TEDxBoston 2011
Jean-Baptiste Michel + Erez Lieberman Aiden: What we learned from 5 million books
Što smo naučili iz 5 milijuna knjiga
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Jeste li se igrali preglednikom Ngram na Google Labs? Navući ćete se na taj alat pomoću kojega možete tražiti riječi i ideje u bazi podataka od pet milijuna knjiga nastalih kroz više stoljeća. Erez Lieberman Aiden i Jean-Baptiste Michel pokazuju nam kako to funkcionira te predstavljaju nekoliko iznenađujućih stvari koje možemo naučiti iz 5 milijardi riječi.
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 Lieberman Aiden: Svi znaju
00:17
that a picture is worth a thousand words.
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da slika vrijedi tisuću riječi.
00:22
But we at Harvard
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No, mi smo se na Harvardu
00:24
were wondering if this was really true.
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zapitali je li to stvarno istina.
00:27
(Laughter)
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(Smijeh)
00:29
So we assembled a team of experts,
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Tako smo okupili tim stručnjaka,
00:33
spanning Harvard, MIT,
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koji obuhvaća ljude na Harvardu i MIT-u,
00:35
The American Heritage Dictionary, The Encyclopedia Britannica
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one koji rade na rječniku American Heritage i Encyclopediji Britannici,
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 tome
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 začuđujućeg zaključka.
00:52
Ladies and gentlemen, a picture is not worth a thousand words.
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Dame i gospodo, slika ne vrijedi tisuću riječi.
00:55
In fact, we found some pictures
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Čak smo pronašli neke slike
00:57
that are worth 500 billion words.
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koje vrijede 500 milijardi riječi.
01:02
Jean-Baptiste Michel: So how did we get to this conclusion?
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Jean-Baptiste Michel: Kako smo došlo do tog zaključka?
01:04
So Erez and I were thinking about ways
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Erez i ja razmišljali smo o načinima
01:06
to get a big picture of human culture
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na koje bismo mogli steći općenitu sliku ljudske kulture
01:08
and human history: change over time.
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i ljudske povijesti: promjene kroz vrijeme.
01:11
So many books actually have been written over the years.
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Kroz vrijeme je zapravo napisano mnogo knjiga.
01:13
So we were thinking, well the best way to learn from them
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Stoga smo mislili kako je najbolji način da nešto naučimo iz njih
01:15
is to read all of these millions of books.
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taj da pročitamo sve te milijune knjiga.
01:17
Now of course, if there's a scale for how awesome that is,
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Naravno, ako postoji ljestvica za mjerenje koliko je to fenomenalno,
01:20
that has to rank extremely, extremely high.
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tako nešto mora biti rangirano vrlo, vrlo visoko.
01:23
Now the problem is there's an X-axis for that,
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Problem je što za to postoji os x
01:25
which is the practical axis.
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ili praktična os.
01:27
This is very, very low.
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Na njoj se to nalazi vrlo, vrlo nisko.
01:29
(Applause)
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(Pljesak)
01:32
Now people tend to use an alternative approach,
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Ljudi su skloni primjenjivanju alternativnog pristupa,
01:35
which is to take a few sources and read them very carefully.
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a to je da izaberu nekoliko izvora i njih pročitaju vrlo pažljivo.
01:37
This is extremely practical, but not so awesome.
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To je vrlo praktično, ali nije baš fenomenalno.
01:39
What you really want to do
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Ono što zapravo želite
01:42
is to get to the awesome yet practical part of this space.
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jest doći do dijela koji je i fenomenalan i praktičan.
01:45
So it turns out there was a company across the river called Google
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Ispada da s druge strane rijeke postoji tvrtka koja se zove Google,
01:48
who had started a digitization project a few years back
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koja je prije nekoliko godina počela s projektom digitalizacije
01:50
that might just enable this approach.
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koji bi mogao omogućiti upravo ovaj pristup.
01:52
They have digitized millions of books.
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Digitalizirali su milijune knjiga.
01:54
So what that means is, one could use computational methods
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A to znači da se možemo služiti računalnim metodama
01:57
to read all of the books in a click of a button.
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kako bismo sve knjige pročitali pritiskom na tipku.
01:59
That's very practical and extremely awesome.
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To je vrlo praktično i poprilično fenomenalno.
02:03
ELA: Let me tell you a little bit about where books come from.
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ELA: Ispričat ću vam malo o tome odakle dolaze knjige.
02:05
Since time immemorial, there have been authors.
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Od pamtivijeka postoje autori.
02:08
These authors have been striving to write books.
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Oni teže tome 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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s razvojem tehnike tiskanja prije nekoliko stoljeća.
02:15
Since then, the authors have won
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Od tada su autori pobijedili
02:18
on 129 million distinct occasions,
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129 milijuna puta
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 se te knjige s vremenom nisu izgubile,
02:24
then they are somewhere in a library,
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znači da su negdje u nekoj knjižnici.
02:26
and many of those books have been getting retrieved from the libraries
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Mnoge od tih knjiga izvučene su iz knjižnica
02:29
and digitized by Google,
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i Google ih je digitalizirao.
02:31
which has scanned 15 million books to date.
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Do danas je skenirano 15 milijuna knjiga.
02:33
Now when Google digitizes a book, they put it into a really nice format.
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Kad Google digitalizira knjigu, stavlja ju u zaista zgodan format.
02:36
Now we've got the data, plus we have metadata.
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Imamo podatke, a imamo i metapodatke.
02:38
We have information about things like where was it published,
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Imamo informacije o stvarima kao što su mjesto izdavanja,
02:41
who was the author, when was it published.
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ime autora, datum izdavanja.
02:43
And what we do is go through all of those records
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I mi tada prolazimo kroz sve te zapise
02:46
and exclude everything that's not the highest quality data.
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i izostavljamo sve što nisu podaci najviše kvalitete.
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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zbirka je od pet milijuna knjiga,
02:55
500 billion words,
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500 milijardi riječi,
02:58
a string of characters a thousand times longer
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niz likova koji je tisuću puta dulji
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, kad bi se ispisao,
03:05
would stretch from here to the Moon and back
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protezao 10 puta odavde do Mjeseca
03:07
10 times over --
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i natrag --
03:09
a veritable shard of our cultural genome.
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zaista tek djelić našeg kulturnog genoma.
03:13
Of course what we did
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Naravno, ono što smo učinili,
03:15
when faced with such outrageous hyperbole ...
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kad smo se suočili s tako skandaloznom hiperbolom...
03:18
(Laughter)
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(Smijeh)
03:20
was what any self-respecting researchers
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bilo je isto što bi učinili bilo koji istraživači
03:23
would have done.
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koji drže do sebe.
03:26
We took a page out of XKCD,
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Uzeli smo jednu stranicu s 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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Pokušat ćemo nešto znanstveno!"
03:32
(Laughter)
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(Smijeh)
03:34
JM: Now of course, we were thinking,
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JM: Naravno, mislili smo,
03:36
well let's just first put the data out there
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hajdemo prvo omogućiti pristup podacima
03:38
for people to do science to it.
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kako bi ih ljudi mogli znanstveno promotriti.
03:40
Now we're thinking, what data can we release?
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Razmišljali smo kojim podacima možemo omogućiti pristup?
03:42
Well of course, you want to take the books
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Naravno, želite uzeti te knjige
03:44
and release the full text of these five million books.
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i omogućiti pristup kompletnom tekstu tih pet milijuna knjiga.
03:46
Now Google, and Jon Orwant in particular,
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Google, a pogotovo Jon Orwant,
03:48
told us a little equation that we should learn.
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pokazali su nam malu jednadžbu koju smo morali naučiti.
03:50
So you have five million, that is, five million authors
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Imate pet milijuna knjiga, odnosno pet miljuna autora
03:53
and five million plaintiffs is a massive lawsuit.
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i pet milijuna tužitelja u masovnoj tužbi.
03:56
So, although that would be really, really awesome,
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Dakle, iako bi to bilo stvarno, stvarno fenomenalno,
03:58
again, that's extremely, extremely impractical.
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to je opet vrlo, vrlo nepraktično.
04:01
(Laughter)
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(Smijeh)
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 primijenili vrlo praktičan pristup, koji je bio nešto manje fenomenalan.
04:08
We said, well instead of releasing the full text,
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Rekli smo, umjesto da omogućimo pristup kompletnom tekstu,
04:10
we're going to release statistics about the books.
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omogućit ćemo pristup statistikama o knjigama.
04:12
So take for instance "A gleam of happiness."
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Uzmite primjerice "tračak sreće" (a gleam of happiness).
04:14
It's four words; we call that a four-gram.
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To su četiri riječi i to zovemo četverogram.
04:16
We're going to tell you how many times a particular four-gram
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Reći ćemo vam koliko se puta određeni četverogram
04:18
appeared in books in 1801, 1802, 1803,
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pojavio u knjigama 1801., 1802., 1803. godine,
04:20
all the way up to 2008.
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i tako sve do 2008.
04:22
That gives us a time series
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Tako dobivamo vremenski niz
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of how frequently this particular sentence was used over time.
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učestalosti korištenja određene rečenice kroz vrijeme,
04:26
We do that for all the words and phrases that appear in those books,
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To smo napravili za sve riječi i izraze koji se pojavljuju u tim knjigama,
04:29
and that gives us a big table of two billion lines
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što nam daje veliku tablicu od dvije milijarde redaka
04:32
that tell us about the way culture has been changing.
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koji nam prikazuju način na koji se kultura mijenja.
04:34
ELA: So those two billion lines,
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ELA: Te dvije milijarde redaka
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we call them two billion n-grams.
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zovemo dvije milijarde n-grama.
04:38
What do they tell us?
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Što nam oni govore?
04:40
Well the individual n-grams measure cultural trends.
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Pojedinačni n-grami mjere kulturne trendove.
04:42
Let me give you an example.
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Dat ću vam primjer.
04:44
Let's suppose that I am thriving,
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Pretpostavimo da ja težim nečemu (thrive),
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then tomorrow I want to tell you about how well I did.
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a sutra vam želim ispričati koliko sam bio uspješan.
04:48
And so I might say, "Yesterday, I throve."
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Mogao bih koristiti oblik za prošlo vrijeme "throve",
04:51
Alternatively, I could say, "Yesterday, I thrived."
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a mogao koristiti i oblike "thrived".
04:54
Well which one should I use?
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Koji bih trebao koristiti?
04:57
How to know?
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Kako to znati?
04:59
As of about six months ago,
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Prije otprilike šest mjeseci,
05:01
the state of the art in this field
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najsuvremeniji podaci u tom polju
05:03
is that you would, for instance,
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kažu da biste, primjerice,
05:05
go up to the following psychologist with fabulous hair,
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otišli do ovog psihologa fantastične kose
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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"Steve, ti si stručnjak za nepravilne glagole.
05:12
What should I do?"
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Što da radim?"
05:14
And he'd tell you, "Well most people say thrived,
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A on bi vam rekao: "Pa, većina ljudi koristi "thrived",
05:16
but some people say throve."
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ali neki ljudi kažu "throve".
05:19
And you also knew, more or less,
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A znali biste i, više-manje,
05:21
that if you were to go back in time 200 years
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da kad biste se vratili 200 godina u prošlost
05:24
and ask the following statesman with equally fabulous hair,
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i pitali ovog državnika jednako fantastične kose,
05:27
(Laughter)
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(Smijeh)
05:30
"Tom, what should I say?"
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"Tome, kako bih trebao govoriti?"
05:32
He'd say, "Well, in my day, most people throve,
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On bi vam rekao: "Pa, u moje vrijeme većina je ljudi koristila "throve",
05:34
but some thrived."
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ali neki su koristili "thrived".
05:37
So now what I'm just going to show you is raw data.
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Sad ću vam pokazati samo sirove podatke.
05:39
Two rows from this table of two billion entries.
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Dva reda iz ove tablice od dvije milijarde unosa.
05:43
What you're seeing is year by year frequency
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Sada gledate učestalost godinu za godinom
05:45
of "thrived" and "throve" over time.
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korištenja "thrived" i "throve" kroz vrijeme.
05:49
Now this is just two
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Dakle, to su samo dva reda
05:51
out of two billion rows.
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od dvije milijarde redova.
05:54
So the entire data set
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Ukupan skup podataka
05:56
is a billion times more awesome than this slide.
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milijardu je puta fenomenalniji od ovog slajda.
05:59
(Laughter)
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(Smijeh)
06:01
(Applause)
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(Pljesak)
06:05
JM: Now there are many other pictures that are worth 500 billion words.
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JM: Postoji mnogo drugih slika koje vrijede 500 milijardi riječi.
06:07
For instance, this one.
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Na primjer, ova ovdje.
06:09
If you just take influenza,
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Ako uzmete samo gripu,
06:11
you will see peaks at the time where you knew
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vidjet ćete vrhove u vrijeme za koje znate
06:13
big flu epidemics were killing people around the globe.
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da su velike epidemije tada ubijale ljude u cijelom svijetu.
06:16
ELA: If you were not yet convinced,
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ELA: Ako vam treba još dokaza,
06:19
sea levels are rising,
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diže se razina mora,
06:21
so is atmospheric CO2 and global temperature.
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kao i CO2 i temperatura u svijetu.
06:24
JM: You might also want to have a look at this particular n-gram,
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JM: Možda ne bi bilo loše da pogledate i ovaj konkretni n-gram,
06:27
and that's to tell Nietzsche that God is not dead,
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koji govori Nietzscheu 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 da bi mu trebao bolji izdavač.
06:33
(Laughter)
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(Smijeh)
06:35
ELA: You can get at some pretty abstract concepts with this sort of thing.
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ELA: Na ovaj način možete dobiti prilično apstraktne koncepte.
06:38
For instance, let me tell you the history
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Na primjer, ispričat ću 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 dio povijesti,
06:44
no one gave a damn about 1950.
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nikoga nije bilo briga za 1950. godinu.
06:46
In 1700, in 1800, in 1900,
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1700. godine, 1800., 1900.,
06:48
no one cared.
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nikoga nije bilo briga.
06:52
Through the 30s and 40s,
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30-ih i 40-ih godina,
06:54
no one cared.
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nikoga nije bilo briga.
06:56
Suddenly, in the mid-40s,
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Odjednom, sredinom 40-ih,
06:58
there started to be a buzz.
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počelo se brujati o tome.
07:00
People realized that 1950 was going to happen,
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Ljudi su shvatili da će doći 1950. godina
07:02
and it could be big.
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i da bi mogla biti važna.
07:04
(Laughter)
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(Smijeh)
07:07
But nothing got people interested in 1950
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Ali ništa nije ljude zainteresiralo za 1950. godinu
07:10
like the year 1950.
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kao 1950. godina.
07:13
(Laughter)
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(Smijeh)
07:16
People were walking around obsessed.
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Ljudi su hodali uokolo opsjednuti.
07:18
They couldn't stop talking
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Nisu mogli prestati govoriti
07:20
about all the things they did in 1950,
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o svim stvarima koje su učinili 1950. godine,
07:23
all the things they were planning to do in 1950,
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o svim stvarima koje planiraju učiniti 1950. godine,
07:26
all the dreams of what they wanted to accomplish in 1950.
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o svim snovima koje žele ostvariti 1950. godine.
07:31
In fact, 1950 was so fascinating
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Zapravo, 1950. godina bila je toliko fascinantna
07:33
that for years thereafter,
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da su i godinama kasnije
07:35
people just kept talking about all the amazing things that happened,
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ljudi i dalje govorili o fantastičnim stvarima koje su se dogodile,
07:38
in '51, '52, '53.
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'51., '52., '53.
07:40
Finally in 1954,
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Na kraju, 1954. godine,
07:42
someone woke up and realized
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netko se otrijeznio i shvatio
07:44
that 1950 had gotten somewhat passé.
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da je 1950. godina postala passé.
07:48
(Laughter)
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(Smijeh)
07:50
And just like that, the bubble burst.
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I tako se iznenada mjehurić rasprsnuo.
07:52
(Laughter)
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(Smijeh)
07:54
And the story of 1950
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Priča o 1950. godini
07:56
is the story of every year that we have on record,
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priča je o svakoj godini koju smo zabilježili,
07:58
with a little twist, because now we've got these nice charts.
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s malom razlikom, jer sad imamo ove krasne grafove.
08:01
And because we have these nice charts, we can measure things.
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A budući da imamo te krasne grafove, možemo mjeriti razne stvari.
08:04
We can say, "Well how fast does the bubble burst?"
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Možemo pitati: "Koliko će se brzo mjehurić rasprsnuti?"
08:06
And it turns out that we can measure that very precisely.
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Ispada da to možemo vrlo precizno izmjeriti.
08:09
Equations were derived, graphs were produced,
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Jednadžbe su se derivirale, grafovi su se crtali,
08:12
and the net result
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a ukupni rezultat
08:14
is that we find that the bubble bursts faster and faster
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jest taj da smo otkrili da se mjehurić rasprsne sve brže
08:17
with each passing year.
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sa svakom godinom koja prođe.
08:19
We are losing interest in the past more rapidly.
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Sve brže gubimo zanimanje za prošlost.
08:24
JM: Now a little piece of career advice.
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JM: A sad mali savjet o odabiru karijere.
08:26
So for those of you who seek to be famous,
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Oni među vama koji žele biti slavni
08:28
we can learn from the 25 most famous political figures,
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mogu ponešto naučiti od 25 najpoznatijih političkih ličnosti,
08:30
authors, actors and so on.
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pisaca, glumaca i drugih.
08:32
So if you want to become famous early on, you should be an actor,
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Dakle, ako želite rano postati slavni, trebate postati glumac
08:35
because then fame starts rising by the end of your 20s --
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jer tada postajete slavni do kraja svojih 20-ih godina --
08:37
you're still young, it's really great.
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još uvijek ste mladi i to je odlično.
08:39
Now if you can wait a little bit, you should be an author,
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Ako možete malo čekati, trebali biste biti pisac
08:41
because then you rise to very great heights,
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jer tada se možete vrlo visoko uzdignuti,
08:43
like Mark Twain, for instance: extremely famous.
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poput primjerice Marka Twaina, on je bio zaista slavan.
08:45
But if you want to reach the very top,
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Ali ako želite dosegnuti sam vrh,
08:47
you should delay gratification
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trebali biste odgoditi zadovoljstvo
08:49
and, of course, become a politician.
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i, naravno, postati političar.
08:51
So here you will become famous by the end of your 50s,
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U tom ćete slučaju postati poznati do kraja svojih 50-ih godina,
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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Znanstvenici uglavnom, isto tako, postaju poznati kad ostare.
08:58
Like for instance, biologists and physics
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Biolozi i fizičari, primjerice,
09:00
tend to be almost as famous as actors.
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znaju biti gotovo jednako slavni kao i glumci.
09:02
One mistake you should not do is become a mathematician.
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Trebate izbjeći samo jednu pogrešku - da postanete matematičar.
09:05
(Laughter)
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(Smijeh)
09:07
If you do that,
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Ako to učinite,
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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možda ćete pomisliti: "Odlično, u 20-ima ću napraviti napraviti svoje najbolje radove."
09:12
But guess what, nobody will really care.
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No, znate što, nikoga neće biti briga.
09:14
(Laughter)
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(Smijeh)
09:17
ELA: There are more sobering notes
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ELA: 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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Primjerice, evo putanje Marca Chagalla,
09:23
an artist born in 1887.
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umjetnika rođenog 1887. godine.
09:25
And this looks like the normal trajectory of a famous person.
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Ovo izgleda kao normalna putanja poznate osobe.
09:28
He gets more and more and more famous,
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Postaje sve poznatiji i poznatiji,
09:32
except if you look in German.
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osim ako gledate za njemački jezik.
09:34
If you look in German, you see something completely bizarre,
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Ako gledate za njemački, vidjet ćete nešto vrlo bizarno,
09:36
something you pretty much never see,
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nešto što gotovo nikad ne vidite,
09:38
which is he becomes extremely famous
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a to je da postaje iznimno poznat,
09:40
and then all of a sudden plummets,
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a nakon toga mu popularnost iznenada padne,
09:42
going through a nadir between 1933 and 1945,
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pri čemu su najniže točke bile između 1933. i 1945. godine,
09:45
before rebounding afterward.
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nakon čega mu se opet vratila popularnost.
09:48
And of course, what we're seeing
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Naravno, on što zapravo vidimo
09:50
is the fact Marc Chagall was a Jewish artist
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jest činjenica da je Marc Chagall bio židovski umjetnik
09:53
in Nazi Germany.
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u nacističkoj Njemačkoj.
09:55
Now these signals
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Ovi su signali
09:57
are actually so strong
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zapravo toliko jaki
09:59
that we don't need to know that someone was censored.
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da ne trebamo ni znati da su nekoga cenzurirali.
10:02
We can actually figure it out
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Zapravo to 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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Evo jednostavnog načina kako to učiniti.
10:08
Well, a reasonable expectation
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Razumno je za očekivati
10:10
is that somebody's fame in a given period of time
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da će nečija slava u određenom razdoblju
10:12
should be roughly the average of their fame before
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biti otprilike prosjek slave te osobe
10:14
and their fame after.
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prije i nakon tog razdoblja.
10:16
So that's sort of what we expect.
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To je otprilike ono što mi očekujemo.
10:18
And we compare that to the fame that we observe.
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I to uspoređujemo sa slavom koju promatramo.
10:21
And we just divide one by the other
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Samo podijelimo jedno drugim
10:23
to produce something we call a suppression index.
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kako bismo dobili takozvani indeks zabrane.
10:25
If the suppression index is very, very, very small,
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Ako je indeks zabrane vrlo, vrlo, vrlo malen,
10:28
then you very well might be being suppressed.
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onda ste vrlo vjerojatno bili zabranjeni.
10:30
If it's very large, maybe you're benefiting from propaganda.
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Ako je vrlo velik, možda profitirate od propagande.
10:34
JM: Now you can actually look at
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JM: Zapravo možete promatrati
10:36
the distribution of suppression indexes over whole populations.
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raspored indeksa zabrane unutar populacija.
10:39
So for instance, here --
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Na primjer, ovdje --
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 u kojima nije zabilježeno zabranjivanje --
10:45
it would be like this, basically tightly centered on one.
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bilo bi ovako, usko centrirano oko jednog.
10:47
What you expect is basically what you observe.
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Ono što očekujete u biti je ono što i vidite.
10:49
This is distribution as seen in Germany --
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Ovo je raspored za Njemačku --
10:51
very different, it's shifted to the left.
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vrlo različito, pomaknuto je ulijevo.
10:53
People talked about it twice less as it should have been.
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Ljudi su o tome razgovarali upola manje nego što su trebali.
10:56
But much more importantly, the distribution is much wider.
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No, mnogo je važnije da je raspored širi.
10:58
There are many people who end up on the far left on this distribution
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Ima mnogo ljudi koji su sasvim na lijevoj strani rasporeda
11:01
who are talked about 10 times fewer than they should have been.
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i o kojima se govori 10 puta manje nego što bi se trebalo.
11:04
But then also many people on the far right
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Ali isto tako ima mnogo ljudi na sasvim desnoj strani
11:06
who seem to benefit from propaganda.
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koji, izgleda, profitiraju od propagande.
11:08
This picture is the hallmark of censorship in the book record.
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Ova je slika glavni simbol cenzure u knjigama.
11:11
ELA: So culturomics
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ELA: Dakle, kulturomika
11:13
is what we call this method.
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jest ime koje smo dali ovoj metodi.
11:15
It's kind of like genomics.
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Nalikuje na genomiku.
11:17
Except genomics is a lens on biology
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Osim što je genomika pogled na biologiju,
11:19
through the window of the sequence of bases in the human genome.
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pogled na slijed baza u ljudskom genomu.
11:22
Culturomics is similar.
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Kulturomika je slična tome.
11:24
It's the application of massive-scale data collection analysis
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To je primjena analize ogromnog skupa podataka
11:27
to the study of human culture.
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na proučavanje ljudske kulture.
11:29
Here, instead of through the lens of a genome,
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Ovdje, umjesto da promatramo genom,
11:31
through the lens of digitized pieces of the historical record.
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promatramo digitalizirane dijelove povijesnih zapisa.
11:34
The great thing about culturomics
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Ono što je odlično kod kulturomike
11:36
is that everyone can do it.
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jest to da se svi mogu njome baviti.
11:38
Why can everyone do it?
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Zašto se svi mogu njome baviti?
11:40
Everyone can do it because three guys,
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Svi se mogu njome baviti jer su tri tipa,
11:42
Jon Orwant, Matt Gray and Will Brockman over at Google,
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Jon Orwant, Matt Gray i Will Brockman iz Googlea
11:45
saw the prototype of the Ngram Viewer,
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vidjeli prototip preglednika Ngram
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 ljudima omogućiti pristup tome."
11:52
So in two weeks flat -- the two weeks before our paper came out --
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Za samo dva tjedna -- dva tjedna prije nego nam je objavljen članak --
11:54
they coded up a version of the Ngram Viewer for the general public.
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iskodirali su verziju pregledika Ngram 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 unijeti bilo koju riječ ili izraz koji vas zanima
12:00
and see its n-gram immediately --
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i odmah vidjeti njegove n-grame --
12:02
also browse examples of all the various books
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isto tako možete pregledavati primjere iz svih knjiga
12:04
in which your n-gram appears.
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u kojima se pojavljuje vaš n-gram.
12:06
JM: Now this was used over a million times on the first day,
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JM: Ovaj je preglednik korišten više od milijun puta prvog dana,
12:08
and this is really the best of all the queries.
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i ovo je zapravo najbolji od svih upita.
12:10
So people want to be their best, put their best foot forward.
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Ljudi žele dati sve od sebe, pokazati se u najboljem svijetlu.
12:13
But it turns out in the 18th century, people didn't really care about that at all.
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Ali ispada da u 18. stoljeću ljudima uopće nije bilo stalo do toga.
12:16
They didn't want to be their best, they wanted to be their beft.
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Nisu željeli dati sve od sebe, željeli su dati fve od sebe.
12:19
So what happened is, of course, this is just a mistake.
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Naravno, ovdje se radi samo o pogrešci.
12:22
It's not that strove for mediocrity,
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Nije da su težili osrednjosti,
12:24
it's just that the S used to be written differently, kind of like an F.
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već se S prije pisao drugačije, pomalo nalik na F.
12:27
Now of course, Google didn't pick this up at the time,
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Naravno, Google to nije prepoznao
12:30
so we reported this in the science article that we wrote.
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i to smo napomenuli u znanstvenom članku koji smo napisali.
12:33
But it turns out this is just a reminder
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No, ispada da je ovo samo podsjetnik da,
12:35
that, although this is a lot of fun,
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iako je ovo vrlo zabavno,
12:37
when you interpret these graphs, you have to be very careful,
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kad tumačite ove grafove, morate biti vrlo oprezni
12:39
and you have to adopt the base standards in the sciences.
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i morate usvojiti ove temeljne znastvene standarde.
12:42
ELA: People have been using this for all kinds of fun purposes.
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ELA: Ljudi ovo koriste za razne zabavne namjene.
12:45
(Laughter)
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(Smijeh)
12:52
Actually, we're not going to have to talk,
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Zapravo, ne moramo ni govoriti,
12:54
we're just going to show you all the slides and remain silent.
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samo ćemo vam pokazati sve slajdove i šutjeti.
12:57
This person was interested in the history of frustration.
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Ovu osobu je zanimala povijest frustracije.
13:00
There's various types of frustration.
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Postoje različite vrste frustracija.
13:03
If you stub your toe, that's a one A "argh."
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Kad se udarite u nožni prst, to je "argh" s jednim A.
13:06
If the planet Earth is annihilated by the Vogons
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Ako plant Zemlju unište Vogonci
13:08
to make room for an interstellar bypass,
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kako bi napravili mjesta za međuzvjezdanu zaobilaznicu,
13:10
that's an eight A "aaaaaaaargh."
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to je "aaaaaaaargh" s 8 A-ova.
13:12
This person studies all the "arghs,"
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Ova osoba proučava sve "arghove",
13:14
from one through eight A's.
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od jednog do 8 A-ova.
13:16
And it turns out
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Ispada da su
13:18
that the less-frequent "arghs"
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manje učestali "arghovi"
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, čudno, početkom 80-ih.
13:26
We think that might have something to do with Reagan.
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Mislimo da to možda ima veze s Reaganom.
13:28
(Laughter)
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(Smijeh)
13:30
JM: There are many usages of this data,
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JM: Ovi se podaci mogu koristiti za razne namjene,
13:33
but the bottom line is that the historical record is being digitized.
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ali ono što je bitno jest da se povijesni zapisi digitaliziraju.
13:36
Google has started to digitize 15 million books.
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Google je počeo digitalizirati 15 milijuna knjiga.
13:38
That's 12 percent of all the books that have ever been published.
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To je 12 posto svih knjiga koje su ikad izdane.
13:40
It's a sizable chunk of human culture.
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To je povelik dio ljudske kulture.
13:43
There's much more in culture: there's manuscripts, there newspapers,
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U kulturi ima još mnogo toga: rukopisi, novine,
13:46
there's things that are not text, like art and paintings.
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postoje stvari koje nisu tekst, poput umjetnosti i slika.
13:48
These all happen to be on our computers,
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To će sve biti na našim računalima,
13:50
on computers across the world.
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na računalima u cijelome svijetu.
13:52
And when that happens, that will transform the way we have
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A kad se to dogodi, promijenit će se način na koji smo
13:55
to understand our past, our present and human culture.
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shvaćali svoju prošlost, svoju sadašnjost i ljudsku kulturu.
13:57
Thank you very much.
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Hvala vam puno.
13:59
(Applause)
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(Pljesak)
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