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Speaker 1: Welcome to stuff to Blow your Mind from how Stuff

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Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

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Speaker 1: My name is Robert Lamb, and we are part of

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Speaker 1: a machine. Um, I think pretty much most unless you

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Speaker 1: happened to be like that. That's sort of that iconic

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Speaker 1: like guy living out in the middle of the desert

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Speaker 1: in a shack or you know, or you know, Georgia

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Speaker 1: O'Keefe shutting herself away off the Yeah, living off the

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Speaker 1: grid and not having any contact with people. Unless you're

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Speaker 1: doing that kind of life, you're probably part of of

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Speaker 1: the machine or a machine. Yeah, Like I got up

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Speaker 1: this morning, got got into Marta. Suddenly I'm in the

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Speaker 1: I'm in the crowd with the with the with the

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Speaker 1: other commuters, and I'm instantly part of something that It's

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Speaker 1: kind of like a big animal, like a big super organism,

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Speaker 1: a big cranky, uh, kind of stinky superganism that crams

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Speaker 1: itself into trains and delivers itself to the front door

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Speaker 1: of its respective workplaces where it becomes part of another

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Speaker 1: super exactly. Yeah, I mean yeah, just like us How

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Speaker 1: Stuff Works, Discovery Communications. It's a giant thing and we're

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Speaker 1: all just like parts of it. We're part of the hive. Yeah,

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Speaker 1: and we're not I'm not. Aga'm not saying that as

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Speaker 1: a as any kind of a slam on on Demand

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Speaker 1: or on Corporate America or any anything like that, because

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Speaker 1: it's it's the more we look, it's like, no matter

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Speaker 1: where you are again, unless you're living in the desert

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Speaker 1: and that's in that shack or or you know, or

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Speaker 1: up in an isolated apartment on the upper east Side

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Speaker 1: with the tinfoil over all the windows, you're a part

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Speaker 1: of some sort of social animal. Right, you're part of

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Speaker 1: a community. We call it a community. Right now, bees

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Speaker 1: do it, we call it a swarm. If ants do it,

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Speaker 1: we call it. I guess that's a swarm too, or

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Speaker 1: an army at any rate. Yeah, and uh, when when

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Speaker 1: birds do it and they do it in mass, in

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Speaker 1: thousands and cousins of birds and they undulate over the horizon,

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Speaker 1: we just call that scary because we're like, what is that?

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Speaker 1: It looks like a funnel cloud of birds coming at us,

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Speaker 1: And then why is it doing that? And beady eyes? Right,

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Speaker 1: my wife finds the the the tongues terrifying on birds,

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Speaker 1: like their black tongues. She's anytime we go to Our Thomas,

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Speaker 1: which is a restaurant here in the Atlanta that birds

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Speaker 1: and cages outside. She's always little freaked out, like the

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Speaker 1: two Cans. Did you know that that guy started the

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Speaker 1: Popeye's chain? And yeah, and Our Thomas is sort of

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Speaker 1: his way of giving back to the community in terms

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Speaker 1: of healthy food and birds. Interesting, Yeah, there you go,

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Speaker 1: but no, but yes, birds, super swarms, super organisms happen

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Speaker 1: every single day. And one of the reasons they're they're

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Speaker 1: kind of that they can often be unnerving is that

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Speaker 1: it's he gets down to the basic idea that an aunt,

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Speaker 1: a single aunt. There's nothing I can out smart in aunt.

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Speaker 1: Most people can't. Everybody can can out smart And then

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Speaker 1: you can take a magnifying glass too and burn it too.

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Speaker 1: If you were a three year old who a single bird,

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Speaker 1: you can take that on with a tennis racket. Um,

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Speaker 1: if it's bees, one b is not an issue. But

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Speaker 1: when they swarm, hey, you're dealing with a large mass

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Speaker 1: of things, but also you're dealing with something you can

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Speaker 1: You're suddenly dealing with something that has um an emergent intelligence,

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Speaker 1: that that has a it has it's it basically is

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Speaker 1: it is a macro intelligence that arises from local interactions, right,

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Speaker 1: and not merely simple interactions, uh you know that are

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Speaker 1: necessary to coordinate movement, but a group intelligence that learns

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Speaker 1: that achieves goals, and it engages in an overall self

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Speaker 1: preservation Okay. And that's what I think is so cool

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Speaker 1: about super organisms, like you said, on their own as

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Speaker 1: individuals and they're a little worthless right in the in

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Speaker 1: the insect community, but together they can accomplish incredible things.

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Speaker 1: And it's still somewhat of a mystery to scientists. I mean,

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Speaker 1: they've begun to figure out why birds and swarms of

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Speaker 1: thousands can uh turn themselves into these crazy vortex um

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Speaker 1: circular shapes and and not run into each other or

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Speaker 1: drop dead in collision, you know. Yeah, Or or like

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Speaker 1: fish that that sort that get into a swarm and

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Speaker 1: it kind of looks like a single thing, like a

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Speaker 1: single larger sea creature. Yeah, which is so cool. If

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Speaker 1: you've never seen footage on that before, it's really worth

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Speaker 1: checking out. Um. But but again it's pointing to that

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Speaker 1: sort of intelligence which is far superior than any individual one. Yeah.

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Speaker 1: In in Steve Johnson's book Emergence, which deals with this

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Speaker 1: and so generally regard is a really good uh introduction

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Speaker 1: to to all this. Uh he uh. He lays out

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Speaker 1: like a few uh necessary items on the checklist for

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Speaker 1: emergence intelligence. One of them is a critical massive participation.

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Speaker 1: So you need a like a minimum number of participants

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Speaker 1: in a social group for like, like like if I

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Speaker 1: was in Marta and it was like three dudes, Uh,

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Speaker 1: there's not really gon be much of a It's it's

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Speaker 1: kind of like a mob, you know, like like if

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Speaker 1: three guys are standing outside of a of a business

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Speaker 1: protesting that people aren't gonna say there's a mob out there.

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Speaker 1: There's no telling what it'll do. No, it's just like Joe,

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Speaker 1: Charlie and Mary and you can sort of deal with

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Speaker 1: them on an individual basis and there's there's not going

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Speaker 1: to be that that thing that takes over um well,

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Speaker 1: and even in riots, right, there seems to be some

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Speaker 1: sort of self organizing aspect to it. Right. Yeah, there's

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Speaker 1: been a there's been some really interesting stuff coming out

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Speaker 1: recently talking about the at the time of this recording,

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Speaker 1: current protests in Egypt. You know, because it gets down

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Speaker 1: to like you can't with a with a group like this,

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Speaker 1: you can't say oh, there's the leader. No, there are

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Speaker 1: money leaders there that are that have sort of organizing

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Speaker 1: roles in it, But you can't. That's I mean, that's

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Speaker 1: why the mob is such a scary prospect to any

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Speaker 1: um seat of power, because what does it want? You can't.

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Speaker 1: It's it's you're dealing with it with an intelligence that

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Speaker 1: can't be boiled down to a single individual, right right,

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Speaker 1: that's right. You can't appease that one single individual. And

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Speaker 1: yet there's an intelligence that comes from that, and there

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Speaker 1: are demands that come from that. Yeah. Like like I

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Speaker 1: would like to just to think back to like a corporation,

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Speaker 1: like like any kind of corporation or big business, Like

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Speaker 1: what motivates everyone? Like the lowly creative type may just

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Speaker 1: want to make something they can feel proud of. The

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Speaker 1: the sales rep might be motivated by, you know, inner

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Speaker 1: department competition, who can get the biggest sale, who's gonna

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Speaker 1: be the top seller? Um, who's gonna get yelled out

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Speaker 1: by Alec Baldwin? Uh. The account it may just have

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Speaker 1: eyes on you know the bonus uh, and the CEO

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Speaker 1: is you know, it might just be chasing the American

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Speaker 1: dream of filling his solid gold submarine with Brazilian supermodels

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Speaker 1: and circus pandas you know, Yeah, that's it's it's it's

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Speaker 1: within their grass. So so yeah, but when you talk

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Speaker 1: about the corporation as a whole, like what does it want? What?

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Speaker 1: You know, you're dealing with a different set of of

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Speaker 1: of of goals. Yeah, that parallels are there, right, I mean,

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Speaker 1: because there's a certain kind of intelligence that that happens

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Speaker 1: when you put a group of people together. I mean

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Speaker 1: that come up with solutions that you might not have

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Speaker 1: come up with yourself, or that people collaborate on and

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Speaker 1: all of a sudden, you've you've created a thing, right now.

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Speaker 1: Other items on Steve Johnson's list a local focus, even

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Speaker 1: if the effects are more widespread. So like the local

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Speaker 1: focus of of a corporation might be just to you know,

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Speaker 1: you may just boil it down to making money or

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Speaker 1: you know, or getting uh you know, or if it's

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Speaker 1: like a nonprofit, it might be to you know, to

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Speaker 1: bring about a certain social change, or to get certain

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Speaker 1: legislature passed. In the in the event of a protest,

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Speaker 1: it may be to alsta a particular individual or to

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Speaker 1: or to oppose a particular unpopular um law or ruling. Um.

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Speaker 1: Then another item on this list is random interactions between

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Speaker 1: groups and individuals outside. Uh so this is necessary for

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Speaker 1: a learning process. You know. It's it's like the mob pushing.

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Speaker 1: It's like you know, any kind of scene where it's

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Speaker 1: like the man with his police and his and his

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Speaker 1: riot shield olds on one side, it's the protesters on

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Speaker 1: the other, and there's like a there's kind of a push.

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Speaker 1: There's a testing of the of the two sides. As

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Speaker 1: as the mob is is learning, it's seeing what it

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Speaker 1: can it can get away with, what it can do, Yeah,

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Speaker 1: learning what his boundaries are. And um, you know there's

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Speaker 1: some there's so many people on the lookout and they're

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Speaker 1: all gathering data and pulling it in one way or another. Right,

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Speaker 1: which is the same thing if you look at and

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Speaker 1: some with some insects, with some species that they're all

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Speaker 1: kind of like ants, for instance, they're communicating with pheromones, right,

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Speaker 1: and they're basically they're saying, Okay, hey, I'm going to

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Speaker 1: leave this chemical trail to this food source that I

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Speaker 1: just found. And then all of a sudden, you've got

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Speaker 1: one ant following that, five ants, ten ants, a thousand ants.

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Speaker 1: And that's how you're getting this sort of group movement

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Speaker 1: toward that food source or the bara metric pressure drops

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Speaker 1: for instance, and the ants start building these little sand

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Speaker 1: turrets to protect themselves from what they think maybe rain

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Speaker 1: in pretty soon. So again, it's that collective information and

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Speaker 1: sharing of it and this self directing movement that comes

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Speaker 1: out of it that's so fascinating. Yeah, with ants, it's

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Speaker 1: important to note and just to remind everybody, they're they're

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Speaker 1: like twelve thousand uh known ant species. They've been ruling

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Speaker 1: the earth for about a hundred and forty million years

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Speaker 1: and uh. And while an ant colony or or or

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Speaker 1: even like a bee hive will have a queen, these

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Speaker 1: are not rulers. Like the queen plays an important reproductive

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Speaker 1: role in a in a in a hive or colony,

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Speaker 1: but they're not calling the shots. Now. She's just a

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Speaker 1: cog in the wheel. Right. Yeah, we we label them

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Speaker 1: a queen and that comes with certain certain anthropomorphic baggage.

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Speaker 1: But just leave that baggage behind, because the queen is

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Speaker 1: just there to to pump out more more bees or ants. Right,

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Speaker 1: that were just that they she's just useful her for

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Speaker 1: her ovaries. Really to throw it down to that and

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Speaker 1: that's true of all these species, right, the insects species

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Speaker 1: that that show emergence. Yeah, there's no one leader, there's

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Speaker 1: no boss. Yeah. She's not saying, hey, I've got an

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Speaker 1: idea how to solve this problem about food foraging. No,

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Speaker 1: it's it's it's emerging from the the the interactions of

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Speaker 1: the group as it pushes against outside stimuli and what

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Speaker 1: the group needs. Yeah, there's an interesting study from US

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Speaker 1: Stanford University's Deborah Gordon. Uh and uh this dealt with

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Speaker 1: with the with particularly with forager ants and figuring the

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Speaker 1: ant colony, figuring out how many foragers it needs to

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Speaker 1: send out. Um. And they found that it came down

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Speaker 1: interactions between foragers and patrollers. So when a forager has

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Speaker 1: contact with patroller you know looking around, um that they

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Speaker 1: end up passing on these these pheromones. But a forager

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Speaker 1: needs several contacts no more than like ten seconds apart

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Speaker 1: before it'll go out. So there has to be like

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Speaker 1: it's it's like a very simple like you know, uh

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Speaker 1: here here the patrollers coming in, patrollers coming in, and

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Speaker 1: once it like counts off enough pheromones within a certain

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Speaker 1: amount of time, then it knows Oh I need to

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Speaker 1: go out and collect Okay. So it's sort of like, again,

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Speaker 1: is that mass movement? Right? So once it starts to

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Speaker 1: accumulate all that data from ten of those and it says, okay,

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Speaker 1: that's the direction I need to go in. Yeah. That

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Speaker 1: They concluded that forgers use the rate of their encounters

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Speaker 1: with patrollers to tell if it's safe to go out. Okay,

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Speaker 1: so you know, in case there's like an ant eater

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Speaker 1: out there just gobbling up everything, they won't just blindly

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Speaker 1: march out into the mall. You know, there's something altruistic

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Speaker 1: about that. It seems you know, again that's that's anthropomorphis

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Speaker 1: morphosizing this. But it does seem like, okay, well I've

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Speaker 1: got your back here, or you know, or I may

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Speaker 1: even die for the cause as I'm patrolling. Yeah, And it's, uh,

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Speaker 1: that was so romantic. I can't have much. It's hard

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Speaker 1: not to get romantic about it. This presentation is brought

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Speaker 1: to you by Intel Sponsors of Tomorrow. Another really interesting

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Speaker 1: part of all this is that but we've been able

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Speaker 1: to to look at some of these systems and learn

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Speaker 1: from them and apply them to, uh, to our own

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Speaker 1: human systems, our own um transportation or delivery UM systems

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Speaker 1: such as UH. There's an industrial medical gas guru by

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Speaker 1: the name of air Liquid and they worked with an

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Speaker 1: AI form firm to develop a computer model based on

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Speaker 1: algorithms that were inspired by the forging behavior of argentine ants.

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Speaker 1: And this is again the species that we're talking about,

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Speaker 1: the deposits chemical pheromones um and and uses that to dictate,

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Speaker 1: you know, how much how many forgers need to go

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Speaker 1: out to get the food. So air Liquid is able

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Speaker 1: to combine the and the ant approach with other artificial

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Speaker 1: intelligence techniques, throw in data about like the weather, customer

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Speaker 1: demand and all, and they use that to figure out

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Speaker 1: how much how much of their product they need to

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Speaker 1: actually ship out. Okay, and this is based on the

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Speaker 1: algorithms that they observed. Yeah, okay, based on algorithm on

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Speaker 1: the on the way the ants are handling their problem,

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Speaker 1: and they're able to up with a way to to

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Speaker 1: handle their uh, their distributing needs. UM. And you've heard

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Speaker 1: of void technology before too, which is sort of similar

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Speaker 1: in that UM BOYD boy. This is bo I D

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Speaker 1: B o I D, which I guess that's sort of

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Speaker 1: a void pronunciation of bird, right, And this is Craig

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Speaker 1: Reynolds who created UM, an artificial life program, and this

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Speaker 1: is a distributed behavioral model to simulate the motion of

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Speaker 1: a flock of birds on a computer. So same thing, right,

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Speaker 1: and nowther space goes back to the the really central

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Speaker 1: human need to make believable batswarms in the movie Batman. Yes, yeah,

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Speaker 1: actually you're right. I mean that that wasn't why he

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Speaker 1: actually created this UH technology, but that's one of the

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Speaker 1: uses for it. Was c G I s to to

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Speaker 1: simulate something that looked realistic in terms of a flock

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Speaker 1: of bats UM. But what he observed is that each

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Speaker 1: boid is implemented as an agent and moves according to

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Speaker 1: its own understanding of the changing environment. So think about

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Speaker 1: bird formations like V formations UM. There are four rules

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Speaker 1: with this. First, a boid must move away from other

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Speaker 1: boyds they're too close stuff because you don't want to

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Speaker 1: applyde Second, they have to fly in a general direction

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Speaker 1: that the flock is moving, okay. Third, they need to

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Speaker 1: minimize their exposure to flox exterior by moving towards the

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Speaker 1: perceived centers, which is interesting if you think about that

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Speaker 1: V formation. I like how your use of BOYD has

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Speaker 1: made you pronounced toward more toward. Of course, that's why

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Speaker 1: he came up with his name. He's like, this is

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Speaker 1: totally going to screw the podcasters in the future. I'm

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Speaker 1: just I'm taking the spirit of it, you know, I'm

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Speaker 1: running with it. UM. And then fourth, a boyd should

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Speaker 1: move laterally away from any boy that blocks its view.

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Speaker 1: So I mean that's very simple, right, taking the forcible

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Speaker 1: rules in creating UM. This this algorithm UM that has

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Speaker 1: also been used by Southwest Airlines. Uh, They've got a

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Speaker 1: routing system that in which the pilots try to find

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Speaker 1: the best airport gate based on the group information coming in.

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Speaker 1: So again you've got the group intelligence aspect of it. Yeah.

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Speaker 1: It's kind of like ants figuring out which entrigued is

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Speaker 1: the colony to take when returning home. Yeah. Yeah, And

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Speaker 1: it's a little bit scary with with you know, with

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Speaker 1: the plane, just because you think, okay, I'm relying on

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Speaker 1: the group intelligence for this, But over and over again,

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Speaker 1: there's so many different studies and counting jelly beans, you know,

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Speaker 1: guessing the weight of something. Yeah, like the just the

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Speaker 1: basic like second third grade activity of getting all the

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Speaker 1: kids to to guess the jelly bean number. And then

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Speaker 1: everybody comes up with these totals, and some kids maybe

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Speaker 1: way out, some kids don't know what numbers are, and

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Speaker 1: they're like, you know, drawing a cat on the ballot.

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Speaker 1: But but when you put them all together and you

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Speaker 1: you get you get an average. You can estimate estimate

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Speaker 1: more or less exactly how many there are in there.

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Speaker 1: The mean of right, the mean of that the group

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Speaker 1: estimate is always closer or the closest to what it

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Speaker 1: actually is, which is astounding. The main key here is

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Speaker 1: that you want a diverse members, you want independent minded members,

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Speaker 1: and you want a mechanism of voting or averaging to

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Speaker 1: figure out how it works. Right. Okay, you don't want

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Speaker 1: like fifty engineers all in the same room, right, I

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Speaker 1: mean you actually want it to be a diverse population

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Speaker 1: of people. Um. I mean, you could have fifty engineers

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Speaker 1: in a room. But the point is is that it

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Speaker 1: doesn't it doesn't really matter. You have people from all

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Speaker 1: social all sort of so so economic strata um giving

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Speaker 1: their information, and the mean will always be more accurate.

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Speaker 1: It's kind of like the whole how many blank does

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Speaker 1: it take to fill in a light bulb? You know?

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Speaker 1: Because I mean it's always a joke about like how

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Speaker 1: many you know, how many hipsters, how many mechanics, how

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Speaker 1: many you know blondes or whatever? Um, you know, does

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Speaker 1: it take? And it's kind of the message is kind

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Speaker 1: of if you get all, if you don't have a

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Speaker 1: diverse group, they're not going to be able to fulfill

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Speaker 1: even a simple task to the best of their ability.

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Speaker 1: But if it were a priest, a rabbi, and uh

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Speaker 1: whatever the third one is, then hey, they were an engineer,

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Speaker 1: an engineer, then they'd have that light screwed in no time.

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Speaker 1: That's right. Yeah, it takes up the logic. I guess

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Speaker 1: it's the point of all emergence here. Um, I mean

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Speaker 1: it really is. It's like it's such a simple construct,

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Speaker 1: but it really has changed the way that we think

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Speaker 1: about intelligence and the way that we operate in our

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Speaker 1: own society and uh, the sort of meaning that we

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Speaker 1: ascribe to CEOs or kings or presidents. Um, if we

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Speaker 1: all are essentially self organizing, uh like ants, then and

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Speaker 1: we don't necessarily need a leader, and of course we

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Speaker 1: need it like psychologically. Right, Um, we're still gonna be

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Speaker 1: okay because we're gonna still come out in theory, um

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Speaker 1: with something that makes sense for our community, right because

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Speaker 1: we're gonna be acting in our best interests. There's along

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Speaker 1: those those lines and kind of taking him to a

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Speaker 1: slightly more troublesome territory. Um, there's the guy, a guy

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Speaker 1: by the name of John Robb. He wrote a book

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Speaker 1: called The Brave New War, and he has a really

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Speaker 1: cool blog called Global Guerrillas. And he made an interesting

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Speaker 1: point where he pointed to the insurgency in a rack

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Speaker 1: in Iraq is a kind of emergence intelligence, uh, he said. Quote.

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Speaker 1: He talked about it as quote a complex series of

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Speaker 1: local interactions that leads to shifts in its behavior that

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Speaker 1: reflect a complex learning goal attainment and self preservation despite

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Speaker 1: a lack of a leadership hierarchy. Uh And and he

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Speaker 1: boils this down into three like key facts about the

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Speaker 1: insurgency in I rock that one the insurgency will continue

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Speaker 1: to improve over time to breakout as possible, and three

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Speaker 1: that it is impossible to discern the motives of this movement.

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Speaker 1: So uh, I found that interesting commentary, you know, because

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Speaker 1: it's it's the idea that you're not you're not fighting

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Speaker 1: a single individual that as much as is everyone like

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Speaker 1: to you know, believe it's like, all right, this this guy,

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Speaker 1: let's find him, uh kill him or or you know,

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Speaker 1: overthrow him, and it's then it's done. But no, you're

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Speaker 1: dealing with a I mean, ay, you're dealing with a

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Speaker 1: far more complicated social and political situation. But also you're

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Speaker 1: you're dealing with a movement that doesn't doesn't have a leader.

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Speaker 1: That's uh, it's you're dealing with the emergence intelligence. And

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Speaker 1: again it's how do you figure out what that wants?

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Speaker 1: And is the second point to break out is that

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Speaker 1: the aspect of almost like when with a bird flock,

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Speaker 1: where it all of a sudden it changes direction and

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Speaker 1: then you know, we maybe have the movement of several

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Speaker 1: birds and then that prompts um the other ones to

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Speaker 1: sort of follow domino like yeah, or like with you know,

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Speaker 1: sometimes you'll have like whales that will watch upon the

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Speaker 1: beach because one has kind of like had gone has

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Speaker 1: gone haywire, and the others just follow. UM. This technology

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Speaker 1: is also being used with robots of course, right right,

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Speaker 1: artificial intelligence. UM. It's based on the collective behavior again

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Speaker 1: of decentralized self organized systems. Just to throw that out

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Speaker 1: there again and again it's identifying best solutions and communicating them.

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Speaker 1: So they're they're hoping to have this same sort of

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Speaker 1: behavior in robots UM, and how it could be applied

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Speaker 1: is that you could use something called morphogen morphogenesis UH

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Speaker 1: to create a group of robots that can reconfigure and

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Speaker 1: undertake different functions. For instance, you could have a group

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Speaker 1: of ten robots that may communicate among each other by

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Speaker 1: admitting different color lights, and that light it's say if

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Speaker 1: it's green and blue, could mean that the other robots

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Speaker 1: need to attach to each other and create some sort

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Speaker 1: of formation. And the formation could be to traverse some

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Speaker 1: sort of gap in the landscape if that's what the

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Speaker 1: robots were doing, or could become up exactly, it becomes

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Speaker 1: a stack up on each other UM, or it could

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Speaker 1: be used to form a shovel shape to push a

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Speaker 1: heavy object. So they're trying to use again that swarm

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Speaker 1: technology there. But more interestingly I think that UM in Georgia,

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Speaker 1: at Georgia Tech of course, where they're doing tons of

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Speaker 1: crazy stuff with robots and creating robot armies. UM they've

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Speaker 1: developed palm sized robots to use in combat zones, and

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Speaker 1: they're smart enough to go up on their own and

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Speaker 1: alert you when they find something. So they're trying to

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Speaker 1: figure out how best to have these bots interact with

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Speaker 1: each other, and right now it's looking like the research

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Speaker 1: is favoring a decentralized approach in which the individual robots

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Speaker 1: would share information among their companions to form a more

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Speaker 1: complete picture. So what are we talking about here? Emergence intelligence? Right? Yeah,

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Speaker 1: so I mean here you go again, robots, emergence intelligence, intelligence,

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Speaker 1: emergence intelligence, military intelligence. I guess you could say that. Yeah. Yeah,

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Speaker 1: So you know, anytime we talk about robots, which I

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Speaker 1: know we've been doing a lot lately, we we sort

400
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Speaker 1: of get a little bit depressed because we think, oh god,

401
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Speaker 1: they're they're being programmed to destroy us again, or destroy

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Speaker 1: someone or do something. But I think that, um, the

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Speaker 1: really cool thing about emergence is that it could actually

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Speaker 1: lead to a better understanding of our own human nature.

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Speaker 1: Right if we apply that sort of intelligence to ourselves.

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Speaker 1: And I am not saying that we don't need presidents,

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Speaker 1: CEOs or power structures are hierarchy in place, Yeah, because

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Speaker 1: without the CEO, the people who make the Golden Submarines

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Speaker 1: are not going to make a living, right, It's very important, Yeah,

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Speaker 1: I mean, the economy will go down and it'll be

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Speaker 1: it'll be a bad thing. Um. But I think that

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Speaker 1: it's interesting to think that we've got the knowledge that

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Speaker 1: we are self directing and there's something comforting in that

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Speaker 1: and that. Um. You know, maybe again here I am

415
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Speaker 1: romanticizing things, but romantic in this one. Um. But given

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Speaker 1: the chance, maybe we could bring up the better angels

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Speaker 1: of our nature. Yeah, as we we figure out how

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Speaker 1: to make a machine culture work, we can see how

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Speaker 1: we should actually make a human culture work. Right, I

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Speaker 1: dig it. Okay, we'll go with it. Let's put it

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Speaker 1: into place. Hey, So let us know what you think

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Speaker 1: if you've seen some some cool examples of emergence intelligence

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Speaker 1: in the people around you and the animals around you,

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Speaker 1: or or the company that you're a part of, or

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Speaker 1: or or even in machines and robots, let us know

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Speaker 1: that's right. And if locust start converging, don't worry. They're

427
00:23:01,280 --> 00:23:03,240
Speaker 1: just they can't help it. That's what they do. Yeah.

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00:23:03,240 --> 00:23:05,679
Speaker 1: I don't hate the individual locusts. It's uh, you know,

429
00:23:05,800 --> 00:23:08,720
Speaker 1: the mob been taught. So hey, I have a couple

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00:23:08,800 --> 00:23:13,320
Speaker 1: of listener mail items here to read. The first comes

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00:23:13,359 --> 00:23:17,760
Speaker 1: from Lisa and This was left on our Facebook page. Um.

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00:23:18,080 --> 00:23:20,560
Speaker 1: She says, I like to listen to your podcast as

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00:23:20,600 --> 00:23:23,080
Speaker 1: I fall asleep, which is great, it helps me sleep.

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00:23:23,160 --> 00:23:25,679
Speaker 1: I listened to it again once I'm awake while I

435
00:23:25,760 --> 00:23:28,480
Speaker 1: learned the stuff while I'm asleep. The side effect to

436
00:23:28,520 --> 00:23:32,679
Speaker 1: all this really strange and scary dreams. So sorry about that,

437
00:23:33,000 --> 00:23:36,280
Speaker 1: which it'll happen. I, Um, I forget if I mentioned this,

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00:23:36,280 --> 00:23:42,439
Speaker 1: but when we were researching our podcast on I was

439
00:23:42,480 --> 00:23:44,560
Speaker 1: really reading that and researching it right before bed and

440
00:23:44,600 --> 00:23:47,119
Speaker 1: I had a bunch of crazy dreams with like atomic

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00:23:47,160 --> 00:23:49,560
Speaker 1: tests going on on a college campus, and it was

442
00:23:50,000 --> 00:23:52,119
Speaker 1: it was kind of unsettling. Yeah. Yeah, I have to

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00:23:52,119 --> 00:23:53,840
Speaker 1: say I haven't been able to access my dreams lately.

444
00:23:53,880 --> 00:23:56,159
Speaker 1: That's probably a good thing. Yeah, because it's just loaded

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00:23:56,200 --> 00:24:00,399
Speaker 1: with robots running away. Yeah, robo Nanny's and uh robot

446
00:24:00,520 --> 00:24:05,960
Speaker 1: locus cool. I have another uh uh. This one is

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00:24:05,960 --> 00:24:08,560
Speaker 1: an email from our listener Chris, and he says, I

448
00:24:08,600 --> 00:24:10,439
Speaker 1: just want to tell you that there are several types

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00:24:10,480 --> 00:24:13,760
Speaker 1: of deafness in your podcast. The werewolf principle. You sayd

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00:24:13,800 --> 00:24:16,520
Speaker 1: that deaf people don't get motion sick. That would only

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00:24:16,600 --> 00:24:20,000
Speaker 1: be if they had neural deafness deafness. There are two

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00:24:20,040 --> 00:24:24,200
Speaker 1: basic types of deafness, neural and conductive. The neural deafness

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00:24:24,240 --> 00:24:29,359
Speaker 1: is when the cochlea doesn't send the sounds to the

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00:24:29,359 --> 00:24:33,080
Speaker 1: brain because the cochlea isn't working or the cochlea is

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00:24:33,119 --> 00:24:37,040
Speaker 1: missing um. The conductive hearing loss is when the sound

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00:24:37,119 --> 00:24:39,560
Speaker 1: doesn't get to the cochlea. This occurs when the little

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00:24:39,600 --> 00:24:44,720
Speaker 1: bones in the ear uh malius incas and states don't

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00:24:44,760 --> 00:24:48,200
Speaker 1: send the sound to the cochlea or the ear drum

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00:24:48,359 --> 00:24:51,000
Speaker 1: is missing. If you are going to make people death,

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00:24:51,560 --> 00:24:55,679
Speaker 1: you would have have to uh do something to the

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00:24:55,720 --> 00:25:00,280
Speaker 1: cochlea or the malius incas or Stapus. Just wanted you

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00:25:00,320 --> 00:25:03,800
Speaker 1: to know, so, all right, cool. I should have maybe

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00:25:03,800 --> 00:25:07,119
Speaker 1: looked up the pronunciation and all those things through that email,

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00:25:07,200 --> 00:25:09,920
Speaker 1: but I felt like you were going Latin there. It's

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00:25:09,960 --> 00:25:14,479
Speaker 1: kind of exciting. Yeah, So hey, if you have anything

466
00:25:14,840 --> 00:25:17,560
Speaker 1: to uh to let us know about, you can drop

467
00:25:17,600 --> 00:25:19,720
Speaker 1: by our Twitter or our Facebook account. We are blow

468
00:25:19,800 --> 00:25:21,760
Speaker 1: the mind on both of those and we keep that

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00:25:21,840 --> 00:25:24,240
Speaker 1: updated with all sorts of cool links to stuff going

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00:25:24,280 --> 00:25:26,680
Speaker 1: on and how stuff works and also you know, neat

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00:25:26,720 --> 00:25:29,879
Speaker 1: little news stories and mind blowing tidnets from throughout the web,

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00:25:30,640 --> 00:25:32,520
Speaker 1: and you can always drop us a line that blow

473
00:25:32,600 --> 00:25:39,879
Speaker 1: the mind at how stuff works dot com for moral this,

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00:25:40,080 --> 00:25:42,480
Speaker 1: and thousands of other topics. Is it how stuff works

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00:25:42,480 --> 00:25:45,119
Speaker 1: dot com. To learn more about the podcast, click on

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00:25:45,160 --> 00:25:48,119
Speaker 1: the podcast icon in the upper right corner of our homepage.

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00:25:48,840 --> 00:25:51,440
Speaker 1: The How stuff Works iPhone app has a ride. Download

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00:25:51,480 --> 00:25:52,920
Speaker 1: it today on iTunes.