TEDxBoston 2011
Jean-Baptiste Michel + Erez Lieberman Aiden: What we learned from 5 million books
Šta smo naučili iz 5 miliona knjiga
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Jeste li se igrali sa Google Labs Ngram pretraživačem? To je jedna zarazna alatka pomoću koje možete da pretražujete reči i ideje u bazi podataka od 5 miliona knjiga nastalih kroz više vekova. Erez Liberman Ejdan i Žan-Baptist Mišel nam pokazuju kako to funkcioniše i nekoliko iznenađujućih stvari koje možemo naučiti iz 500 milijardi reči.
Jean-Baptiste Michel - Data researcher
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bioErez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ... Full bio
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bioErez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ... Full bio
Double-click the English transcript below to play the video.
00:15
Erez Lieberman Aiden: Everyone knows
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Erez Liberman Ejdan: Svi znaju
00:17
that a picture is worth a thousand words.
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da slika vredi hiljadu reči.
00:22
But we at Harvard
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Ali mi na Harvadu
00:24
were wondering if this was really true.
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smo se pitali da li je ovo stvarno tačno.
00:27
(Laughter)
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(Smeh)
00:29
So we assembled a team of experts,
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Okupili smo tim stručnjaka,
00:33
spanning Harvard, MIT,
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iz Harvarda, MIT-a,
00:35
The American Heritage Dictionary, The Encyclopedia Britannica
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Rečnika američkog nasleđa, Enciklopedije Britanika
00:38
and even our proud sponsors,
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čak i naše ponosne sponzore,
00:40
the Google.
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Google.
00:43
And we cogitated about this
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Razmišljali smo o ovome
00:45
for about four years.
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oko četiri godine.
00:47
And we came to a startling conclusion.
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I došli smo do zapanjujućeg zaključka.
00:52
Ladies and gentlemen, a picture is not worth a thousand words.
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Dame i gospodo, slika ne vredi hiljadu reči.
00:55
In fact, we found some pictures
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U stvari, našli smo neke slike
00:57
that are worth 500 billion words.
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koje vrede 500 milijardi reči.
01:02
Jean-Baptiste Michel: So how did we get to this conclusion?
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Žan-Baptist Mišel: Kako smo došli do ovog zaključka?
01:04
So Erez and I were thinking about ways
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Erez i ja smo razmišljali o načinima
01:06
to get a big picture of human culture
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na koje bismo mogli steći opštu sliku ljudske kulture
01:08
and human history: change over time.
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i ljudske istorije: promene kroz vreme.
01:11
So many books actually have been written over the years.
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Mnoštvo knjiga je napisano tokom godina.
01:13
So we were thinking, well the best way to learn from them
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Mislili smo, najbolji način da iz njih naučimo
01:15
is to read all of these millions of books.
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je da pročitamo sve te milione knjiga.
01:17
Now of course, if there's a scale for how awesome that is,
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Kada bi postojala skala koja pokazuje koliko je to izuzetno,
01:20
that has to rank extremely, extremely high.
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rangirala bi to vrlo, vrlo visoko.
01:23
Now the problem is there's an X-axis for that,
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Problem je u tome što postoji X-osa za to,
01:25
which is the practical axis.
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koja je osa praktičnosti.
01:27
This is very, very low.
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Ovo je veoma, veoma nisko.
01:29
(Applause)
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(Aplauz)
01:32
Now people tend to use an alternative approach,
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Ljudi uglavnom nastoje da koriste alternativni pristup,
01:35
which is to take a few sources and read them very carefully.
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koriste nekoliko izvora i pažljivo ih pročitaju.
01:37
This is extremely practical, but not so awesome.
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Ovo je izuzetno praktično, ali ne tako izvanredno.
01:39
What you really want to do
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Ono što zaista želite da uradite
01:42
is to get to the awesome yet practical part of this space.
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je da dođete do onog izuzetnog, ali i praktičnog dela ovog prostora.
01:45
So it turns out there was a company across the river called Google
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Ispostavilo se da tamo, preko reke, postoji kompanija po imenu Google,
01:48
who had started a digitization project a few years back
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koja je pre nekoliko godina započela projekat digitalizacije,
01:50
that might just enable this approach.
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koji bi mogao omogućiti ovakav pristup.
01:52
They have digitized millions of books.
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Oni su digitalizovali nekoliko miliona knjiga.
01:54
So what that means is, one could use computational methods
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A to znači da se računske metode mogu koristiti
01:57
to read all of the books in a click of a button.
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za čitanje svih knjiga samo jednim klikom na dugme.
01:59
That's very practical and extremely awesome.
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Što je vrlo praktično i totalno fantastično.
02:03
ELA: Let me tell you a little bit about where books come from.
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ELE: Pričaću vam malo o tome odakle knjige dolaze.
02:05
Since time immemorial, there have been authors.
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Još od pamtiveka, postoje pisci.
02:08
These authors have been striving to write books.
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Oni se trude da pišu knjige.
02:11
And this became considerably easier
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To je postalo znatno lakše
02:13
with the development of the printing press some centuries ago.
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sa razvojem štamparske tehnike pre nekoliko vekova.
02:15
Since then, the authors have won
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Od tada, pisci su pobedili
02:18
on 129 million distinct occasions,
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u 129 miliona različitih prilika
02:20
publishing books.
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i objavili su knjige.
02:22
Now if those books are not lost to history,
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Ako te knjige nisu zaboravljene,
02:24
then they are somewhere in a library,
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onda se nalaze negde u biblioteci
02:26
and many of those books have been getting retrieved from the libraries
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i mnoge od ovih knjiga Google uzima iz biblioteka,
02:29
and digitized by Google,
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digitalizuje ih
02:31
which has scanned 15 million books to date.
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i do danas je skenirano 15 miliona knjiga.
02:33
Now when Google digitizes a book, they put it into a really nice format.
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Kad Google digitalizuje knjigu, stavlja je u jedan zgodan format.
02:36
Now we've got the data, plus we have metadata.
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Imamo podatke i meta-podatke.
02:38
We have information about things like where was it published,
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Imamo informacije o stvarima kao što su mesto izdavanja,
02:41
who was the author, when was it published.
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ime autora, vreme izdavanja.
02:43
And what we do is go through all of those records
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Mi prolazimo kroz sve ove zapise
02:46
and exclude everything that's not the highest quality data.
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i izbacujemo sve ono što nije visokokvalitetan podatak.
02:50
What we're left with
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Ono što nam ostaje
02:52
is a collection of five million books,
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jeste kolekcija od 5 miliona knjiga,
02:55
500 billion words,
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500 milijardi reči,
02:58
a string of characters a thousand times longer
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niz slova hiljadu puta duži
03:00
than the human genome --
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od ljudskog genoma --
03:03
a text which, when written out,
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tekst koji bi se, kada bi bio ispisan,
03:05
would stretch from here to the Moon and back
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prostirao odavde do Meseca i nazad
03:07
10 times over --
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puta 10 --
03:09
a veritable shard of our cultural genome.
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zaista tek delić našeg kulturnog genoma.
03:13
Of course what we did
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Naravno, ono što smo uradili
03:15
when faced with such outrageous hyperbole ...
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kada smo se suočili sa tako preteranom hiperbolom...
03:18
(Laughter)
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(Smeh)
03:20
was what any self-respecting researchers
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je ono što bi svi istraživači koji drže do sebe
03:23
would have done.
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uradili.
03:26
We took a page out of XKCD,
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Uzeli smo stranicu iz XKCD-a
03:28
and we said, "Stand back.
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i rekli: "Odmaknite se.
03:30
We're going to try science."
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Sad ćemo da isprobamo nauku."
03:32
(Laughter)
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(Smeh)
03:34
JM: Now of course, we were thinking,
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ŽM: Naravno, mislili smo,
03:36
well let's just first put the data out there
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hajde prvo samo da objavimo podatke
03:38
for people to do science to it.
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da bi ih ljudi naučno proučili.
03:40
Now we're thinking, what data can we release?
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Razmišljali smo, kakve podatke možemo da objavimo?
03:42
Well of course, you want to take the books
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Pa svakako, želite da uzmete knjige
03:44
and release the full text of these five million books.
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i objavite čitave tekstove ovih 5 miliona knjiga.
03:46
Now Google, and Jon Orwant in particular,
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Google, tačnije Džon Orvant,
03:48
told us a little equation that we should learn.
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rekao nam je za malu jednačinu koju treba da naučimo.
03:50
So you have five million, that is, five million authors
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Imate pet miliona knjiga, to je pet miliona autora,
03:53
and five million plaintiffs is a massive lawsuit.
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a pet miliona tužilaca čini masivnu parnicu.
03:56
So, although that would be really, really awesome,
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Iako bi to bilo jako, jako fantastično,
03:58
again, that's extremely, extremely impractical.
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opet, to je izuzetno nepraktično.
04:01
(Laughter)
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(Smeh)
04:03
Now again, we kind of caved in,
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Opet smo popustilli
04:05
and we did the very practical approach, which was a bit less awesome.
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i pristupili smo tome veoma praktično, što je bilo malo manje izuzetno.
04:08
We said, well instead of releasing the full text,
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Rekli smo, umesto objavljivanja kompletnog teksta,
04:10
we're going to release statistics about the books.
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objavićemo statistike o knjigama.
04:12
So take for instance "A gleam of happiness."
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Uzmite na primer "Zračak sreće."
04:14
It's four words; we call that a four-gram.
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To su dve reči; zovemo ih bigram.
04:16
We're going to tell you how many times a particular four-gram
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Reći ćemo vam koliko puta se određeni bigram
04:18
appeared in books in 1801, 1802, 1803,
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pojavljuje u knjigama iz 1801.,1802., 1803.,
04:20
all the way up to 2008.
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sve do 2008. godine.
04:22
That gives us a time series
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To nam daje vremenske serije
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of how frequently this particular sentence was used over time.
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učestalosti korišćenja ove rečenice kroz vreme.
04:26
We do that for all the words and phrases that appear in those books,
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To uradimo sa svim rečima i frazama koje se pojavljuju u tim knjigama
04:29
and that gives us a big table of two billion lines
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i to nam daje veliku tabelu od dve milijarde redova
04:32
that tell us about the way culture has been changing.
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koji nam prikazuju način na koji se kultura menja.
04:34
ELA: So those two billion lines,
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ELE: Te dve milijarde redova,
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we call them two billion n-grams.
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zovemo dve milijarde n-grama.
04:38
What do they tell us?
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Šta nam oni govore?
04:40
Well the individual n-grams measure cultural trends.
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Pojedinačni n-grami mere trendove u kulturi.
04:42
Let me give you an example.
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Daću vam primer.
04:44
Let's suppose that I am thriving,
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Pretpostavimo da ja uspevam,
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then tomorrow I want to tell you about how well I did.
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te sutradan želim da vam kažem kako sam uspeo.
04:48
And so I might say, "Yesterday, I throve."
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Mogu da kažem: "Juče, ja sam uspeo."
04:51
Alternatively, I could say, "Yesterday, I thrived."
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Ili mogu da kažem: "Juče, ja uspeh."
04:54
Well which one should I use?
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Koje od ova dva bi trebalo da upotrebim?
04:57
How to know?
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Kako da znam?
04:59
As of about six months ago,
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Pre nekih šest meseci,
05:01
the state of the art in this field
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stanje u ovom području je takvo
05:03
is that you would, for instance,
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da biste, na primer,
05:05
go up to the following psychologist with fabulous hair,
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otišli kod psihologa sjajne frizure
05:07
and you'd say,
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i rekli biste:
05:09
"Steve, you're an expert on the irregular verbs.
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"Stiv, Vi ste stručnjak za glagole.
05:12
What should I do?"
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Šta da radim?"
05:14
And he'd tell you, "Well most people say thrived,
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A on bi vam rekao: "Većina ljudi kaže 'uspeo sam',
05:16
but some people say throve."
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ali neki kažu 'ja uspeh'."
05:19
And you also knew, more or less,
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Takođe biste znali, manje-više,
05:21
that if you were to go back in time 200 years
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da ako biste se vratili 200 godina unazad
05:24
and ask the following statesman with equally fabulous hair,
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i pitali državnika jednako sjajne frizure:
05:27
(Laughter)
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(Smeh)
05:30
"Tom, what should I say?"
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"Tom, kako treba da kažem?"
05:32
He'd say, "Well, in my day, most people throve,
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On bi rekao: "Pa, u moje vreme, mnogi uspeše,
05:34
but some thrived."
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ali neki su uspeli."
05:37
So now what I'm just going to show you is raw data.
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Ono što ću sada da vam pokažem su neobrađeni podaci.
05:39
Two rows from this table of two billion entries.
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Dva reda ove tabele od dve milijarde unosa.
05:43
What you're seeing is year by year frequency
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Možete videti učestalost godinu za godinom
05:45
of "thrived" and "throve" over time.
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za reči "uspeo" i "uspeh".
05:49
Now this is just two
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Ovo su samo dva
05:51
out of two billion rows.
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od dve milijarde redova.
05:54
So the entire data set
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Dakle, kompletan set podataka
05:56
is a billion times more awesome than this slide.
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milijardu puta je izuzetniji od ovog slajda.
05:59
(Laughter)
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(Smeh)
06:01
(Applause)
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(Aplauz)
06:05
JM: Now there are many other pictures that are worth 500 billion words.
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ŽM: Postoje mnoge druge slike koje vrede 500 milijardi reči.
06:07
For instance, this one.
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Na primer, ova.
06:09
If you just take influenza,
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Ako samo uzmete u obzir grip,
06:11
you will see peaks at the time where you knew
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videćete maksimume u razdobljima za koje znate
06:13
big flu epidemics were killing people around the globe.
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da su velike epidemije ubijale ljude širom planete.
06:16
ELA: If you were not yet convinced,
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ELE: Ako pak i dalje niste ubeđeni,
06:19
sea levels are rising,
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nivoi mora rastu,
06:21
so is atmospheric CO2 and global temperature.
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kao i nivo CO2 u atmosferi i globalna temperatura.
06:24
JM: You might also want to have a look at this particular n-gram,
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ŽM: Možda takođe želite da pogledate konkretno ovaj n-gram,
06:27
and that's to tell Nietzsche that God is not dead,
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koji pokazuje Ničeu da bog nije mrtav,
06:30
although you might agree that he might need a better publicist.
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iako se možda slažete sa tim da mu treba bolji izdavač.
06:33
(Laughter)
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(Smeh)
06:35
ELA: You can get at some pretty abstract concepts with this sort of thing.
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ELE: Na ovaj način možete doći do nekih prilično apstraktnih koncepata.
06:38
For instance, let me tell you the history
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Na primer, ispričaću vam priču
06:40
of the year 1950.
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o 1950. godini.
06:42
Pretty much for the vast majority of history,
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Veliki deo istorije,
06:44
no one gave a damn about 1950.
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nikog nije bilo briga za 1950.
06:46
In 1700, in 1800, in 1900,
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Tokom 1700. godine, 1800., 1900.,
06:48
no one cared.
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niko nije mario.
06:52
Through the 30s and 40s,
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tokom '30-ih i '40-ih,
06:54
no one cared.
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niko nije mario.
06:56
Suddenly, in the mid-40s,
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Iznenada, sredinom '40-ih,
06:58
there started to be a buzz.
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počelo je da se šuška.
07:00
People realized that 1950 was going to happen,
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Ljudi su shvatili da će se 1950. dogoditi.
07:02
and it could be big.
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i da bi mogla biti važna.
07:04
(Laughter)
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(Smeh)
07:07
But nothing got people interested in 1950
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Ali ništa nije zainteresovalo ljude za 1950.
07:10
like the year 1950.
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kao sama 1950. godina.
07:13
(Laughter)
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(Smeh)
07:16
People were walking around obsessed.
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Ljudi su hodali naokolo opsednuti.
07:18
They couldn't stop talking
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Nisu mogli da prestanu da pričaju
07:20
about all the things they did in 1950,
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o stvarima koje su radili 1950.
07:23
all the things they were planning to do in 1950,
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o svim stvarima koje su planirali da urade 1950.
07:26
all the dreams of what they wanted to accomplish in 1950.
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o svim snovima koje su želeli da ostvare te 1950.
07:31
In fact, 1950 was so fascinating
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U stvari, 1950. je bila tako fascinantna
07:33
that for years thereafter,
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da su, godinama nakon što je prošla,
07:35
people just kept talking about all the amazing things that happened,
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ljudi nastavljali da pričaju o neverovatnim stvarima koje su se dogodile
07:38
in '51, '52, '53.
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u '51., '52., '53.
07:40
Finally in 1954,
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Konačno 1954. godine,
07:42
someone woke up and realized
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neko se probudio i shvatio
07:44
that 1950 had gotten somewhat passé.
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da je 1950. postala nekako passé.
07:48
(Laughter)
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(Smeh)
07:50
And just like that, the bubble burst.
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I tek tako, mehurić je pukao.
07:52
(Laughter)
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(Smeh)
07:54
And the story of 1950
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A priča o 1950.
07:56
is the story of every year that we have on record,
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je priča o svakoj godini koju imamo zabeleženu,
07:58
with a little twist, because now we've got these nice charts.
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sa malim preokretom, jer sad imamo ove lepe grafikone.
08:01
And because we have these nice charts, we can measure things.
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A zbog toga što imamo ove lepe grafikone, možemo da merimo stvari.
08:04
We can say, "Well how fast does the bubble burst?"
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Možemo pitati: "Koliko se brzo mehurić rasprsne?"
08:06
And it turns out that we can measure that very precisely.
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Ispostavlja se da to možemo izmeriti vrlo precizno.
08:09
Equations were derived, graphs were produced,
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Jednačine su izvedene, grafikoni su napravljeni
08:12
and the net result
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i krajnji rezultat
08:14
is that we find that the bubble bursts faster and faster
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je da smo izračunali da mehurić puca sve brže i brže
08:17
with each passing year.
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svake godine.
08:19
We are losing interest in the past more rapidly.
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Gubimo interesovanje za prošlost sve brže.
08:24
JM: Now a little piece of career advice.
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ŽM: Sad mali savet vezan za izbor karijere.
08:26
So for those of you who seek to be famous,
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Za sve vas koji želite da budete slavni,
08:28
we can learn from the 25 most famous political figures,
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saznali smo od 25 najpoznatijih političkih figura,
08:30
authors, actors and so on.
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pisaca, glumaca i tako dalje.
08:32
So if you want to become famous early on, you should be an actor,
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Dakle, ako želite rano da postanete slavni, treba da se bavite glumom,
08:35
because then fame starts rising by the end of your 20s --
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jer u tom slučaju, slava počinje da raste do kraja vaših 20-ih --
08:37
you're still young, it's really great.
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a još uvek ste mladi, to je stvarno sjajno.
08:39
Now if you can wait a little bit, you should be an author,
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A ako možete da čekate malo, onda treba da budete pisac,
08:41
because then you rise to very great heights,
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jer onda slava doseže velike visine,
08:43
like Mark Twain, for instance: extremely famous.
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kao Mark Tven, na primer, bio je izuzetno slavan.
08:45
But if you want to reach the very top,
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Ali ako želite da dosegnete sam vrh,
08:47
you should delay gratification
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trebalo bi da odgodite zadovoljstvo
08:49
and, of course, become a politician.
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i da, naravno, postanete političar.
08:51
So here you will become famous by the end of your 50s,
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Na taj način ćete postati poznati do kraja svojih 50-ih
08:53
and become very, very famous afterward.
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i ostati vrlo, vrlo poznati nakon toga.
08:55
So scientists also tend to get famous when they're much older.
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Naučnici takođe obično postanu poznati tek kada ostare.
08:58
Like for instance, biologists and physics
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Kao, na primer, biolozi i fizičari
09:00
tend to be almost as famous as actors.
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koji mogu biti poznati skoro koliko i glumci.
09:02
One mistake you should not do is become a mathematician.
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Jedina greška koju ne smete da napravite je da postanete matematičar.
09:05
(Laughter)
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(Smeh)
09:07
If you do that,
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Ako to uradite,
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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možete pomisliti: "Oh, sjajno. U svojim 20-im ću napraviti svoj najbolji rad."
09:12
But guess what, nobody will really care.
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Ali pogodite, nikoga neće biti briga.
09:14
(Laughter)
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(Smeh)
09:17
ELA: There are more sobering notes
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ELE: Postoje i neke ozbiljnije činjenice
09:19
among the n-grams.
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među n-gramima.
09:21
For instance, here's the trajectory of Marc Chagall,
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Na primer, ovo je putanja Marka Šagala,
09:23
an artist born in 1887.
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umetnika rođenog 1887.
09:25
And this looks like the normal trajectory of a famous person.
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I ona izgleda kao obična putanja slavne osobe.
09:28
He gets more and more and more famous,
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Postaje sve poznatiji,
09:32
except if you look in German.
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osim ako pogledate na nemačkom.
09:34
If you look in German, you see something completely bizarre,
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Ako pogledate na nemačkom, možete videti nešto stvarno bizarno,
09:36
something you pretty much never see,
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nešto što se baš i ne viđa,
09:38
which is he becomes extremely famous
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a to je da on postaje izuzetno slavan,
09:40
and then all of a sudden plummets,
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a onda iznenada, njegova popularnost naglo opada,
09:42
going through a nadir between 1933 and 1945,
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prolazi kroz najnižu tačku između 1933. i 1945. godine
09:45
before rebounding afterward.
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i nakon toga ponovo naglo raste.
09:48
And of course, what we're seeing
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Naravno, uviđamo
09:50
is the fact Marc Chagall was a Jewish artist
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činjenicu da je Mark Šagal bio jevrejski umetnik
09:53
in Nazi Germany.
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u nacističkoj Nemačkoj.
09:55
Now these signals
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Ovi znaci
09:57
are actually so strong
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su toliko jaki
09:59
that we don't need to know that someone was censored.
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da ne moramo da znamo da li je neko bio cenzurisan.
10:02
We can actually figure it out
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To u stvari možemo zaključiti
10:04
using really basic signal processing.
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koristeći osnovnu obradu znakova.
10:06
Here's a simple way to do it.
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Ovo je jednostavan način da se to uradi.
10:08
Well, a reasonable expectation
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Opravdano očekivanje je da
10:10
is that somebody's fame in a given period of time
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nečija slava u određenom periodu,
10:12
should be roughly the average of their fame before
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treba da, otprilike, bude prosek njegove slave
10:14
and their fame after.
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pre i posle tog perioda.
10:16
So that's sort of what we expect.
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Dakle, to je ono što očekujemo.
10:18
And we compare that to the fame that we observe.
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Uporedimo to sa slavom koju opažamo.
10:21
And we just divide one by the other
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I onda samo podelimo jednu sa drugom
10:23
to produce something we call a suppression index.
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da bismo dobili nešto što se zove indeks zabrane.
10:25
If the suppression index is very, very, very small,
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Ako je indeks zabrane jako, jako mali,
10:28
then you very well might be being suppressed.
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onda najverovatnije bivate zabranjeni.
10:30
If it's very large, maybe you're benefiting from propaganda.
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Ako je indeks velik, možda vam propaganda ide u korist.
10:34
JM: Now you can actually look at
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ŽM: Sada možete pogledati
10:36
the distribution of suppression indexes over whole populations.
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raspodelu indeksa zabrane po čitavim populacijama.
10:39
So for instance, here --
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Tako, na primer, ovde --
10:41
this suppression index is for 5,000 people
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ovo je indeks zabrane za 5 000 ljudi
10:43
picked in English books where there's no known suppression --
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odabranih u engleskim knjigama gde nije poznata zabrana --
10:45
it would be like this, basically tightly centered on one.
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bilo bi to ovako, u suštini, usko centrisano na jednu tačku.
10:47
What you expect is basically what you observe.
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Ono što očekujete je, praktično, ono što zapažate.
10:49
This is distribution as seen in Germany --
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Ovako je raspodela viđena u Nemačkoj --
10:51
very different, it's shifted to the left.
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dosta drugačije, pomerena je ulevo.
10:53
People talked about it twice less as it should have been.
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Ljudi su o njoj pričali dvaput manje nego što je trebalo.
10:56
But much more importantly, the distribution is much wider.
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Ali što je još važnije, raspodela je mnogo šira.
10:58
There are many people who end up on the far left on this distribution
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Ima dosta ljudi koji su završili na samom kraju s leve strane ove raspodele
11:01
who are talked about 10 times fewer than they should have been.
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o kojima se pričalo 10 puta manje nego što je trebalo.
11:04
But then also many people on the far right
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Ali ima i dosta ljudi na krajnjoj desnoj strani
11:06
who seem to benefit from propaganda.
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koji su, čini se, imali veliku korist od propagande.
11:08
This picture is the hallmark of censorship in the book record.
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Ova slika je glavni znak cenzure u knjigama.
11:11
ELA: So culturomics
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ELE: Ovu metodu
11:13
is what we call this method.
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zovemo kulturomika.
11:15
It's kind of like genomics.
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Podseća na genomiku.
11:17
Except genomics is a lens on biology
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Samo što je genomika deo biologije
11:19
through the window of the sequence of bases in the human genome.
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koji izučava nizove podataka u ljudskom genomu.
11:22
Culturomics is similar.
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Kulturomika je slična.
11:24
It's the application of massive-scale data collection analysis
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To je primena kolekcije podataka masovnih razmera
11:27
to the study of human culture.
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za izučavanje ljudske kulture.
11:29
Here, instead of through the lens of a genome,
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Ovde se, umesto kroz prizmu jednog genoma,
11:31
through the lens of digitized pieces of the historical record.
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to radi kroz prizmu digitalizovanih delova istorijskog zapisa.
11:34
The great thing about culturomics
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Odlična stvar kod kulturomike
11:36
is that everyone can do it.
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jeste to da svi mogu njome da se bave.
11:38
Why can everyone do it?
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Zašto mogu svi?
11:40
Everyone can do it because three guys,
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Zato što su tri lika,
11:42
Jon Orwant, Matt Gray and Will Brockman over at Google,
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Džon Orvant, Met Grej i Vil Brokmen iz Goole-a,
11:45
saw the prototype of the Ngram Viewer,
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videli prototip Ngram pretraživača,
11:47
and they said, "This is so fun.
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i rekli: "Ovo je tako zabavno.
11:49
We have to make this available for people."
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Moramo ovo da učinimo dostupnim za sve ljude."
11:52
So in two weeks flat -- the two weeks before our paper came out --
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I nakon dve nedelje -- dve nedelje pre nego što je naša studija izašla --
11:54
they coded up a version of the Ngram Viewer for the general public.
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programirali su verziju Ngram pretraživača za javnost.
11:57
And so you too can type in any word or phrase that you're interested in
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Tako da i vi možete ukucati bilo koju reč ili frazu koja vas interesuje
12:00
and see its n-gram immediately --
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i odmah videti njen n-gram --
12:02
also browse examples of all the various books
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takođe možete pretraživati različite knjige
12:04
in which your n-gram appears.
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u kojima se vaš n-gram pojavljuje.
12:06
JM: Now this was used over a million times on the first day,
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ŽM: Ovo je prvog dana bilo korišćeno preko milion puta,
12:08
and this is really the best of all the queries.
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a ovo je zaista najbolje o svih pitanja.
12:10
So people want to be their best, put their best foot forward.
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Ljudi hoće da daju sve od sebe, da urade sve što je bolje moguće.
12:13
But it turns out in the 18th century, people didn't really care about that at all.
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Ali ispostavilo se da u 18. veku, ljudi uopšte nisu marili za to.
12:16
They didn't want to be their best, they wanted to be their beft.
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Nisu želeli da budu najbolji (best), želeli su da budu osrednji (beft).
12:19
So what happened is, of course, this is just a mistake.
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Ono što se desilo je, naravno, samo greška.
12:22
It's not that strove for mediocrity,
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Nije težnja ka prosečnosti,
12:24
it's just that the S used to be written differently, kind of like an F.
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već je samo S bilo pisano drugačije, slično kao F. (beSt - beFt)
12:27
Now of course, Google didn't pick this up at the time,
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Naravno, Google to nije prepoznao na vreme,
12:30
so we reported this in the science article that we wrote.
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pa smo ovo objavili u naučnom članku koji smo napisali.
12:33
But it turns out this is just a reminder
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Ali ispostavlja se da je ovo samo podsetnik
12:35
that, although this is a lot of fun,
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da, iako je jako zabavno,
12:37
when you interpret these graphs, you have to be very careful,
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kada interpretirate ove grafikone, morate biti veoma oprezni,
12:39
and you have to adopt the base standards in the sciences.
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i morate usvojiti osnovne naučne standarde.
12:42
ELA: People have been using this for all kinds of fun purposes.
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ELE: Ljudi koriste ovaj program u razne zabavne svrhe.
12:45
(Laughter)
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(Smeh)
12:52
Actually, we're not going to have to talk,
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U stvari, nećemo morati da pričamo,
12:54
we're just going to show you all the slides and remain silent.
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samo ćemo da vam pokažemo sve slajdove i da ćutimo.
12:57
This person was interested in the history of frustration.
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Ova osoba je bila zainteresovana za istoriju frustracije.
13:00
There's various types of frustration.
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Postoje razni tipovi frustracije.
13:03
If you stub your toe, that's a one A "argh."
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Ako udarite nožni prst, onda je to jedno A "ah."
13:06
If the planet Earth is annihilated by the Vogons
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Ako su planetu Zemlju razorili Vogoni
13:08
to make room for an interstellar bypass,
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da bi napravili mesta za međuzvezdanu zaobilaznicu,
13:10
that's an eight A "aaaaaaaargh."
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onda imamo 8 A "aaaaaaaah."
13:12
This person studies all the "arghs,"
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Ova osoba izučava sve "ah-ove",
13:14
from one through eight A's.
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od jednog do 8 A.
13:16
And it turns out
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I ispostavlja se
13:18
that the less-frequent "arghs"
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da su ređi "ah-ovi"
13:20
are, of course, the ones that correspond to things that are more frustrating --
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naravno, oni koji odgovaraju stvarima koje izazivaju veću frustraciju --
13:23
except, oddly, in the early 80s.
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osim, što je čudno, u ranim '80-im.
13:26
We think that might have something to do with Reagan.
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Mi mislimo da to ima veze sa Reganom.
13:28
(Laughter)
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(Smeh)
13:30
JM: There are many usages of this data,
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ŽM: Ovi podaci se mogu koristiti u razne svrhe,
13:33
but the bottom line is that the historical record is being digitized.
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ali poenta je da se istorijski zapis digitalizuje.
13:36
Google has started to digitize 15 million books.
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Google je počeo da digitalizuje 15 miliona knjiga.
13:38
That's 12 percent of all the books that have ever been published.
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To je 12 procenata svih knjiga koje su ikada objavljene.
13:40
It's a sizable chunk of human culture.
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Što je povelik deo ljudske kulture.
13:43
There's much more in culture: there's manuscripts, there newspapers,
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Ali postoji još mnogo više: postoje rukopisi, novine,
13:46
there's things that are not text, like art and paintings.
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stvari koje su bez teksta, kao što su umetnička dela, slike.
13:48
These all happen to be on our computers,
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Sve te stvari će se naći na našim računarima,
13:50
on computers across the world.
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na računarima širom sveta.
13:52
And when that happens, that will transform the way we have
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A kada se to desi, promeniće se način na koji
13:55
to understand our past, our present and human culture.
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shvatamo našu prošlost, sadašnjost i našu kulturu.
13:57
Thank you very much.
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Hvala vam mnogo.
13:59
(Applause)
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(Aplauz)
ABOUT THE SPEAKERS
Jean-Baptiste Michel - Data researcherJean-Baptiste Michel looks at how we can use large volumes of data to better understand our world.
Why you should listen
Jean-Baptiste Michel holds joint academic appointments at Harvard (FQEB Fellow) and Google (Visiting Faculty). His research focusses on using large volumes of data as tools that help better understand the world around us -- from the way diseases progress in patients over years, to the way cultures change in human societies over centuries. With his colleague Erez Lieberman Aiden, Jean-Baptiste is a Founding Director of Harvard's Cultural Observatory, where their research team pioneers the use of quantitative methods for the study of human culture, language and history. His research was featured on the covers of Science and Nature, on the front pages of the New York Times and the Boston Globe, in The Economist, Wired and many other venues. The online tool he helped create -- ngrams.googlelabs.com -- was used millions of times to browse cultural trends. Jean-Baptiste is an Engineer from Ecole Polytechnique (Paris), and holds an MS in Applied Mathematics and a PhD in Systems Biology from Harvard.
Jean-Baptiste Michel | Speaker | TED.com
Erez Lieberman Aiden - Researcher
Erez Lieberman Aiden pursues a broad range of research interests, spanning genomics, linguistics, mathematics ...
Why you should listen
Erez Lieberman Aiden is a fellow at the Harvard Society of Fellows and Visiting Faculty at Google. His research spans many disciplines and has won numerous awards, including recognition for one of the top 20 "Biotech Breakthroughs that will Change Medicine", by Popular Mechanics; the Lemelson-MIT prize for the best student inventor at MIT; the American Physical Society's Award for the Best Doctoral Dissertation in Biological Physics; and membership in Technology Review's 2009 TR35, recognizing the top 35 innovators under 35. His last three papers -- two with JB Michel -- have all appeared on the cover of Nature and Science.
Erez Lieberman Aiden | Speaker | TED.com