1 00:00:00,000 --> 00:00:02,000 I think I'll start out and just talk a little bit about 2 00:00:02,000 --> 00:00:04,000 what exactly autism is. 3 00:00:04,000 --> 00:00:07,000 Autism is a very big continuum 4 00:00:07,000 --> 00:00:10,000 that goes from very severe -- the child remains non-verbal -- 5 00:00:10,000 --> 00:00:13,000 all the way up to brilliant scientists and engineers. 6 00:00:13,000 --> 00:00:15,000 And I actually feel at home here, 7 00:00:15,000 --> 00:00:17,000 because there's a lot of autism genetics here. 8 00:00:17,000 --> 00:00:19,000 You wouldn't have any... 9 00:00:19,000 --> 00:00:23,000 (Applause) 10 00:00:23,000 --> 00:00:25,000 It's a continuum of traits. 11 00:00:25,000 --> 00:00:28,000 When does a nerd turn into 12 00:00:28,000 --> 00:00:30,000 Asperger, which is just mild autism? 13 00:00:30,000 --> 00:00:33,000 I mean, Einstein and Mozart 14 00:00:33,000 --> 00:00:35,000 and Tesla would all be probably diagnosed 15 00:00:35,000 --> 00:00:37,000 as autistic spectrum today. 16 00:00:37,000 --> 00:00:40,000 And one of the things that is really going to concern me is 17 00:00:40,000 --> 00:00:43,000 getting these kids to be the ones that are going to invent 18 00:00:43,000 --> 00:00:45,000 the next energy things, 19 00:00:45,000 --> 00:00:49,000 you know, that Bill Gates talked about this morning. 20 00:00:49,000 --> 00:00:51,000 OK. Now, if you want to understand 21 00:00:51,000 --> 00:00:53,000 autism, animals. 22 00:00:53,000 --> 00:00:55,000 And I want to talk to you now about different ways of thinking. 23 00:00:55,000 --> 00:00:58,000 You have to get away from verbal language. 24 00:00:58,000 --> 00:01:00,000 I think in pictures, 25 00:01:00,000 --> 00:01:03,000 I don't think in language. 26 00:01:03,000 --> 00:01:05,000 Now, the thing about the autistic mind 27 00:01:05,000 --> 00:01:08,000 is it attends to details. 28 00:01:08,000 --> 00:01:10,000 OK, this is a test where you either have to 29 00:01:10,000 --> 00:01:12,000 pick out the big letters, or pick out the little letters, 30 00:01:12,000 --> 00:01:14,000 and the autistic mind picks out the 31 00:01:14,000 --> 00:01:16,000 little letters more quickly. 32 00:01:16,000 --> 00:01:20,000 And the thing is, the normal brain ignores the details. 33 00:01:20,000 --> 00:01:22,000 Well, if you're building a bridge, details are pretty important 34 00:01:22,000 --> 00:01:25,000 because it will fall down if you ignore the details. 35 00:01:25,000 --> 00:01:28,000 And one of my big concerns with a lot of policy things today 36 00:01:28,000 --> 00:01:30,000 is things are getting too abstract. 37 00:01:30,000 --> 00:01:32,000 People are getting away from doing 38 00:01:32,000 --> 00:01:34,000 hands-on stuff. 39 00:01:34,000 --> 00:01:36,000 I'm really concerned that a lot of the schools have taken out 40 00:01:36,000 --> 00:01:38,000 the hands-on classes, 41 00:01:38,000 --> 00:01:40,000 because art, and classes like that, 42 00:01:40,000 --> 00:01:42,000 those are the classes where I excelled. 43 00:01:42,000 --> 00:01:44,000 In my work with cattle, 44 00:01:44,000 --> 00:01:47,000 I noticed a lot of little things that most people don't notice 45 00:01:47,000 --> 00:01:49,000 would make the cattle balk. Like, for example, 46 00:01:49,000 --> 00:01:52,000 this flag waving, right in front of the veterinary facility. 47 00:01:52,000 --> 00:01:55,000 This feed yard was going to tear down their whole veterinary facility; 48 00:01:55,000 --> 00:01:57,000 all they needed to do was move the flag. 49 00:01:57,000 --> 00:02:00,000 Rapid movement, contrast. 50 00:02:00,000 --> 00:02:02,000 In the early '70s when I started, I got right down 51 00:02:02,000 --> 00:02:04,000 in the chutes to see what cattle were seeing. 52 00:02:04,000 --> 00:02:07,000 People thought that was crazy. A coat on a fence would make them balk, 53 00:02:07,000 --> 00:02:10,000 shadows would make them balk, a hose on the floor ... 54 00:02:10,000 --> 00:02:12,000 people weren't noticing these things -- 55 00:02:12,000 --> 00:02:14,000 a chain hanging down -- 56 00:02:14,000 --> 00:02:16,000 and that's shown very, very nicely in the movie. 57 00:02:16,000 --> 00:02:18,000 In fact, I loved the movie, how they 58 00:02:18,000 --> 00:02:20,000 duplicated all my projects. That's the geek side. 59 00:02:20,000 --> 00:02:23,000 My drawings got to star in the movie too. 60 00:02:23,000 --> 00:02:25,000 And actually it's called "Temple Grandin," 61 00:02:25,000 --> 00:02:27,000 not "Thinking In Pictures." 62 00:02:27,000 --> 00:02:29,000 So, what is thinking in pictures? It's literally movies 63 00:02:29,000 --> 00:02:31,000 in your head. 64 00:02:31,000 --> 00:02:33,000 My mind works like Google for images. 65 00:02:33,000 --> 00:02:36,000 Now, when I was a young kid I didn't know my thinking was different. 66 00:02:36,000 --> 00:02:38,000 I thought everybody thought in pictures. 67 00:02:38,000 --> 00:02:40,000 And then when I did my book, "Thinking In Pictures," 68 00:02:40,000 --> 00:02:43,000 I start interviewing people about how they think. 69 00:02:43,000 --> 00:02:45,000 And I was shocked to find out that 70 00:02:45,000 --> 00:02:47,000 my thinking was quite different. Like if I say, 71 00:02:47,000 --> 00:02:49,000 "Think about a church steeple" 72 00:02:49,000 --> 00:02:51,000 most people get this sort of generalized generic one. 73 00:02:51,000 --> 00:02:53,000 Now, maybe that's not true in this room, 74 00:02:53,000 --> 00:02:57,000 but it's going to be true in a lot of different places. 75 00:02:57,000 --> 00:02:59,000 I see only specific pictures. 76 00:02:59,000 --> 00:03:03,000 They flash up into my memory, just like Google for pictures. 77 00:03:03,000 --> 00:03:05,000 And in the movie, they've got a great scene in there 78 00:03:05,000 --> 00:03:09,000 where the word "shoe" is said, and a whole bunch of '50s and '60s shoes 79 00:03:09,000 --> 00:03:11,000 pop into my imagination. 80 00:03:11,000 --> 00:03:13,000 OK, there is my childhood church, 81 00:03:13,000 --> 00:03:16,000 that's specific. There's some more, Fort Collins. 82 00:03:16,000 --> 00:03:18,000 OK, how about famous ones? 83 00:03:18,000 --> 00:03:21,000 And they just kind of come up, kind of like this. 84 00:03:21,000 --> 00:03:24,000 Just really quickly, like Google for pictures. 85 00:03:24,000 --> 00:03:26,000 And they come up one at a time, 86 00:03:26,000 --> 00:03:28,000 and then I think, "OK, well maybe we can have it snow, 87 00:03:28,000 --> 00:03:30,000 or we can have a thunderstorm," 88 00:03:30,000 --> 00:03:33,000 and I can hold it there and turn them into videos. 89 00:03:33,000 --> 00:03:36,000 Now, visual thinking was a tremendous asset 90 00:03:36,000 --> 00:03:39,000 in my work designing cattle-handling facilities. 91 00:03:39,000 --> 00:03:41,000 And I've worked really hard on improving 92 00:03:41,000 --> 00:03:43,000 how cattle are treated at the slaughter plant. 93 00:03:43,000 --> 00:03:46,000 I'm not going to go into any gucky slaughter slides. 94 00:03:46,000 --> 00:03:48,000 I've got that stuff up on YouTube if you want to look at it. 95 00:03:48,000 --> 00:03:52,000 But, one of the things that I was able to do in my design work 96 00:03:52,000 --> 00:03:54,000 is I could actually test run 97 00:03:54,000 --> 00:03:56,000 a piece of equipment in my mind, 98 00:03:56,000 --> 00:03:59,000 just like a virtual reality computer system. 99 00:03:59,000 --> 00:04:01,000 And this is an aerial view 100 00:04:01,000 --> 00:04:04,000 of a recreation of one of my projects that was used in the movie. 101 00:04:04,000 --> 00:04:06,000 That was like just so super cool. 102 00:04:06,000 --> 00:04:08,000 And there were a lot of kind of Asperger types 103 00:04:08,000 --> 00:04:11,000 and autism types working out there on the movie set too. 104 00:04:11,000 --> 00:04:13,000 (Laughter) 105 00:04:13,000 --> 00:04:15,000 But one of the things that really worries me 106 00:04:15,000 --> 00:04:19,000 is: Where's the younger version of those kids going today? 107 00:04:19,000 --> 00:04:22,000 They're not ending up in Silicon Valley, where they belong. 108 00:04:22,000 --> 00:04:25,000 (Laughter) 109 00:04:25,000 --> 00:04:30,000 (Applause) 110 00:04:30,000 --> 00:04:33,000 Now, one of the things I learned very early on because I wasn't that social, 111 00:04:33,000 --> 00:04:37,000 is I had to sell my work, and not myself. 112 00:04:37,000 --> 00:04:39,000 And the way I sold livestock jobs 113 00:04:39,000 --> 00:04:42,000 is I showed off my drawings, I showed off pictures of things. 114 00:04:42,000 --> 00:04:44,000 Another thing that helped me as a little kid 115 00:04:44,000 --> 00:04:46,000 is, boy, in the '50s, you were taught manners. 116 00:04:46,000 --> 00:04:48,000 You were taught you can't pull the merchandise off the shelves 117 00:04:48,000 --> 00:04:50,000 in the store and throw it around. 118 00:04:50,000 --> 00:04:53,000 Now, when kids get to be in third or fourth grade, 119 00:04:53,000 --> 00:04:56,000 you might see that this kid's going to be a visual thinker, 120 00:04:56,000 --> 00:04:58,000 drawing in perspective. Now, I want to 121 00:04:58,000 --> 00:05:00,000 emphasize that not every autistic kid 122 00:05:00,000 --> 00:05:02,000 is going to be a visual thinker. 123 00:05:02,000 --> 00:05:06,000 Now, I had this brain scan done several years ago, 124 00:05:06,000 --> 00:05:08,000 and I used to joke around about having a 125 00:05:08,000 --> 00:05:10,000 gigantic Internet trunk line 126 00:05:10,000 --> 00:05:12,000 going deep into my visual cortex. 127 00:05:12,000 --> 00:05:14,000 This is tensor imaging. 128 00:05:14,000 --> 00:05:16,000 And my great big internet trunk line 129 00:05:16,000 --> 00:05:18,000 is twice as big as the control's. 130 00:05:18,000 --> 00:05:20,000 The red lines there are me, 131 00:05:20,000 --> 00:05:24,000 and the blue lines are the sex and age-matched control. 132 00:05:24,000 --> 00:05:26,000 And there I got a gigantic one, 133 00:05:26,000 --> 00:05:28,000 and the control over there, the blue one, 134 00:05:28,000 --> 00:05:32,000 has got a really small one. 135 00:05:32,000 --> 00:05:34,000 And some of the research now is showing 136 00:05:34,000 --> 00:05:38,000 is that people on the spectrum actually think with primary visual cortex. 137 00:05:38,000 --> 00:05:41,000 Now, the thing is, the visual thinker's just one kind of mind. 138 00:05:41,000 --> 00:05:44,000 You see, the autistic mind tends to be a specialist mind -- 139 00:05:44,000 --> 00:05:48,000 good at one thing, bad at something else. 140 00:05:48,000 --> 00:05:50,000 And where I was bad was algebra. And I was never allowed 141 00:05:50,000 --> 00:05:52,000 to take geometry or trig. 142 00:05:52,000 --> 00:05:55,000 Gigantic mistake: I'm finding a lot of kids who need to skip algebra, 143 00:05:55,000 --> 00:05:57,000 go right to geometry and trig. 144 00:05:57,000 --> 00:06:00,000 Now, another kind of mind is the pattern thinker. 145 00:06:00,000 --> 00:06:02,000 More abstract. These are your engineers, 146 00:06:02,000 --> 00:06:04,000 your computer programmers. 147 00:06:04,000 --> 00:06:06,000 Now, this is pattern thinking. That praying mantis 148 00:06:06,000 --> 00:06:08,000 is made from a single sheet of paper -- 149 00:06:08,000 --> 00:06:10,000 no scotch tape, no cuts. 150 00:06:10,000 --> 00:06:13,000 And there in the background is the pattern for folding it. 151 00:06:13,000 --> 00:06:15,000 Here are the types of thinking: 152 00:06:15,000 --> 00:06:18,000 photo-realistic visual thinkers, like me; 153 00:06:18,000 --> 00:06:22,000 pattern thinkers, music and math minds. 154 00:06:22,000 --> 00:06:24,000 Some of these oftentimes have problems with reading. 155 00:06:24,000 --> 00:06:26,000 You also will see these kind of problems 156 00:06:26,000 --> 00:06:29,000 with kids that are dyslexic. 157 00:06:29,000 --> 00:06:31,000 You'll see these different kinds of minds. 158 00:06:31,000 --> 00:06:34,000 And then there's a verbal mind, they know every fact about everything. 159 00:06:34,000 --> 00:06:36,000 Now, another thing is the sensory issues. 160 00:06:36,000 --> 00:06:40,000 I was really concerned about having to wear this gadget on my face. 161 00:06:40,000 --> 00:06:43,000 And I came in half an hour beforehand 162 00:06:43,000 --> 00:06:45,000 so I could have it put on and kind of get used to it, 163 00:06:45,000 --> 00:06:48,000 and they got it bent so it's not hitting my chin. 164 00:06:48,000 --> 00:06:51,000 But sensory is an issue. Some kids are bothered by fluorescent lights; 165 00:06:51,000 --> 00:06:54,000 others have problems with sound sensitivity. 166 00:06:54,000 --> 00:06:57,000 You know, it's going to be variable. 167 00:06:57,000 --> 00:07:01,000 Now, visual thinking gave me a whole lot of insight 168 00:07:01,000 --> 00:07:03,000 into the animal mind. 169 00:07:03,000 --> 00:07:06,000 Because think about it: An animal is a sensory-based thinker, 170 00:07:06,000 --> 00:07:10,000 not verbal -- thinks in pictures, 171 00:07:10,000 --> 00:07:13,000 thinks in sounds, thinks in smells. 172 00:07:13,000 --> 00:07:16,000 Think about how much information there is there on the local fire hydrant. 173 00:07:16,000 --> 00:07:19,000 He knows who's been there, when they were there. 174 00:07:19,000 --> 00:07:22,000 Are they friend or foe? Is there anybody he can go mate with? 175 00:07:22,000 --> 00:07:25,000 There's a ton of information on that fire hydrant. 176 00:07:25,000 --> 00:07:29,000 It's all very detailed information, 177 00:07:29,000 --> 00:07:31,000 and, looking at these kind of details 178 00:07:31,000 --> 00:07:33,000 gave me a lot of insight into animals. 179 00:07:33,000 --> 00:07:37,000 Now, the animal mind, and also my mind, 180 00:07:37,000 --> 00:07:39,000 puts sensory-based information 181 00:07:39,000 --> 00:07:41,000 into categories. 182 00:07:41,000 --> 00:07:43,000 Man on a horse 183 00:07:43,000 --> 00:07:45,000 and a man on the ground -- 184 00:07:45,000 --> 00:07:47,000 that is viewed as two totally different things. 185 00:07:47,000 --> 00:07:50,000 You could have a horse that's been abused by a rider. 186 00:07:50,000 --> 00:07:52,000 They'll be absolutely fine with the veterinarian 187 00:07:52,000 --> 00:07:55,000 and with the horseshoer, but you can't ride him. 188 00:07:55,000 --> 00:07:58,000 You have another horse, where maybe the horseshoer beat him up 189 00:07:58,000 --> 00:08:00,000 and he'll be terrible for anything on the ground, 190 00:08:00,000 --> 00:08:03,000 with the veterinarian, but a person can ride him. 191 00:08:03,000 --> 00:08:05,000 Cattle are the same way. 192 00:08:05,000 --> 00:08:07,000 Man on a horse, 193 00:08:07,000 --> 00:08:09,000 a man on foot -- they're two different things. 194 00:08:09,000 --> 00:08:11,000 You see, it's a different picture. 195 00:08:11,000 --> 00:08:14,000 See, I want you to think about just how specific this is. 196 00:08:14,000 --> 00:08:18,000 Now, this ability to put information into categories, 197 00:08:18,000 --> 00:08:21,000 I find a lot of people are not very good at this. 198 00:08:21,000 --> 00:08:23,000 When I'm out troubleshooting equipment 199 00:08:23,000 --> 00:08:25,000 or problems with something in a plant, 200 00:08:25,000 --> 00:08:29,000 they don't seem to be able to figure out, "Do I have a training people issue? 201 00:08:29,000 --> 00:08:31,000 Or do I have something wrong with the equipment?" 202 00:08:31,000 --> 00:08:33,000 In other words, categorize equipment problem 203 00:08:33,000 --> 00:08:35,000 from a people problem. 204 00:08:35,000 --> 00:08:38,000 I find a lot of people have difficulty doing that. 205 00:08:38,000 --> 00:08:41,000 Now, let's say I figure out it's an equipment problem. 206 00:08:41,000 --> 00:08:43,000 Is it a minor problem, with something simple I can fix? 207 00:08:43,000 --> 00:08:46,000 Or is the whole design of the system wrong? 208 00:08:46,000 --> 00:08:49,000 People have a hard time figuring that out. 209 00:08:49,000 --> 00:08:51,000 Let's just look at something like, you know, 210 00:08:51,000 --> 00:08:53,000 solving problems with making airlines safer. 211 00:08:53,000 --> 00:08:55,000 Yeah, I'm a million-mile flyer. 212 00:08:55,000 --> 00:08:57,000 I do lots and lots of flying, 213 00:08:57,000 --> 00:09:00,000 and if I was at the FAA, 214 00:09:00,000 --> 00:09:04,000 what would I be doing a lot of direct observation of? 215 00:09:04,000 --> 00:09:06,000 It would be their airplane tails. 216 00:09:06,000 --> 00:09:09,000 You know, five fatal wrecks in the last 20 years, 217 00:09:09,000 --> 00:09:13,000 the tail either came off or steering stuff inside the tail broke 218 00:09:13,000 --> 00:09:15,000 in some way. 219 00:09:15,000 --> 00:09:17,000 It's tails, pure and simple. 220 00:09:17,000 --> 00:09:19,000 And when the pilots walk around the plane, guess what? They can't see 221 00:09:19,000 --> 00:09:21,000 that stuff inside the tail. 222 00:09:21,000 --> 00:09:23,000 You know, now as I think about that, 223 00:09:23,000 --> 00:09:26,000 I'm pulling up all of that specific information. 224 00:09:26,000 --> 00:09:29,000 It's specific. See, my thinking's bottom-up. 225 00:09:29,000 --> 00:09:33,000 I take all the little pieces and I put the pieces together like a puzzle. 226 00:09:33,000 --> 00:09:35,000 Now, here is a horse that was deathly afraid 227 00:09:35,000 --> 00:09:37,000 of black cowboy hats. 228 00:09:37,000 --> 00:09:39,000 He'd been abused by somebody with a black cowboy hat. 229 00:09:39,000 --> 00:09:42,000 White cowboy hats, that was absolutely fine. 230 00:09:42,000 --> 00:09:45,000 Now, the thing is, the world is going to need 231 00:09:45,000 --> 00:09:47,000 all of the different kinds of minds 232 00:09:47,000 --> 00:09:49,000 to work together. 233 00:09:49,000 --> 00:09:52,000 We've got to work on developing all these different kinds of minds. 234 00:09:52,000 --> 00:09:55,000 And one of the things that is driving me really crazy, 235 00:09:55,000 --> 00:09:57,000 as I travel around and I do autism meetings, 236 00:09:57,000 --> 00:10:00,000 is I'm seeing a lot of smart, geeky, nerdy kids, 237 00:10:00,000 --> 00:10:03,000 and they just aren't very social, 238 00:10:03,000 --> 00:10:05,000 and nobody's working on developing their interest 239 00:10:05,000 --> 00:10:07,000 in something like science. 240 00:10:07,000 --> 00:10:10,000 And this brings up the whole thing of my science teacher. 241 00:10:10,000 --> 00:10:13,000 My science teacher is shown absolutely beautifully in the movie. 242 00:10:13,000 --> 00:10:15,000 I was a goofball student. When I was in high school 243 00:10:15,000 --> 00:10:18,000 I just didn't care at all about studying, 244 00:10:18,000 --> 00:10:21,000 until I had Mr. Carlock's science class. 245 00:10:21,000 --> 00:10:24,000 He was now Dr. Carlock in the movie. 246 00:10:24,000 --> 00:10:27,000 And he got me challenged 247 00:10:27,000 --> 00:10:30,000 to figure out an optical illusion room. 248 00:10:30,000 --> 00:10:32,000 This brings up the whole thing of you've got to show kids 249 00:10:32,000 --> 00:10:34,000 interesting stuff. 250 00:10:34,000 --> 00:10:37,000 You know, one of the things that I think maybe TED ought to do 251 00:10:37,000 --> 00:10:40,000 is tell all the schools about all the great lectures that are on TED, 252 00:10:40,000 --> 00:10:42,000 and there's all kinds of great stuff on the Internet 253 00:10:42,000 --> 00:10:44,000 to get these kids turned on. 254 00:10:44,000 --> 00:10:47,000 Because I'm seeing a lot of these geeky nerdy kids, 255 00:10:47,000 --> 00:10:50,000 and the teachers out in the Midwest, and the other parts of the country, 256 00:10:50,000 --> 00:10:52,000 when you get away from these tech areas, 257 00:10:52,000 --> 00:10:54,000 they don't know what to do with these kids. 258 00:10:54,000 --> 00:10:56,000 And they're not going down the right path. 259 00:10:56,000 --> 00:10:58,000 The thing is, you can make a mind 260 00:10:58,000 --> 00:11:01,000 to be more of a thinking and cognitive mind, 261 00:11:01,000 --> 00:11:04,000 or your mind can be wired to be more social. 262 00:11:04,000 --> 00:11:06,000 And what some of the research now has shown in autism 263 00:11:06,000 --> 00:11:08,000 is there may by extra wiring back here, 264 00:11:08,000 --> 00:11:11,000 in the really brilliant mind, and we lose a few social circuits here. 265 00:11:11,000 --> 00:11:15,000 It's kind of a trade-off between thinking and social. 266 00:11:15,000 --> 00:11:17,000 And then you can get into the point where it's so severe 267 00:11:17,000 --> 00:11:20,000 you're going to have a person that's going to be non-verbal. 268 00:11:20,000 --> 00:11:22,000 In the normal human mind 269 00:11:22,000 --> 00:11:25,000 language covers up the visual thinking we share with animals. 270 00:11:25,000 --> 00:11:28,000 This is the work of Dr. Bruce Miller. 271 00:11:28,000 --> 00:11:31,000 And he studied Alzheimer's patients 272 00:11:31,000 --> 00:11:33,000 that had frontal temporal lobe dementia. 273 00:11:33,000 --> 00:11:36,000 And the dementia ate out the language parts of the brain, 274 00:11:36,000 --> 00:11:41,000 and then this artwork came out of somebody who used to install stereos in cars. 275 00:11:41,000 --> 00:11:45,000 Now, Van Gogh doesn't know anything about physics, 276 00:11:45,000 --> 00:11:47,000 but I think it's very interesting 277 00:11:47,000 --> 00:11:49,000 that there was some work done to show that 278 00:11:49,000 --> 00:11:51,000 this eddy pattern in this painting 279 00:11:51,000 --> 00:11:54,000 followed a statistical model of turbulence, 280 00:11:54,000 --> 00:11:56,000 which brings up the whole interesting idea 281 00:11:56,000 --> 00:11:58,000 of maybe some of this mathematical patterns 282 00:11:58,000 --> 00:12:00,000 is in our own head. 283 00:12:00,000 --> 00:12:02,000 And the Wolfram stuff -- I was taking 284 00:12:02,000 --> 00:12:04,000 notes and I was writing down all the 285 00:12:04,000 --> 00:12:06,000 search words I could use, 286 00:12:06,000 --> 00:12:10,000 because I think that's going to go on in my autism lectures. 287 00:12:10,000 --> 00:12:12,000 We've got to show these kids interesting stuff. 288 00:12:12,000 --> 00:12:14,000 And they've taken out the autoshop class 289 00:12:14,000 --> 00:12:16,000 and the drafting class and the art class. 290 00:12:16,000 --> 00:12:19,000 I mean art was my best subject in school. 291 00:12:19,000 --> 00:12:21,000 We've got to think about all these different kinds of minds, 292 00:12:21,000 --> 00:12:24,000 and we've got to absolutely work with these kind of minds, 293 00:12:24,000 --> 00:12:27,000 because we absolutely are going to need 294 00:12:27,000 --> 00:12:30,000 these kind of people in the future. 295 00:12:30,000 --> 00:12:32,000 And let's talk about jobs. 296 00:12:32,000 --> 00:12:34,000 OK, my science teacher got me studying 297 00:12:34,000 --> 00:12:37,000 because I was a goofball that didn't want to study. 298 00:12:37,000 --> 00:12:39,000 But you know what? I was getting work experience. 299 00:12:39,000 --> 00:12:41,000 I'm seeing too many of these smart kids who haven't learned basic things, 300 00:12:41,000 --> 00:12:43,000 like how to be on time. 301 00:12:43,000 --> 00:12:45,000 I was taught that when I was eight years old. 302 00:12:45,000 --> 00:12:48,000 You know, how to have table manners at granny's Sunday party. 303 00:12:48,000 --> 00:12:51,000 I was taught that when I was very, very young. 304 00:12:51,000 --> 00:12:54,000 And when I was 13, I had a job at a dressmaker's shop 305 00:12:54,000 --> 00:12:56,000 sewing clothes. 306 00:12:56,000 --> 00:12:59,000 I did internships in college, 307 00:12:59,000 --> 00:13:02,000 I was building things, 308 00:13:02,000 --> 00:13:05,000 and I also had to learn how to do assignments. 309 00:13:05,000 --> 00:13:09,000 You know, all I wanted to do was draw pictures of horses when I was little. 310 00:13:09,000 --> 00:13:11,000 My mother said, "Well let's do a picture of something else." 311 00:13:11,000 --> 00:13:13,000 They've got to learn how to do something else. 312 00:13:13,000 --> 00:13:15,000 Let's say the kid is fixated on Legos. 313 00:13:15,000 --> 00:13:18,000 Let's get him working on building different things. 314 00:13:18,000 --> 00:13:20,000 The thing about the autistic mind 315 00:13:20,000 --> 00:13:22,000 is it tends to be fixated. 316 00:13:22,000 --> 00:13:24,000 Like if a kid loves racecars, 317 00:13:24,000 --> 00:13:26,000 let's use racecars for math. 318 00:13:26,000 --> 00:13:29,000 Let's figure out how long it takes a racecar to go a certain distance. 319 00:13:29,000 --> 00:13:33,000 In other words, use that fixation 320 00:13:33,000 --> 00:13:36,000 in order to motivate that kid, that's one of the things we need to do. 321 00:13:36,000 --> 00:13:39,000 I really get fed up when they, you know, the teachers, 322 00:13:39,000 --> 00:13:42,000 especially when you get away from this part of the country, 323 00:13:42,000 --> 00:13:44,000 they don't know what to do with these smart kids. 324 00:13:44,000 --> 00:13:46,000 It just drives me crazy. 325 00:13:46,000 --> 00:13:48,000 What can visual thinkers do when they grow up? 326 00:13:48,000 --> 00:13:51,000 They can do graphic design, all kinds of stuff with computers, 327 00:13:51,000 --> 00:13:56,000 photography, industrial design. 328 00:13:56,000 --> 00:13:58,000 The pattern thinkers, they're the ones that are going to be 329 00:13:58,000 --> 00:14:01,000 your mathematicians, your software engineers, 330 00:14:01,000 --> 00:14:05,000 your computer programmers, all of those kinds of jobs. 331 00:14:05,000 --> 00:14:08,000 And then you've got the word minds. They make great journalists, 332 00:14:08,000 --> 00:14:11,000 and they also make really, really good stage actors. 333 00:14:11,000 --> 00:14:13,000 Because the thing about being autistic is, 334 00:14:13,000 --> 00:14:16,000 I had to learn social skills like being in a play. 335 00:14:16,000 --> 00:14:19,000 It's just kind of -- you just have to learn it. 336 00:14:19,000 --> 00:14:22,000 And we need to be working with these students. 337 00:14:22,000 --> 00:14:24,000 And this brings up mentors. 338 00:14:24,000 --> 00:14:27,000 You know, my science teacher was not an accredited teacher. 339 00:14:27,000 --> 00:14:29,000 He was a NASA space scientist. 340 00:14:29,000 --> 00:14:31,000 Now, some states now are getting it to where 341 00:14:31,000 --> 00:14:33,000 if you have a degree in biology, or a degree in chemistry, 342 00:14:33,000 --> 00:14:36,000 you can come into the school and teach biology or chemistry. 343 00:14:36,000 --> 00:14:38,000 We need to be doing that. 344 00:14:38,000 --> 00:14:40,000 Because what I'm observing is 345 00:14:40,000 --> 00:14:42,000 the good teachers, for a lot of these kids, 346 00:14:42,000 --> 00:14:44,000 are out in the community colleges, 347 00:14:44,000 --> 00:14:47,000 but we need to be getting some of these good teachers into the high schools. 348 00:14:47,000 --> 00:14:50,000 Another thing that can be very, very, very successful is 349 00:14:50,000 --> 00:14:53,000 there is a lot of people that may have retired 350 00:14:53,000 --> 00:14:56,000 from working in the software industry, and they can teach your kid. 351 00:14:56,000 --> 00:14:59,000 And it doesn't matter if what they teach them is old, 352 00:14:59,000 --> 00:15:02,000 because what you're doing is you're lighting the spark. 353 00:15:02,000 --> 00:15:05,000 You're getting that kid turned on. 354 00:15:05,000 --> 00:15:08,000 And you get him turned on, then he'll learn all the new stuff. 355 00:15:08,000 --> 00:15:10,000 Mentors are just essential. 356 00:15:10,000 --> 00:15:12,000 I cannot emphasize enough 357 00:15:12,000 --> 00:15:15,000 what my science teacher did for me. 358 00:15:15,000 --> 00:15:18,000 And we've got to mentor them, hire them. 359 00:15:18,000 --> 00:15:20,000 And if you bring them in for internships in your companies, 360 00:15:20,000 --> 00:15:23,000 the thing about the autism, Asperger-y kind of mind, 361 00:15:23,000 --> 00:15:26,000 you've got to give them a specific task. Don't just say, "Design new software." 362 00:15:26,000 --> 00:15:28,000 You've got to tell them something a lot more specific: 363 00:15:28,000 --> 00:15:31,000 "Well, we're designing a software for a phone 364 00:15:31,000 --> 00:15:33,000 and it has to do some specific thing. 365 00:15:33,000 --> 00:15:35,000 And it can only use so much memory." 366 00:15:35,000 --> 00:15:37,000 That's the kind of specificity you need. 367 00:15:37,000 --> 00:15:39,000 Well, that's the end of my talk. 368 00:15:39,000 --> 00:15:41,000 And I just want to thank everybody for coming. 369 00:15:41,000 --> 00:15:43,000 It was great to be here. 370 00:15:43,000 --> 00:15:55,000 (Applause) 371 00:15:55,000 --> 00:15:58,000 Oh, you've got a question for me? OK. 372 00:15:58,000 --> 00:15:59,000 (Applause) 373 00:15:59,000 --> 00:16:03,000 Chris Anderson: Thank you so much for that. 374 00:16:03,000 --> 00:16:05,000 You know, you once wrote, I like this quote, 375 00:16:05,000 --> 00:16:07,000 "If by some magic, autism had been 376 00:16:07,000 --> 00:16:10,000 eradicated from the face of the Earth, 377 00:16:10,000 --> 00:16:13,000 then men would still be socializing in front of a wood fire 378 00:16:13,000 --> 00:16:15,000 at the entrance to a cave." 379 00:16:15,000 --> 00:16:17,000 Temple Grandin: Because who do you think made the first stone spears? 380 00:16:17,000 --> 00:16:20,000 The Asperger guy. And if you were to get rid of all the autism genetics 381 00:16:20,000 --> 00:16:22,000 there would be no more Silicon Valley, 382 00:16:22,000 --> 00:16:24,000 and the energy crisis would not be solved. 383 00:16:24,000 --> 00:16:27,000 (Applause) 384 00:16:27,000 --> 00:16:29,000 CA: So, I want to ask you a couple other questions, 385 00:16:29,000 --> 00:16:31,000 and if any of these feel inappropriate, 386 00:16:31,000 --> 00:16:33,000 it's okay just to say, "Next question." 387 00:16:33,000 --> 00:16:35,000 But if there is someone here 388 00:16:35,000 --> 00:16:37,000 who has an autistic child, 389 00:16:37,000 --> 00:16:39,000 or knows an autistic child 390 00:16:39,000 --> 00:16:42,000 and feels kind of cut off from them, 391 00:16:42,000 --> 00:16:44,000 what advice would you give them? 392 00:16:44,000 --> 00:16:46,000 TG: Well, first of all, you've got to look at age. 393 00:16:46,000 --> 00:16:48,000 If you have a two, three or four year old 394 00:16:48,000 --> 00:16:50,000 you know, no speech, no social interaction, 395 00:16:50,000 --> 00:16:52,000 I can't emphasize enough: 396 00:16:52,000 --> 00:16:56,000 Don't wait, you need at least 20 hours a week of one-to-one teaching. 397 00:16:56,000 --> 00:16:59,000 You know, the thing is, autism comes in different degrees. 398 00:16:59,000 --> 00:17:01,000 There's going to be about half the people on the spectrum 399 00:17:01,000 --> 00:17:03,000 that are not going to learn to talk, and they're not going to be working 400 00:17:03,000 --> 00:17:06,000 Silicon Valley, that would not be a reasonable thing for them to do. 401 00:17:06,000 --> 00:17:08,000 But then you get the smart, geeky kids 402 00:17:08,000 --> 00:17:10,000 that have a touch of autism, 403 00:17:10,000 --> 00:17:12,000 and that's where you've got to get them turned on 404 00:17:12,000 --> 00:17:14,000 with doing interesting things. 405 00:17:14,000 --> 00:17:17,000 I got social interaction through shared interest. 406 00:17:17,000 --> 00:17:21,000 I rode horses with other kids, I made model rockets with other kids, 407 00:17:21,000 --> 00:17:23,000 did electronics lab with other kids, 408 00:17:23,000 --> 00:17:25,000 and in the '60s, it was gluing mirrors 409 00:17:25,000 --> 00:17:28,000 onto a rubber membrane on a speaker to make a light show. 410 00:17:28,000 --> 00:17:31,000 That was like, we considered that super cool. 411 00:17:31,000 --> 00:17:33,000 CA: Is it unrealistic for them 412 00:17:33,000 --> 00:17:35,000 to hope or think that that child 413 00:17:35,000 --> 00:17:38,000 loves them, as some might, as most, wish? 414 00:17:38,000 --> 00:17:40,000 TG: Well let me tell you, that child will be loyal, 415 00:17:40,000 --> 00:17:42,000 and if your house is burning down, they're going to get you out of it. 416 00:17:42,000 --> 00:17:45,000 CA: Wow. So, most people, if you ask them 417 00:17:45,000 --> 00:17:47,000 what are they most passionate about, they'd say things like, 418 00:17:47,000 --> 00:17:50,000 "My kids" or "My lover." 419 00:17:50,000 --> 00:17:53,000 What are you most passionate about? 420 00:17:53,000 --> 00:17:55,000 TG: I'm passionate about that the things I do 421 00:17:55,000 --> 00:17:57,000 are going to make the world a better place. 422 00:17:57,000 --> 00:17:59,000 When I have a mother of an autistic child say, 423 00:17:59,000 --> 00:18:01,000 "My kid went to college because of your book, 424 00:18:01,000 --> 00:18:03,000 or one of your lectures," that makes me happy. 425 00:18:03,000 --> 00:18:06,000 You know, the slaughter plants, I've worked with them 426 00:18:06,000 --> 00:18:08,000 in the '80s; they were absolutely awful. 427 00:18:08,000 --> 00:18:12,000 I developed a really simple scoring system for slaughter plants 428 00:18:12,000 --> 00:18:14,000 where you just measure outcomes: How many cattle fell down? 429 00:18:14,000 --> 00:18:16,000 How many cattle got poked with the prodder? 430 00:18:16,000 --> 00:18:18,000 How many cattle are mooing their heads off? 431 00:18:18,000 --> 00:18:20,000 And it's very, very simple. 432 00:18:20,000 --> 00:18:22,000 You directly observe a few simple things. 433 00:18:22,000 --> 00:18:24,000 It's worked really well. I get satisfaction out of 434 00:18:24,000 --> 00:18:27,000 seeing stuff that makes real change 435 00:18:27,000 --> 00:18:29,000 in the real world. We need a lot more of that, 436 00:18:29,000 --> 00:18:31,000 and a lot less abstract stuff. 437 00:18:31,000 --> 00:18:38,000 (Applause) 438 00:18:38,000 --> 00:18:40,000 CA: When we were talking on the phone, one of the things you said that 439 00:18:40,000 --> 00:18:42,000 really astonished me was you said one thing 440 00:18:42,000 --> 00:18:46,000 you were passionate about was server farms. Tell me about that. 441 00:18:46,000 --> 00:18:49,000 TG: Well the reason why I got really excited when I read about that, 442 00:18:49,000 --> 00:18:52,000 it contains knowledge. 443 00:18:52,000 --> 00:18:54,000 It's libraries. 444 00:18:54,000 --> 00:18:56,000 And to me, knowledge is something 445 00:18:56,000 --> 00:18:58,000 that is extremely valuable. So, maybe, over 10 years ago 446 00:18:58,000 --> 00:19:00,000 now our library got flooded. 447 00:19:00,000 --> 00:19:02,000 And this is before the Internet got really big. 448 00:19:02,000 --> 00:19:04,000 And I was really upset about all the books being wrecked, 449 00:19:04,000 --> 00:19:06,000 because it was knowledge being destroyed. 450 00:19:06,000 --> 00:19:08,000 And server farms, or data centers 451 00:19:08,000 --> 00:19:11,000 are great libraries of knowledge. 452 00:19:11,000 --> 00:19:14,000 CA: Temple, can I just say it's an absolute delight to have you at TED. 453 00:19:14,000 --> 00:19:17,000 TG: Well thank you so much. Thank you. 454 00:19:17,000 --> 00:19:23,000 (Applause)