What we learned from 5 million books

236,154 views ・ 2011-09-20

TED


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Translator: Bjarne Poulsen Reviewer: Jonas Tholstrup Christensen
00:15
Erez Lieberman Aiden: Everyone knows
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Erez Lieberman Aiden: Alle ved
00:17
that a picture is worth a thousand words.
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at et billede siger mere end tusind ord
00:22
But we at Harvard
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Men på Harvard
00:24
were wondering if this was really true.
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spurgte vi os selv, om det egentlig er sandt.
00:27
(Laughter)
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(Latter)
00:29
So we assembled a team of experts,
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Så vi samlede et hold eksperter,
00:33
spanning Harvard, MIT,
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både fra Harvard, MIT,
00:35
The American Heritage Dictionary, The Encyclopedia Britannica
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The American Heritage Dictionary, The Encyclopedia Britannica
00:38
and even our proud sponsors,
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og sågar vores stolte sponsor...
00:40
the Google.
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The Google.
00:43
And we cogitated about this
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Og vi har funderet over dette
00:45
for about four years.
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i cirka fire år.
00:47
And we came to a startling conclusion.
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Og vores konklusion er overraskende.
00:52
Ladies and gentlemen, a picture is not worth a thousand words.
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Mine damer og herrer, et billede siger ikke mere end tusind ord.
00:55
In fact, we found some pictures
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Det viste sig faktisk at nogle billeder
00:57
that are worth 500 billion words.
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siger mere end 500 milliarder ord.
01:02
Jean-Baptiste Michel: So how did we get to this conclusion?
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Jean-Baptiste Michel: Hvordan når vi denne konklusion?
01:04
So Erez and I were thinking about ways
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Erez og jeg tænkte på, hvordan man
01:06
to get a big picture of human culture
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kunne få overblik over menneskets kultur og historie -
01:08
and human history: change over time.
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- og ændringen over tid.
01:11
So many books actually have been written over the years.
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Der skrevet så mange bøger gennem tiderne.
01:13
So we were thinking, well the best way to learn from them
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Så vi tænkte at man kan lære mest af alle disse bøger
01:15
is to read all of these millions of books.
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ved at læse dem alle sammen.
01:17
Now of course, if there's a scale for how awesome that is,
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Hvis der er en skala for, hvor fantastisk det er
01:20
that has to rank extremely, extremely high.
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må det selvfølgelig ligge meget, meget højt (Awesome).
01:23
Now the problem is there's an X-axis for that,
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Problemet er, at der også er en X-akse,
01:25
which is the practical axis.
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og det aksen for, om det også er praktisk.
01:27
This is very, very low.
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Den er meget, meget lav.
01:29
(Applause)
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(Bifald)
01:32
Now people tend to use an alternative approach,
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Folk bruger som regel en anden tilgang,
01:35
which is to take a few sources and read them very carefully.
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Man tager nogle få kilder og læser dem meget omhyggeligt.
01:37
This is extremely practical, but not so awesome.
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Dette er meget praktisk, men ikke særlig fantastisk.
01:39
What you really want to do
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Det bedste må være
01:42
is to get to the awesome yet practical part of this space.
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at nå til dette fantastiske men alligevel praktiske område.
01:45
So it turns out there was a company across the river called Google
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Et firma på den anden side af floden - Google -
01:48
who had started a digitization project a few years back
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startede et digitaliseringsprojekt for nogle år siden
01:50
that might just enable this approach.
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og det kan måske gøre denne tilgang mulig.
01:52
They have digitized millions of books.
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De har digitaliseret millioner af bøger.
01:54
So what that means is, one could use computational methods
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Man kan således bruge computerbaserede metoder
01:57
to read all of the books in a click of a button.
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til at læse alle bøgerne med et enkelt klik.
01:59
That's very practical and extremely awesome.
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Det er meget praktisk og ekstremt fantastisk.
02:03
ELA: Let me tell you a little bit about where books come from.
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ELA: Nu skal I høre, hvor bøger stammer fra.
02:05
Since time immemorial, there have been authors.
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Der har altid eksisteret forfattere.
02:08
These authors have been striving to write books.
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Disse forfattere har bestræbt sig på at skrive bøger.
02:11
And this became considerably easier
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Og det blev væsentligt nemmere
02:13
with the development of the printing press some centuries ago.
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da trykpressen blev opfundet for nogle hundrede år siden.
02:15
Since then, the authors have won
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Siden da, er det lykkedes forfattere
02:18
on 129 million distinct occasions,
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at udgive bøger
02:20
publishing books.
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129 millioner gange.
02:22
Now if those books are not lost to history,
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Hvis disse bøger ikke er gået tabt for historien,
02:24
then they are somewhere in a library,
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findes de på et bibliotek et sted,
02:26
and many of those books have been getting retrieved from the libraries
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og mange bøgerne er blevet taget fra hylderne
02:29
and digitized by Google,
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og er blevet digitaliseret af Google,
02:31
which has scanned 15 million books to date.
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som til dato har scannet 15 millioner bøger.
02:33
Now when Google digitizes a book, they put it into a really nice format.
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Når Google digitaliserer en bog, får den et rigtig fint format.
02:36
Now we've got the data, plus we have metadata.
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Nu har vi både data og metada.
02:38
We have information about things like where was it published,
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Vi har f.eks. oplysninger om, hvor den blev udgivet,
02:41
who was the author, when was it published.
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hvem forfatteren var, og hvornår den blev udgivet.
02:43
And what we do is go through all of those records
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Og vi går gennem alle disse arkiver
02:46
and exclude everything that's not the highest quality data.
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og udelukker alle data, der ikke er af højeste kvalitet.
02:50
What we're left with
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Det, der er tilbage, er en samling
02:52
is a collection of five million books,
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på fem millioner bøger,
02:55
500 billion words,
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500 milliarder ord,
02:58
a string of characters a thousand times longer
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en tegnstreng, der er tusind gange længere
03:00
than the human genome --
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end menneskets arvemasse.
03:03
a text which, when written out,
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Hvis teksten blev skrevet ud,
03:05
would stretch from here to the Moon and back
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ville den nå herfra til månen og tilbage igen
03:07
10 times over --
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10 gange!
03:09
a veritable shard of our cultural genome.
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- Et sandt brudstykke af vores kulturelle arvemasse.
03:13
Of course what we did
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Det vi gjorde,
03:15
when faced with such outrageous hyperbole ...
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da vi stod over for så vanvittige sammenligninger...
03:18
(Laughter)
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(Latter)
03:20
was what any self-respecting researchers
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var, hvad enhver forskere med respekt for sig selv
03:23
would have done.
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ville have gjort.
03:26
We took a page out of XKCD,
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Vi gjorde som i tegneserien XKCD,
03:28
and we said, "Stand back.
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og sagde "Gør plads!
03:30
We're going to try science."
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Vi prøver med videnskab".
03:32
(Laughter)
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(Latter)
03:34
JM: Now of course, we were thinking,
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JM: Først tænkte vi selvfølgelig,
03:36
well let's just first put the data out there
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"Vi gør bare data tilgængelige,
03:38
for people to do science to it.
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så andre kan bruge videnskab på dem."
03:40
Now we're thinking, what data can we release?
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Nu tænker vi "Hvilke data kan vi lægge ud?"
03:42
Well of course, you want to take the books
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Egentlig vil vi gerne tage bøgerne
03:44
and release the full text of these five million books.
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og lægge teksten fra alle fem millioner bøger ud.
03:46
Now Google, and Jon Orwant in particular,
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Men Google - og særligt Jon Orwant -
03:48
told us a little equation that we should learn.
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fortalte om en ligning, vi skulle lære.
03:50
So you have five million, that is, five million authors
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Vi har altså fem millioner forfattere
03:53
and five million plaintiffs is a massive lawsuit.
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altså fem millioner, der gerne vil sagsøge os.
03:56
So, although that would be really, really awesome,
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Så selvom det ville være virkelig, virkelig fantastisk,
03:58
again, that's extremely, extremely impractical.
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ville det også være helt ekstremt upraktisk.
04:01
(Laughter)
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(Latter)
04:03
Now again, we kind of caved in,
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Igen lod vi os overtale
04:05
and we did the very practical approach, which was a bit less awesome.
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og fulgte den praktiske tilgang, der var lidt mindre fantastisk.
04:08
We said, well instead of releasing the full text,
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I stedet for at lægge den fulde tekst ud ville vi
04:10
we're going to release statistics about the books.
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gøre statistikker om bøgerne tilgængelige.
04:12
So take for instance "A gleam of happiness."
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Et eksempel er "A gleam of happiness" - Et glimpt af lykke
04:14
It's four words; we call that a four-gram.
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Det er fire ord - det vi kalder et fire-gram
04:16
We're going to tell you how many times a particular four-gram
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Vi vil nu fortælle jer, hvor mange gange et bestemt fire-gram
04:18
appeared in books in 1801, 1802, 1803,
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optrådte i bøger i 1801, 1802, 1803,
04:20
all the way up to 2008.
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og helt op til 2008
04:22
That gives us a time series
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Det giver os en tidsserie, der viser hvor hyppigt
04:24
of how frequently this particular sentence was used over time.
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denne ene sætning er blevet brugt over tid.
04:26
We do that for all the words and phrases that appear in those books,
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Det gør vi for alle ord og udtryk i disse bøger.
04:29
and that gives us a big table of two billion lines
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Det giver os en stor tabel med to milliarder linjer
04:32
that tell us about the way culture has been changing.
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som viser hvordan kulturen har ændret sig.
04:34
ELA: So those two billion lines,
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ELA: Disse to milliarder linjer
04:36
we call them two billion n-grams.
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som vi kalder to milliarder n-grammer...
04:38
What do they tell us?
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Hvad fortæller de os?
04:40
Well the individual n-grams measure cultural trends.
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De enkelte n-grammer måler kulturelle tendenser.
04:42
Let me give you an example.
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Lad mig give et eksempel.
04:44
Let's suppose that I am thriving,
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Jeg vil sige, at jeg trives,
04:46
then tomorrow I want to tell you about how well I did.
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i morgen siger jeg så, hvor godt jeg havde det.
04:48
And so I might say, "Yesterday, I throve."
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Jeg ville sige "I går trivedes (throve) jeg".
04:51
Alternatively, I could say, "Yesterday, I thrived."
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Man kan også bruge "thrived" i stedet for "throve".
04:54
Well which one should I use?
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Hvilket af de to ord skal jeg bruge?
04:57
How to know?
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Hvor skulle jeg vide det fra?
04:59
As of about six months ago,
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Indtil for seks måneder siden
05:01
the state of the art in this field
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var den anerkendte metode på dette område
05:03
is that you would, for instance,
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at du f.eks. kunne få fat i
05:05
go up to the following psychologist with fabulous hair,
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denne psykolog med lækkert hår
05:07
and you'd say,
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og spørge ham:
05:09
"Steve, you're an expert on the irregular verbs.
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"Steve, du er ekspert i uregelmæssige verber.
05:12
What should I do?"
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Hvad skal jeg gøre?"
05:14
And he'd tell you, "Well most people say thrived,
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Og han ville sige: "De fleste mennesker bruger "thrived"
05:16
but some people say throve."
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men nogle siger "throve".
05:19
And you also knew, more or less,
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Og du vidste også - mere eller mindre -
05:21
that if you were to go back in time 200 years
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at hvis du gik 200 år tilbage i tiden
05:24
and ask the following statesman with equally fabulous hair,
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og spurgte denne statsmand med ligeså lækkert hår:
05:27
(Laughter)
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(Latter)
05:30
"Tom, what should I say?"
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"Tom, hvad ville du sige?"
05:32
He'd say, "Well, in my day, most people throve,
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Han ville sige: "På min tid brugte de fleste "throve,
05:34
but some thrived."
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mens andre brugte "thrived".
05:37
So now what I'm just going to show you is raw data.
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Så nu vil jeg bare vise jer rå data.
05:39
Two rows from this table of two billion entries.
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To rækker i denne tabel ud af to millarder poster.
05:43
What you're seeing is year by year frequency
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Den viser hyppigheden pr. år
05:45
of "thrived" and "throve" over time.
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af "thrived" og "throve" over tid.
05:49
Now this is just two
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Det her er kun to
05:51
out of two billion rows.
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ud af to milliarder rækker.
05:54
So the entire data set
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Så hele datasættet
05:56
is a billion times more awesome than this slide.
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er en milliard gange mere fantastisk end dette slide.
05:59
(Laughter)
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(Latter)
06:01
(Applause)
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(Bifald)
06:05
JM: Now there are many other pictures that are worth 500 billion words.
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JM: Der er jo mange andre billeder, der siger mere end 500 milliarder ord.
06:07
For instance, this one.
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For eksempel dette.
06:09
If you just take influenza,
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Hvis vi bare ser på influenza,
06:11
you will see peaks at the time where you knew
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vil I se høje udslag på de tidspunkter, hvor I vidste
06:13
big flu epidemics were killing people around the globe.
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at der var store globale influenzaepidemier.
06:16
ELA: If you were not yet convinced,
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ELA: Hvis du ikke er overbevist,
06:19
sea levels are rising,
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stiger vandstanden i havene -
06:21
so is atmospheric CO2 and global temperature.
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det gør CO2-indholdet i atmosfæren og den globale temperatur også.
06:24
JM: You might also want to have a look at this particular n-gram,
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JM: Prøv også at kaste et blik på dette n-gram,
06:27
and that's to tell Nietzsche that God is not dead,
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og det fortæller Nietzsche, at Gud ikke er død,
06:30
although you might agree that he might need a better publicist.
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selvom du måske også synes, han har brug for en bedre ///presseagent.
06:33
(Laughter)
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(Latter)
06:35
ELA: You can get at some pretty abstract concepts with this sort of thing.
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ELA: Man kan få nogle ret abstrakte begreber med disse ting.
06:38
For instance, let me tell you the history
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Lad mig f.eks. fortælle jer historien
06:40
of the year 1950.
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om året 1950.
06:42
Pretty much for the vast majority of history,
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I den største del af vores historie
06:44
no one gave a damn about 1950.
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har ingen interesseret sig en pind for 1950.
06:46
In 1700, in 1800, in 1900,
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I 1700 og 1800 og 1900
06:48
no one cared.
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var ingen interesseret.
06:52
Through the 30s and 40s,
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Op gennem 30'erne og 40'erne
06:54
no one cared.
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var ingen interesseret.
06:56
Suddenly, in the mid-40s,
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Pludselig, midt i 40'erne,
06:58
there started to be a buzz.
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blev der hvisket i krogene.
07:00
People realized that 1950 was going to happen,
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Folk indså at 1950 var noget, der ville ske,
07:02
and it could be big.
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og det kunne være noget stort.
07:04
(Laughter)
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(Latter)
07:07
But nothing got people interested in 1950
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Men det der gjorde folk allermest interesseret i 1950
07:10
like the year 1950.
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var året 1950.
07:13
(Laughter)
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(Latter)
07:16
People were walking around obsessed.
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Folk var som besat.
07:18
They couldn't stop talking
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De kunne ikke lade være med at tale
07:20
about all the things they did in 1950,
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om alt det, de lavede i 1950,
07:23
all the things they were planning to do in 1950,
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alt det de planlagde at skulle gøre i 1950,
07:26
all the dreams of what they wanted to accomplish in 1950.
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og alle drømmene om, hvad de ville opnå i 1950.
07:31
In fact, 1950 was so fascinating
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Faktisk var 1950 så fascinerende
07:33
that for years thereafter,
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at folk i flere år efter
07:35
people just kept talking about all the amazing things that happened,
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bare blev ved med at tale om alle de utrolige ting, der skete -
07:38
in '51, '52, '53.
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i 1951, 1952 og 1953.
07:40
Finally in 1954,
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Omsider i 1954
07:42
someone woke up and realized
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var der en der vågnede op og indså
07:44
that 1950 had gotten somewhat passé.
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at 1950 var blevet noget passé.
07:48
(Laughter)
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(Latter)
07:50
And just like that, the bubble burst.
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Og uden videre sprang boblen.
07:52
(Laughter)
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(Latter)
07:54
And the story of 1950
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Og historien om 1950
07:56
is the story of every year that we have on record,
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er historien om alle de år, vi har registreret,
07:58
with a little twist, because now we've got these nice charts.
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med et lille tvist, fordi vi nu har disse fine grafer.
08:01
And because we have these nice charts, we can measure things.
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Og fordi vi har disse fine grafer, kan vi nu måle ting.
08:04
We can say, "Well how fast does the bubble burst?"
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Vi kan sige "Hvor hurtigt springer boblen?"
08:06
And it turns out that we can measure that very precisely.
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Og de viser sig, at vi kan måle dette meget præcist.
08:09
Equations were derived, graphs were produced,
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Der blev udledt ligninger, og der opstillet grafer,
08:12
and the net result
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og nettoresultatet er
08:14
is that we find that the bubble bursts faster and faster
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at det viser sig, at boblen springer hurtigere og hurtigere
08:17
with each passing year.
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for hvert år der går.
08:19
We are losing interest in the past more rapidly.
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Vi mister interessen for fortiden hurtigere.
08:24
JM: Now a little piece of career advice.
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JM: Og nu et godt karrieretip:
08:26
So for those of you who seek to be famous,
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For de af jer, der vil være berømte,
08:28
we can learn from the 25 most famous political figures,
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kan vi lære af de 25 mest berømte politiske personligheder,
08:30
authors, actors and so on.
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forfattere, skuespillere osv.
08:32
So if you want to become famous early on, you should be an actor,
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Så hvis du vil være berømt tidligt, skal du være skuespiller,
08:35
because then fame starts rising by the end of your 20s --
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fordi berømmelsen så begynder at stige, nrå du er sidst i 20'erne –
08:37
you're still young, it's really great.
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Du er stadig ung, og det er virkelig skønt.
08:39
Now if you can wait a little bit, you should be an author,
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Men hvis du kan vente lidt, skal du blive forfatter,
08:41
because then you rise to very great heights,
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fordi så opnår meget stor berømmelse,
08:43
like Mark Twain, for instance: extremely famous.
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som f.eks. Mark Twain: Ekstremt berømt.
08:45
But if you want to reach the very top,
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Men hvis du vil helt til toppen,
08:47
you should delay gratification
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skal du udskyde den tilfredsstillelse, det er
08:49
and, of course, become a politician.
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at blive berømt - og selvfølgelig blive politiker.
08:51
So here you will become famous by the end of your 50s,
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Her vil du blive berømt, når du er i slutningen af 50'erne,
08:53
and become very, very famous afterward.
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og blive meget, meget berømt derefter.
08:55
So scientists also tend to get famous when they're much older.
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Videnskabsfolk plejer også at blive berømte, når de er meget ældre.
08:58
Like for instance, biologists and physics
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For eksempel biologer og fysikere
09:00
tend to be almost as famous as actors.
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bliver næsten ligeså berømte som skuespillere.
09:02
One mistake you should not do is become a mathematician.
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En fejl, du ikke skal begå, er at blive matematiker.
09:05
(Laughter)
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(Latter)
09:07
If you do that,
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Hvis du gør det,
09:09
you might think, "Oh great. I'm going to do my best work when I'm in my 20s."
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tænker du måske "Herligt! Jeg leverer mit bedste arbejde, når jeg er i 20'erne"
09:12
But guess what, nobody will really care.
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Men tænk engang... stort set ingen lægger mærke til det.
09:14
(Laughter)
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(Latter)
09:17
ELA: There are more sobering notes
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ELA: Der er mere nøgterne observationer
09:19
among the n-grams.
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blandt n-grammerne.
09:21
For instance, here's the trajectory of Marc Chagall,
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Her er f.eks. Marc Chagalls livsforløb,
09:23
an artist born in 1887.
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som kunster født i 1887.
09:25
And this looks like the normal trajectory of a famous person.
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Og dette ligner det normale forløb for en berømt person.
09:28
He gets more and more and more famous,
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Han bliver mere og mere berømt,
09:32
except if you look in German.
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bare ikke hvis vi ser på tysk.
09:34
If you look in German, you see something completely bizarre,
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På tysk ser vi noget ganske bizart,
09:36
something you pretty much never see,
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noget man stort set aldrig ser,
09:38
which is he becomes extremely famous
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og det er, at han bliver ekstremt berømt
09:40
and then all of a sudden plummets,
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hvorefter berømmelsen falder brat
09:42
going through a nadir between 1933 and 1945,
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og er på nulpunktet mellem 1933 og 1945,
09:45
before rebounding afterward.
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hvorefter berømmelsen vender tilbage.
09:48
And of course, what we're seeing
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Og de vi selvfølgelig kan se
09:50
is the fact Marc Chagall was a Jewish artist
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er at Marc Chagall var jødisk kunstner
09:53
in Nazi Germany.
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i nazi-Tyskland
09:55
Now these signals
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Disse signaler
09:57
are actually so strong
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er faktisk så stærk,
09:59
that we don't need to know that someone was censored.
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at vi ikke behøver at vide, at en person er blevet censureret.
10:02
We can actually figure it out
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Vi kan faktisk regne det ud
10:04
using really basic signal processing.
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ved hjælp af meget grundlæggende behandling af signalerne.
10:06
Here's a simple way to do it.
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Her er en simpel måde at gøre det på.
10:08
Well, a reasonable expectation
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Det er rimeligt at forvente
10:10
is that somebody's fame in a given period of time
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at en persons berømmelse i en given periode
10:12
should be roughly the average of their fame before
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vil være nogenlunde gennemsnittet af berømmelsen før
10:14
and their fame after.
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og berømmelsen efter perioden.
10:16
So that's sort of what we expect.
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Så det er nogenlunde, det vi forventer.
10:18
And we compare that to the fame that we observe.
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Og vi sammenligner med den berømmelse, vi kan aflæse.
10:21
And we just divide one by the other
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Og så dividerer vi bare den ene med den anden
10:23
to produce something we call a suppression index.
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så vi får noget, vi kalder et undertrykkelsesindeks.
10:25
If the suppression index is very, very, very small,
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Hvis undertrykkelsesindekset er meget, meget, meget lavt,
10:28
then you very well might be being suppressed.
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er der stor sandsynlighed for at du er undertrykt.
10:30
If it's very large, maybe you're benefiting from propaganda.
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Hvis det er meget højt, får du måske hjælp af propaganda.
10:34
JM: Now you can actually look at
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JM: Nu kan man faktisk se på
10:36
the distribution of suppression indexes over whole populations.
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fordelingen af undertrykkelsesindekser over hele populationer.
10:39
So for instance, here --
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For eksempel her:
10:41
this suppression index is for 5,000 people
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Dette undertrykkelsesindeks er for 5.000 personer
10:43
picked in English books where there's no known suppression --
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taget fra engelske bøger uden nogen kendt undertrykkelse.
10:45
it would be like this, basically tightly centered on one.
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Det ville være på denne måde, tæt centreret om ét.
10:47
What you expect is basically what you observe.
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Det man kan aflæse, er grundlæggende som forventet.
10:49
This is distribution as seen in Germany --
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Dette er fordelingen, som den ses i Tyskland.
10:51
very different, it's shifted to the left.
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Meget anderledes... den er forskudt til venstre.
10:53
People talked about it twice less as it should have been.
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Folk talte dobbelt så lidt om det, som de burde.
10:56
But much more importantly, the distribution is much wider.
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Men vigtigere er, at fordelingen er meget bredere.
10:58
There are many people who end up on the far left on this distribution
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Der er mange personer, der ender ude til venstre i fordelingen,
11:01
who are talked about 10 times fewer than they should have been.
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som der bliver talt 10 gange så lidt om, som der burde.
11:04
But then also many people on the far right
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Men der er også personer ude til højre,
11:06
who seem to benefit from propaganda.
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som synes at være hjulpet af propaganda.
11:08
This picture is the hallmark of censorship in the book record.
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Dette er kendetegnende for censur i bogregisteret.
11:11
ELA: So culturomics
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ELA: Denne metode
11:13
is what we call this method.
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kalder vi "culturomics".
11:15
It's kind of like genomics.
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Det er lidt ligesom genforskning
11:17
Except genomics is a lens on biology
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Genomics - genforskning - er et nærbillede af biologi
11:19
through the window of the sequence of bases in the human genome.
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hvor man ser på sekvenser af baser i arvemassen.
11:22
Culturomics is similar.
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Culturomics minder om dette.
11:24
It's the application of massive-scale data collection analysis
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Det er en analyse af en kæmpe samling data
11:27
to the study of human culture.
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anvendt på studiet af menneskets kultur.
11:29
Here, instead of through the lens of a genome,
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I stedet for at bruge arvemassen som perspektiv,
11:31
through the lens of digitized pieces of the historical record.
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bruges digitaliserede stykker af historisk materiale.
11:34
The great thing about culturomics
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Det gode ved culturomics er
11:36
is that everyone can do it.
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at alle kan gøre det.
11:38
Why can everyone do it?
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Hvorfor kan alle gøre det?
11:40
Everyone can do it because three guys,
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Alle kan gøre det, fordi disse tre herrer,
11:42
Jon Orwant, Matt Gray and Will Brockman over at Google,
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Jon Orwant, Matt Gray og Will Brockman hos Google,
11:45
saw the prototype of the Ngram Viewer,
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så prototypen af Ngram Viewer,
11:47
and they said, "This is so fun.
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og sagde, "Det er så sjovt,
11:49
We have to make this available for people."
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at vi må gøre det tilgængeligt for alle."
11:52
So in two weeks flat -- the two weeks before our paper came out --
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På nøjagtig de to uger inden offentliggørelsen af vores rapport
11:54
they coded up a version of the Ngram Viewer for the general public.
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kodede de en version af Ngram Viewer til almen brug.
11:57
And so you too can type in any word or phrase that you're interested in
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Du kan så skrive et vilkårligt ord, du er interesseret i
12:00
and see its n-gram immediately --
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og straks se det tilhørende n-gram,
12:02
also browse examples of all the various books
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og du kan gennemse eksempler på alle bøger
12:04
in which your n-gram appears.
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som dit n-gram optræder i.
12:06
JM: Now this was used over a million times on the first day,
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Dette blev brugt over en million gang første dag,
12:08
and this is really the best of all the queries.
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og dette er den bedste af alle søgninger.
12:10
So people want to be their best, put their best foot forward.
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Så folk ønsker at yde deres bedste.
12:13
But it turns out in the 18th century, people didn't really care about that at all.
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Men i det 18. årh. var folk ligeglade med alt det.
12:16
They didn't want to be their best, they wanted to be their beft.
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De ville ikke gøre bedste, de ville være "beft".
12:19
So what happened is, of course, this is just a mistake.
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Dette var selvfølgelig bare en fejl.
12:22
It's not that strove for mediocrity,
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Man stræbte ikke efter middelmådighed,
12:24
it's just that the S used to be written differently, kind of like an F.
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men tidligere skrev man S anderledes, nærmest som et f.
12:27
Now of course, Google didn't pick this up at the time,
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Det opdagede Google selvfølgelig ikke dengang,
12:30
so we reported this in the science article that we wrote.
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så vi skrev det i den videnskabelige artikel.
12:33
But it turns out this is just a reminder
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Dette minder os om, at
12:35
that, although this is a lot of fun,
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selvom det er rigtig sjovt,
12:37
when you interpret these graphs, you have to be very careful,
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at fortolke disse grafer, skal man være forsigtig
12:39
and you have to adopt the base standards in the sciences.
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og overholde de videnskabelige standarder.
12:42
ELA: People have been using this for all kinds of fun purposes.
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Folk har brugt dette til mange sjove formål.
12:45
(Laughter)
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(Latter)
12:52
Actually, we're not going to have to talk,
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Vi behøver faktisk ikke tale,
12:54
we're just going to show you all the slides and remain silent.
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vi viser bare alle slides og tier stille.
12:57
This person was interested in the history of frustration.
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Denne person var interesseret i frustrationens historie.
13:00
There's various types of frustration.
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Der er forskellige typer frustration.
13:03
If you stub your toe, that's a one A "argh."
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Hvis slår tåen, er der ét A i "argh".
13:06
If the planet Earth is annihilated by the Vogons
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Hvis Jorden udslettes af Vogonerne
13:08
to make room for an interstellar bypass,
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for at gøre plads til en intergalaktisk ekspresrute,
13:10
that's an eight A "aaaaaaaargh."
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er det et "aaaaaaaargh" med otte A'er.
13:12
This person studies all the "arghs,"
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Personen undersøger alle udgaver af "argh"
13:14
from one through eight A's.
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fra ét til otte A'er.
13:16
And it turns out
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Og det viser sig
13:18
that the less-frequent "arghs"
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at de mindst hyppige "argh" vedrører
13:20
are, of course, the ones that correspond to things that are more frustrating --
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vedrører ting, der er mere frustrerende
13:23
except, oddly, in the early 80s.
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men sjovt nok ikke i de tidlige 80'ere.
13:26
We think that might have something to do with Reagan.
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Vi tror det kan være noget med Reagan.
13:28
(Laughter)
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(Latter)
13:30
JM: There are many usages of this data,
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Disse data kan bruges til mange ting,
13:33
but the bottom line is that the historical record is being digitized.
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men grundlaget er, at historien bliver digitaliseret.
13:36
Google has started to digitize 15 million books.
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Google er begyndt at digitalisere 15 millioner bøger.
13:38
That's 12 percent of all the books that have ever been published.
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Det er 12 % af alle bøger, der er udgivet.
13:40
It's a sizable chunk of human culture.
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Det er en god klump af menneskets kultur.
13:43
There's much more in culture: there's manuscripts, there newspapers,
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Kultur er meget mere: manuskripter, aviser
13:46
there's things that are not text, like art and paintings.
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noget er ikke tekst, f.eks. kunst og malerier.
13:48
These all happen to be on our computers,
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Disse vil alle findes på vores computere,
13:50
on computers across the world.
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på computere i hele verden.
13:52
And when that happens, that will transform the way we have
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Og når det sker, ændrer det den måde
13:55
to understand our past, our present and human culture.
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vi forstår vores fortid, vores nutid og menneskets kultur.
13:57
Thank you very much.
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Mange tak.
13:59
(Applause)
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(Bifald)
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