The Jordan B. Peterson Podcast - 318. Autism, Academics, and Animals ｜ Dr. Temple Grandin

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00:00:57.420 Hello everyone. I'm usually excited to have whoever I'm talking to that day on my show because I pick people I'm excited about talking to.

00:01:17.840 But I'm particularly excited about my guest today, Dr. Temple Grandin.

00:01:22.780 Temple Grandin revolutionized the animal handling industry over the last 40 years and has done more for animal welfare in a practical sense than anybody that I know of and perhaps anybody on the planet.

00:01:34.960 I think you could make a case for that.

00:01:36.640 She's a remarkable person. I saw her first in Tucson, Arizona. I'll talk about that a little bit in our interview.

00:01:41.380 She gave one of the most compelling presentations I'd ever seen in an academic setting at a conference on consciousness and saw that about 15 years ago.

00:01:50.220 And ever since then, I'd really been wanting to meet her, and I got to do that today, so that's so exciting.

00:01:56.060 So I'll just give you a brief bio and we'll pop into the interview.

00:01:59.580 Dr. Temple Grandin is a professor of animal sciences at Colorado State University.

00:02:05.580 Facilities she has designed for handling livestock are used by companies all around the world.

00:02:09.760 Her work has been instrumental in implementing animal welfare auditing programs, now used by McDonald's, Wendy's, Whole Foods, and many other major corporations.

00:02:20.560 She has appeared on numerous shows across platforms, such as 2020, Larry King Live, and Primetime.

00:02:27.940 Dr. Grandin is also an accomplished author, with books such as Thinking in Pictures, Livestock Handling in Transport, and The Autistic Brain.

00:02:36.220 A few of her other publications, Animals in Translation, as well as Visual Thinking, have even made it to the New York Times bestseller list.

00:02:46.700 In 2017, Dr. Grandin was inducted into the National Women's Hall of Fame.

00:02:51.540 And in 2022, she was honored once again as a Colorado State Distinguished Professor.

00:02:57.040 We're going to walk through her book today, and we're going to learn what she has to say now about thinking.

00:03:07.740 Let's start with visual thinking and language.

00:03:10.220 So do you want to tell people about your realization, about different thought patterns?

00:03:16.480 When I was in my 20s, I thought everybody thought in pictures the way I thought.

00:03:21.840 I didn't know that other people thought in words until I was in my late 30s.

00:03:27.960 Now, you've already mentioned, how do I categorize?

00:03:31.020 Well, I'll explain as a child how I learned to categorize, categorizing with individual pictures.

00:03:38.320 So as a very young child, I categorized cats, dogs, and horses by size,

00:03:44.560 because in our neighborhood at that time, there were no cattle, and there were no small dogs.

00:03:49.020 Then a dachshund came into my neighborhood, and she's the same size as a cat.

00:03:56.640 And I remember looking at the dachshund, she was a black dachshund, 0.98

00:04:00.440 and trying to figure out, now, what features does she share with the dog?

00:04:04.960 She barks. 1.00

00:04:07.000 Her nose shape is the same as a dog, and she smells like a dog. 0.81

00:04:12.140 So I had to take other sensory-based things, like smell and what the dog sounded like,

00:04:19.720 to put the dachshund in the dog category.

00:04:24.000 So the way I form categories is I have to have a bunch of information.

00:04:27.040 Let's take the cat category.

00:04:29.620 And you look at a leopard's face, a lion's face, and even a house cat's face.

00:04:35.740 There's similarities, and they also smell all the same, too.

00:04:43.260 If you go to the zoo, you can smell how they are different.

00:04:46.740 So the first step for abstract thinking is put making categories.

00:04:52.700 So when I finally figured out that other people did not think in pictures,

00:04:57.580 if I ask most people, visualize your own home, your dog, or your car, you will do it,

00:05:04.360 because you're so familiar with that.

00:05:07.260 But one time at an autism conference, when I was in my late 30s,

00:05:11.040 I asked a speech therapist, think about a church steeple,

00:05:14.560 and I was shocked that the only image he saw was a very vague two lines like this,

00:05:21.740 where I see specific churches.

00:05:24.080 They come up like a series of, well, back then, 35-millimeter slides.

00:05:29.400 Now it would be PowerPoint slides.

00:05:31.960 And then I can start, as I see more and more of these churches,

00:05:35.180 I can put them into New England-type category, stone cathedral-type.

00:05:41.180 Looks like a warehouse, and it has a little plastic steeple type.

00:05:45.640 I can make finer categories as I get more and more specific images.

00:05:51.860 It's bottom-up thinking.

00:05:53.040 And I learned that that's exactly how an artificial intelligence program

00:05:57.280 diagnoses melanoma cancer.

00:06:00.060 It's given a training set of 2,000 melanomas

00:06:03.060 and another training set of every kind of skin rash and mosquito bite

00:06:06.900 and infected whatever.

00:06:09.660 And then it learns to categorize melanoma from non-melanoma.

00:06:14.460 And that was very insightful to me

00:06:17.020 when I learned that that's how the simple type of artificial intelligence programs work.

00:06:21.740 You're diagnosing something like melanoma.

00:06:24.700 So let me ask you, when I'm thinking something through,

00:06:30.480 now I can think in pictures, and if I'm building something

00:06:33.460 or trying to design something to build, then I tend to think in pictures.

00:06:38.200 But I would say 90% of the time, my proclivity is to think in words.

00:06:45.340 And I would say, in part, that's because my word thinking is so dominant.

00:06:52.040 It just suppresses the image thinking.

00:06:54.700 And so a lot of my thinking, I would also say,

00:06:57.740 takes the form of something like internal argumentation.

00:07:01.380 So I'll ask myself a question.

00:07:04.640 I'll think up an answer in words.

00:07:07.560 And then I'll think up a bunch of reasons why that answer isn't sufficient.

00:07:11.860 And then I'll conduct an internal argument.

00:07:14.460 And that's occupying me continually, like 16 hours a day, nonstop.

00:07:19.820 And it's been like that ever since I was two years old,

00:07:22.880 except for those times, let's say.

00:07:24.500 So, yeah, so how do you, do you conduct internal arguments?

00:07:30.580 Or like what accounts for the, so part of the creativity in my thinking

00:07:36.480 is the outcome of these arguments.

00:07:38.520 But you're thinking in pictures, so you're not having internal arguments.

00:07:43.320 Nope.

00:07:44.060 What's the stream of your thought like?

00:07:46.420 Well, when it comes to things like designing equipment,

00:07:49.720 I often will kind of, a lot of equipment design,

00:07:54.300 someone gets an idea from something else.

00:07:56.540 In one of my other books, I wrote about the inventor of Velcro,

00:08:00.060 and he saw how burdocks stuck on his clothes.

00:08:04.320 You know, cockle burrs on those kind of things stuck on his clothes.

00:08:07.300 And that's where he got the idea for making Velcro.

00:08:10.600 That would be visual thinking.

00:08:13.880 The burdocks and Velcro,

00:08:15.860 well, they've got similarities on how they stick together.

00:08:20.040 In designing equipment, I can just see how it operates.

00:08:24.600 I worked with welders that had 20 patents each

00:08:28.160 that barely graduated from high school.

00:08:30.920 But they could build any kind of a machine

00:08:32.900 and invent industrial machinery.

00:08:36.000 They just see it in their head.

00:08:39.080 Right.

00:08:39.700 Now, will you see, okay,

00:08:41.140 so I read Nikolai Tesla's biography a long while back,

00:08:45.680 and it was extremely interesting.

00:08:47.380 And he claimed, and I have no reason to disbelieve him,

00:08:51.160 that entire inventions, machines,

00:08:56.380 would pop into the theater of his imagination,

00:08:59.720 detailed out to the point where he knew the angles on the screws

00:09:03.180 that held the metal together.

00:09:05.160 And he would try to write them down in something,

00:09:08.040 draw them out in something approximating,

00:09:10.600 something that would be,

00:09:12.240 that you could make a blueprint out of, let's say,

00:09:14.260 in that much detail.

00:09:16.240 And sometimes a new invention would pop into his head

00:09:19.040 so quickly that it would obliterate the previous one.

00:09:22.820 He had to work very quickly to keep up.

00:09:25.280 And so there's that incredible fluency in visualization.

00:09:30.140 But then, as I said, in the verbal world,

00:09:34.280 I'll think of argument and counter-arguments.

00:09:36.980 When you're thinking of industrial design,

00:09:39.680 do you think of the object that you're attempting to design

00:09:44.160 and then multiple variants of it and test them against each other?

00:09:47.680 Or does it just come to you as a solution for a given problem?

00:09:51.880 It almost sort of just pops into my head.

00:09:55.360 I also see mechanical abnormalities.

00:09:57.400 I remember walking in,

00:09:58.200 and someone had made a cardboard old-fashioned locomotive,

00:10:01.180 you know, and the wheels have those links between them.

00:10:03.500 And I go, that's, they drew that wrong.

00:10:07.120 Wheels are not going to turn.

00:10:09.340 You know, and it was just a cartoon train for a party.

00:10:13.520 But I immediately noticed that they didn't draw it right.

00:10:16.940 The other thing is,

00:10:18.420 is my thinking's associative.

00:10:23.380 All right, give me a key word.

00:10:25.840 Pretend I'm Google for images.

00:10:27.800 Not something, not something I can see in here

00:10:30.900 with books and papers all around

00:10:32.560 and photography equipment and stuff all around me,

00:10:36.340 but something kind of a creative key word.

00:10:39.360 And I'll tell you how I access my memory for that key word.

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00:12:21.280 So, and with regard,

00:12:25.220 so my wife has a very powerful visual imagination.

00:12:29.400 She's able to do all sorts of remarkable things with it.

00:12:33.900 And one of the things that strikes me as highly probable

00:12:37.280 is that her capacity for visual imagery

00:12:40.660 is a lot more intense than mine.

00:12:43.060 Yes.

00:12:43.280 And so, like, I was a fairly vivid dreamer for years,

00:12:48.760 especially when I was reading Carl Jung's work

00:12:50.800 in graduate school.

00:12:52.160 Don't seem to dream much now or not.

00:12:54.100 Well, I don't remember them much now.

00:12:56.360 When I do think in pictures,

00:12:58.760 it's not as vivid as seeing the real world.

00:13:02.660 It's maybe 5% of that.

00:13:05.020 It's sort of, it's,

00:13:08.120 well, it just doesn't have that intensity.

00:13:09.840 What, and you, and you?

00:13:13.080 No, it's very, very vivid.

00:13:15.200 And, and while I was,

00:13:17.640 while you were setting up all the camera equipment,

00:13:20.000 here I found, here's a whole bunch of papers,

00:13:22.760 more papers I've found online

00:13:24.960 supporting the, a lot of the things that are in the book.

00:13:28.180 I just got surfing around

00:13:29.480 and, boy, you can sometimes find some great stuff

00:13:32.380 when you look at the citations.

00:13:33.840 No, but it's very, very vivid.

00:13:37.700 I don't know, it's, it's completely vivid.

00:13:39.620 Now, when you mentioned the word imagination,

00:13:42.520 I remember going to a long time ago,

00:13:45.320 Disney World, a kind of a ride

00:13:48.340 that had imagination with people

00:13:50.340 and dirigibles and things like that.

00:13:52.880 You know, then I visited a studio

00:13:54.340 where they, where Disney made a whole bunch of their stuff.

00:13:58.040 I'm now seeing that.

00:13:59.100 Yes, I'm now seeing a nondisclosure agreement.

00:14:00.840 So I can't tell you what I did there.

00:14:04.580 And I, but I, now I'm seeing

00:14:07.720 a very interesting discussion we had

00:14:09.560 about designing things.

00:14:12.200 Where, where you're designing things,

00:14:15.240 there's the decoration part of it.

00:14:17.300 And then there's, then there's the mechanical part

00:14:19.960 of, of a machine.

00:14:21.380 Of course, at an amusement park,

00:14:23.520 you want to have decorations,

00:14:24.900 but you also, the rides have to mechanically work.

00:14:28.080 They're kind of two separate things.

00:14:29.560 They're kind of done by two separate departments.

00:14:32.460 You, you also spoke in your book

00:14:34.480 about your ability to see things out of place.

00:14:39.340 And so one of the things that characterizes autism

00:14:42.400 is the fact that at least some autistic people

00:14:47.060 are not very happy when they walk into a room,

00:14:51.320 say, that they're familiar with,

00:14:52.660 and one thing is out of place.

00:14:54.220 So for example, an autistic child

00:14:56.060 might walk into a dining room

00:14:57.520 and one of the chairs is, you know,

00:15:00.620 tilted 45 degrees to the right or to the left

00:15:03.120 instead of being put straight in.

00:15:05.200 And what seems to happen,

00:15:07.140 correct me if you feel that I'm wrong,

00:15:09.120 is that the fact that that chair is now askew

00:15:12.860 means that the entire room is different

00:15:16.340 in some important and emotionally significant way.

00:15:19.820 So there's this response to anomaly.

00:15:22.140 I don't have that issue.

00:15:23.380 That's not an issue for me.

00:15:25.200 But if there's one pixel off on an electronic sign,

00:15:29.780 I will notice that.

00:15:31.900 I remember one time walking into the airport

00:15:34.140 with somebody else,

00:15:36.040 and the United ticket counter sign

00:15:38.880 was a whole row of television monitors.

00:15:41.860 And there must have been 20 TV monitors

00:15:43.860 making the word United over and over again.

00:15:46.480 And one of the screens was scrambled,

00:15:49.160 the whole screen.

00:15:50.420 And I immediately saw it.

00:15:52.240 And I said to the person beside me,

00:15:53.620 did you see that that sign was messed up?

00:15:56.280 No, they did not see it.

00:15:58.220 And they were right beside me when we walked in.

00:16:00.880 I immediately saw it.

00:16:02.320 Now that it upset me, no.

00:16:04.180 I just noticed that the sign was scrambled.

00:16:06.640 Right.

00:16:07.240 But it didn't upset you.

00:16:08.620 No.

00:16:09.020 So, yeah.

00:16:09.940 So that's that interesting.

00:16:11.740 So for me, I think I can detect anomalies visually.

00:16:18.540 So, for example, one of the things I learned to do

00:16:20.620 when I was setting up my house and renovating places

00:16:24.720 and setting up my offices,

00:16:26.840 like arranging my local environment,

00:16:28.580 was to sit in the room

00:16:29.700 and become meditative in some sense,

00:16:33.480 and then to feel out

00:16:35.020 what was bothering me about the room,

00:16:39.780 like what ugly feature might pop out,

00:16:42.160 what part of it needed to be attended to.

00:16:45.200 Right.

00:16:45.420 And I think I was tuning myself

00:16:47.360 to detect what was abnormal

00:16:51.000 in relationship to the underlying aesthetic

00:16:53.220 or the pattern of the room.

00:16:54.840 But that pops out for you

00:16:57.400 almost instantaneously.

00:17:00.420 For me, anomaly detection is mostly verbal.

00:17:03.280 Again, it's like all think up an argument

00:17:05.600 and then think up counter arguments.

00:17:08.000 And if one argument and the other don't jive,

00:17:12.240 if they're contradictory,

00:17:13.560 then that pops out for me.

00:17:15.900 Well, it's sort of like I fly all the time.

00:17:18.820 So like I know when the pilots do the checklist

00:17:21.100 and like we push back

00:17:23.040 and we just take too long to turn,

00:17:26.140 I'm going, uh-oh,

00:17:27.840 we're going to have an air traffic control delay.

00:17:31.060 And I can often predict that

00:17:32.660 before they announce it.

00:17:36.360 And I also can,

00:17:37.740 I'm very conscious of,

00:17:39.740 on the biggest airplane,

00:17:42.160 when they start the little gentle push

00:17:43.880 to push it back from the gate,

00:17:44.940 I can feel it on the biggest aircraft there is.

00:17:49.360 Right.

00:17:49.920 And I-

00:17:50.840 Right, so now-

00:17:51.680 I just want to get there.

00:17:53.220 And I'm going,

00:17:53.660 oh, please push back.

00:17:57.480 But it's sort of like,

00:17:58.680 I don't get upset about it.

00:18:00.340 It's just anything that's not routine.

00:18:03.720 I instantly notice it. 0.99

00:18:05.320 And I go, oh, crap. 0.96

00:18:06.840 I see a vest that says Tech Ops. 0.82

00:18:09.120 We may have a delayed flight.

00:18:11.940 Now that brings up another thing

00:18:13.180 I want to talk about.

00:18:14.700 In the last month,

00:18:15.880 I've had, okay,

00:18:16.760 a mechanic come on a plane twice.

00:18:18.500 And both mechanics had gray hair.

00:18:21.080 And this brings up a major issue

00:18:23.820 that's in my book

00:18:24.700 about skill loss,

00:18:27.140 especially skill loss

00:18:28.900 with mechanical things.

00:18:31.140 I've been on a lot of

00:18:32.500 questionable elevators 0.97

00:18:33.900 and escalators lately

00:18:35.300 that definitely needed servicing.

00:18:38.820 And what's happening,

00:18:40.300 and this is in my screened out chapter,

00:18:42.400 is the kids are getting screened out

00:18:44.220 of these trades

00:18:45.780 because they've taken the shop classes

00:18:47.340 out of the schools 20 years ago.

00:18:48.920 And we have so many

00:18:50.520 higher math requirements

00:18:51.700 that you don't need

00:18:52.980 for something like fixing elevators

00:18:54.600 that the kids are playing video games

00:18:57.220 in the basement

00:18:57.760 on an autism diagnosis

00:18:59.080 instead of fixing elevators.

00:19:01.560 There's a relationship here

00:19:03.380 between what goes on

00:19:04.700 with industrial things

00:19:05.900 and what's going on at school,

00:19:07.660 which is a major,

00:19:08.920 major thing

00:19:09.840 that I'm interested

00:19:10.820 in talking about.

00:19:12.920 Yeah, so let's talk

00:19:14.700 about that a little bit.

00:19:15.840 That's one of the things

00:19:16.800 that popped out for me

00:19:17.980 from your book.

00:19:20.000 So I was trying

00:19:20.720 to think that through.

00:19:22.580 So you make the claim

00:19:23.640 as you just laid out

00:19:24.720 that our education system

00:19:27.320 and maybe our entire culture

00:19:28.660 as it's hypothetically

00:19:30.980 de-industrializing

00:19:32.140 is actually working

00:19:34.460 against the best interests

00:19:35.920 of those who think mechanically,

00:19:38.160 those who think in pictures,

00:19:39.820 and those who can do

00:19:41.000 hands-on work.

00:19:42.460 Here's a paper

00:19:43.400 I just dug off

00:19:44.360 of Google Scholar

00:19:45.400 on visual object ability,

00:19:47.900 a new dimension

00:19:48.540 of nonverbal intelligence.

00:19:50.400 And what a lot of educators

00:19:52.460 don't understand

00:19:53.920 is that object visualization,

00:19:56.780 especially on solving

00:19:57.600 mechanical problems,

00:19:58.900 it is a different way

00:20:00.660 of thinking.

00:20:02.100 I worked with people

00:20:03.100 that barely graduated

00:20:04.200 from high school, 0.99

00:20:05.160 stutterers. 0.99

00:20:06.240 They'd be labeled autistic today. 0.75

00:20:07.880 They'd be in special ed.

00:20:08.820 But they had big metal

00:20:10.940 working shops

00:20:11.640 and 20 patents each

00:20:13.320 for mechanically

00:20:14.660 complicated equipment

00:20:15.660 that they are selling

00:20:16.860 around the world.

00:20:18.040 And this is something

00:20:19.300 that educators

00:20:20.660 just don't get it.

00:20:23.040 Great.

00:20:23.840 And the reason why

00:20:25.560 I wrote this book,

00:20:27.620 I'll tell you

00:20:28.080 what was the reason.

00:20:30.020 In 2019,

00:20:32.180 just before COVID

00:20:32.960 shut everything down,

00:20:34.260 I went to four places

00:20:35.820 and I realized

00:20:37.200 the magnitude

00:20:37.860 of the skill loss.

00:20:39.980 The first one

00:20:41.140 was a pork processing plant

00:20:43.240 where most of the equipment

00:20:45.080 came from Holland.

00:20:46.720 I went to another

00:20:47.480 pork processing plant,

00:20:49.120 equipment coming from Holland.

00:20:50.720 Then I went to

00:20:51.380 a poultry processing plant

00:20:52.800 where all of the specialized

00:20:54.300 equipment came from Holland

00:20:55.640 in a hundred shipping containers.

00:20:58.100 And then I went

00:20:58.820 to the Steve Jobs Theater

00:21:00.200 and the structural glass walls

00:21:02.340 are from Italy and Germany.

00:21:04.260 Then, after reading

00:21:06.200 more stuff,

00:21:07.520 I found out

00:21:08.160 that the state-of-the-art

00:21:09.200 electronic chip-making machine

00:21:10.960 comes from Holland.

00:21:12.920 And that goes back

00:21:14.480 to their educational system.

00:21:17.120 They don't stick their nose up

00:21:18.700 at the high-end skilled trades

00:21:20.340 and look at it

00:21:21.260 sort of like a lesser form

00:21:22.280 of intelligence.

00:21:23.620 When the kids get

00:21:24.520 to about ninth grade,

00:21:25.840 they can go university route

00:21:27.080 or they can go tech route.

00:21:29.900 And I want to emphasize

00:21:30.900 high-end skilled trades

00:21:33.480 where you're really

00:21:34.540 using that visual thinking

00:21:36.020 for mechanical devices.

00:21:39.600 So, I was trying

00:21:41.060 to think through

00:21:42.000 why this might be happening.

00:21:44.060 So, let me offer

00:21:44.840 some hypotheses to you

00:21:46.460 and you tell me

00:21:47.740 what you think about this.

00:21:48.880 So, first of all,

00:21:50.780 we have learned

00:21:51.640 and you draw on

00:21:53.060 Simon Baron Cohen's work

00:21:55.180 in your book.

00:21:56.280 We have learned

00:21:57.200 that there is a difference

00:21:59.400 and we can try

00:22:00.460 to map this

00:22:01.080 onto verbal thinking

00:22:02.500 versus imagistic thinking.

00:22:05.420 Simon Baron Cohen

00:22:06.240 talked about

00:22:07.020 systemizing

00:22:08.400 versus empathizing.

00:22:10.640 And he considers

00:22:12.480 that something

00:22:13.440 akin to the continuum

00:22:15.440 between autistic thinking

00:22:17.900 and normal thinking.

00:22:19.020 So, the autistic types 1.00

00:22:20.220 are more systematizing

00:22:23.480 and my suspicions are

00:22:25.160 they're also the ones

00:22:26.160 who are more likely

00:22:26.860 to think in images

00:22:27.680 in the manner

00:22:28.420 that you described.

00:22:29.200 Wait a minute.

00:22:29.960 Now, we've got

00:22:30.340 two kinds of thinking

00:22:32.440 in images.

00:22:34.960 And there's a nice paper

00:22:36.300 that just came out

00:22:37.160 relatively recently,

00:22:38.640 Seeing and Thinking in Pictures

00:22:39.880 to Review of Visual Information

00:22:41.360 Processing

00:22:42.280 that came out in 2018.

00:22:45.060 I like to keep my stuff

00:22:46.140 up to date.

00:22:47.500 Now, I think

00:22:48.340 in photorealistic pictures

00:22:50.340 where the more mathematical

00:22:51.960 type of thinking

00:22:53.080 thinks in patterns.

00:22:55.820 See, in your brain

00:22:57.220 you've got circuits

00:22:57.920 for what is something.

00:22:59.860 Okay, so I see a dog

00:23:01.380 and I go,

00:23:01.740 yeah, that's a dog.

00:23:02.980 Or I've got some

00:23:04.980 china ceramic cattle

00:23:06.800 on my coffee table here.

00:23:09.580 Okay, and I just look

00:23:10.880 at the animals

00:23:11.480 and name them.

00:23:13.040 Then, the visual spatial

00:23:14.700 is where is something?

00:23:16.480 Where are you located

00:23:17.440 in space?

00:23:19.340 There are actually

00:23:19.940 two different kinds

00:23:21.360 of visualization.

00:23:22.660 visualization.

00:23:23.380 And I have a whole chapter

00:23:24.620 in Visual Thinking book

00:23:26.140 about this

00:23:27.480 and research

00:23:28.620 to back this up.

00:23:31.580 Now, the visual spatial

00:23:33.300 type pattern thinking

00:23:35.040 or sort of

00:23:35.800 where is something

00:23:36.900 in space,

00:23:37.900 those kids do well

00:23:38.860 in math.

00:23:39.560 So, they're going

00:23:40.340 to STEM right. 0.83

00:23:41.540 But let me tell you

00:23:42.320 what's going on

00:23:42.940 out in the food

00:23:43.380 processing plants.

00:23:45.040 The people I've worked

00:23:46.240 with in the big steel shops

00:23:47.540 now that a lot

00:23:48.220 of us closed down,

00:23:49.180 we are paying the price

00:23:50.080 now for taking out

00:23:50.740 the shop classes

00:23:51.620 on designed

00:23:53.560 mechanically clever

00:23:54.800 equipment.

00:23:56.400 The people I worked

00:23:57.340 with in May,

00:23:57.880 we never worked

00:23:58.480 on boilers

00:23:59.000 or refrigeration.

00:24:00.060 We don't understand

00:24:00.800 that stuff.

00:24:01.800 That requires

00:24:02.880 a lot more

00:24:03.780 mathematical thinking

00:24:04.980 or the load

00:24:06.380 on the roof

00:24:06.920 so the factory

00:24:08.040 doesn't fall down

00:24:08.940 if we get

00:24:09.380 two feet of snow.

00:24:10.860 You see,

00:24:11.280 then I have seen

00:24:12.200 this division

00:24:13.000 of engineering labor

00:24:13.940 in every single

00:24:14.700 meat company

00:24:15.200 I have worked with.

00:24:16.100 They're all the same.

00:24:17.120 So, do you think

00:24:18.300 that's a matter

00:24:19.720 of, let's say,

00:24:22.140 something approximating

00:24:23.220 focal depth?

00:24:24.600 So, is it possible

00:24:25.500 that the first

00:24:27.080 kind of people

00:24:27.800 that you talked

00:24:28.500 about are dealing

00:24:31.360 at the object

00:24:33.420 level itself?

00:24:34.280 So, they're dealing,

00:24:35.020 say, with a boiler

00:24:35.700 or with a particular

00:24:36.480 piece of equipment

00:24:37.320 whereas the other

00:24:38.740 types are dealing

00:24:39.500 with the relationship

00:24:40.620 between pieces

00:24:41.560 of equipment?

00:24:42.240 No, it's just...

00:24:43.040 No, you don't think

00:24:43.700 that's it?

00:24:44.180 No, I think it's

00:24:45.020 just real simple.

00:24:45.880 It's, you just

00:24:47.160 see the objects

00:24:48.240 and then after

00:24:49.460 you work...

00:24:50.160 You see, an object

00:24:51.000 visualizer gets

00:24:51.820 better and better

00:24:52.420 at designing

00:24:52.920 mechanical equipment

00:24:53.840 the more things

00:24:55.680 you go out and see.

00:24:56.640 Like, when I started

00:24:57.360 working my designing

00:24:58.320 cattle handling

00:24:59.040 facilities, I went

00:25:00.040 to every feed yard

00:25:00.840 in Arizona

00:25:01.460 and I worked cattle

00:25:02.320 and I go,

00:25:03.940 this kind of a design

00:25:04.840 doesn't work.

00:25:05.880 This worked.

00:25:06.840 So, then I took

00:25:07.360 all the good bits

00:25:08.260 and, like, recombined them.

00:25:09.800 It's bottom-up thinking.

00:25:11.080 The more stuff

00:25:11.760 I get exposed to,

00:25:12.920 okay, whether it's

00:25:13.520 cattle handling facility

00:25:14.500 or maybe I'll look

00:25:16.040 at how water flows

00:25:16.980 through something

00:25:17.640 and then you watch

00:25:18.800 how cattle move.

00:25:20.100 I like to look

00:25:20.800 at drone footage

00:25:21.740 and a lot of that

00:25:23.600 resembles water flow

00:25:26.060 and the visual spatial,

00:25:29.680 they see patterns.

00:25:31.420 It's actually

00:25:31.980 very different

00:25:33.920 and what we're losing

00:25:35.360 is the object visualizer.

00:25:39.540 That person that can just...

00:25:41.700 You see, I'm very aware

00:25:43.320 of things like

00:25:43.860 I go in an elevator now

00:25:44.980 that hasn't been serviced

00:25:45.920 and it's scraping

00:25:46.740 in the shaft.

00:25:47.480 You better believe it.

00:25:48.280 Right, right, right.

00:25:49.100 I hear it

00:25:49.980 and I go,

00:25:50.740 they haven't serviced

00:25:51.460 that elevator

00:25:52.120 and I was at a fancy hotel

00:25:54.040 recently

00:25:54.580 and the bellhop goes,

00:25:57.580 oh, it skips that floor.

00:25:58.820 We have to get that floor

00:25:59.880 on the way down.

00:26:01.440 Yeah, real nice hotel,

00:26:02.680 major city.

00:26:05.040 Right, right.

00:26:06.100 Okay, so let me...

00:26:07.360 I don't quite understand

00:26:08.540 that distinction yet

00:26:09.420 so I'm going to...

00:26:10.260 All right.

00:26:10.400 I'm going to push

00:26:10.880 a little bit more on that.

00:26:11.960 So on the visual side,

00:26:14.960 you have the visual spatial types

00:26:16.760 if I'm remembering this correctly

00:26:19.240 and that's the category

00:26:20.120 that you fall into

00:26:21.000 and then you have people

00:26:22.340 who are,

00:26:23.180 what are they,

00:26:23.540 higher up on the abstraction chain

00:26:25.380 in some sense,

00:26:26.180 the ones who can think

00:26:27.000 more mathematically?

00:26:28.660 No, but basically,

00:26:29.680 object visualizers,

00:26:30.660 I can tell you this

00:26:31.360 from experience,

00:26:32.380 we're good at mechanical devices,

00:26:34.720 art, photography.

00:26:36.000 I have talked to many,

00:26:37.580 many photographers

00:26:38.620 because I do a lot of interviews,

00:26:41.400 find out they're dyslexic,

00:26:42.720 they're about flunked out of school

00:26:43.900 and fortunately,

00:26:45.140 somebody introduced them

00:26:46.220 to photography

00:26:46.880 and the other thing

00:26:48.120 that my kind of mind's good at

00:26:49.320 is animals.

00:26:50.640 Then you're visual spatial,

00:26:52.500 mathematics, algebra,

00:26:53.720 I can't do algebra.

00:26:55.100 There's nothing there

00:26:55.720 to visualize.

00:26:56.960 Mathematics, calculus,

00:26:58.440 I took a computer programming course

00:27:00.940 when I was in college.

00:27:01.960 I couldn't do it.

00:27:02.940 I had to drop it.

00:27:03.740 I was exposed to the same exact computer

00:27:06.080 that Bill Gates had

00:27:07.040 and he could do it

00:27:08.320 and I had to drop the class.

00:27:10.640 You know,

00:27:10.900 these are the things

00:27:12.560 that the more mathematical

00:27:14.160 pattern thinking mind

00:27:15.540 is good at

00:27:16.360 and some of these

00:27:18.540 really smart kids,

00:27:19.580 they can just look

00:27:20.160 at algebra formulas

00:27:21.340 and just see it.

00:27:22.500 They don't have to do it

00:27:23.140 step by step.

00:27:23.920 They just see it

00:27:24.520 and that's how they think

00:27:26.200 and then the teachers

00:27:27.000 try to make them do it

00:27:27.800 step by step.

00:27:28.660 That gets the kids frustrated.

00:27:30.720 You know,

00:27:31.000 they don't think the same way

00:27:32.420 and

00:27:33.660 and my thinking

00:27:35.260 is also associative.

00:27:36.820 I tend to jump around

00:27:37.820 but there's a logic

00:27:39.020 to the association

00:27:40.780 and I think

00:27:41.800 the best way

00:27:42.400 to illustrate

00:27:43.120 that associative

00:27:44.180 kind of thinking

00:27:44.820 is give me

00:27:45.520 a single keyword

00:27:46.600 like on Google

00:27:48.000 for images

00:27:48.540 and think up

00:27:49.800 a creative keyword

00:27:50.740 and I will tell you

00:27:52.060 exactly how my mind

00:27:53.160 associates

00:27:53.960 and how my mind

00:27:56.060 can get off the subject

00:27:57.500 but there is a logic

00:27:58.720 to the getting off

00:27:59.960 of the subject.

00:28:01.600 So give me a keyword,

00:28:02.700 a single keyword.

00:28:03.660 Okay, how about rose?

00:28:07.600 Okay, I'm seeing

00:28:08.340 some rose bushes

00:28:09.140 that we had

00:28:09.920 in our backyard

00:28:10.720 when I was a child

00:28:12.180 and we had a lot

00:28:14.720 of thorns on them

00:28:15.660 and we also like

00:28:17.260 dig around in the grass

00:28:18.400 that was back there

00:28:19.040 so now I'm seeing

00:28:19.740 the grass

00:28:20.480 behind our house

00:28:22.560 and we used to go

00:28:23.600 play in it.

00:28:24.880 We'd catch some insects

00:28:26.080 sometimes.

00:28:27.680 Okay, now you can see

00:28:28.740 how it's jumping around.

00:28:30.580 I don't have

00:28:31.140 that big a visual

00:28:32.060 I don't have that big

00:28:33.380 a visual library

00:28:34.460 of roses.

00:28:36.120 So then I tend

00:28:37.640 to then go

00:28:39.620 to something else.

00:28:41.820 How about cows?

00:28:43.460 What?

00:28:44.940 Cows?

00:28:45.300 All right, now I'm seeing

00:28:46.760 like 10 cow statues

00:28:49.320 right here in front of me

00:28:50.340 and so obviously

00:28:52.580 those are coming up

00:28:53.560 in my mind.

00:28:55.560 Just recently,

00:28:56.880 I tend to bring up

00:28:57.600 recent memories,

00:28:58.280 I visited with a

00:28:59.240 black Angus bull

00:29:01.000 that was a pet

00:29:01.660 and he wasn't

00:29:02.740 very happy with us

00:29:03.640 because we didn't

00:29:04.180 bring him any carrots.

00:29:05.680 So my friend

00:29:06.480 gave him one of those

00:29:07.440 disgusting soy protein bars

00:29:09.720 and he goes,

00:29:10.520 I'm not going to eat that.

00:29:13.160 He wouldn't eat it

00:29:14.180 no matter how much

00:29:14.680 we tried to feed it to him

00:29:15.820 and he was annoyed

00:29:17.540 because we didn't

00:29:18.240 bring him any carrots.

00:29:21.420 Okay, then I'm getting,

00:29:22.980 now I'm on the,

00:29:23.920 now I've got the carrot

00:29:24.760 word in my head

00:29:25.640 and when I was

00:29:26.660 in fourth grade

00:29:27.340 I used my singer

00:29:28.080 Sew Handy

00:29:28.760 to sew green crepe paper

00:29:31.040 so students could be carrots

00:29:32.840 and I made the green

00:29:33.980 carrot tops

00:29:34.860 out of green crepe paper.

00:29:38.200 So that's how I got

00:29:39.100 my cattle

00:29:39.860 to the carrot.

00:29:41.580 So talking to you,

00:29:42.740 talking to you that way

00:29:44.300 reminds me very much

00:29:46.020 of what used to happen

00:29:47.240 in my therapy sessions

00:29:48.540 when I was helping people

00:29:49.860 interpret their dreams

00:29:51.640 and so dreams have

00:29:53.640 the same quality of thought

00:29:56.020 that your thought had

00:29:58.840 when you just put it

00:29:59.660 on display there.

00:30:00.980 So because the dream

00:30:02.700 tends to be an intermingling,

00:30:04.280 so imagine there's

00:30:05.080 a center category

00:30:06.280 and the nature

00:30:08.080 of the category

00:30:08.760 is somewhat unclear.

00:30:10.280 Maybe that's partly

00:30:11.040 what the dream

00:30:11.580 is trying to puzzle out

00:30:12.620 but what you get

00:30:13.780 is a web of associations

00:30:15.440 some of which

00:30:16.940 are autobiographical

00:30:18.080 that are sort of circulating

00:30:19.740 around a main theme

00:30:21.140 and partly what you do

00:30:22.580 when you analyze

00:30:23.620 a dream

00:30:24.100 is you walk through

00:30:25.780 all the associated images.

00:30:27.660 You also ask the person

00:30:29.160 to let their mind loose

00:30:30.820 to generate more associations

00:30:32.940 and then you try

00:30:34.160 to use the associational web

00:30:35.960 to triangulate

00:30:36.920 on the central theme

00:30:38.120 and to haul out the gist

00:30:40.620 and the gist of that

00:30:42.260 array of images

00:30:43.800 would be something

00:30:44.500 like the interpretation

00:30:45.420 of the dream.

00:30:46.560 If you get it right

00:30:47.240 then it snaps into people.

00:30:48.900 They think,

00:30:49.560 oh yes,

00:30:50.020 that's definitely

00:30:50.660 what that was about.

00:30:52.220 But the dream

00:30:53.000 is attempting

00:30:53.840 to put something together

00:30:55.240 that has a central

00:30:57.060 categorical structure.

00:30:57.280 And there also

00:30:57.980 are sensory things

00:30:59.360 because I have

00:31:01.380 some balance issues

00:31:02.900 so I often get dreams

00:31:04.900 where I'm like

00:31:05.480 riding a bike

00:31:06.480 down a hill

00:31:07.000 that's super steep

00:31:07.720 like that

00:31:08.220 and I wish

00:31:08.560 I wasn't doing that

00:31:09.560 and I know

00:31:10.780 that that has to do

00:31:11.860 with the fact

00:31:12.720 that I have balance issues.

00:31:14.200 I don't think

00:31:14.540 it means anything

00:31:15.720 but I'm always up

00:31:17.120 on some high place

00:31:18.000 that I wish

00:31:18.440 I wasn't up on

00:31:19.260 and I've got to

00:31:20.160 try to walk down it

00:31:21.300 and then I have

00:31:23.300 other dreams

00:31:23.860 where I can see

00:31:24.440 it might have

00:31:24.960 some meaning

00:31:25.580 and other times

00:31:27.220 where it doesn't.

00:31:30.100 But the other thing

00:31:31.660 with my thinking

00:31:32.420 when I'm working

00:31:33.380 on design work

00:31:34.160 I can control

00:31:35.040 the associations.

00:31:37.500 I can control them.

00:31:39.500 I've had before

00:31:40.460 you know

00:31:41.700 there's now 3D simulations

00:31:43.240 so okay

00:31:43.720 let's say

00:31:44.280 the company

00:31:45.260 that's building

00:31:45.880 this plant

00:31:46.800 wants to show off

00:31:48.000 how their equipment works

00:31:48.800 they can do

00:31:49.340 a 3D simulation

00:31:50.780 showing how

00:31:51.860 the equipment works.

00:31:53.640 I can remember

00:31:54.460 sitting in a conference room

00:31:55.660 and we were trying

00:31:56.060 to discuss

00:31:56.600 how to do

00:31:57.080 some conveyors

00:31:57.940 and the other guys

00:32:01.000 there were coming up

00:32:01.780 with ideas

00:32:02.340 for the conveyors

00:32:03.020 and I go

00:32:03.220 no no

00:32:03.500 if you do that

00:32:04.100 you're going to

00:32:04.740 yank the rails

00:32:05.340 out of the ceiling

00:32:06.060 oh no no no

00:32:07.020 that won't work.

00:32:08.280 They were almost

00:32:08.980 using my mind

00:32:09.800 like a 3D CAD program

00:32:11.960 that was animated

00:32:13.600 and I could

00:32:14.760 test run

00:32:15.820 in my head

00:32:16.500 on these different conveyors.

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00:33:25.800 So let's go back

00:33:30.840 to the issue

00:33:31.920 of say

00:33:32.920 taking shop class

00:33:34.060 out of school.

00:33:34.940 So Barron Cohen

00:33:36.500 talked about

00:33:37.320 systematizers

00:33:38.520 versus empathizers

00:33:39.760 Yeah, but what

00:33:40.180 Barron Cohen did

00:33:41.100 It's also the case

00:33:41.520 He left out

00:33:42.460 the object visualizers.

00:33:44.800 He was looking more

00:33:45.720 at the mathematical

00:33:47.480 visual spatial

00:33:48.240 pattern thinkers

00:33:49.360 I agree with him

00:33:50.520 about systematizing

00:33:51.640 and verbal

00:33:53.940 but the object visualizer

00:33:57.420 But he didn't

00:33:57.860 differentiate

00:33:58.420 he didn't differentiate

00:33:59.560 the systematizers

00:34:00.740 properly

00:34:01.280 that should be more

00:34:02.060 differentiated

00:34:02.680 Yeah, he didn't

00:34:02.700 systematizers

00:34:03.800 would have two categories

00:34:05.340 the object visualizer

00:34:06.680 and the visual spatial

00:34:08.040 and a big mistake

00:34:09.300 in a lot of perceptual studies

00:34:10.660 is that they're not

00:34:11.600 differentiating them

00:34:12.700 and some people

00:34:13.920 there's some verbal people

00:34:16.880 in psychology

00:34:17.740 that don't want to believe

00:34:18.460 this stuff exists

00:34:19.280 Just while you were

00:34:20.480 setting up the cameras

00:34:21.320 I downloaded

00:34:22.400 six new abstracts

00:34:25.500 that aren't even

00:34:26.180 in my book on this

00:34:27.320 that show that

00:34:28.880 they are different

00:34:29.780 Right, okay

00:34:31.760 Got them right here

00:34:32.300 on my lap

00:34:33.000 So Cohen also talks

00:34:36.400 about gender differences

00:34:37.720 in relationship

00:34:38.680 to this continuous

00:34:40.060 So we can break

00:34:40.860 the continuum

00:34:41.360 into two parts

00:34:42.060 on the one end

00:34:42.740 and it is the case

00:34:45.120 that autism

00:34:46.400 tends to be

00:34:47.560 preferentially

00:34:48.300 a male disorder 0.62

00:34:49.940 Yes, that's right

00:34:50.240 Although there are

00:34:50.960 females as well 0.97

00:34:51.860 But so that

00:34:52.940 systematizing mode

00:34:54.340 of thinking

00:34:54.860 that you've

00:34:55.620 differentiated

00:34:56.280 into the two categories

00:34:57.400 also tends to be

00:34:59.040 gender stereotyped

00:35:00.680 to some degree

00:35:01.400 at a biological level

00:35:01.960 I avoid that issue

00:35:02.720 as too controversial

00:35:03.320 because right now

00:35:05.200 I'm interested

00:35:07.440 in one thing

00:35:08.000 at the age of 75

00:35:09.120 of helping

00:35:10.380 the students

00:35:10.880 who think differently

00:35:11.600 get into really good careers

00:35:13.220 where they can have

00:35:14.200 satisfying lives

00:35:15.320 and I avoid

00:35:17.020 the controversial stuff

00:35:18.340 Yeah, well

00:35:19.980 I'm not so much

00:35:21.300 interested in the controversy

00:35:22.560 I'm interested

00:35:23.420 in trying to address

00:35:24.520 the issue

00:35:25.100 of why

00:35:26.060 shop courses

00:35:27.580 for example

00:35:28.360 have been taken

00:35:28.980 out of schools

00:35:29.740 Now, we do know

00:35:31.200 that schools

00:35:32.900 are predominantly

00:35:34.020 run by women

00:35:35.040 and women

00:35:35.880 are more likely

00:35:36.500 to be empathizing

00:35:37.300 No, I think

00:35:37.960 they're less likely

00:35:38.620 to be systematizing

00:35:39.780 So I'm wondering

00:35:40.500 if there's a gender issue

00:35:41.760 going on there

00:35:42.540 and a prejudice

00:35:43.240 against a certain

00:35:44.000 way of thinking

00:35:44.680 Okay

00:35:45.100 What's that?

00:35:46.180 I think that

00:35:47.640 one of the reasons

00:35:49.400 they took out

00:35:49.880 the shop classes

00:35:50.600 they kind of just go

00:35:51.440 everybody's going to go

00:35:52.380 the university route

00:35:53.580 and cost

00:35:55.300 Right

00:35:55.580 Good shop classes

00:35:56.940 cost a lot of money

00:35:58.040 Now, people are starting

00:35:59.960 to put shop classes

00:36:01.020 back in

00:36:01.580 and you know

00:36:02.060 what they're finding?

00:36:02.820 They can't find anybody

00:36:03.840 to teach the shop classes

00:36:05.260 I just heard about

00:36:06.680 a brand new

00:36:07.560 beautiful welding shop

00:36:08.740 built here in Colorado

00:36:09.680 at a community college

00:36:10.620 and they can't find

00:36:12.000 somebody to teach it

00:36:13.120 even after they drop

00:36:14.300 the university requirement

00:36:15.440 and I can tell you

00:36:16.560 right now

00:36:17.080 we need people

00:36:18.320 that do these things

00:36:19.160 before the power grid

00:36:20.360 and the water systems

00:36:21.240 fall apart

00:36:21.800 I'll tell you

00:36:22.500 that's stuff

00:36:22.980 I care about

00:36:23.340 Right

00:36:23.640 Right

00:36:25.420 Right

00:36:25.860 Yeah

00:36:26.160 Well, it's

00:36:26.660 Okay

00:36:27.180 So you

00:36:28.020 Well, virtualization

00:36:29.260 Now, do you think

00:36:30.420 virtualization

00:36:31.260 has also played

00:36:32.420 a role in this?

00:36:33.240 I mean

00:36:33.420 The systematizing types

00:36:35.000 Some of the 3D drawings

00:36:36.980 I see are wrong

00:36:38.180 And that's not going to fix

00:36:41.620 some of the serious problems

00:36:42.560 we've got with infrastructure

00:36:43.500 right now

00:36:44.080 And you need both kinds

00:36:45.960 of thinkers

00:36:46.400 Like one of the things

00:36:47.220 I've got in my book

00:36:48.120 is where a bridge

00:36:49.760 fell down in Minneapolis

00:36:50.760 and the workers

00:36:52.840 were complaining

00:36:53.660 They were worried

00:36:54.540 that when they were

00:36:55.600 working on this bridge

00:36:56.260 it was going to collapse

00:36:57.100 Well, I looked up

00:36:57.900 that bridge collapse

00:36:59.040 and I saw

00:37:00.580 all the twisted metal

00:37:01.660 and I took one look

00:37:03.940 at that

00:37:04.220 and I went

00:37:05.000 It's too light

00:37:05.900 It's too cheap

00:37:07.140 That was just

00:37:09.280 from looking

00:37:10.240 at the pictures

00:37:11.040 Then I found

00:37:12.380 the engineering report

00:37:13.620 on why that bridge

00:37:14.940 fell down

00:37:15.500 and they cheated

00:37:16.460 on the gusset plates

00:37:17.860 that hold

00:37:18.840 the beams together

00:37:19.980 and they were

00:37:20.560 way thinner

00:37:21.140 than the spec

00:37:22.020 But I had already

00:37:24.100 looked at the pictures

00:37:24.980 and said

00:37:25.500 that bridge 0.57

00:37:25.880 is too light

00:37:26.420 it's cheap

00:37:27.100 before I read

00:37:29.720 any engineering report

00:37:30.980 So how was it

00:37:34.760 in your life

00:37:35.920 that you

00:37:37.040 attained the

00:37:38.500 practical knowledge

00:37:40.060 necessary

00:37:40.680 to facilitate

00:37:41.480 your thinking

00:37:42.240 So we're talking

00:37:42.940 about how

00:37:43.580 kids who think

00:37:45.220 in the vision

00:37:45.880 I'll tell you

00:37:46.420 how you do it

00:37:46.980 It's real simple

00:37:47.680 You've got to get out

00:37:48.800 and experience

00:37:49.440 all kinds of stuff

00:37:50.360 because the more

00:37:51.240 information you put

00:37:52.180 in the database

00:37:52.800 the better you get

00:37:53.640 and going back

00:37:54.840 to teaching a computer

00:37:55.900 how to diagnose

00:37:56.940 melanoma

00:37:57.440 you had to give it

00:37:58.140 like a couple

00:37:59.160 of thousand

00:37:59.700 melanoma examples

00:38:01.060 and a couple

00:38:01.560 of thousand

00:38:02.060 mosquito bites

00:38:03.120 infected boils

00:38:04.080 and everything else

00:38:04.860 examples

00:38:05.800 In other words

00:38:06.680 as I got older

00:38:08.480 and I got more

00:38:09.520 and more information

00:38:10.540 in my database

00:38:11.120 I could think

00:38:12.640 better and better

00:38:13.560 and better

00:38:14.040 and I could make

00:38:14.980 smaller categories

00:38:16.280 of things

00:38:17.160 Now a lot

00:38:18.340 of the people

00:38:18.720 that I worked

00:38:19.360 with in construction

00:38:20.100 that built

00:38:20.560 equipment for me

00:38:21.320 that's used

00:38:22.040 in every large

00:38:22.620 beef plant

00:38:23.140 now in the US

00:38:23.980 Some one of them

00:38:25.620 started

00:38:26.000 and some of them

00:38:27.120 would definitely

00:38:27.600 be autistic 0.96

00:38:28.260 One of them

00:38:29.700 started out

00:38:30.160 working on cars

00:38:31.020 Another one

00:38:31.860 took a single

00:38:32.440 welding class

00:38:33.400 and now he's

00:38:34.760 selling mechanical

00:38:35.520 equipment all

00:38:36.240 over the world

00:38:37.020 Started with a tiny

00:38:39.620 shop that then

00:38:40.400 grew into a big

00:38:41.100 shop

00:38:41.420 and what's

00:38:42.260 happening now

00:38:43.240 is

00:38:44.400 is the little

00:38:46.240 shops are not

00:38:46.980 forming

00:38:47.440 and that's why

00:38:49.580 we're importing

00:38:50.160 all this equipment

00:38:50.760 from Holland

00:38:51.340 and Italy

00:38:51.960 because when

00:38:53.700 you look at

00:38:54.140 their educational

00:38:54.860 system

00:38:55.320 and I looked

00:38:55.760 it up again

00:38:56.420 recently online

00:38:57.460 Italy actually

00:38:59.520 has three routes

00:39:00.360 university route

00:39:01.540 tech route

00:39:02.560 mechanical

00:39:03.260 and art route

00:39:04.740 you know like

00:39:05.980 for their fashion

00:39:06.600 industry

00:39:07.080 and the

00:39:07.740 Holland

00:39:08.720 and Netherlands

00:39:09.460 you can go

00:39:10.060 either university

00:39:10.740 route

00:39:11.040 or tech route

00:39:11.780 and that's why

00:39:12.740 they're making

00:39:13.320 the state-of-the-art

00:39:14.480 chip making machine

00:39:15.580 that we don't

00:39:16.600 make

00:39:16.960 right

00:39:18.780 and I'm not

00:39:19.540 talking potato

00:39:20.200 chips

00:39:20.480 I'm talking

00:39:21.060 electronic chips

00:39:22.180 right

00:39:23.800 I wonder if

00:39:24.640 this is also

00:39:25.280 a consequence

00:39:26.060 of people

00:39:27.580 increasingly

00:39:28.180 moving away

00:39:28.920 from farms

00:39:29.860 you know

00:39:30.600 because when

00:39:30.940 you're on a

00:39:31.380 farm you'd

00:39:31.860 have to do

00:39:32.220 a lot of

00:39:32.580 hands-on

00:39:33.060 stuff

00:39:33.460 you have

00:39:33.720 to do a

00:39:33.940 lot of

00:39:34.180 fence

00:39:34.420 repair

00:39:34.780 you've got

00:39:35.100 to take

00:39:35.360 care of

00:39:35.620 your own

00:39:35.900 machinery

00:39:36.300 and you know

00:39:37.380 as you move

00:39:37.960 into the urban

00:39:38.540 environment

00:39:39.040 everything

00:39:39.480 in some sense

00:39:40.640 even in the

00:39:41.120 real world

00:39:41.580 is virtualized

00:39:42.380 because you can

00:39:42.820 always call

00:39:43.420 on other people

00:39:44.080 to do the

00:39:44.720 day-to-day

00:39:45.160 things that you

00:39:45.820 need to keep

00:39:46.400 the infrastructure

00:39:47.160 well I agree

00:39:47.940 a lot of

00:39:49.140 you know

00:39:49.940 kids are

00:39:50.880 growing up

00:39:51.260 today

00:39:51.560 totally removed

00:39:52.720 from the

00:39:53.600 practical

00:39:54.220 and one of

00:39:55.560 the things

00:39:55.860 I talk about

00:39:56.420 in the visual

00:39:56.840 thinking book

00:39:57.840 is I

00:40:00.060 talked to a

00:40:01.860 doctor

00:40:02.220 and he told me

00:40:03.300 he had trouble

00:40:03.840 training interns

00:40:04.920 to sew up cuts

00:40:05.800 because the

00:40:07.080 interns had

00:40:07.940 never used

00:40:08.580 scissors as a

00:40:09.540 young child

00:40:10.260 I had a

00:40:11.580 student in my

00:40:12.060 class that had

00:40:12.600 never used a

00:40:13.100 tape measure

00:40:13.600 to measure

00:40:14.120 anything

00:40:14.960 and they're

00:40:16.920 totally removed

00:40:17.800 from the world

00:40:18.240 of the practical

00:40:18.880 where those

00:40:20.180 kids that came

00:40:20.720 off the farms

00:40:21.260 yes they had

00:40:22.240 to figure out

00:40:22.780 how to fix

00:40:23.320 things

00:40:23.740 absolutely

00:40:24.680 but I think

00:40:26.680 what's happening

00:40:27.320 now in the

00:40:27.980 schools is things

00:40:28.700 are getting so

00:40:29.300 verbal and

00:40:30.540 they're going

00:40:31.060 absolutely crazy

00:40:32.000 on math

00:40:32.500 requirements

00:40:33.180 because I

00:40:34.800 know people

00:40:35.460 with 20

00:40:35.980 patents and

00:40:36.940 they could

00:40:37.200 basically do

00:40:37.760 sixth grade

00:40:38.460 arithmetic

00:40:39.960 that I could

00:40:41.180 do because

00:40:41.720 I can relate

00:40:42.280 that back

00:40:42.720 to real

00:40:43.060 things

00:40:43.540 and things

00:40:44.660 like

00:40:45.000 do you

00:40:45.600 do you

00:40:47.520 know of

00:40:47.900 any research

00:40:48.780 pertaining

00:40:49.360 on how

00:40:49.960 the more

00:40:51.280 the people

00:40:52.160 who

00:40:52.500 visualize

00:40:53.920 objects

00:40:54.660 might be

00:40:55.300 assessed

00:40:55.760 for their

00:40:56.320 ability

00:40:56.800 yes

00:40:57.680 there's a

00:40:58.600 whole

00:40:58.780 there's a whole

00:40:59.700 chapter in here

00:41:01.060 there's a whole

00:41:01.980 chapter in here

00:41:03.200 and I never

00:41:04.700 can pronounce

00:41:05.340 their names

00:41:06.040 right blank and

00:41:06.760 and I never

00:41:07.960 can say the

00:41:08.500 names correctly

00:41:09.240 but I have a

00:41:10.480 whole list of

00:41:11.100 references in

00:41:11.800 there where

00:41:12.780 the difference

00:41:14.160 between the

00:41:14.660 object visualizer

00:41:15.600 and the

00:41:16.080 visual spatial

00:41:16.620 is being

00:41:17.540 assessed

00:41:18.080 and there's a

00:41:19.420 whole bunch

00:41:19.880 of references

00:41:20.420 on that

00:41:20.940 now I have to

00:41:21.780 look these

00:41:22.440 names up

00:41:22.880 because I

00:41:23.180 can't

00:41:24.060 I can't

00:41:25.360 pronounce them

00:41:26.040 correctly

00:41:26.600 let me find

00:41:29.500 the reference

00:41:30.000 list here for

00:41:30.600 chapter two

00:41:31.520 okay it's

00:41:33.320 Blazhenkova

00:41:34.940 I've got

00:41:36.300 one two

00:41:36.820 three references

00:41:37.980 in here from

00:41:38.620 Blazhenkova

00:41:40.100 on types of

00:41:41.340 creativity

00:41:41.900 and then the

00:41:43.100 other big

00:41:44.280 reference I

00:41:46.020 have lots of

00:41:46.820 references

00:41:47.320 would be

00:41:50.220 Kozhevnikov

00:41:53.820 if she's got

00:41:55.500 trade-offs

00:41:56.220 object versus

00:41:57.320 spatial

00:41:57.780 visualization

00:41:58.360 reviewing the

00:41:59.920 visual verbal

00:42:01.140 dimension

00:42:01.660 evidence for

00:42:02.600 two types of

00:42:03.220 visualizers

00:42:03.740 that's another

00:42:04.280 paper

00:42:04.780 spatial

00:42:06.060 versus object

00:42:07.480 visualization

00:42:08.240 a new

00:42:09.240 characterization

00:42:09.840 of visual

00:42:10.580 cognitive

00:42:11.080 style

00:42:11.600 that's three

00:42:12.460 papers right

00:42:13.040 there from

00:42:13.440 Kohesnikov

00:42:14.320 that are in

00:42:15.060 my reference

00:42:15.600 list

00:42:16.000 and

00:42:16.960 okay

00:42:17.560 and I've

00:42:18.560 got a lot

00:42:19.480 of references

00:42:20.580 where they

00:42:21.660 were actual

00:42:22.180 you know

00:42:22.620 tests were

00:42:23.380 done

00:42:23.840 you know

00:42:25.000 when I go

00:42:25.460 through the

00:42:25.820 citation lists

00:42:26.760 I just went

00:42:27.380 in and

00:42:28.360 we're working

00:42:29.320 on the

00:42:29.580 children's edition

00:42:30.240 of the book

00:42:30.700 right now

00:42:31.340 and one

00:42:32.440 of the

00:42:32.620 copy

00:42:32.880 editors

00:42:33.100 had a

00:42:33.500 query

00:42:33.820 about a

00:42:34.820 reference

00:42:35.260 and I

00:42:36.780 had to

00:42:37.640 look that

00:42:38.060 up

00:42:38.320 and then

00:42:38.740 I decided

00:42:39.860 to just

00:42:40.320 type in

00:42:40.860 object

00:42:41.520 visualizer

00:42:42.240 visual

00:42:42.740 spatial

00:42:43.240 into

00:42:44.460 Google

00:42:44.780 Scholar

00:42:45.180 again

00:42:45.720 and find

00:42:47.160 the same

00:42:47.540 old

00:42:47.760 papers

00:42:48.180 and then

00:42:49.060 I found

00:42:49.460 some

00:42:49.700 citations

00:42:50.460 it's kind

00:42:51.260 of a cool

00:42:51.660 paper here

00:42:52.260 in the

00:42:52.440 journal

00:42:52.640 cognition

00:42:53.280 it's an

00:42:54.660 old paper

00:42:55.140 actually

00:42:55.460 visual object

00:42:56.320 ability

00:42:56.760 a new

00:42:57.200 dimension

00:42:57.740 of nonverbal

00:42:58.540 intelligence

00:42:59.080 I know

00:42:59.940 from working

00:43:00.460 in factories

00:43:01.180 I spent

00:43:02.140 25 years

00:43:02.920 in heavy

00:43:03.220 construction

00:43:03.720 and that

00:43:04.800 is something

00:43:05.320 that I don't

00:43:06.400 think many

00:43:06.760 teachers have

00:43:07.300 done that

00:43:07.740 and seeing

00:43:08.720 how these

00:43:09.120 guys think

00:43:09.820 big complicated

00:43:11.280 Cargill

00:43:11.960 plants

00:43:12.480 IBP plants

00:43:14.260 which are now

00:43:14.700 Tyson

00:43:15.200 Montfort

00:43:16.380 plants

00:43:16.980 and that

00:43:18.120 company is

00:43:18.720 now JBS

00:43:19.560 figuring out

00:43:21.600 complicated

00:43:22.200 things with

00:43:23.180 equipment

00:43:23.580 it's a

00:43:24.700 different

00:43:25.100 type of

00:43:26.240 intelligence

00:43:27.080 intelligence

00:43:27.260 and I

00:43:28.560 think

00:43:28.760 when I

00:43:30.100 worked on

00:43:30.500 the book

00:43:30.800 with Betsy

00:43:31.320 Lerner

00:43:31.660 my super

00:43:32.740 verbal

00:43:33.640 co-writer

00:43:34.400 who helped

00:43:35.100 me organize

00:43:35.700 things

00:43:36.300 and she

00:43:37.400 had someone

00:43:37.940 come in

00:43:38.380 to fix

00:43:38.760 a bunch

00:43:39.060 of stuff

00:43:39.380 in her

00:43:39.640 house

00:43:40.120 and after

00:43:41.380 we had

00:43:41.800 discussed

00:43:42.240 this

00:43:42.520 Betsy

00:43:42.900 was telling

00:43:43.380 me

00:43:43.620 well I

00:43:44.200 watched

00:43:44.560 how he

00:43:44.860 figured

00:43:45.140 out

00:43:45.380 how to

00:43:45.700 fix

00:43:45.920 the

00:43:46.200 stones

00:43:46.940 on the

00:43:47.280 chimney

00:43:47.620 I had

00:43:48.720 never

00:43:48.960 really

00:43:49.260 thought

00:43:49.540 about

00:43:49.800 it

00:43:49.940 before

00:43:50.240 but she

00:43:51.080 watched

00:43:51.500 how he

00:43:51.900 did

00:43:52.220 things

00:43:52.720 and then

00:43:53.340 she

00:43:53.480 started

00:43:53.800 to

00:43:54.020 understand

00:43:54.540 there's

00:43:55.060 a form

00:43:55.320 of intelligence

00:43:56.000 there

00:43:56.420 that's

00:43:57.240 absolutely

00:43:57.760 not

00:43:58.100 verbal

00:43:58.440 when she

00:43:59.040 watched

00:43:59.480 a person

00:44:00.380 she hired 0.89

00:44:00.900 to fix

00:44:01.320 stuff

00:44:01.580 in her

00:44:01.780 house

00:44:02.180 right

00:44:04.240 right

00:44:04.620 yeah well

00:44:05.220 you can

00:44:05.480 imagine

00:44:05.820 something

00:44:06.280 and this

00:44:06.980 did pop

00:44:07.440 into my

00:44:07.820 mind

00:44:08.160 visually

00:44:08.800 can imagine

00:44:09.660 somebody

00:44:10.080 who's trying

00:44:10.580 to put

00:44:10.880 together

00:44:11.200 a stone

00:44:11.640 chimney

00:44:12.080 has to

00:44:12.900 rotate

00:44:13.220 the stones

00:44:13.860 to make

00:44:15.040 to make

00:44:15.060 sure

00:44:15.240 like a

00:44:16.220 Tetris

00:44:16.600 game

00:44:16.900 that's

00:44:17.200 a good

00:44:17.400 way

00:44:17.580 of

00:44:17.680 thinking

00:44:17.920 about

00:44:18.180 it

00:44:18.320 I was

00:44:18.820 thinking

00:44:19.060 then again

00:44:19.600 because I'm

00:44:20.400 thinking in images

00:44:21.080 now that we're

00:44:21.700 talking

00:44:22.100 I was thinking

00:44:22.840 about my young

00:44:23.500 grandson

00:44:23.920 he's only

00:44:24.840 two years

00:44:25.320 old

00:44:25.620 he had his

00:44:26.400 Legos

00:44:26.960 laid out

00:44:28.580 in front

00:44:28.900 of him

00:44:29.180 and when I

00:44:30.320 was a kid

00:44:30.740 I played

00:44:31.120 a lot

00:44:31.460 with Legos

00:44:32.060 and I

00:44:32.280 played a fair

00:44:32.800 bit with

00:44:33.220 this

00:44:33.440 Meccano

00:44:33.960 set

00:44:34.340 that was

00:44:35.100 like a

00:44:35.900 junior

00:44:36.180 engineering

00:44:36.700 set

00:44:37.100 and it

00:44:37.900 was certainly

00:44:38.280 the case

00:44:38.700 that working

00:44:39.160 with Legos

00:44:39.880 was nonverbal

00:44:40.860 because you're

00:44:42.180 rotating shapes

00:44:43.620 in space

00:44:44.320 and having

00:44:45.060 them fit

00:44:45.460 into one

00:44:45.860 another

00:44:46.180 towards some

00:44:47.400 design end

00:44:48.360 and that's

00:44:49.100 a nice

00:44:49.340 kind of

00:44:49.700 hands-on

00:44:50.320 learning

00:44:52.980 and exposure

00:44:54.280 to different

00:44:54.940 mechanical

00:44:55.400 principles

00:44:55.920 and so

00:44:56.500 do you have

00:44:57.840 recommendations

00:44:58.520 for people

00:44:59.600 who want

00:45:00.500 to help

00:45:01.000 their children

00:45:01.720 train

00:45:03.020 their

00:45:04.120 visual

00:45:05.000 spatial

00:45:05.520 and object

00:45:07.160 visualization

00:45:07.960 abilities

00:45:08.660 let's get

00:45:09.140 them out

00:45:09.440 building

00:45:09.800 things

00:45:10.200 the big

00:45:10.820 mistake

00:45:11.180 I see

00:45:11.500 with a lot

00:45:11.900 of kids

00:45:12.260 is they're

00:45:12.560 super good

00:45:12.980 with Legos

00:45:13.560 they don't

00:45:14.120 think to

00:45:14.400 introduce

00:45:14.780 tools

00:45:15.260 I was

00:45:16.140 using

00:45:16.380 tools

00:45:16.740 by second

00:45:17.260 grade

00:45:17.460 I was

00:45:17.720 not using

00:45:18.120 a saw

00:45:18.600 but I was

00:45:19.320 using hammer

00:45:19.860 screwdriver

00:45:20.340 and pliers

00:45:20.920 I was taught

00:45:22.100 how to use

00:45:22.540 them safely

00:45:23.180 I've got

00:45:23.900 another book

00:45:24.460 of children's

00:45:25.160 projects

00:45:25.660 called

00:45:25.960 Calling All

00:45:26.600 Minds

00:45:27.280 where I

00:45:28.060 describe

00:45:28.460 bird kites

00:45:29.280 that I

00:45:29.800 spent hours

00:45:30.520 with tinkering

00:45:31.300 to get

00:45:31.720 them to

00:45:32.040 work

00:45:32.420 tinkering

00:45:34.760 with parachutes

00:45:35.640 to get

00:45:36.000 them to

00:45:36.300 open up

00:45:36.740 more easily

00:45:37.460 kids

00:45:38.780 aren't

00:45:39.180 kids today

00:45:41.400 are totally

00:45:41.900 separated

00:45:42.420 in the world

00:45:43.300 of physical

00:45:44.040 things

00:45:44.920 they're not

00:45:46.620 getting out

00:45:47.100 and observing

00:45:47.540 stuff out

00:45:48.120 in nature

00:45:48.640 this is part

00:45:52.160 of the problem

00:45:52.820 and I just

00:45:55.380 went to a

00:45:55.840 veterinary

00:45:56.240 school

00:45:56.740 where

00:45:57.640 the students

00:45:58.880 are so far

00:45:59.480 removed from

00:45:59.900 practical things

00:46:00.760 that to give

00:46:01.520 them dexterity

00:46:02.420 skills

00:46:02.920 and surgical

00:46:03.920 skills

00:46:04.420 they have

00:46:04.900 these plastic

00:46:05.480 tote boxes

00:46:06.300 and they put

00:46:07.120 children's puzzles

00:46:07.980 inside them

00:46:08.760 and they've got

00:46:09.420 to just reach

00:46:09.960 in and by

00:46:10.640 feel

00:46:11.080 put these

00:46:11.700 children's

00:46:12.120 puzzles

00:46:12.380 together

00:46:12.860 because when

00:46:14.480 they were

00:46:14.800 in kindergarten

00:46:15.500 they never

00:46:15.980 did this

00:46:16.620 right

00:46:17.900 right

00:46:18.300 right

00:46:18.720 yeah well

00:46:19.360 my parents

00:46:20.280 told me

00:46:20.780 that when

00:46:21.100 I was four

00:46:21.780 my favorite

00:46:22.520 toy was a

00:46:23.400 screwdriver

00:46:23.780 and that I

00:46:24.620 used to take

00:46:25.280 all the cupboard

00:46:26.020 doors off

00:46:26.740 the cupboards

00:46:27.240 in the kitchens

00:46:27.860 and so I was

00:46:29.260 introduced to

00:46:29.940 tools at a

00:46:30.520 very early age

00:46:31.420 and that there's

00:46:32.580 a real practical

00:46:33.480 utility in that

00:46:34.420 too because now

00:46:35.180 if I visualize

00:46:36.080 a project in my

00:46:37.240 house shelving

00:46:38.160 or something like

00:46:38.840 that or any

00:46:39.440 construction

00:46:41.540 project of any

00:46:42.520 sort

00:46:42.800 well I can

00:46:43.520 visualize the

00:46:44.200 array of tools

00:46:44.900 that's necessary

00:46:45.700 to make that

00:46:47.340 come about

00:46:48.280 and then I

00:46:49.280 have the

00:46:49.740 tools at

00:46:50.260 hand and I

00:46:50.760 know how

00:46:51.040 to use

00:46:51.440 them

00:46:51.640 it's and

00:46:52.500 and that

00:46:53.600 is a

00:46:54.060 well

00:46:54.560 it's also

00:46:56.460 I really find

00:46:57.380 that kind of

00:46:57.820 work calming

00:46:58.760 and engrossing

00:47:00.960 well the problem

00:47:02.960 is we got kids

00:47:03.860 growing up today

00:47:04.640 totally removed

00:47:05.800 from the world

00:47:06.320 of the practical

00:47:06.980 they don't use

00:47:08.340 tools

00:47:09.020 they don't use

00:47:10.060 scissors

00:47:10.580 I had a student

00:47:12.420 in a class

00:47:12.940 who had never

00:47:13.340 used a ruler

00:47:13.940 or tape measure

00:47:14.620 to measure

00:47:15.020 anything

00:47:15.820 ever

00:47:16.500 and

00:47:18.280 I think

00:47:18.700 that's a

00:47:19.160 problem

00:47:19.540 because

00:47:21.360 if you

00:47:21.920 haven't done

00:47:22.460 practical things

00:47:23.400 then you

00:47:24.560 don't understand

00:47:25.320 how to fix

00:47:25.860 things

00:47:26.140 okay like 0.93

00:47:26.960 two years

00:47:27.340 ago they

00:47:27.640 had the

00:47:27.820 horrible mess

00:47:28.420 with a

00:47:28.700 power you

00:47:29.260 know like

00:47:29.460 a bunch

00:47:29.740 of different

00:47:30.140 power stations

00:47:30.980 froze

00:47:31.380 well I

00:47:32.840 never heard

00:47:33.280 so much

00:47:33.600 abstract

00:47:34.220 gobbledygook

00:47:34.940 about that

00:47:35.620 because the

00:47:36.740 way I would

00:47:37.420 work on

00:47:38.060 fixing it

00:47:38.620 would be

00:47:38.940 all right

00:47:39.140 let's look

00:47:39.640 at each

00:47:39.940 power station

00:47:40.640 what piece

00:47:41.800 of equipment

00:47:42.340 actually froze

00:47:43.780 I never saw

00:47:45.640 anything written

00:47:46.320 in the press

00:47:46.780 that described

00:47:47.220 okay what

00:47:47.680 frozen

00:47:48.020 this plant

00:47:48.560 what frozen

00:47:49.260 this plant

00:47:49.860 because my

00:47:51.040 inclination would

00:47:51.820 be to

00:47:52.200 kind of rank

00:47:53.220 them on

00:47:53.700 okay a

00:47:54.460 turbine hall

00:47:55.180 that froze

00:47:55.620 I can build

00:47:56.000 a building

00:47:56.380 over it

00:47:56.740 that's an

00:47:57.040 easy one

00:47:57.640 you know a

00:47:58.500 whole bunch

00:47:58.880 of gas wells

00:47:59.580 froze that's

00:48:00.420 going to be

00:48:00.680 like really

00:48:01.120 difficult to

00:48:01.760 fix

00:48:02.060 but you see

00:48:03.720 nothing's

00:48:04.360 abstract

00:48:04.980 because okay

00:48:06.500 if I'm going

00:48:06.820 to try to

00:48:07.160 figure out

00:48:07.600 how to fix

00:48:08.200 it

00:48:08.420 I don't

00:48:10.220 really want

00:48:10.520 to argue

00:48:10.820 who owns

00:48:11.340 them I'm

00:48:11.700 not interested

00:48:12.100 in the

00:48:12.320 politics

00:48:12.660 I would

00:48:13.100 just rank

00:48:13.660 the power

00:48:14.340 stations

00:48:14.780 on

00:48:15.340 expensiveness

00:48:16.040 and difficulty

00:48:16.800 to winterize

00:48:17.600 and say

00:48:18.920 exactly what

00:48:20.000 piece of equipment

00:48:21.100 froze and you

00:48:22.100 know how I can

00:48:22.560 find out

00:48:23.060 you let me

00:48:24.060 loosen there

00:48:24.640 away from the

00:48:25.400 managers

00:48:25.720 I'll find the

00:48:26.420 maintenance shop

00:48:27.100 they'll show me

00:48:27.920 everything

00:48:28.220 and I know

00:48:28.920 enough about

00:48:29.340 equipment that

00:48:29.980 they can't

00:48:31.140 BS me

00:48:31.720 right so

00:48:33.820 maybe part of

00:48:34.940 the advantage

00:48:35.480 to that

00:48:36.100 more visual

00:48:37.500 form of

00:48:38.220 processing

00:48:38.720 is also

00:48:39.540 its association

00:48:40.300 with that

00:48:40.980 kind of

00:48:41.400 practical

00:48:41.940 particularization

00:48:43.200 that's right

00:48:44.120 now on the

00:48:45.360 other hand

00:48:47.040 I have no

00:48:47.600 idea how to

00:48:48.100 balance a

00:48:48.520 power grid

00:48:48.980 that's a job

00:48:49.880 for the

00:48:50.140 mathematicians

00:48:50.900 you see

00:48:51.740 this is where

00:48:52.280 we need to

00:48:52.880 work together

00:48:53.760 and in a

00:48:56.160 complementary

00:48:56.840 factor

00:48:57.460 in a

00:48:58.440 complementary

00:48:58.820 way

00:48:59.400 and I tell

00:49:00.460 people in

00:49:00.900 big corporations

00:49:01.620 and I've

00:49:01.940 done a lot

00:49:02.260 of talks

00:49:02.580 for businesses

00:49:03.080 your first

00:49:04.000 step is

00:49:05.340 that you've

00:49:05.860 got to

00:49:06.580 recognize

00:49:07.740 different kinds

00:49:08.420 of thinking

00:49:08.900 exists

00:49:09.820 and let's

00:49:12.280 take another

00:49:13.320 example

00:49:13.800 recently I

00:49:14.440 visited two

00:49:15.020 really nice

00:49:15.680 dairies up

00:49:16.240 in Quebec

00:49:16.600 Canada

00:49:17.100 that have

00:49:17.760 the robotic

00:49:18.300 milking machines

00:49:19.460 where the

00:49:20.220 cow can go

00:49:20.760 in and she

00:49:21.140 decides when

00:49:21.840 she wants 0.85

00:49:22.320 to get milk

00:49:22.880 and get fed

00:49:23.580 and both

00:49:24.900 dairies had

00:49:25.520 actually made

00:49:26.080 some good

00:49:26.500 mechanical

00:49:27.100 modifications

00:49:28.460 on that

00:49:29.140 equipment

00:49:29.600 which the

00:49:30.580 equipment

00:49:30.860 company

00:49:31.320 finally

00:49:31.920 adopted

00:49:32.460 but one

00:49:33.320 of the

00:49:33.540 dairy

00:49:33.780 producers

00:49:34.320 said to

00:49:34.760 me

00:49:34.900 I stop

00:49:35.560 at the

00:49:35.780 computer

00:49:36.060 stuff

00:49:36.560 I don't

00:49:37.260 mess with

00:49:37.600 the program

00:49:38.060 that's

00:49:39.360 somebody else's

00:49:40.520 job to

00:49:41.320 work on the

00:49:41.820 software

00:49:42.240 but the

00:49:43.140 mechanical

00:49:43.620 parts of

00:49:44.220 the device

00:49:44.760 they figured

00:49:45.820 out ways

00:49:46.220 to improve

00:49:46.780 it

00:49:47.080 yeah

00:49:49.100 it's like

00:49:50.180 a it's

00:49:50.780 like a

00:49:51.160 hierarchy

00:49:51.700 man you

00:49:52.140 can imagine

00:49:52.560 a hierarchy

00:49:53.240 of generalization

00:49:54.400 with the

00:49:55.780 highest resolution

00:49:57.160 lowest level

00:49:58.040 being the

00:49:58.540 particulars

00:49:59.040 of a given

00:49:59.540 machine

00:50:00.080 and you

00:50:00.840 can imagine

00:50:01.360 people

00:50:02.000 might specialize

00:50:03.940 to operate

00:50:04.640 at different

00:50:05.100 levels of the

00:50:05.680 hierarchy

00:50:06.180 maybe the

00:50:07.080 verbal types

00:50:07.700 are operating

00:50:08.280 at the

00:50:08.600 highest level

00:50:09.620 of abstraction

00:50:10.080 but they

00:50:11.300 lose that

00:50:11.820 particularization

00:50:12.620 that's right

00:50:13.260 that's basically

00:50:14.020 right

00:50:14.440 because the

00:50:14.780 verbalizer tends

00:50:15.720 to overgeneralize

00:50:16.560 one of the

00:50:17.380 papers that I

00:50:18.020 reviewed in my

00:50:19.020 book on visual

00:50:19.660 thinking was an

00:50:20.900 interesting study

00:50:21.920 where high school

00:50:24.920 students that came

00:50:25.900 like for school

00:50:26.660 specializing in the

00:50:27.600 arts another

00:50:28.600 school specializing

00:50:29.660 in the sciences

00:50:30.460 and another one

00:50:31.480 specializing in the

00:50:32.120 humanities which

00:50:32.940 would be more

00:50:33.420 verbal on those

00:50:35.300 teams of students

00:50:36.140 and their

00:50:36.580 assignment was to

00:50:37.740 create a new

00:50:38.980 planet so the

00:50:41.060 art students

00:50:41.640 heavily visualizing

00:50:42.740 they made a

00:50:43.900 planet with

00:50:44.600 crystals on it

00:50:45.860 another one made

00:50:46.680 a skyscraper

00:50:47.560 planet and then

00:50:49.000 the more

00:50:49.300 mathematical

00:50:49.780 science students

00:50:50.800 just painted a

00:50:51.700 round made a

00:50:52.720 round ball and

00:50:54.360 described the

00:50:55.060 gravity and the

00:50:56.180 atmosphere you

00:50:57.760 know kind of

00:50:58.160 boring pictures

00:50:59.000 and the verbal

00:50:59.900 thinkers started

00:51:00.600 write it down

00:51:01.380 and then they go

00:51:02.420 oh wait a minute

00:51:02.960 we're supposed to

00:51:03.520 draw the planet

00:51:04.380 so they made

00:51:05.260 kind of splotchy

00:51:06.200 stuff on it

00:51:07.220 but the thing

00:51:08.200 that was interesting

00:51:09.000 is that the

00:51:10.180 arts minds

00:51:11.560 more my kind of

00:51:12.420 mind and the

00:51:13.400 mathematical minds

00:51:14.440 they had big

00:51:15.780 planning sessions

00:51:16.840 on how to

00:51:18.340 design their

00:51:18.980 planet where the

00:51:20.340 verbal thinkers

00:51:21.040 they didn't do

00:51:22.000 any planning

00:51:22.660 you see this is

00:51:23.760 the problem

00:51:24.220 the verbal

00:51:24.860 thinker would

00:51:25.240 get big broad

00:51:26.120 concepts of

00:51:26.900 something we

00:51:27.280 need to do

00:51:27.840 right but how

00:51:29.080 do you actually

00:51:29.920 implement those

00:51:31.500 concepts yeah

00:51:32.980 right there's no

00:51:33.760 detail there now

00:51:35.320 in food safety we

00:51:36.680 have a a thing

00:51:38.060 that I really like

00:51:38.760 it's called

00:51:39.060 hazard analysis

00:51:39.920 critical control

00:51:40.660 points so let's

00:51:42.120 say I'm out there

00:51:43.180 in a sea of

00:51:44.360 things I can do

00:51:45.920 well I can't do

00:51:47.260 all that stuff

00:51:47.920 see now the

00:51:49.000 critical control

00:51:49.760 point let's say

00:51:51.000 in food safety

00:51:51.740 in a food factory

00:51:53.460 I can't measure

00:51:54.840 microbes and

00:51:55.700 bacteria on

00:51:56.560 everything in

00:51:57.340 that plant I

00:51:59.020 have to pick

00:51:59.720 out the places

00:52:00.720 where I'm most

00:52:01.660 likely to have

00:52:02.760 contamination

00:52:03.480 that would be

00:52:05.520 the critical

00:52:06.340 control points

00:52:07.320 and one of

00:52:08.800 them is

00:52:09.200 doorknobs

00:52:10.140 yeah that's

00:52:11.960 why some

00:52:12.360 places food

00:52:13.440 factories have

00:52:14.000 automatic doors

00:52:14.920 because that gets

00:52:16.020 rid of touching

00:52:16.800 the door

00:52:17.820 yeah well one

00:52:20.300 of one of the

00:52:20.940 things that I

00:52:21.520 always found a

00:52:22.900 relief in

00:52:23.960 working with

00:52:24.800 engineers is

00:52:26.260 that they were

00:52:26.820 good and I

00:52:27.920 can see it in

00:52:28.720 the terms that

00:52:29.340 you're describing

00:52:29.920 they were very

00:52:30.540 good at rank

00:52:32.120 ordering practical

00:52:33.280 priorities right

00:52:34.880 and that that

00:52:35.520 seems to be part

00:52:36.280 of this

00:52:36.660 particularization

00:52:37.960 so I have this

00:52:39.160 company it's a

00:52:40.300 software company

00:52:41.140 that sells

00:52:42.600 personality tests

00:52:44.100 and and writing

00:52:46.400 programs for people

00:52:47.440 to help them plan

00:52:48.200 their lives and

00:52:48.960 my business

00:52:49.800 partner is an

00:52:50.600 engineer and

00:52:52.100 although he's also

00:52:53.060 very intelligent

00:52:53.720 verbally he's more

00:52:54.580 intelligent non-verbally

00:52:55.720 and he knows the

00:52:57.400 systems right from

00:52:58.520 the from the code

00:53:00.100 upward at every

00:53:01.940 level of the

00:53:02.520 machine instantiation

00:53:03.780 and so the huge

00:53:05.080 advantage to that is

00:53:06.080 that if anything ever

00:53:07.380 goes wrong he knows

00:53:08.820 exactly what goes

00:53:09.840 wrong and he knows

00:53:10.480 exactly how to fix

00:53:11.640 it it's particularized

00:53:13.040 down to the point

00:53:13.860 where that makes

00:53:14.800 action possible

00:53:16.120 way and that's

00:53:17.080 part of the problem

00:53:17.780 with the with the

00:53:18.900 verbal abstraction is

00:53:20.220 that it makes sense

00:53:21.660 conceptually but that

00:53:22.740 doesn't mean that it's

00:53:24.060 detailed to the point

00:53:24.980 where it's actually

00:53:25.640 implementable and then

00:53:27.000 it's like a pseudo

00:53:28.080 knowledge right because

00:53:29.080 it sounds like you've

00:53:31.140 got the picture right

00:53:32.380 but when you actually

00:53:34.820 try to implement it

00:53:35.700 you find out that

00:53:36.520 that it's really a

00:53:38.000 hollow a hollow shell

00:53:39.440 of conceptualization

00:53:40.420 well I kind of look at

00:53:41.540 like this is why when

00:53:43.780 I learned about the

00:53:44.600 food safety concept

00:53:45.680 of hazard analysis

00:53:46.800 critical control

00:53:47.600 points or let's just

00:53:48.380 call it critical

00:53:49.280 control points so

00:53:50.640 I've got a big sea

00:53:52.300 of stuff out there

00:53:53.260 what's the really

00:53:54.620 important thing okay

00:53:55.880 now I only think

00:53:57.420 in specific examples

00:53:58.460 so let's take frozen

00:53:59.760 Texas power stations

00:54:02.300 the critical control

00:54:04.060 point is and I want

00:54:05.260 it like in two

00:54:05.940 sentences what piece

00:54:08.180 of equipment froze

00:54:09.520 and then you can

00:54:12.260 very easily figure

00:54:13.880 out the ones that

00:54:14.480 will be easy to

00:54:15.040 winterize and I've

00:54:16.900 actually talked to

00:54:17.580 somebody who installs

00:54:18.740 gas wells and it

00:54:20.660 only costs $5,000

00:54:21.820 to winterize a well

00:54:22.880 when you build it

00:54:23.680 to retrofit it's a

00:54:24.940 complete mess he

00:54:26.520 also said you have

00:54:28.180 to have someone who

00:54:28.860 knows how to turn

00:54:29.520 the valves he said

00:54:30.600 that's me I call him

00:54:32.660 the guy in the pickup

00:54:33.420 and that person in

00:54:35.740 that pickup truck is

00:54:37.180 not getting enough

00:54:38.080 credit he knows how

00:54:39.460 to turn those valves

00:54:40.480 right

00:54:40.800 right

00:54:42.260 yeah so if you

00:54:43.360 conceptualize that

00:54:44.360 verbally you end up

00:54:45.300 saying something like

00:54:46.280 a bad winter storm

00:54:48.480 took out the Texas

00:54:49.620 power grid

00:54:50.600 and that sort of

00:54:52.200 sounds like you've

00:54:53.000 said something but

00:54:53.800 it's nothing like

00:54:54.740 saying well the grid

00:54:56.400 is made out of 200

00:54:57.680 different like

00:54:59.700 industrial assemblies

00:55:00.900 each of those is made

00:55:02.840 of a variety of parts

00:55:03.900 that's differentially

00:55:04.840 susceptible to winter

00:55:06.060 all right well that's

00:55:07.240 still gobbledygook

00:55:08.240 because first of all

00:55:09.420 they wouldn't have 200

00:55:10.340 separate power stations

00:55:11.740 you know it's more

00:55:13.420 like 10

00:55:14.260 something like that

00:55:16.060 and

00:55:17.000 and

00:55:18.020 I would just

00:55:20.100 I'd list

00:55:20.940 well write down the

00:55:22.100 name of the station

00:55:22.940 because I don't really

00:55:23.820 care who owns it

00:55:24.600 and what we used to do

00:55:25.860 with meat packing plants

00:55:26.800 when we were doing

00:55:27.320 welfare audits

00:55:28.040 is we'd always call them

00:55:29.360 by their town names

00:55:30.600 because you're auditing

00:55:32.180 that particular plant

00:55:33.320 and I'd say well this

00:55:34.000 power station had a

00:55:35.800 frozen turbine haul

00:55:36.860 that's an easy one to

00:55:37.920 fix on the cold

00:55:40.300 fire that froze

00:55:41.320 well they freeze up

00:55:42.660 the where you

00:55:43.340 how the cold bumps

00:55:44.700 in that's probably

00:55:46.320 fairly easy to fix

00:55:47.560 and if I'm you know

00:55:49.940 50 gas wells froze

00:55:51.960 up the feet of plant

00:55:53.100 that's going to be

00:55:53.560 a mess to fix

00:55:54.440 on but you see

00:55:56.880 as I talk about

00:55:57.820 these things I see

00:55:59.300 it and I happen to

00:56:00.400 know what some of

00:56:00.920 the equipment looks

00:56:01.700 like you see that

00:56:03.020 also makes me a

00:56:03.880 better troubleshooter

00:56:04.740 you know if I know

00:56:06.060 what stuff looks like

00:56:07.160 you see this is where

00:56:08.200 you've got to put

00:56:09.720 things in my database

00:56:10.700 but I but I worked

00:56:13.840 25 years out in

00:56:15.180 factories on heavy

00:56:16.060 construction stuff

00:56:16.940 so I know what a lot

00:56:18.580 of stuff looks like

00:56:19.600 right so so it means

00:56:22.060 in some sense that to

00:56:23.240 solve the problem is

00:56:24.300 you take the verbal

00:56:25.200 representation so the

00:56:27.420 verbal representation is

00:56:28.620 a storm took out the

00:56:30.040 Texas power grid and

00:56:31.280 then you say well

00:56:32.020 there's 10 key

00:56:32.940 components to the

00:56:33.840 Texas power grid so

00:56:35.100 that differentiates that

00:56:36.300 and then you say in

00:56:38.080 each of the 10

00:56:39.580 systems there are

00:56:41.820 critically vulnerable

00:56:43.480 points that are

00:56:44.500 specifically sensitive

00:56:45.580 to cold weather we

00:56:46.940 need to differentiate

00:56:47.720 and find out what

00:56:48.620 those key ones are

00:56:49.600 then we have to

00:56:50.360 differentiate that

00:56:51.180 further you say now

00:56:52.620 okay now when I

00:56:53.500 first started my work

00:56:54.340 there was no Google

00:56:55.040 Earth well the first

00:56:56.700 thing I'd do probably

00:56:57.820 Google Earth and

00:56:58.620 street view the power

00:56:59.680 stations and the

00:57:02.080 other thing I've

00:57:02.800 learned if I want to

00:57:04.080 get accurate information

00:57:05.480 I don't you don't

00:57:06.200 talk to the managers

00:57:07.140 you got to get down

00:57:08.420 in the shop and and

00:57:10.940 then the shop guys

00:57:11.980 have to be not worried

00:57:13.000 about getting fired

00:57:13.880 that's another right

00:57:15.800 right nasty issue that

00:57:17.340 I'm going to have to

00:57:18.000 deal with but you know

00:57:20.560 let's say I get three

00:57:21.280 shop guys together and

00:57:22.780 we've okay my industry

00:57:24.580 say when we get rid of

00:57:25.440 all the suits you know

00:57:27.280 that's going to be the

00:57:27.900 managers and the verbal

00:57:28.760 thinkers they won't talk

00:57:29.960 in front of suits they're

00:57:31.660 afraid they're going to

00:57:32.220 lose their jobs you know

00:57:34.180 and this is something I

00:57:35.020 know from all the

00:57:36.500 years I've worked in

00:57:37.300 this stuff and then

00:57:38.460 they'll take you out

00:57:39.140 there and show you what

00:57:40.100 froze and then oh

00:57:42.280 there's creative things

00:57:43.320 I talked to a guy who

00:57:44.240 worked at a power plant

00:57:45.020 and there was this one

00:57:46.540 sensor very important

00:57:48.260 electronic sensor and

00:57:49.780 it'd get cold and when

00:57:51.400 it got cold he put

00:57:52.160 plastic garbage bags

00:57:53.420 over it and I'm going

00:57:55.980 right well I think we

00:57:57.400 need to have something a

00:57:58.940 little more permanent to

00:58:00.080 keep that sensor warm

00:58:01.120 than black plastic

00:58:02.480 garbage bags and that's

00:58:04.000 what I told them

00:58:04.860 yeah so so that that

00:58:07.900 issue that the the guys

00:58:09.480 with the hands-on

00:58:10.700 knowledge have to worry

00:58:12.100 about being fired is

00:58:13.300 interesting too because

00:58:14.380 it seems like so they

00:58:16.320 have this extremely

00:58:17.240 detailed knowledge that's

00:58:19.060 practical about how

00:58:20.060 these systems operate and

00:58:21.860 what happens is if they

00:58:22.940 bring that knowledge up

00:58:24.180 the abstraction hierarchy

00:58:25.900 what they're doing in some

00:58:27.580 sense is is pointing out

00:58:29.320 the manner in which

00:58:30.640 their superiors their

00:58:31.980 hypothetically well that's

00:58:33.160 the problem a very big

00:58:35.760 problem I've had is I

00:58:39.420 they're they're worried

00:58:41.360 about losing their jobs

00:58:43.080 yeah and and but you

00:58:46.000 really want to solve the

00:58:47.120 problem they need to be

00:58:48.980 able to talk to you

00:58:50.520 freely because they're the

00:58:53.100 ones that tell you exactly

00:58:54.360 what froze right well the

00:58:57.380 question is why would

00:58:58.860 their attempts to bring

00:59:00.360 their practical reality to

00:59:02.440 bear as they move

00:59:03.920 information up the

00:59:05.100 information hierarchy why

00:59:07.120 would that threaten people

00:59:09.080 to the point where they

00:59:10.940 would be intimidated in

00:59:12.240 relationship to their job

00:59:13.600 I mean why is there this

00:59:15.740 gap the psychological gap

00:59:17.560 between the practical and

00:59:18.680 the abstract and you'd

00:59:20.260 think that the managers

00:59:21.160 would be calling on these

00:59:22.320 people all the time you

00:59:23.780 got to drag those suits out

00:59:25.200 of the office to just when

00:59:27.120 things are working

00:59:28.180 normally and I'm so they

00:59:31.860 get a better understanding

00:59:33.640 of what the practical

00:59:34.680 people are doing now my

00:59:36.520 animal welfare work on one

00:59:39.420 of the things where I made

00:59:40.180 some of the biggest

00:59:40.740 difference in animal

00:59:41.460 welfare work is auditing

00:59:42.740 programs I helped develop

00:59:43.960 with McDonald's Wendy's and

00:59:45.460 Burger King back over 20 0.99

00:59:47.520 years ago and in this

00:59:50.660 situation we were taking

00:59:51.700 vice president level

00:59:53.080 managers out to the

00:59:54.840 plant and they'd have

00:59:56.880 these undercover boss

00:59:58.140 moments just like that

00:59:59.220 show I'll never forget

01:00:00.920 the day when the

01:00:01.540 McDonald's vice president

01:00:02.880 saw a half dead dairy cow

01:00:04.760 go into their product and

01:00:06.480 he goes yeah we got some

01:00:08.420 things here we have to

01:00:09.500 fix you see before it was

01:00:11.840 all an abstraction

01:00:12.700 spreadsheets numbers okay

01:00:15.320 animal welfare give it to

01:00:16.880 lawyers give it to the

01:00:17.980 public relations department

01:00:19.040 but when they came out of

01:00:21.160 the office and they saw

01:00:22.220 something bad now it was

01:00:23.780 real now they had to act

01:00:25.800 it was no longer abstract

01:00:28.040 anymore there seems to be

01:00:29.960 a kind of pride in that

01:00:31.440 abstraction in some sense I

01:00:33.100 mean I've also noticed that

01:00:34.680 highly intellectual people

01:00:36.080 and maybe those are ones

01:00:37.080 who think primarily in words

01:00:38.440 tend to be rather dismissive

01:00:40.580 of the intelligence of more

01:00:42.160 practical people like working

01:00:43.620 people and that's I come

01:00:45.380 from a working class

01:00:46.280 environment so although I've

01:00:48.860 hung around verbal people

01:00:49.880 most of my life so I can

01:00:51.040 see that dichotomy do you

01:00:52.700 have any sense of why it

01:00:54.060 is that the more abstract

01:00:56.820 thinkers are have a

01:00:59.080 contemplative of the

01:01:00.920 practical because they used

01:01:02.020 to say okay the kids that

01:01:03.180 are like failing in high 1.00

01:01:04.220 school stupid kids go and 1.00

01:01:05.480 shop well I can tell you 1.00

01:01:07.440 right now I work big metal

01:01:10.220 fabrication companies owned 1.00

01:01:11.800 by these so-called stupid 1.00

01:01:12.920 people and what they they 1.00

01:01:14.580 were inventing equipment they

01:01:15.760 had 20 patents and they

01:01:16.740 were very proud of their

01:01:17.480 patents and one of them

01:01:18.560 made posters out of them

01:01:19.660 put them up all over his

01:01:20.880 place it's a different kind

01:01:24.380 of intelligence and and it's

01:01:28.620 not you know there's things

01:01:31.200 they can do that the verbal

01:01:32.180 thinkers can't do and what I'm

01:01:34.600 what I'm really interested in

01:01:35.700 is we need all the different

01:01:37.160 kinds of minds because one of

01:01:39.360 the problems with my kind of

01:01:40.560 thinker since I'm

01:01:41.580 associative is the point in a

01:01:43.660 company let's say I had a

01:01:45.500 metal fabrication shop I'd have

01:01:46.720 to hire a verbal thinker just

01:01:47.860 to run the business the

01:01:49.300 payroll ordering materials

01:01:51.060 things all of that part of

01:01:52.960 it do you think it's a

01:01:54.720 competition do you think it's

01:01:56.540 a competition for status

01:01:57.960 between different forms of

01:02:00.000 neurodiversity I mean if I can

01:02:02.380 claim that my intelligence is

01:02:03.860 paramount that that increases my

01:02:06.140 social status in some sense

01:02:07.920 right you can imagine there's a

01:02:09.540 competition for that broadly

01:02:11.040 going on in society what I have

01:02:12.720 found is when you got the

01:02:14.080 verbal thinkers out in the

01:02:15.780 field their eyes got opened

01:02:19.080 that the important thing is

01:02:22.660 you got to drag the suits out

01:02:25.100 of the office I've done a whole

01:02:27.040 lot of that and let me tell you

01:02:28.500 they changed okay I've done a lot

01:02:32.020 of work on supply chain

01:02:33.040 management and when there's

01:02:35.120 supply chain disasters like a

01:02:37.080 factory burning up and a

01:02:38.620 clothing industry is disgusting

01:02:39.840 industry they need to clean up a

01:02:41.200 lot of stuff on a factory burns

01:02:44.040 up a hundred people selling

01:02:45.640 a blue jeans well the suits

01:02:48.680 didn't get out of the office and

01:02:51.440 see what was going on in these

01:02:53.600 factories until there was a

01:02:56.760 disaster the one they should

01:02:57.840 have been preventing a disaster

01:02:59.180 like that right well yeah so

01:03:01.780 once you build up hierarchical

01:03:03.160 organizations it's very easy for

01:03:04.980 the people who are operating near

01:03:07.200 the top of the abstraction chain

01:03:08.900 not to pay attention to the

01:03:10.460 details and hopefully the

01:03:12.200 details are working out so well

01:03:13.660 that you don't have to pay

01:03:14.560 attention to them I mean that's

01:03:15.880 in some sense the point of

01:03:17.160 building a hierarchical

01:03:18.140 organization but but what you

01:03:19.840 need to do obviously the

01:03:22.320 manager can't be you know doing

01:03:24.180 every job out there in a plant

01:03:25.520 but they need to have enough

01:03:26.960 contact with the field and I'm

01:03:29.120 going to use the field be like

01:03:30.400 the factory floor farming you know

01:03:33.020 waterworks any of these kind of

01:03:35.260 things so they they understand

01:03:39.260 the different there can be things

01:03:41.400 bad going on but I tell business

01:03:43.700 managers and I've talked to many

01:03:45.260 big corporations computer companies

01:03:47.020 steel companies on pharmaceutical

01:03:50.100 companies that the first step is

01:03:52.780 realizing that different kinds of

01:03:55.180 thinking exist and that they can

01:03:57.100 work together in complementary ways

01:04:00.720 and you actually you need the whole

01:04:03.780 team you need the whole team

01:04:05.460 because we're kind of disorganized

01:04:08.180 and associational I'm going to need

01:04:10.960 a verbal thinker if I got a really

01:04:12.600 big business to you know keep the

01:04:16.160 business going

01:04:16.900 so what do you think what do you

01:04:20.820 think of the suppositions and you

01:04:22.660 talk about them a bit in your book

01:04:24.240 that the visual versus verbal modes of

01:04:29.340 cognition map to some degree onto

01:04:31.380 hemispheric specialization well that

01:04:33.860 the right is more oriented towards

01:04:35.540 your kind the right hemisphere is more

01:04:37.160 oriented broadly speaking toward

01:04:39.320 broadly speaking broadly dominates your

01:04:41.460 mind yeah but broadly speaking that's

01:04:43.960 true you know there except there

01:04:46.140 exceptions to some of that that stuff

01:04:48.340 but I'm the thing that I want to talk

01:04:50.820 about is is right now I'm seeing too

01:04:54.120 many kids that are dyslexic autistic

01:04:56.400 um or whatever I I'm ending up playing

01:05:01.120 video games on on a disability check

01:05:03.720 when they could be photographers I got

01:05:06.420 four person crew here right now doing

01:05:09.860 this um filming and that's an interesting

01:05:13.200 career or they could be designing

01:05:15.160 mechanical equipment and they never get

01:05:18.500 an opportunity to do photography art or

01:05:20.680 mechanical equipment because it kind of 1.00

01:05:23.340 look at these stupid kids right right 1.00

01:05:27.440 okay so there's a privileging of the 1.00

01:05:30.020 semantic and that's partly because it's

01:05:32.160 cheaper it's easier it's detached from

01:05:34.720 the world in some sense how do you think

01:05:37.200 we should redesign schools for say

01:05:40.440 kindergarten kids and elementary kids in

01:05:42.360 some practical way you tend to think

01:05:43.940 practically what do you what do you

01:05:45.740 think would be good start well like in

01:05:49.000 the 50s when I went to school we had all

01:05:51.660 kinds of craft projects I was learning

01:05:53.420 to use little blunt scissors probably in

01:05:55.480 first grade I'd be putting all the

01:05:58.040 hands-on classes back into schools and

01:05:59.900 that's going to include theater music

01:06:02.440 cooking sewing when I went to elementary

01:06:06.300 school I loved art sewing and woodworking

01:06:09.240 and if I hadn't had those classes I would

01:06:11.300 have hated elementary school and I loved

01:06:14.120 sewing and I'm when I was in fourth

01:06:16.560 grade I had a singer so handy a toy

01:06:19.160 sewing machine it actually sewed and it's

01:06:21.880 one of my favorite things because I could

01:06:23.580 make things with it kids are not doing

01:06:26.660 enough of that kind of stuff today

01:06:29.140 right okay so there's a return to the

01:06:32.180 there's there's a need for a return to

01:06:33.980 the practical on in some sense on both

01:06:36.660 sides of the gender spectrum on everybody

01:06:38.540 on everybody sewing with and I would now I

01:06:42.220 went when I was doing a book signing for

01:06:44.320 visual thinking I went to a physics lab in

01:06:48.780 Harvard this room's labeled physics lab and

01:06:52.160 they had all kinds of 3d printers in

01:06:54.240 there but they also had a sewing machine and

01:06:56.420 they also had a station for crocheting this

01:06:59.260 is the building labeled engineering

01:07:01.300 department at Harvard maybe they're

01:07:03.540 realizing they've got to get them doing

01:07:05.100 some hands-on things this is when I did the

01:07:07.260 book signing for this it's part of the book

01:07:09.360 tour the other thing I've noticed I stayed

01:07:12.180 I got into some interesting places I stayed

01:07:15.040 at this hotel where where they is in

01:07:18.860 Evansville Illinois where they had

01:07:21.620 textbooks in the rooms from the 1930s I

01:07:26.300 wish I'd had more time to look at it and I

01:07:28.280 pulled out an electrical engineering book

01:07:30.500 and it had a lot of math in it but it was

01:07:33.340 much more applied they'd say oh this is how

01:07:37.760 this is the generator works this is the

01:07:39.520 math that goes with it but it would

01:07:40.900 describe how the generator actually worked

01:07:43.620 it was much more applied and I now the

01:07:49.120 physiology book that I had in the 70s you

01:07:52.080 know explain how the kidney works how the

01:07:53.940 heart works and then explain the chemistry

01:07:55.940 now I look at a physiology book and it's

01:07:58.100 much more verbal a lot more math and

01:07:59.940 chemistry but how does the kidney actually

01:08:02.220 work I still have my old Duke's physiology

01:08:05.400 of domestic animals from 1970 and I want to

01:08:08.640 go back and compare that to the Duke's

01:08:10.200 physiology now and and it's like we're

01:08:14.200 taking the practical out I just got an email

01:08:18.040 yesterday from the UK that they wanted to

01:08:21.740 take a technology and design course out of a

01:08:24.040 high school you see I think this what's

01:08:26.680 happening now is mathematics is totally

01:08:28.080 taking over yes we need mathematics because

01:08:31.620 my kind of mind is not going to touch boilers and

01:08:33.880 refrigeration in that food processing plant

01:08:36.500 that's a job for the mathematicians but what

01:08:39.940 we're losing is the object visualizer right

01:08:44.040 right right yeah well I wonder I wonder too if

01:08:48.280 part of this is the fact that you know for a long

01:08:50.860 time in our society a lot of this practical

01:08:53.560 machinery just worked and so we could afford to

01:08:56.680 ignore it in some abstract sense right because

01:08:59.920 our cars worked and our power grids worked and we

01:09:03.440 could take all of this low-level infrastructure for

01:09:05.920 granted now that meant there were a lot of people on

01:09:08.240 the shop floors who were busily working making sure it

01:09:11.400 worked but it did mean that we had the luxury to engage in

01:09:14.640 abstract specialization and maybe we could fall prey to the

01:09:18.780 psychological tendency to just dismiss all that

01:09:21.520 well you see when they first started about 20 years ago is

01:09:25.920 when they started taking shop classes out of the schools well

01:09:30.880 you can get away with that for a while and then the people I

01:09:34.320 worked with I'm gray now are retiring they're retiring out and

01:09:40.240 they're not getting replaced that's happening with elevator and

01:09:43.520 escalator mechanics that's happening with airplane mechanics and

01:09:48.400 I'm seeing that more and more and more and more as I travel these

01:09:51.360 are free things that I see all the time and they are getting gray

01:09:56.320 right right so yeah the retirement problem is going to be

01:10:02.000 the retirement problem is going to be a big one

01:10:04.320 when it comes to industry there's two gigantic mistakes that were made

01:10:08.160 shutting down in-house engineering shops

01:10:11.840 20 years ago we had a huge metal working shop called the Montfort Fab Shop

01:10:16.960 and it was part of the engineering of a company called Montfort so they no

01:10:21.200 longer exist now well that's been shut down and then at the same time we

01:10:25.920 took out shop classes now in the short run it was cheaper for

01:10:30.800 these companies to just farm out engineering work they need to do in

01:10:34.560 their plant yeah that works fine until the shops retire out and now what's

01:10:40.560 happened like I can't go in the name give you the name of the company but I

01:10:44.040 have a client right now where the one shop that's left is ripping 0.62

01:10:48.280 people off at five times the price and that's happening right now right

01:10:53.000 do you see any positive consequences of computer technology for object

01:11:00.680 visualizers and for the for the people who are working more in the visual

01:11:04.040 spatial end of things well it's definitely useful to you know like the

01:11:08.920 visualization and stuff you can do on computers but that doesn't replace

01:11:14.120 real things let me tell you power grid I lay awake at night about that and

01:11:19.800 that's so fragile that I'm not I'm not going to go into any detail because it's

01:11:23.240 too fragile and I'm not going to discuss the things that I visualize and lay

01:11:28.040 awake at night about the power grid because it's just okay fragile

01:11:34.200 okay I'm curious about that why not discuss them because I don't want to

01:11:39.000 give I don't want to give people that have bad intentions any information on how

01:11:43.080 to hurt the power grid okay okay okay that's the reason I see

01:11:46.280 that is the reason right because it's very easy so when you

01:11:50.760 hurt it and I don't want to give out any information that would help somebody

01:11:54.200 damage the power grid so I don't discuss the details here but I'm seeing them

01:11:58.680 right now right right right yeah well that's part of that ability to think

01:12:04.600 about critical points of failure well there's so much characteristic of

01:12:08.200 engineering mind I know where the critical points of failure are and I'm not

01:12:12.040 going to discuss them right right so we can all be thankful that

01:12:16.840 you're not a terrorist yeah I'll be thankful I'm not a criminal yes exactly

01:12:23.400 exactly yeah well I've always often been afraid when thinking along the same

01:12:28.360 lines that you're describing of just exactly how fragile things are in that

01:12:33.320 regard and how much someone with a good imagination how much damage they could

01:12:37.320 do if they targeted things properly it is really quite frightening to apprehend

01:12:42.040 well that's why there's certain things about the power grid I am not going to

01:12:45.480 discuss and I'm seeing three big fat critical control points right now I'm

01:12:50.600 seeing them in my mind and they make me cringe

01:12:55.560 right so so could we talk a little bit about the specifics of your work I

01:13:00.360 remember one of the stories you told at this Tucson conference was about

01:13:04.520 you talked about cows that they would do such things as go into a field and look at

01:13:09.000 a if you look left a briefcase on the field for example the cows would

01:13:12.280 eventually come and look at it that's right a cows in a in a line might be 1.00

01:13:17.320 stopped by something like a coke bottle in their path and so you used your

01:13:23.240 ability to think like animals to design systems for animal handling that were

01:13:29.560 much more humane so could you walk us through that a little bit

01:13:32.600 well the first work I did with cattle was in the 70s in Arizona I when I

01:13:38.520 didn't even know that I had was a visual thinker and other people were not a

01:13:41.640 visual thinker and I noticed that if there was a coat hung on the fence the

01:13:46.200 cattle would stop on if there was a shadow or reflection off a vehicle so I

01:13:52.760 got down in the chutes to see what they were seeing and I would take pictures

01:13:56.760 down inside the chute and people thought that was kind of crazy but now I'm I like

01:14:04.520 right just recently did start up at a really big plant and at 10 o'clock in the

01:14:08.920 morning everything was working fine and then at 3 o'clock in the afternoon a big

01:14:13.480 shadow appeared I call it the spider monster and it was just a shadow when

01:14:17.960 these cattle decided they weren't going to walk over that so they'll have to build

01:14:21.880 a roof over the facility so that the cattle can't see the spider monster

01:14:27.000 uh the other thing I'm I show I show people how to do is watch your lead animal

01:14:32.280 your lead animal will come up and stop and look at the thing they don't like

01:14:36.040 the same plant on the night shift okay the shadow monster wouldn't be there

01:14:40.520 they either the guy calls me and he goes they go halfway up the chute and stop

01:14:46.120 all right what do I do I said now bring up a nice calm bunch of cattle watch your leader

01:14:51.000 really carefully he'll look right at the thing he doesn't like and there was an LED light on the

01:14:57.160 corner of a building and they took that down and then everything worked fine how do you identify

01:15:04.920 the leader well there's always a leader when when all right let's say I take a 20 head of cattle out

01:15:11.320 of a pan there's a lead animal that walks out first usually one of the more bold animals not the

01:15:18.760 dominant animal the dominant animal that pushes the others away from the feed trough he's in the

01:15:23.320 middle of the herd or she's in the middle of the herd but the leader will come out of the pen first

01:15:27.800 and the other cat will follow and it's just the first animal in the group is a leader it's that

01:15:32.920 simple you see as we're talking about that I'm seeing it okay so so is that leader stable across

01:15:40.600 instances of leadership and is that associated with this ability to find what's anomalous and to deal

01:15:46.440 with well in cattle that have lived together for a while tend to stay in the same order you know

01:15:54.360 years ago when your tags came out that were sequentially numbered in other words you could

01:15:58.440 just buy a package of ear tags labeled one through 200 and they put them on their cattle and one of

01:16:05.480 the things that surprised ranchers when maybe 200 cattle came back the following year to go through

01:16:10.360 the shoots to get vaccinated and they were coming through in almost the same order you know that's

01:16:17.320 a group of cattle that live together okay is there something okay so you made the observation that the

01:16:25.080 lead animal will stop and look at the thing that he doesn't like and so are the other animals and but

01:16:32.040 also that lead animal wasn't the physically dominant animal he's got some other characteristics

01:16:37.160 the animal is dominant at the pushing others away from the water trough or the feed trough

01:16:41.720 but the but once the lead animal walks the other cattle usually will follow same thing with shape

01:16:47.720 the other animals will usually follow right so you remove the distractions that the lead animals reacting

01:16:53.000 to and and at this particular plant we had two things we had to get rid of we had to get rid of this

01:16:58.520 shadow the spider monster shadow and we had to get rid of an led light that was on the corner of a building

01:17:03.880 so do you have any sense on of why the other animals come to rely on the lead animal like what's the

01:17:13.320 lead animal being selected for or is it just a first mover like what's the nature of this lead animal

01:17:18.760 you see there's different levels of fear in animals genetically some animals are more bold other

01:17:24.280 animals are more shy or you can call those high and low fear and the animal that's more bold is more

01:17:31.880 likely to be the leader than the animal that's more anxious um and and uh you know if you know

01:17:41.640 a bunch of cattle that have lived together all the time you know that there'll be certain animals that

01:17:46.200 tend to be the leaders and then you've got the great big one with the giant horns and she shoves 1.00

01:17:50.920 everybody else away from the feed trough there's something that's very profound about that because

01:17:55.640 you're laying out the fact that it isn't the dominant animal that leads no and that it's the animal

01:18:01.000 that's exploratory and that's right somewhat willing to take risks that that's right but also absolutely

01:18:07.320 also that the that the lead animal will spot anomaly right like the spider monster that you're describing

01:18:14.040 and so it's not like they're they're so bold that they're completely without fear they're still acting

01:18:20.600 cautiously in some sense all right so you can walk the lead animal down a chute and you can see what it's

01:18:26.120 going to see and you can actually do that because you go down there and do it but you also think that

01:18:30.760 way you gotta bring your cat up really calmly to see because if you bring them up at a run then the

01:18:35.080 leader just turns back and you don't know what it's reacting to oh yeah i said to him now bring him up

01:18:39.640 nice and calm watch the leader come up the chute and when he stops he'll look right at the thing he

01:18:45.640 doesn't like and he looked at the led light on the corner of the building then they texted me a picture of

01:18:50.600 it and they got rid of that and that fixed the problem now have you have you have you developed

01:18:59.240 some sort of picture of the class of things that stop cattle yes yes i talked about the spider okay

01:19:06.200 so tell me what sort of things tend to stop cattle i have pictures i have checklists of things to look

01:19:12.760 for reflections on water uh at this particular plant there was a gate handle a gate handle that

01:19:19.960 jiggled and it was right by the chute entrance i said that needs to be fixed so this gate handle

01:19:25.640 doesn't vibrate so what's common do you what's common about the things that stop cattle in their

01:19:33.800 tracks or or can you extract out let's look at let's look at cattle's a prey species animal so you're

01:19:40.840 looking for things that might be a danger some little little bits of rapid movement set them off

01:19:48.840 and and something that sort of like shouldn't be there like you can put a white plastic bottle

01:19:55.720 in the entrance of the chute now about shut a meat plant down

01:20:00.440 i they'll just keep turning back away from it turning back away from it

01:20:05.560 right so they're looking for something that doesn't fit the environment that's right and that's

01:20:09.880 probably doing something like activating predator detection well that's right it's like that you

01:20:14.600 know they they're looking for stuff that uh you know a movement in the bushes maybe that's a mountain

01:20:20.440 lion or a wolf some little movement in the bushes the other thing about new experiences if you take

01:20:27.480 something like camera equipment cattle love camera equipment you put an expensive camera in the middle of

01:20:33.080 the pasture they will come up and knock it over and lick it to death that's what they will do see 0.96

01:20:39.720 things that are novel are attractive when the animal can voluntarily approach and scary if you

01:20:46.760 suddenly shove it in their face you suddenly shove it in their face then it's scary

01:20:53.080 right and so the best way that's a introduce new things to cattle is to let them voluntarily approach

01:20:59.560 it i don't know how many times people say to me my horse was fine at home he went crazy at the show

01:21:05.480 well you've got a lot of novel stuff there like flags for example so you better get your horse used

01:21:10.600 to flags before you go there and the best way to get them used to flags would be to decorate the

01:21:15.080 pasture with flags and let your animal walk up and voluntarily approach them right right well you know

01:21:21.800 that's exactly what you do in psychotherapy when you're trying to help people overcome a phobia

01:21:26.360 right so if someone's afraid of an elevator and won't get in it that often happens with agoraphobia

01:21:32.040 what you do is you say to them okay um let's start by imagining elevators so that's going to make you

01:21:39.560 a bit nervous but imagine an elevator at some distance that doesn't make you uncomfortable okay

01:21:46.360 okay and then you say okay well now you've done that see if you can move yourself in your mind closer to

01:21:52.920 the elevator door and then you keep doing that but it has to be voluntary it's 100 absolutely

01:21:59.080 necessary for it to be okay so maybe you run them through this imaginal exposure therapy and then you

01:22:05.240 say okay for our next session what we're going to do is we're going to go out in the hallway you know

01:22:08.680 that elevator you wouldn't take we're going to go out the hallway we're going to stand 200 feet away

01:22:13.000 from the elevator and you're just going to look at it if you can and so they'll do that has to be

01:22:18.760 voluntary and then you can get them 150 feet away and 100 feet away and soon they'll be right up to

01:22:24.200 the elevator door i was very anxious uh as you know in my 20s and i got terrified of airplanes

01:22:32.360 because i was an extremely scary emergency landing when i was a senior in high school

01:22:37.480 they put the slides down and the whole thing very very scary and one of the ways i got over that is i had

01:22:43.720 to make aviation interesting and when i got the ride in the cockpit of a plane flying uh holstein

01:22:50.920 heifers to puerto rico uh that made it interesting you make something scary interesting because i know

01:22:59.640 another thing they do on the elevator phobias is they show them how the safety mechanisms work

01:23:04.760 that the elevator is not going to fall down the shaft

01:23:06.920 yeah well you you you know you even compel that interest to some degree so for example

01:23:14.440 once you get a phobic person inside an elevator what they'll tend to do is look at their feet

01:23:21.640 and so you say to them look quit looking at your feet look at each corner of the elevator look at all

01:23:27.640 the numbers look at the display panel like you have to facilitate their voluntary visual exploration okay

01:23:35.000 and to some degree what you're doing is you're calling out their interest to say attend to all

01:23:40.600 of these things as if they're interesting and then that's how they familiarize themselves with the

01:23:45.640 elevator and they also note that while they're in there because you have to look at the elevator to

01:23:49.960 know you're in an elevator right literally you have to move your eyes and point at all the different

01:23:55.000 parts of the elevator and the more that you can help people do that at a high level of detailed

01:23:59.800 resolution voluntarily the more likely they are not not only to become less afraid of the elevator is

01:24:06.360 that that actually isn't what happens is you actually train them in a form of exploratory bravery

01:24:12.600 because what you teach them is that if they use their eyes voluntarily to scan what they're afraid of

01:24:18.200 they'll become braver and then that generalizes to all sorts of other instances too so if you train

01:24:25.560 someone to be less afraid of an elevator they're much less afraid of other things as well

01:24:29.480 well that's right and the thing that we're seeing i'm seeing right now in dogs you know we have very

01:24:35.640 strict lease laws here and there's more problems with dogs being afraid of the veterinarian because

01:24:40.760 they haven't been out experiencing enough stuff like strange people touching them for example just

01:24:45.320 going to lots of different places you know this is the reason why when they train service dog puppies

01:24:51.000 you take them everywhere so that there's almost nothing that will frighten them

01:24:55.640 yes yeah well that that that's the same argument you were making earlier about the fact that to train

01:25:03.960 people practically we have to put them in a lot of different practical hands-on situations

01:25:08.840 and so that they can generalize across all those instances and so experience that's too narrow is too

01:25:15.160 is too restrictive well when i get worried that we're going to have people making policy on all kinds of

01:25:20.280 important stuff um when they're so far removed from the world of the practical you see we need to have

01:25:29.000 both because all the problem with us practical people is we're not organized enough that's where

01:25:35.080 just about every tech company has to hire a suit eventually just to keep the company organized

01:25:41.400 somebody's got to pay the payroll somebody's got to pay the taxes somebody has to make sure the rent is

01:25:46.680 paid you know if they need more office space then they've got to go out and go shopping for office

01:25:52.440 space you know there there's uh well and we really do need all the different kinds of minds

01:25:59.960 well when i look at the ideological solutions that are being put forward to the world's problems

01:26:05.080 continually i do wonder the same thing you're wondering about which is is this empty ideological

01:26:12.840 representation a consequence of the fact that the people who are doing this have no practical

01:26:17.400 experience at all it's like they're not thinking at the level of detail well i think that it's a

01:26:22.680 problem because when i worked originally this is over 20 years ago with mcdonald's burger king and wendy's

01:26:28.120 and they took the top managers out into the field and implemented the auditing program it was

01:26:34.920 interesting to see how the animal welfare uh issue went from an abstraction give it to legal give it to

01:26:41.960 public relations to something real that they really needed to address you know half dead animals going

01:26:48.680 into your products not okay right right right broken stunning equipment is totally terrible and not okay

01:26:56.040 and that's mainly a management problem and failure to do maintenance and and i and and when i worked on

01:27:03.400 that in 1999 i got five journal articles published on this and i saw more change than i'd seen in

01:27:09.960 in in in my whole career when these big companies were inspecting these plants but i figured out a

01:27:16.920 very simple scoring system if you couldn't shoot 95 of those cattle dead on the first shot you failed

01:27:23.160 the mcdonald's audit it was that simple some very simple critical control points if you had more than

01:27:28.600 three percent of your cattle bellering their heads off when you're handling them you failed the mcdonald's

01:27:32.840 audit okay so why why why did that turn out to be the critical issue well they um broken stunners

01:27:40.200 were a big issue now on the handling i figured out a way to score that that's very simple vocalization

01:27:45.960 if you're poking cattle with too many electric prods or you're slamming doors on them or whatever

01:27:51.160 they're going to be bellering their heads off and i better not hear any bellering coming out of the 0.87

01:27:56.760 place where they kill them i hear bellering coming out of there somebody needs to get kicked off the 0.53

01:28:01.400 approved supplier list it's that simple that's one of the critical control right so you used so you used

01:28:07.880 animal distress as an indication of that's right efficiency of process okay can you can you walk us

01:28:14.120 through some of your designs i mean you designed these these circular um cattle enclosures as well to

01:28:20.680 to calm them down the circular designs are really nice but i'm also very proud of the fact there were 75

01:28:26.280 plants on the mcdonald's approved supplier list only three had to buy fancy expensive equipment

01:28:33.800 everybody else we fixed with management yeah three managers had to be removed i call that manager

01:28:38.280 ectomy um a lot of non-slip flooring had to go in because one of the things we measure is slipping

01:28:43.880 and falling and lighting cattle are scared of the dark um training people to move smaller groups of

01:28:51.960 animals and and uh put a solid side up so they don't see the vehicles passing by and these very

01:28:59.400 simple changes we were able to fix some of the older places and i'm very i used all my design ability to

01:29:05.320 figure out how to make some of the older facilities work even though they did not have the fancy new

01:29:10.280 equipment and then we had three plants only three plants had to do a front-end remodel that was very

01:29:17.560 very expensive that's three out of 75 i'm really proud of that oh yeah that's all the big pork plants

01:29:23.320 in the u.s right so so tell me tell me tell me tell me what your goals were okay so let's walk this

01:29:30.920 through at the level of detail so why don't you tell people about how these cattle handling plants

01:29:35.640 work broadly speaking from from the time the cattle arrived till the time they were processed let's say

01:29:41.320 and then how are you brought in to fix them well unloading a truck make sure you have a non-slip

01:29:48.680 unloading ramp open the gates up let them out you do not need to scream at them pound on the vehicle

01:29:53.960 or stick electric pads in through the holes in the side of the truck so let the cattle just get off

01:29:58.920 and they will and then they should walk quietly out of the truck if they got a scale weigh them on the

01:30:05.080 scale and then quietly walk into a holding pen get a drink of water maybe lay down now when it's

01:30:11.080 time to go up to the plant somebody should come down bring a group of 20 out not a group of 50

01:30:17.640 and you quietly walk them up the alley to where they get to where the round crowd pen is

01:30:23.720 and the whole thing should be a calm walk without slipping and falling and without mooing and bellering

01:30:30.280 and and almost no electric prods that and it should all be very calm walking is what it should be

01:30:38.440 right and and how many plants did you go analyze we they we we had 75 plants on the on the approved

01:30:47.480 supplier list and um only three of them had to have extensive renovations but we did have three

01:30:54.360 plants where the plant manager had to be removed right and so in those cases yeah why did you remove

01:31:00.120 the managers well the ones where we were able to get rid of the plant manager was the corporate ones

01:31:05.880 uh the plant then we had one plant that we used to call the problem child and management was family

01:31:11.320 and we couldn't get rid of it and that plant would like you know fail an audit and then pass an audit and

01:31:16.440 ah uh but management has to decide that they're going to do things right you can have the best equipment

01:31:23.720 and it's not going to work if it's not managed in fact before we started these audits i had a lot of

01:31:30.760 equipment out in the field lots of equipment half my clients tore it up and wrecked it and one of the

01:31:35.880 what the customer inspections and audits did is force the plant to manage the stuff they had either brand new

01:31:43.800 fancy stuff or older stuff and so how broadly did your innovation spread and how rapidly and what were the

01:31:52.280 consequences of that for the meat handling industry in general well the auditing program was within six

01:31:58.920 months as the year of 1999 was adopted by mcdonald's wendy's and burger king and that covers just about

01:32:05.160 all the big beef plants and i saw more improvement than i'd seen in a 25 year career prior to that

01:32:13.000 people were no longer using broken equipment they were moving smaller groups of animals

01:32:18.120 uh things were kept prepared and um and employees were better supervised and boy it made a big

01:32:25.720 difference so why do you think you were able to do this i mean you obviously can solve the problem

01:32:32.760 practically but how were you able to um make your way in the corporate world in a manner that actually

01:32:39.720 resulted a and people listening to you and b and changes actually being made because that's that's

01:32:45.320 quite a remarkable combination of unlikely achievements all right let's start off i started

01:32:50.440 out with equipment this was in my 20s and i found that selling equipment was much easier than getting

01:32:56.680 people to manage equipment correctly and early in my career i made the mistake that a lot of young

01:33:01.720 engineers make i thought i could make a self-managing cattle handling facility that is bs and i got a lot of

01:33:09.720 systems out there the other thing that helped me to get systems out into the industry is i wrote about

01:33:15.400 them and i wrote about them in the meat industry and cattle industry trade press wrote all kinds of

01:33:21.960 articles just about how to do it so now i had a lot of equipment out there center track restrainer and

01:33:28.520 lots and lots of these big plants had one it's a piece of equipment i developed with the guys in the

01:33:33.480 shop that i've talked to you about earlier but half my clients tore it up and wrecked it then i worked

01:33:40.360 with a lady named janet riley at the american meat institute and i came up with this very very simple

01:33:47.800 scoring system for evaluating meat packing plants and we wrote it up in our guidelines nobody used it for

01:33:54.680 two years then mcdonald's approached me to implement their animal welfare auditing program

01:34:04.120 and it started out taking vice president level managers out of the office they saw some bad

01:34:10.520 stuff and it started out as little training program they already had food safety auditors in the plants

01:34:18.040 auditing them that was already being done and so i took the trained the food safety auditors to do the

01:34:24.920 animal welfare audit and within six months i saw more change that i had seen in my whole career prior to

01:34:32.680 that then wendy's got on board then burger king got on board and i made sure that everybody used the

01:34:38.200 exact same score so it was absolutely clear it was like traffic rules you know you know they measure

01:34:44.440 speeding we measured how many vocalizations the cattle did we measured how many animals fell down those

01:34:50.360 things were measured and the plants had to make certain numbers it was absolutely clear there were five

01:34:56.520 critical control points and they had to do all five of them to pass the audit i kind of sometimes

01:35:02.200 can't believe i pulled it off it worked me on my wildest dreams but it was absolutely practical

01:35:08.040 right right yeah well it did it is it is quite a remarkable thing to pull off to to be able to

01:35:14.840 make that kind of change in the corporate world so quickly that's that's really quite remarkable the

01:35:19.400 other reason i was able to make change i i practiced reverse conflict of interest i had a lot of expensive

01:35:25.640 equipment already out in the plants i bent over backwards in the older facilities to figure out how to make

01:35:32.280 that older facility work without buying expensive equipment let me tell you non-slip flooring it can

01:35:38.120 work wonders you know we did quite a lot of that right right but that's not that's not expensive

01:35:43.880 equipment and i'm really proud of the fact that took some of the older facilities and we made them work

01:35:49.160 and then we did have three only three out of 75 plants that had to build an expensive front end remodel

01:35:56.280 and that was very expensive so what would you say if if you had to put it into a few phrases

01:36:05.080 you you weren't obviously pursuing mere narrow profit at that time not that there's anything

01:36:10.680 wrong with profit you you were serving some other goal i was serving the goal of improving animal

01:36:16.760 welfare and i've been over backwards to do reverse conflict of interest and i tried to take some of

01:36:23.960 those older facilities right some of them a bit shabby and make them work with simple changes

01:36:31.400 like repairs non-slip flooring changing the lighting and three plants had to have the plant

01:36:37.240 manager removed and that solved the problem so what why were you cons why were you so concerned with

01:36:43.240 animal welfare and how would you define animal welfare what why did that become paramount in your

01:36:48.440 in your hierarchy of goals well one of the reasons why i started working on the equipment is like the

01:36:54.040 way cattle were being handled was horrible you know electric prods on 100 percent of them falling down

01:36:58.760 crashing into things people screaming at them uh cattle handling in the 80s was terrible absolutely

01:37:05.000 terrible and i i saw that is something that i could fix now i talked to a lot of young people today

01:37:12.840 that want to you know do activism about some specific thing and it's way too broad i want justice in

01:37:18.760 the world for example right yes might be something they would say yes yes and i say why don't you do

01:37:23.720 something more targeted like using dna to show that this prisoner was falsely accused you see now that's

01:37:31.000 something a lot more targeted that you can actually do yes absolutely and i think that is a huge problem

01:37:38.040 with the way that kids are trained morally in universities is that that grandiose vague

01:37:44.680 activism replaces the actual practicalities of problem solving that you're describing that

01:37:50.280 actually make a difference why do you think it was that animal suffering stood out for you is it is

01:37:55.800 it partly because you you can think like animals like what why do you think well you would how would

01:38:00.440 you like to get shocked to electric prods and be slipping and falling and crashing into fences and things

01:38:04.840 like that you'd be terrified um and my goal was to improve how the cattle were treated when i talked

01:38:14.360 to students about you know activism i said what i worked on wasn't everything bad happening to animals

01:38:22.280 i worked on something targeted the thing that i'm seeing now with young people that want to make

01:38:26.760 a difference they say i want to have justice in the world or i want to like animals are treated terrible

01:38:32.280 we got to do something about it and i'm saying you're going to be more effective if you pick out

01:38:38.440 something relatively targeted i worked on cattle handling to start with that's not everything to

01:38:44.840 do with animals yes exactly or the example of using dna to show that this criminal was innocent

01:38:52.360 that's something much more doable and targeted that you can actually do right so how was it that the

01:38:59.160 suffering of animals in meat tracking in meat packing plants came to your attention to begin

01:39:04.280 with so you said the suffering you found that unbearable first of all it started out um when

01:39:09.480 i went out to the feed yards uh handling cattle back in the 70s there's a lot of really horrible cattle

01:39:15.160 handling and i i i made a mistake in the beginning that a lot of young engineers make they think technology

01:39:23.320 can solve all their problems and i mistakenly believed that i could build a self-managing

01:39:29.560 cattle handling facility that's nonsense i know that now that's nonsense good equipment makes good

01:39:35.800 handling better but it doesn't replace management and what the auditing program did is it forced people

01:39:41.960 to manage the facilities and why why were you at the cattle handling facilities to begin with i mean

01:39:48.680 was this part of your academic training or was this part of the fact that you'd grown up on a farm

01:39:53.800 well i got interested in going out to my aunt's ranch and this brings up the other things students

01:39:58.840 get interested in stuff they get exposed to it's that simple in in one of the people i profiled in

01:40:06.920 visual thinking was michelangelo grubby little 12 year old dropped out of school but he was running

01:40:13.960 around all the churches seeing great art and he grew up with stone cutting tools okay that's the

01:40:19.800 exposure then he started making some stuff and then an artist took him in as an apprentice that's

01:40:25.320 mentoring good careers start first with exposure and the other reason why i'm so concerned about

01:40:31.400 taking all the hands-on stuff out of the schools is those things like let's say theater for example

01:40:36.440 expose students i didn't care about being in the play but i loved making scenery and costumes

01:40:43.080 that i loved doing now that's something that can become a career so let let me we're running out

01:40:50.360 of time on this segment let me ask you one more question and then maybe i'll sum up our discussion

01:40:54.680 for everybody or try to extract out the gist you think primarily in pictures but you're also an

01:41:00.600 extremely effective verbal communicator i mean you've written a number of books you can obviously talk

01:41:05.320 your way into corporate environments and help people walk through the complex process of restructuring

01:41:11.080 say animal handling on a pretty much on a national scale how did you how do you think you've been

01:41:17.160 able to develop your verbal ability like what what did you do to manage that all right it goes back to

01:41:22.760 when i was seven years old in my neighborhood all the kids when they were seven when the parents had a

01:41:27.560 party you had to put your good clothes on greet the guests and serve the snacks be little hosts and

01:41:32.760 hostesses that's what you had to do um i sold candy for charity that helped me on talking to people

01:41:40.440 the other thing i figured out very early on is back doors into jobs and most people don't see this

01:41:46.680 in the hbo movie temple grandin there's a scene where i go up to the editor of our state farm magazine

01:41:51.960 and i get his card because i knew if i wrote for that magazine that would help my career

01:41:56.760 that's a back door writing i did a lot of writing this how to handle cattle how to design facilities

01:42:06.360 and i was so happy when one of my very early articles got picked up by two other magazines

01:42:11.880 you know and it made the national scene of course this is all you know pre-internet and then that press

01:42:18.120 pass got me into all kinds of meetings that helped me to get into my self-made internship with a swift

01:42:24.680 plant i recognized the back doors most people don't recognize the back doors but lots of good

01:42:31.000 jobs are gotten through the back door right so you allied your ability to concentrate on focal details

01:42:39.400 practically with the ability and the willingness to communicate on at all sorts of different levels

01:42:45.080 verbally you're able to bring that's getting too abstract for me i just go okay i got the card

01:42:51.400 within a week i made i had done a master's thesis on cattle behavior in different types of squeeze shoots

01:42:58.200 and i i sent them a good article and they published it then i went back to them and said

01:43:04.040 maybe i could just write something for you every month i just walked into the office and asked that job

01:43:10.440 so i did that as a volunteer for about three or four months and then they started paying me

01:43:13.720 i wasn't shy to walk up and ask right right so so you developed a communication communication

01:43:23.720 expertise and a communication network at the same time that you were trying to implement your practical

01:43:28.920 solutions and both of those facilitated each other on the very beginning i wasn't even designing

01:43:34.360 facilities and then i i was um just visiting all these feed yards and writing for the magazine and that

01:43:41.880 press pass got me into all kinds of places i recognized the value of that press pass it got

01:43:49.240 me into meetings in the 70s with 600 registration fees i was no way i could have afforded that this is

01:43:55.640 seven right right right so what what are you working on now you finished this this new book that came out

01:44:02.360 in 2022 what are you doing now well i'm very interested in seeing the kids who think differently

01:44:08.280 get into good careers and that's going to be a major emphasis of the things that i do now be a lot of

01:44:14.760 speaking engagements a lot of you know interviews like this because i want to see those kids that are

01:44:20.600 different get into good jobs okay if they're visual object visualizer like me maybe an art job maybe

01:44:27.240 photography job or a job building things if they're the more mathematical visual spatial a good programming

01:44:33.720 job a good um uh mathematics or chemistry job where you need the mathematics there's too many kids that

01:44:42.040 think differently um some of them are just kind of going nowhere i want to see them get into good careers and do

01:44:48.760 things that will be constructive in the world do something constructive

01:44:54.120 so we're we're going to switch over to the additional half an hour that i produced for

01:45:00.760 the daily wire plus platform to delve into some of the biographical details of your life

01:45:06.760 before we close i'd like to just sort of wander over the territory that we've covered and see if

01:45:11.160 there's anything else see if you think this is a reasonable summary and if there's anything else you

01:45:15.720 want to add i've been talking with dr temple grandin today who's who's developed a spectacular career in

01:45:23.880 modifying animal handling and also managed a lot on the more purely intellectual front as well in terms

01:45:31.320 of conceptualization of information processing she's uh we talked a lot today about the difference in

01:45:38.520 in in in the ways that people think concentrating mostly on the distinction between visual thinkers

01:45:45.000 who tend to be more practical and detail oriented and who can be broadly differentiated into two

01:45:51.240 categories and those would be object visualizers and people who think more visual spatially and

01:45:57.000 mathematically contrasting them with people who think more verbally we talked a fair bit about the

01:46:03.080 prioritization of more verbal and abstract thinking at the cost of this practical thinking and

01:46:09.880 training in that practical thinking we discussed how that's affected the school system and

01:46:14.760 broader culture we discussed the dangers that poses to the integrity of our society as we

01:46:20.680 lose the people who have the hands-on knowledge we talked about the psychological danger that poses

01:46:26.440 to people who think more practically concretely and ver and visually who are in school systems that

01:46:33.480 are optimized for the verbal thinkers we talked about temple's um career at the detail level um

01:46:44.200 ameliorating the suffering of animals that across like nationally and internationally as it turned out

01:46:51.640 partly because she decided not to chase mere generalities but to focus on an actual problem which

01:46:58.200 was the suffering of actual animals in actual plants was willing to focus her um emotional concerns on

01:47:07.080 something that was practical and to marry that with a strategy that involved particularization and visualization and

01:47:13.400 and verbal communication and practical interactions with corporations and also we close that with a

01:47:20.360 discussion of the fact that what she's doing now is trying to bring to people's attention in podcasts

01:47:25.800 like this the fact that we seem to be um working contrary to our own best interests by not uh building educational

01:47:37.640 facilities that help optimize the ability of visual thinkers to function but also

01:47:44.440 for society more broadly to take advantage of the talents and skills of those people

01:47:50.840 in the in innovation and in the maintenance of the infrastructure that we already have around us and so

01:47:56.920 i think that about summarizes what we talked about today is there anything you want to add to that

01:48:01.160 that definitely you know kind of um kind of summarizes that we need all the different kinds of minds and

01:48:07.880 they can when we understand that different people think differently they can work in teams where they can

01:48:12.440 collaborate and have complementary skills i think that's that's uh something that's really important

01:48:19.720 also right right the thing i wanted one thing i would do with the schools is i'd put a lot of the hands-on

01:48:27.080 classes back in like art sewing woodworking shop welding auto mechanics theater because these are all things that

01:48:35.400 expose kids to to things that can become possible careers too

01:48:42.040 right and those are all things that have to be done there in an embodied sense you actually have to

01:48:46.760 it's not purely abstract any any it's not abstract you like none of those things are abstract

01:48:53.400 yeah well that's i suppose a danger of moving so much education online as well as that it's going to

01:48:58.440 increase the degree well that's right the other thing when i went to did the book signing for visual

01:49:04.680 thinking i told you about the electrical engineering book from the 30s i found in this unique hotel room

01:49:10.120 but i also got put in the office of a professor in political science and i looked at some of those books

01:49:16.760 and it was so abstract theories about politics i i didn't even understand it it had nothing to do

01:49:23.640 with right or left it had to do with just abstractions that were so abstract it made no sense to me

01:49:30.280 and i'm going oh i wouldn't want this person in charge of figuring out with the power grid

01:49:35.640 you know i remember at this tucson conference where i first saw you speak after you spoke very

01:49:42.120 practical talk very much like the one that you you delivered today when we were talking a philosophy

01:49:47.720 student got up because there were a lot of abstract thinkers at this consciousness conference and asked

01:49:52.600 you something extremely abstract and philosophical and you did exactly what i would expect a good

01:49:58.360 engineer to do which was to say you know i really don't understand anything that you just said i don't

01:50:04.280 know how to associate it with anything practical and i'm completely unable to answer your question

01:50:09.480 which i thought was just it was ridiculously comical and i also thought um what would you say well

01:50:15.960 targeted because it was the case that you know you had been talking about real practical realities your

01:50:21.720 ability to think like an animal the fact that you had taken these practical set steps to ameliorate

01:50:27.480 animal suffering and that that had been so consequential and and so of obvious worth and then

01:50:34.680 you were faced with this flight into abstraction and and did what engineers always did do which is

01:50:41.720 something like well yeah but i don't understand that what does it mean practically which is a really

01:50:45.960 good question that you know it's a question that should be asked of abstract thinkers all the time

01:50:50.600 what are the devils in the details here that you're overlooking

01:50:55.000 how much do you know about the systems that you're abstractly representing and the answer to that is usually

01:50:59.240 almost nothing well it's sort of like you know we need all the three different bases you know

01:51:06.040 different kinds of minds you need the object visualizers and we're going to get the arts

01:51:10.680 mechanical and photography and animals you need the visual spatial mathematicians computer programmers

01:51:18.040 chemists things that require mathematics and we need the verbal thinkers uh because i'm

01:51:23.960 they're going to help organize things you see you need you need all three different kinds of minds

01:51:30.040 and they should work together in a complementary fashion all right well that's a good place to end

01:51:36.840 this segment i would say i'm going to go thank you thank you to everyone on youtube and the associated

01:51:42.600 podcasts for your time and attention i hope you found this discussion interesting and engaging

01:51:49.080 and practically useful as well i'm going to switch switch over to the daily wire plus platform

01:51:55.240 and i'm going to talk to dr temple grandin a little bit more on the biographical end i want to

01:52:00.360 lay out how her how her interest in the issues that she did pursue professionally made themselves manifest

01:52:06.600 in her life hello everyone i would encourage you to continue listening to my conversation with my guest

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The Jordan B. Peterson Podcast - December 29, 2022

318. Autism, Academics, and Animals ｜ Dr. Temple Grandin

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