Eric Berlow and Sean Gourley: Mapping ideas worth spreading

71,083 views ใƒป 2013-09-18

TED


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

ืžืชืจื’ื: Ron Bentata ืžื‘ืงืจ: Ido Dekkers
00:12
Eric Berlow: I'm an ecologist, and Sean's a physicist,
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ืืจื™ืง ื‘ืจืœื•: ืื ื™ ืืงื•ืœื•ื’, ื•ืฉื•ืŸ ืคื™ื–ื™ืงืื™,
00:15
and we both study complex networks.
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ื•ืฉื ื™ื ื• ื—ื•ืงืจื™ื ืจืฉืชื•ืช ืžื•ืจื›ื‘ื•ืช.
00:17
And we met a couple years ago when we discovered
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ื•ื ืคื’ืฉื ื• ืœืคื ื™ ืฉื ื™ื ื›ืืฉืจ ื’ื™ืœื™ื ื•
00:19
that we had both given a short TED Talk
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ืฉืฉื ื™ื ื• ื”ืขื‘ืจื ื• ื”ืจืฆืืช ื˜ื“ ืงืฆืจื”
00:21
about the ecology of war,
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ื‘ื ื•ืฉื ื”ืืงื•ืœื•ื’ื™ื” ืฉืœ ื”ืžืœื—ืžื”,
00:23
and we realized that we were connected
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ื•ื”ื‘ื ื• ืฉืื ื—ื ื• ืžื—ื•ื‘ืจื™ื
00:25
by the ideas we shared before we ever met.
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ืขืœ ื™ื“ื™ ื”ืจืขื™ื•ื ื•ืช ืฉืฉื™ืชืคื ื• ืขื•ื“ ืœืคื ื™ ืฉื ืคื’ืฉื ื•.
00:28
And then we thought, you know, there are thousands
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ื•ืื– ื—ืฉื‘ื ื•, ืืชื ื™ื•ื“ืขื™ื, ื™ืฉื ืŸ ืืœืคื™
00:29
of other talks out there, especially TEDx Talks,
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ื”ืจืฆืื•ืช ืื—ืจื•ืช, ื‘ืžื™ื•ื—ื“ ื”ืจืฆืื•ืช TEDx,
00:31
that are popping up all over the world.
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ืฉืฆืฆื•ืช ื‘ื›ืœ ืจื—ื‘ื™ ื”ืขื•ืœื.
00:34
How are they connected,
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00:34
and what does that global conversation look like?
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ืื™ืš ื”ืŸ ืžืชื—ื‘ืจื•ืช,
ื•ืื™ืš ื”ืฉื™ื—ื” ื”ื’ืœื•ื‘ืืœื™ืช ื”ื–ื• ื ืจืื™ืช?
00:36
So Sean's going to tell you a little bit about how we did that.
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ืื– ืฉื•ืŸ ื™ืกืคืจ ืœื›ื ืงืฆืช ืขืœ ืื™ืš ืขืฉื™ื ื• ืืช ื–ื”.
00:39
Sean Gourley: Exactly. So we took 24,000 TEDx Talks
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ืฉื•ืŸ ื’ื•ืจืœื™: "ื‘ื“ื™ื•ืง". ืื– ืื ื—ื ื• ืœืงื—ื ื• 24,000 ื”ืจืฆืื•ืช TEDx
00:43
from around the world, 147 different countries,
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ืžื›ืœ ืจื—ื‘ื™ ื”ืขื•ืœื, 147 ืžื“ื™ื ื•ืช ืฉื•ื ื•ืช,
00:46
and we took these talks and we wanted to find
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ื•ืœืงื—ื ื• ืืช ื”ื”ืจืฆืื•ืช ื”ืืœื• ื•ืจืฆื™ื ื• ืœืžืฆื•ื
00:48
the mathematical structures that underly
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ืืช ื”ืžื‘ื ื™ื ื”ืžืชืžื˜ื™ื™ื ื”ืžื”ื•ื•ื™ื ื‘ืกื™ืก
00:50
the ideas behind them.
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ืœืจืขื™ื•ื ื•ืช ืฉืžืื—ื•ืจื™ื”ื.
00:52
And we wanted to do that so we could see how
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ื•ืจืฆื™ื ื• ืœืขืฉื•ืช ื–ืืช ืขืœ ืžื ืช ืœืจืื•ืช ืื™ืš
00:53
they connected with each other.
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ื”ืŸ ืžืชื—ื‘ืจื•ืช ื‘ื™ื ื™ื”ืŸ.
00:55
And so, of course, if you're going to do this kind of stuff,
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ื•ืœื›ืŸ, ื‘ื•ื•ื“ืื™, ืื ื”ื•ืœื›ื™ื ืœืขืฉื•ืช ื›ื–ื” ื“ื‘ืจ,
00:57
you need a lot of data.
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ืฆืจื™ืš ื”ืจื‘ื” ื ืชื•ื ื™ื.
00:58
So the data that you've got is a great thing called YouTube,
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ืื– ื”ืžื™ื“ืข ืฉื™ืฉ ืœืš ื”ื•ื ื”ื“ื‘ืจ ื”ื ืคืœื ืฉื ืงืจื ื™ื•ื˜ื™ื•ื‘,
01:02
and we can go down and basically pull
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื”ื™ื›ื ืก ื•ืœืžืฉื•ืš
01:03
all the open information from YouTube,
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ืืช ื›ืœ ื”ืžื™ื“ืข ื”ืคืชื•ื— ืžื™ื•ื˜ื™ื•ื‘,
01:06
all the comments, all the views, who's watching it,
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ื›ืœ ื”ืชื’ื•ื‘ื•ืช, ื›ืœ ื”ืฆืคื™ื•ืช, ืžื™ ืฆื•ืคื” ื‘ื–ื”,
01:08
where are they watching it, what are they saying in the comments.
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ืื™ืคื” ื”ื ืฆื•ืคื™ื ื‘ื–ื”, ืžื” ื”ื ืื•ืžืจื™ื ื‘ืชื’ื•ื‘ื•ืช.
01:11
But we can also pull up, using speech-to-text translation,
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ืื‘ืœ ืื ื—ื ื• ื’ื ื™ื›ื•ืœื™ื ืœืžืฉื•ืš, ื‘ืืžืฆืขื•ืช ืชืจื’ื•ื ืฉืœ ื˜ืงืกื˜ ืœื“ื™ื‘ื•ืจ,
01:14
we can pull the entire transcript,
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืžืฉื•ืš ืืช ื›ืœ ื”ืชืžืœื™ืœ,
01:16
and that works even for people with kind of funny accents like myself.
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ื•ื–ื” ืขื•ื‘ื“ ื’ื ืขื‘ื•ืจ ืื ืฉื™ื ืขื ืžื‘ื˜ื ืžื•ื–ืจ ื›ืžื•ื ื™.
01:19
So we can take their transcript
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ืื– ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืงื—ืช ืืช ื”ืชืžืœื™ืœ ืฉืœื”ื
01:21
and actually do some pretty cool things.
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ื•ืœืžืขืฉื” ืœืขืฉื•ืช ื“ื‘ืจื™ื ื“ื™ ืžื’ื ื™ื‘ื™ื.
01:23
We can take natural language processing algorithms
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืงื—ืช ืืœื’ื•ืจื™ืชืžื™ื ืฉืœ ืขื™ื‘ื•ื“ ืฉืคื” ื˜ื‘ืขื™ืช
01:25
to kind of read through with a computer, line by line,
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ืขืœ ืžื ืช ืœืงืจื•ื ื‘ืืžืฆืขื•ืช ืžื—ืฉื‘, ืฉื•ืจื” ืื—ืจ ืฉื•ืจื”,
01:28
extracting key concepts from this.
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ืชื•ืš ื”ื•ืฆืืช ืจืขื™ื•ื ื•ืช ืžืจื›ื–ื™ื™ื ืžืชื•ืš ื–ื”.
01:30
And we take those key concepts and they sort of form
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ื•ืื ื—ื ื• ืœื•ืงื—ื™ื ืืช ื”ืจืขื™ื•ื ื•ืช ื”ืžืจื›ื–ื™ื™ื ื”ืืœื• ื•ื”ื ืžืจื›ื™ื‘ื™ื
01:33
this mathematical structure of an idea.
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ืžื‘ื ื” ืžืชืžื˜ื™ ืฉืœ ืจืขื™ื•ืŸ.
01:36
And we call that the meme-ome.
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ื•ืื ื—ื ื• ืžื›ื ื™ื ืืช ื–ื” ืžื™ืž-ืื•ื.
01:38
And the meme-ome, you know, quite simply,
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ื•ื”ืžื™ืž-ืื•ื, ืืชื ื™ื•ื“ืขื™ื, ืœืžืขืฉื”,
01:40
is the mathematics that underlies an idea,
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ื”ื•ื ื”ืžืชืžื˜ื™ืงื” ืขืœื™ื” ืžื‘ื•ืกืก ืจืขื™ื•ืŸ,
01:43
and we can do some pretty interesting analysis with it,
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ืžื—ืงืจ ื“ื™ ืžืขื ื™ื™ืŸ ืขื ื–ื”,
01:45
which I want to share with you now.
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ืฉืื ื™ ืจื•ืฆื” ืœืฉืชืฃ ืืชื›ื ืขื›ืฉื™ื•.
01:47
So each idea has its own meme-ome,
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ืื– ืœื›ืœ ืจืขื™ื•ืŸ ื™ืฉ ืืช ื”ืžื™ืž-ืื•ื ืฉืœื•,
01:49
and each idea is unique with that,
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ื•ื›ืœ ืจืขื™ื•ืŸ ื”ื•ื ื™ื™ื—ื•ื“ื™ ืขื ื–ื”,
01:51
but of course, ideas, they borrow from each other,
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ืื‘ืœ ื›ืžื•ื‘ืŸ, ืจืขื™ื•ื ื•ืช, ืฉื•ืืœื™ื ื–ื” ืžื–ื”,
01:53
they kind of steal sometimes,
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ื”ื ื‘ืื•ืคืŸ ืžืกื•ื™ื ื’ื•ื ื‘ื™ื ืœืคืขืžื™ื,
01:54
and they certainly build on each other,
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ื•ื”ื ื‘ื•ื•ื“ืื™ ื ื‘ื ื™ื ืื—ื“ ืขืœ ื”ืฉื ื™,
01:56
and we can go through mathematically
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื‘ื—ื•ืŸ ื–ืืช ืžืชืžื˜ื™ืช
01:58
and take the meme-ome from one talk
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ื•ืœืงื—ืช ืืช ื”ืžื™ืž-ืื•ื ืžืฉื™ื—ื” ืื—ืช
02:00
and compare it to the meme-ome from every other talk,
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ื•ืœื”ืฉื•ื•ืช ืื•ืชื• ืœืžื™ืž-ืื•ื ืฉืœ ืฉื™ื—ื” ืื—ืจืช,
02:02
and if there's a similarity between the two of them,
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ื•ืื ื™ืฉ ื“ืžื™ื•ืŸ ื‘ื™ืŸ ื”ืฉื ื™ื™ื,
02:04
we can create a link and represent that as a graph,
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื™ืฆื•ืจ ื—ื™ื‘ื•ืจ ื•ืœื”ืฆื™ื’ ื–ืืช ื‘ืืžืฆืขื•ืช ื’ืจืฃ,
02:07
just like Eric and I are connected.
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ื‘ื“ื™ื•ืง ื›ืคื™ ืฉืืจื™ืง ื•ืื ื™ ืžื—ื•ื‘ืจื™ื.
02:10
So that's theory, that's great.
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ืื–, ื–ื• ืชื™ืื•ืจื™ื”, ื–ื” ืžืขื•ืœื”.
02:11
Let's see how it works in actual practice.
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ื‘ื•ืื• ื ืจืื” ืื™ืš ื–ื” ืขื•ื‘ื“ ื‘ืžืฆื™ืื•ืช.
02:14
So what we've got here now is the global footprint
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ืื– ืžื” ืฉื™ืฉ ืœื ื• ื›ืืŸ ืขื›ืฉื™ื• ื–ื” ื˜ื‘ื™ืขืช ื”ืจื’ืœ ื”ื’ืœื•ื‘ืœื™ืช
02:17
of all the TEDx Talks over the last four years
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ืฉืœ ื›ืœ ื”ืจืฆืื•ืช TEDx ืž-4 ื”ืฉื ื™ื ื”ืื—ืจื•ื ื•ืช
02:19
exploding out around the world
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ืžื•ืคืฆื™ื ืœื›ืœ ื”ืขื•ืœื
02:20
from New York all the way down to little old New Zealand in the corner.
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ืžื ื™ื•-ื™ื•ืจืง ื›ืœ ื”ื“ืจืš ืœืžื˜ื” ืขื“ ืœื ื™ื• ื–ื™ืœื ื“ ื”ืงื˜ื ื” ื‘ืคื™ื ื”.
02:24
And what we did on this is we analyzed the top 25 percent of these,
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ื•ืžื” ืฉืขืฉื™ื ื• ืขื ื–ื” ื”ื•ื ืฉื ื™ืชื—ื ื• ืืช 25% ื”ืขืœื™ื•ื ื™ื ืฉืœ ื–ื”,
02:28
and we started to see where the connections occurred,
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ื•ื”ืชื—ืœื ื• ืœืจืื•ืช ืื™ืคื” ื”ื—ื™ื‘ื•ืจื™ื ืžืชืจื—ืฉื™ื,
02:30
where they connected with each other.
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ืื™ืคื” ื”ื ืžืชื—ื‘ืจื™ื ืื—ื“ ืขื ื”ืฉื ื™.
02:32
Cameron Russell talking about image and beauty
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ืงืžืจื•ืŸ ืจืืกืœ ืžื“ื‘ืจ ืขืœ ืชืžื•ื ื” ื•ื™ื•ืคื™
02:33
connected over into Europe.
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ืžืชื—ื‘ืจ ืœืชื•ืš ืื™ืจื•ืคื”.
02:35
We've got a bigger conversation about Israel and Palestine
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ื™ืฉ ืœื ื• ืฉื™ื—ื” ื’ื“ื•ืœื” ื™ื•ืชืจ ืขืœ ื™ืฉืจืืœ ื•ืคืœืกื˜ื™ืŸ
02:37
radiating outwards from the Middle East.
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ืžื•ืคืฆืช ื›ืœืคื™ ื—ื•ืฅ ืžื”ืžื–ืจื— ื”ืชื™ื›ื•ืŸ.
02:40
And we've got something a little broader
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ื•ื™ืฉ ืœื ื• ืžืฉื”ื• ืงืฆืช ืจื—ื‘ ื™ื•ืชืจ
02:41
like big data with a truly global footprint
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ื›ืžื• "ืžื™ื“ืข ื’ื“ื•ืœ" ืขื ื˜ื‘ื™ืขืช ืจื’ืœ ื‘ืืžืช ื’ืœื•ื‘ืืœื™ืช
02:43
reminiscent of a conversation
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ื–ื›ืจื•ืŸ ืฉืœ ืฉื™ื—ื”
02:45
that is happening everywhere.
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ืฉืžืชืจื—ืฉืช ื‘ื›ืœ ืžืงื•ื.
02:47
So from this, we kind of run up against the limits
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ืื– ืžื–ื”, ืื ื—ื ื• ื“ื™ ื ืชืงืœื™ื ื‘ื’ื‘ื•ืœื•ืช
02:50
of what we can actually do with a geographic projection,
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ืฉืœ ืžื” ืฉืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ืขื ืชื—ื–ื™ื•ืช ื’ืื•ื’ืจืคื™ื•ืช,
02:52
but luckily, computer technology allows us to go out
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ืื‘ืœ ืœืžืจื‘ื” ื”ืžื–ืœ, ื˜ื›ื ื•ืœื•ื’ื™ืช ื”ืžื—ืฉื‘ ืžืืคืฉืจืช ืœื ื• ืœืฆืืช
02:54
into multidimensional space.
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ืœืžืจื—ื‘ ื”ืจื‘-ืžื™ืžื“ื™.
02:56
So we can take in our network projection
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ืœื›ืŸ ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืงื—ืช ืืช ื”ืชื—ื–ื™ืช ื”ืจืฉืชื™ืช ืฉืœื ื•
02:58
and apply a physics engine to this,
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ื•ืœื™ื™ืฉื ืžื ื•ืข ืคื™ื–ื™ืงืœื™ ืœื–ื”,
02:59
and the similar talks kind of smash together,
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ื•ื”ืฉื™ื—ื•ืช ื”ื“ื•ืžื•ืช ื“ื™ ืžืชืื—ื“ื•ืช ื™ื—ื“ื™ื•,
03:01
and the different ones fly apart,
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ื•ื”ืฉื•ื ื•ืช ืžืชืคื–ืจื•ืช ืœื”ืŸ,
03:03
and what we're left with is something quite beautiful.
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ื•ืžื” ืฉืื ื—ื ื• ื ืฉืืจื™ื ืื™ืชื• ื–ื” ื“ื‘ืจ ื“ื™ ื™ืคื”.
03:05
EB: So I want to just point out here that every node is a talk,
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ืืจื™ืง: ืื– ืื ื™ ืจืง ืจื•ืฆื” ืœืฆื™ื™ืŸ ื›ืืŸ ืฉื›ืœ ื ืงื•ื“ืช ืงืฆื” ื”ื™ื ืฉื™ื—ื”,
03:08
they're linked if they share similar ideas,
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ื”ืŸ ืžื—ื•ื‘ืจื•ืช ืื ื”ืŸ ื—ื•ืœืงื•ืช ืจืขื™ื•ื ื•ืช ื“ื•ืžื™ื,
03:11
and that comes from a machine reading
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ื•ื–ื” ืžื’ื™ืข ืžืงืจื™ืื” ืฉืœ ืžื›ื•ื ื”
03:13
of entire talk transcripts,
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ืฉืœ ืชืžืœื™ืœ ื”ืฉื™ื—ื” ื‘ืžืœื•ืื”,
03:15
and then all these topics that pop out,
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ื•ืื– ื›ืœ ื”ื ื•ืฉืื™ื ื”ืืœื• ืฉืงื•ืคืฆื™ื,
03:17
they're not from tags and keywords.
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ื”ื ืœื ืžืชื’ื™ื•ืช ื•ืžื™ืœื•ืช ืžืคืชื—.
03:19
They come from the network structure
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ื”ื ื ื•ื‘ืขื™ื ืžืžื‘ื ื” ื”ืจืฉืช
03:21
of interconnected ideas. Keep going.
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ืฉืœ ืจืขื™ื•ื ื•ืช ืžื—ื•ื‘ืจื™ื. ืชืžืฉื™ืš.
03:23
SG: Absolutely. So I got a little quick on that,
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ืฉื•ืŸ: ืœื—ืœื•ื˜ื™ืŸ. ืื– ืื ื™ ื”ื–ื“ืจื–ืชื™ ืขื ื–ื”,
03:25
but he's going to slow me down.
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ืื‘ืœ ื”ื•ื ืขื•ืžื“ ืœื”ืื˜ ืื•ืชื™.
03:26
We've got education connected to storytelling
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ื™ืฉ ืœื ื• ืืช ื—ื™ื ื•ืš ืฉืžืชื—ื‘ืจ ืœืกื™ืคื•ืจืช
03:28
triangulated next to social media.
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ื•ืฉื ื™ื”ื ืžืชื—ื‘ืจื™ื ืœืžื“ื™ื” ื—ื‘ืจืชื™ืช.
03:30
You've got, of course, the human brain right next to healthcare,
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ื™ืฉ ืœื›ื, ื›ืžื•ื‘ืŸ, ืืช ื”ืžื•ื— ื”ืื ื•ืฉื™ ืžืžืฉ ืœื™ื“ ื‘ืจื™ืื•ืช,
03:33
which you might expect,
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ืžื” ืฉื”ื™ื™ืชื ืžืฆืคื™ื,
03:34
but also you've got video games, which is sort of adjacent,
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ืื‘ืœ ื’ื ื™ืฉ ืœื›ื ืžืฉื—ืงื™ ื•ื™ื“ื™ืื•, ืฉื”ื ื“ื™ ื—ื•ืคืคื™ื,
03:36
as those two spaces interface with each other.
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ื›ืืฉืจ ืฉื ื™ ืžื™ืžื“ื™ื ืืœื• ืžืชืžืžืฉืงื™ื ื”ืื—ื“ ืœืฉื ื™.
03:39
But I want to take you into one cluster
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ืื‘ืœ ืื ื™ ืจื•ืฆื” ืœืงื—ืช ืืชื›ื ืœืชื•ืš ืื—ื“ ื”ืืฉื›ื•ืœื•ืช
03:41
that's particularly important to me, and that's the environment.
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ืืฉืจ ืœืžืขืฉื” ื—ืฉื•ื‘ ืœื™, ื•ื–ื” ื”ืกื‘ื™ื‘ื”.
03:43
And I want to kind of zoom in on that
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ื•ืื ื™ ืจื•ืฆื” ืœืขืฉื•ืช ื–ื•ื ืœืชื•ืš ื–ื”
03:45
and see if we can get a little more resolution.
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ื•ืœืจืื•ืช ืื ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืงื‘ืœ ืจื–ื•ืœื•ืฆื™ื” ื˜ื•ื‘ื” ื™ื•ืชืจ.
03:47
So as we go in here, what we start to see,
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ืื– ืื™ืš ืฉืื ื—ื ื• ื ื›ื ืกื™ื ืœื›ืืŸ, ืžื” ืฉืื ื—ื ื• ืžืชื—ื™ืœื™ื ืœืจืื•ืช,
03:50
apply the physics engine again,
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ืžื™ื™ืฉื ืืช ืžื ื•ืข ื”ืคื™ื–ื™ืงื” ืฉื•ื‘,
03:51
we see what's one conversation
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ืื ื—ื ื• ืจื•ืื™ื ืฉืฉื™ื—ื” ืื—ืช
03:53
is actually composed of many smaller ones.
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ืœืžืขืฉื” ืžื•ืจื›ื‘ืช ืžื›ืžื” ืฉื™ื—ื•ืช ืงื˜ื ื•ืช.
03:55
The structure starts to emerge
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ื”ืžื‘ื ื” ืžืชื—ื™ืœ ืœื”ืชื’ืœื•ืช
03:57
where we see a kind of fractal behavior
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ืื™ืคื” ืฉืื ื—ื ื• ืจื•ืื™ื ืกื•ื’ ืฉืœ ื”ืชื ื”ื’ื•ืช ืคืจืงื˜ืœื™ืช
03:59
of the words and the language that we use
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ืฉืœ ื”ืžื™ืœื™ื ื•ื”ืฉืคื” ืฉืื ื—ื ื• ืžืฉืชืžืฉื™ื
04:01
to describe the things that are important to us
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ืœืชืืจ ืืช ื”ื“ื‘ืจื™ื ืฉื—ืฉื•ื‘ื™ื ืœื ื•
04:03
all around this world.
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ื‘ื›ืœ ื”ืขื•ืœื.
04:04
So you've got food economy and local food at the top,
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ืื– ื™ืฉ ืœื ื• ื›ืœื›ืœืช ืžื–ื•ืŸ ื•ืื•ื›ืœ ืžืงื•ืžื™ ืœืžืขืœื”,
04:06
you've got greenhouse gases, solar and nuclear waste.
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ื™ืฉ ื’ื–ื™ ื—ืžืžื”, ืกื•ืœืืจื™ ื•ืคืกื•ืœืช ื’ืจืขื™ื ื™ืช.
04:09
What you're getting is a range of smaller conversations,
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ืžื” ืฉืžืชืงื‘ืœ ื”ื•ื ืžื’ื•ื•ืŸ ืฉืœ ืฉื™ื—ื•ืช ืงื˜ื ื•ืช,
04:12
each connected to each other through the ideas
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ื›ืœ ืื—ืช ืžื—ื•ื‘ืจื•ืช ืœืฉื ื™ื” ื‘ืืžืฆืขื•ืช ื”ืจืขื™ื•ื ื•ืช
04:14
and the language they share,
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ื•ื”ืฉืคื” ืฉื”ื ืžืฉืชืคื™ื,
04:15
creating a broader concept of the environment.
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ื™ื•ืฆืจื™ื ืžื•ืฉื’ ืจื—ื‘ ื™ื•ืชืจ ืฉืœ ื”ืกื‘ื™ื‘ื”.
04:18
And of course, from here, we can go
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ื•ื›ืžื•ื‘ืŸ, ืฉืžื›ืืŸ, ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืœื›ืช
04:19
and zoom in and see, well, what are young people looking at?
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ื•ืœื”ืชืงืจื‘ ื•ืœืจืื•ืช, ื•ื‘ื›ืŸ, ืขืœ ืžื” ืื ืฉื™ื ืฆืขื™ืจื™ื ืžืกืชื›ืœื™ื?
04:23
And they're looking at energy technology and nuclear fusion.
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ื•ื”ื ืžืกืชื›ืœื™ื ืขืœ ื˜ื›ื ื•ืœื•ื’ื™ืช ืื ืจื’ื™ื” ื•ื”ื™ืชื•ืš ื’ืจืขื™ื ื™.
04:25
This is their kind of resonance
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ื–ื• ื”ื™ื ื”ืชืขื•ื“ื” ืฉืœื”ื
04:27
for the conversation around the environment.
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ืฉืœ ื”ืฉื™ื—ื” ื‘ื ื•ื’ืข ืœืกื‘ื™ื‘ื”.
04:29
If we split along gender lines,
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ืื ืื ื—ื ื• ืžืคืฆืœื™ื ืœืคื™ ืงื•ื•ื™ ืžื’ื“ืจ,
04:31
we can see females resonating heavily
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืจืื•ืช ืฉื”ื ืฉื™ื ืžื•ื‘ื™ืœื•ืช ื‘ื”ืจื‘ื”
04:33
with food economy, but also out there in hope and optimism.
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ืขื ื›ืœื›ืœืช ืžื–ื•ืŸ, ืื‘ืœ ื’ื ืื™ ืฉื ื‘ืชืงื•ื•ื” ื•ืื•ืคื˜ื™ืžื™ื•ืช.
04:37
And so there's a lot of exciting stuff we can do here,
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ื•ื›ืŸ ื™ืฉ ื”ืจื‘ื” ื“ื‘ืจื™ื ืžืจื’ืฉื™ื ืฉืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ื›ืืŸ,
04:39
and I'll throw to Eric for the next part.
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ื•ืื ื™ ืื–ืจื•ืง ืœืืจื™ืง ืœื—ืœืง ื”ื‘ื.
04:41
EB: Yeah, I mean, just to point out here,
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ืืจื™ืง: ื›ืŸ, ืื ื™ ืžืชื›ื•ื•ืŸ, ืจืง ืœื”ื“ื’ื™ืฉ ื›ืืŸ,
04:43
you cannot get this kind of perspective
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ืืชื ืœื ื™ื›ื•ืœื™ื ืœืงื‘ืœ ืคืจืกืคืงื˜ื™ื‘ื” ืฉื›ื–ื•
04:44
from a simple tag search on YouTube.
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ืžื—ื™ืคื•ืฉ ืชื’ื™ื™ื•ืช ืคืฉื•ื˜ ื‘ื™ื•ื˜ื™ื•ื‘.
04:48
Let's now zoom back out to the entire global conversation
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ื‘ื•ืื• ื ืขืฉื” ืขื›ืฉื™ื• ื–ื•ื ืื—ื•ืจื” ืœืฉื™ื—ื” ื”ื’ืœื•ื‘ืœื™ืช
04:52
out of environment, and look at all the talks together.
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ืžื”ืกื‘ื™ื‘ื”, ื•ื ืกืชื›ืœ ืขืœ ื›ืœ ื”ืฉื™ื—ื•ืช ื‘ื™ื—ื“.
04:54
Now often, when we're faced with this amount of content,
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ืขื›ืฉื™ื• ืœืขื™ืชื™ื, ื›ืืฉืจ ืื ื—ื ื• ื ืฆื‘ื™ื ืžื•ืœ ื›ืžื•ืช ื›ื–ื• ืฉืœ ืชื•ื›ืŸ,
04:57
we do a couple of things to simplify it.
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ืื ื—ื ื• ืขื•ืฉื™ื ืžืกืคืจ ื“ื‘ืจื™ื ืœืคืฉื˜ ื–ืืช.
05:00
We might just say, well,
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืคืฉื•ื˜ ืœื•ืžืจ, ื•ื‘ื›ืŸ,
05:01
what are the most popular talks out there?
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ืžื” ื”ืŸ ื”ืฉื™ื—ื•ืช ื”ืคื•ืคื•ืœืืจื™ื•ืช ื‘ื™ื•ืชืจ?
05:04
And a few rise to the surface.
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ื•ื‘ื•ื“ื“ื•ืช ืฆืคื•ืช ืœืคื ื™ ื”ืฉื˜ื—.
05:05
There's a talk about gratitude.
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ื™ืฉื ื” ืฉื™ื—ื” ื‘ื ื•ื’ืข ืœื”ื›ืจืช ืชื•ื“ื”.
05:07
There's another one about personal health and nutrition.
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ื™ืฉื ื” ืื—ืจืช ื‘ื ื•ื’ืข ืœื‘ืจื™ืื•ืช ืื™ืฉื™ืช ื•ืชื–ื•ื ื”.
05:10
And of course, there's got to be one about porn, right?
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ื•ื›ืžื•ื‘ืŸ, ื—ื™ื™ื‘ืช ืœื”ื™ื•ืช ืื—ืช ื‘ื ื•ื’ืข ืœืคื•ืจื ื•, ื ื›ื•ืŸ?
05:13
And so then we might say, well, gratitude, that was last year.
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ื•ืื– ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื•ืžืจ, ื•ื‘ื›ืŸ, ื”ื›ืจืช ืชื•ื“ื”, ื–ื” ื”ื™ื” ืฉื ื” ืฉืขื‘ืจื”.
05:17
What's trending now? What's the popular talk now?
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ืžื” ื”ื˜ืจื ื“ ื”ื ื•ื›ื—ื™? ืžื” ื”ืฉื™ื—ื” ื”ืคื•ืคื•ืœืืจื™ืช ืขื›ืฉื™ื•?
05:19
And we can see that the new, emerging, top trending topic
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืจืื•ืช ืฉื”ื ื•ืฉื ื”ื—ื“ืฉ, ื”ื˜ืจื ื“ื™
05:22
is about digital privacy.
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ื”ื•ื ื‘ื ื•ื’ืข ืœืคืจื˜ื™ื•ืช ื“ื™ื’ื™ื˜ืœื™ืช.
05:25
So this is great. It simplifies things.
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ืื– ื–ื” ืคืฉื•ื˜. ื–ื” ืžืคืฉื˜ ื“ื‘ืจื™ื.
05:27
But there's so much creative content
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ืื‘ืœ ื™ืฉื ื• ื›ืœ ื›ืš ื”ืจื‘ื” ืชื•ื›ืŸ ื™ืฆื™ืจืชื™
05:29
that's just buried at the bottom.
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ืฉืคืฉื•ื˜ ืงื‘ื•ืจ ื‘ืชื—ืชื™ืช.
05:31
And I hate that. How do we bubble stuff up to the surface
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ื•ืื ื™ ืฉื•ื ื ืืช ื–ื”. ืื™ืš ืื ื—ื ื• ืžืขืœื™ื ื“ื‘ืจื™ื ืœืคื ื™ ื”ืฉื˜ื—
05:34
that's maybe really creative and interesting?
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ืฉืื•ืœื™ ืžืžืฉ ื™ืฆื™ืจืชื™ื™ื ื•ืžืขื ื™ื™ื ื™ื?
05:36
Well, we can go back to the network structure of ideas
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ื•ื‘ื›ืŸ, ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื—ื–ื•ืจ ืœืžื‘ื ื” ื”ืจืฉืชื™ ืฉืœ ื”ืจืขื™ื•ื ื•ืช
05:39
to do that.
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ื•ืœืขืฉื•ืช ื–ืืช.
05:41
Remember, it's that network structure
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ื–ื›ืจื•, ื–ื”ื• ื”ืžื‘ื ื” ื”ืจืฉืชื™
05:43
that is creating these emergent topics,
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ืืฉืจ ื™ื•ืฆืจ ืืช ื”ื ื•ืฉืื™ื ื”ืžืชื”ื•ื•ื™ื ื”ืœืœื•,
05:45
and let's say we could take two of them,
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ื•ื‘ื•ืื• ื ื’ื™ื“ ืฉื”ื™ื™ื ื• ื™ื›ื•ืœื™ื ืœืงื—ืช ืฉื ื™ื™ื ืžื”ื,
05:47
like cities and genetics, and say, well, are there any talks
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ื›ืžื• ืขืจื™ื ื•ื’ื ื˜ื™ืงื”, ื•ืœื•ืžืจ, ื•ื‘ื›ืŸ, ื”ืื ืงื™ื™ืžื•ืช ืฉื™ื—ื•ืช
05:50
that creatively bridge these two really different disciplines.
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ืฉืžื’ืฉืจื•ืช ื‘ืื•ืคืŸ ื™ืฆื™ืจืชื™ ืืช ืฉืชื™ ื”ื“ื™ืกื™ืคืœื™ื ื•ืช ื”ืฉื•ื ื•ืช ื”ืœืœื•?
05:52
And that's -- Essentially, this kind of creative remix
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ื•ื–ื” -- ืœืžืขืฉื”, ื”ืฉื™ืœื•ื‘ ื”ื™ืฆื™ืจืชื™ ืžืกื•ื’ ื–ื”
05:54
is one of the hallmarks of innovation.
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ื”ื•ื ืื—ื“ ื”ืกื™ืžื ื™ ื”ื”ื™ื›ืจ ืฉืœ ื—ื“ืฉื ื•ืช.
05:56
Well here's one by Jessica Green
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ื•ื‘ื›ืŸ ื”ื ื” ืื—ื“ ืฉืœ ื’'ืกื™ืงื” ื’ืจื™ืŸ
05:58
about the microbial ecology of buildings.
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ื‘ื ื•ื’ืข ืœืืงื•ืœื•ื’ื™ื” ืฉืœ ื—ื™ื™ื“ืงื™ื ื‘ื‘ื ื™ื™ื ื™ื.
06:00
It's literally defining a new field.
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ื–ื” ืžืžืฉ ืžื’ื“ื™ืจ ืชื—ื•ื ื—ื“ืฉ.
06:02
And we could go back to those topics and say, well,
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื—ื–ื•ืจ ืœื ื•ืฉืื™ื ืืœื• ื•ืœื•ืžืจ, ื•ื‘ื›ืŸ,
06:04
what talks are central to those conversations?
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ืื™ืœื• ืฉื™ื—ื•ืช ืกืคืฆื™ืคื™ื•ืช ืžื”ื•ื•ืช ืืช ื”ืžืจื›ื– ืœืฉื™ื—ื•ืช ืืœื•?
06:07
In the cities cluster, one of the most central
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ื‘ืืฉื›ื•ืœ ื”ืขืจื™ื, ืื—ืช ื”ืžืจื›ื–ื™ื•ืช ื‘ื™ื•ืชืจ
06:09
was one by Mitch Joachim about ecological cities,
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ื”ื™ื™ืชื” ืฉืœ ืžื™ื˜ืฉ ื’'ื•ืืงื™ื ื‘ื ื•ื’ืข ืœืขืจื™ื ืืงื•ืœื•ื’ื™ื•ืช,
06:13
and in the genetics cluster,
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ื•ื‘ืืฉื›ื•ืœ ื”ื’ื ื˜ื™ืงื”,
06:15
we have a talk about synthetic biology by Craig Venter.
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ืื ื—ื ื• ืžื•ืฆืื™ื ืฉื™ื—ื” ื‘ื ื•ื’ืข ืœื‘ื™ื•ืœื•ื’ื™ื” ืกื™ื ื˜ื˜ื™ืช ืฉืœ ืงืจื™ื™ื’ ื•ื ื˜ืจ.
06:18
These are talks that are linking many talks within their discipline.
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ืืœื• ืฉื™ื—ื•ืช ืฉืžื—ื‘ืจื•ืช ืฉื™ื—ื•ืช ืจื‘ื•ืช ื‘ืชื•ืš ื”ื“ื™ืกื™ืคืœื™ื ื” ืฉืœื”ื.
06:21
We could go the other direction and say, well,
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ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืœื›ืช ืœื›ื™ื•ื•ืŸ ื”ืฉื ื™ ื•ืœื•ืžืจ, ื•ื‘ื›ืŸ,
06:23
what are talks that are broadly synthesizing
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ืืœื• ืฉื™ื—ื•ืช ืžื—ื‘ืจื•ืช ื‘ืื•ืคืŸ ืจื—ื‘
06:25
a lot of different kinds of fields.
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ืกื•ื’ื™ื ืจื‘ื™ื ื•ืฉื•ื ื™ื ืฉืœ ืชื—ื•ืžื™ื.
06:27
We used a measure of ecological diversity to get this.
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ืื ื—ื ื• ื”ืฉืชืžืฉื ื• ื‘ืžื™ื“ื” ืฉืœ ืฉื•ื ื•ืช ืืงื•ืœื•ื’ื™ืช ืขืœ ืžื ืช ืœืงื‘ืœ ื–ืืช.
06:29
Like, a talk by Steven Pinker on the history of violence,
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ื›ื’ื•ืŸ, ืฉื™ื—ื” ืฉืœ ืกื˜ื™ื‘ืŸ ืคื™ื ืงืจ ืื•ื“ื•ืช ื”ื”ื™ืกื˜ื•ืจื™ื” ืฉืœ ื”ืืœื™ืžื•ืช,
06:32
very synthetic.
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ืกื™ื ื˜ื˜ื™ ืžืื•ื“.
06:33
And then, of course, there are talks that are so unique
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ื•ืื–, ื›ืžื•ื‘ืŸ, ื™ืฉื ืŸ ืฉื™ื—ื•ืช ืฉื”ืŸ ืžืื•ื“ ื™ื™ื—ื•ื“ื™ื•ืช
06:35
they're kind of out in the stratosphere, in their own special place,
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ื›ืžื• ืžืขื™ื™ืŸ ืžื—ื•ืฅ ืœื˜ื•ื•ื—, ื‘ื—ืœืœ ืžืฉืœื”ื,
06:38
and we call that the Colleen Flanagan index.
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ื•ืื ื—ื ื• ืงื•ืจืื™ื ืœื–ื” ืื™ื ื“ืงืก ืงื•ืœื™ืŸ ืคืœื ื’ืŸ.
06:41
And if you don't know Colleen, she's an artist,
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ื•ืื ืืชื ืœื ืžื›ื™ืจื™ื ืืช ืงื•ืœื™ืŸ, ื”ื™ื ืืžื ื™ืช,
06:44
and I asked her, "Well, what's it like out there
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ื•ืื ื™ ืฉืืœืชื™ ืื•ืชื”, "ื•ื‘ื›ืŸ, ืื™ืš ื–ื” ืฉื ืžื—ื•ืฅ
06:45
in the stratosphere of our idea space?"
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ืœื—ืœืœ ื”ืจืขื™ื•ื ื•ืช ืฉืœื ื•?"
06:47
And apparently it smells like bacon.
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ื•ื›ื›ืœ ื”ื ืจืื” ื–ื” ืžืจื™ื— ื›ืžื• ื‘ื™ื™ืงื•ืŸ.
06:50
I wouldn't know.
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ืื ื™ ืœื ื”ื™ื™ืชื™ ื™ื•ื“ืข.
06:52
So we're using these network motifs
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ืื– ืื ื—ื ื• ืžืฉืชืžืฉื™ื ื‘ืžื•ื˜ื™ื‘ื™ื
06:54
to find talks that are unique,
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ืœืžืฆื•ื ืฉื™ื—ื•ืช ืฉื”ืŸ ื™ื™ื—ื•ื“ื™ื•ืช,
06:56
ones that are creatively synthesizing a lot of different fields,
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ื›ืืœื• ืฉื‘ืื•ืคืŸ ื™ืฆื™ืจืชื™ ืžืฉืœื‘ื•ืช ืชื—ื•ืžื™ื ืจื‘ื™ื,
06:58
ones that are central to their topic,
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ื›ืืœื• ืฉื”ืŸ ืžืจื›ื–ื™ื•ืช ืœื ื•ืฉืื™ื ืฉืœื”ืŸ,
07:00
and ones that are really creatively bridging disparate fields.
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ื•ื›ืืœื• ืฉืžืžืฉ ื‘ืื•ืคืŸ ื™ืฆื™ืจืชื™ ืžื’ืฉืจื•ืช ื‘ื™ืŸ ืชื—ื•ืžื™ื ื ืคืจื“ื™ื.
07:03
Okay? We never would have found those with our obsession
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ืื•ืงื™? ืœืขื•ืœื ืœื ื”ื™ื™ื ื• ืžื•ืฆืื™ื ื–ืืช ืขื ื”ืื•ื‘ืกืกื™ื” ืฉืœื ื•
07:05
with what's trending now.
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ืœืžื” ืฉื˜ืจื ื“ื™ ืขื›ืฉื™ื•.
07:08
And all of this comes from the architecture of complexity,
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ื•ื›ืœ ื–ื” ืžื’ื™ืข ืžื”ืืจื›ื™ื˜ืงื˜ื•ืจื” ืฉืœ ื”ืžื•ืจื›ื‘ื•ืช,
07:11
or the patterns of how things are connected.
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ืฉืœ ื”ืชื‘ื ื™ื•ืช ืฉืœ ืื™ืš ื“ื‘ืจื™ื ืžื—ื•ื‘ืจื™ื.
07:14
SG: So that's exactly right.
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ืฉื•ืŸ: ืื– ื–ื” ื‘ื“ื™ื•ืง ื ื›ื•ืŸ.
07:15
We've got ourselves in a world
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ืื ื—ื ื• ื ืžืฆืื™ื ื‘ืขื•ืœื
07:18
that's massively complex,
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ืฉืžื•ืจื›ื‘ ื‘ืื•ืคืŸ ืžืกื™ื‘ื™,
07:20
and we've been using algorithms to kind of filter it down
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ื•ืื ื—ื ื• ืžืฉืชืžืฉื™ื ื‘ืืœื’ื•ืจื™ืชืžื™ื ืœืกื ืŸ ืื•ืชื•
07:23
so we can navigate through it.
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ื›ืš ืฉื ื•ื›ืœ ืœื ื•ื•ื˜ ื‘ืชื•ื›ื•.
07:24
And those algorithms, whilst being kind of useful,
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ื•ื‘ืืœื’ื•ืจื™ืชืžื™ื ื”ืืœื•, ืขื“ ื›ืžื” ืฉื”ื ืฉื™ืžื•ืฉื™ื™ื,
07:27
are also very, very narrow, and we can do better than that,
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ื”ื ืžืื•ื“, ืžืื•ื“ ืฆืจื™ื, ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ื˜ื•ื‘ ื™ื•ืชืจ ืžื–ื”
07:30
because we can realize that their complexity is not random.
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ื›ื™ ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื”ื‘ื™ืŸ ืฉื”ืžื•ืจื›ื‘ื•ืช ืฉืœื”ื ื”ื™ื ืœื ืืงืจืื™ืช.
07:33
It has mathematical structure,
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ื™ืฉ ืœื–ื” ืžื‘ื ื” ืžืชืžื˜ื™,
07:35
and we can use that mathematical structure
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ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื ืœื”ืฉืชืžืฉ ื‘ืžื‘ื ื” ื”ืžืชืžื˜ื™ ื”ื–ื”,
07:36
to go and explore things like the world of ideas
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ืœื’ืฉืช ื•ืœื—ืงื•ืจ ื“ื‘ืจื™ื ื›ืžื• ืขื•ืœื ื”ืจืขื™ื•ื ื•ืช
07:39
to see what's being said, to see what's not being said,
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ืœืจืื•ืช ืžื” ื ืืžืจ, ืœืจืื•ืช ืžื” ืœื ื ืืžืจ,
07:42
and to be a little bit more human
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ื•ืœื”ื™ื•ืช ืงืฆืช ื™ื•ืชืจ ืื ื•ืฉื™ื™ื
07:43
and, hopefully, a little smarter.
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ื•ื’ื, ื‘ืชืงื•ื•ื”, ืงืฆืช ื—ื›ืžื™ื ื™ื•ืชืจ.
07:45
Thank you.
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ืชื•ื“ื” ืœื›ื.
07:46
(Applause)
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(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

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