:18:14.829
How many cattle fell down?

0:18:14.853,0:18:16.601
How many got poked with the prodder?

0:18:16.625,0:18:18.656
How many cattle[br]are mooing their heads off?

0:18:19.018,0:18:20.352
And it's very, very simple.

0:18:20.376,0:18:22.353
You directly observe a few simple things.

0:18:22.377,0:18:23.541
It's worked really well.

0:18:23.565,0:18:25.904
I get satisfaction out of seeing stuff

0:18:25.928,0:18:28.648
that makes real change in the real world.

0:18:28.672,0:18:31.421
We need a lot more of that,[br]and a lot less abstract stuff.

0:18:31.445,0:18:32.596
CA: Totally.

0:18:32.620,0:18:37.703
(Applause)

0:18:37.727,0:18:40.826
CA: When we were talking on the phone,[br]one of the things you said

0:18:40.850,0:18:42.052
that really astonished me

0:18:42.076,0:18:45.237
was that one thing you were passionate[br]about was server farms.

0:18:45.843,0:18:46.994
Tell me about that.

0:18:47.018,0:18:50.395
TG: Well, the reason why I got really[br]excited when I read about that,

0:18:50.419,0:18:52.075
it contains knowledge.

0:18:52.099,0:18:53.757
It's libraries.

0:18:53.781,0:18:57.300
And to me, knowledge is something[br]that is extremely valuable.

0:18:57.324,0:19:00.440
So, maybe over 10 years ago[br]now, our library got flooded.

0:19:00.464,0:19:02.515
This is before the Internet[br]got really big.

0:19:02.539,0:19:05.220
And I was really upset[br]about all the books being wrecked,

0:19:05.244,0:19:07.220
because it was knowledge being destroyed.

0:19:07.244,0:19:11.657
And server farms, or data centers,[br]are great libraries of knowledge.

0:19:11.681,0:19:12.972
CA: Temple, can I just say,

0:19:12.996,0:19:15.082
it's an absolute delight[br]to have you at TED.

0:19:15.106,0:19:16.257
Thank you so much.

0:19:16.281,0:19:18.176
TG: Well, thank you so much. Thank you.

0:19:18.200,0:19:23.436
(Applause)