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 isprobali Google-ov Labs' Ngram Viewer? To je zarazna alatka koja vam omogućava da tražite riječi i ideje u bazi od 5 miliona knjiga iz različitih stoljeća. Erez Lieberman Aiden and Jean-Baptiste Michel nam pokazuju kako funkcioniše, i nekoliko začuđujućih stvari koje možemo naučiti iz 500 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 Aide: Svako zna
00:17
that a picture is worth a thousand words.
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da jedna slika vrijedi hiljadu riječi.
00:22
But we at Harvard
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Ali mi na Harvardu
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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(Smijeh)
00:29
So we assembled a team of experts,
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Stoga smo skupili tim eksperata,
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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The American Heritage Dictionary, Enciklopedije Britannica,
00:38
and even our proud sponsors,
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i naših ponosnih sponzora,
00:40
the Google.
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Googlea.
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 4 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 vrijedi hiljadu riječi.
00:55
In fact, we found some pictures
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Zapravo, našli smo 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 došli do ovog zaključka?
01:04
So Erez and I were thinking about ways
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Erez i ja smo razmišljali kako da pronađemo načine
01:06
to get a big picture of human culture
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da napravimo sliku ljudske kulture
01:08
and human history: change over time.
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i ljudske historije: promjenu tokom vremena.
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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Pa smo razmišljali da je najbolji način da se iz njih uči
01:15
is to read all of these millions of books.
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jeste da pročitamo sve ove knjige.
01:17
Now of course, if there's a scale for how awesome that is,
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Naravno, ako postoji skala fenomenalnosti,
01:20
that has to rank extremely, extremely high.
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mora biti jako, jako visoko.
01:23
Now the problem is there's an X-axis for that,
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Problem je što za to postoji X-osa,
01:25
which is the practical axis.
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stvarna osa.
01:27
This is very, very low.
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Koja 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 obično koriste drugi pristup,
01:35
which is to take a few sources and read them very carefully.
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uzmu par izvora i pažljivo ih čitaju.
01:37
This is extremely practical, but not so awesome.
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Ovo je veoma praktično, ali nije tako fenomenalno.
01:39
What you really want to do
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Ono što zapravo želite postići
01:42
is to get to the awesome yet practical part of this space.
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jeste fenomenalno, ali praktični dio ovog prostora.
01:45
So it turns out there was a company across the river called Google
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Postoji kompanija koja se zove Google
01:48
who had started a digitization project a few years back
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i koja je prije nekoliko godina krenula sa digitalizacijom
01:50
that might just enable this approach.
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koja bi pomogla ovom pristupu.
01:52
They have digitized millions of books.
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Digitalizirali su milione knjiga.
01:54
So what that means is, one could use computational methods
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To znači da možemo kompjuterski
01:57
to read all of the books in a click of a button.
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pročitati sve knjige u samo jednom kliku.
01:59
That's very practical and extremely awesome.
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To je veoma praktično i fenomenalno.
02:03
ELA: Let me tell you a little bit about where books come from.
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ELA: Dozvolite mi da nešto kažem o tome odakle su potjekle knjige.
02:05
Since time immemorial, there have been authors.
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Od prastarih vremena, postojali su autori.
02:08
These authors have been striving to write books.
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Ovi autori su težili da pišu knjige.
02:11
And this became considerably easier
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Ovo je postalo znatno lakše
02:13
with the development of the printing press some centuries ago.
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od kada se, prije nekoliko stoljeća, pojavila mašina za štampanje.
02:15
Since then, the authors have won
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Od tada, autori su
02:18
on 129 million distinct occasions,
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objavili oko 129 miliona
02:20
publishing books.
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knjiga.
02:22
Now if those books are not lost to history,
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Ako se ove knjige nisu izgubile u prošlosti,
02:24
then they are somewhere in a library,
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onda su negdje u knjižari,
02:26
and many of those books have been getting retrieved from the libraries
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a mnoge knjige su podizane iz bibilioteka
02:29
and digitized by Google,
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i digitalizovane od strane Goolgea,
02:31
which has scanned 15 million books to date.
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koji je do sada skenirao 15 miliona knjiga.
02:33
Now when Google digitizes a book, they put it into a really nice format.
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Kada Google digitalizuje knjigu, stave je u veoma dobar format.
02:36
Now we've got the data, plus we have metadata.
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Sada imamo podatke i meta-podatke.
02:38
We have information about things like where was it published,
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Imamo podatke o tome gdje je objavljena,
02:41
who was the author, when was it published.
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ko je autor, kada je objavljena.
02:43
And what we do is go through all of those records
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I mi prelazimo sve ove podatke
02:46
and exclude everything that's not the highest quality data.
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i izbacujemo sve one podatke koji nisu kvalitetni.
02:50
What we're left with
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Ono što nam preostaje je
02:52
is a collection of five million books,
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kolekcija od 5 miliona 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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i niz slova, 1000 puta duži od
03:00
than the human genome --
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ljudskog genoma --
03:03
a text which, when written out,
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tekst koji, kada se ispiše,
03:05
would stretch from here to the Moon and back
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bi se protezao do Mjeseca i nazad
03:07
10 times over --
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10 puta --
03:09
a veritable shard of our cultural genome.
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prava krhotina našeg kulturnog genoma.
03:13
Of course what we did
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Naravno,
03:15
when faced with such outrageous hyperbole ...
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kada smo se suočili sa ovakvom nečuvenom hiperbolom...
03:18
(Laughter)
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(Smijeh)
03:20
was what any self-respecting researchers
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uradili smo ono
03:23
would have done.
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što bi svaki istraživač uradio.
03:26
We took a page out of XKCD,
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Uzeli smo stranicu iz XKCD,
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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Isprobat ćemo nauku."
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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hajmo prvo ubaciti podatke
03:38
for people to do science to it.
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koji bi ih iskoristili u nauci.
03:40
Now we're thinking, what data can we release?
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Razmišljali smo, koje podatke možemo obajaviti?
03:42
Well of course, you want to take the books
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Naravno, želite objaviti
03:44
and release the full text of these five million books.
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cijeli tekst ovih 5 miliona knjiga.
03:46
Now Google, and Jon Orwant in particular,
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Google, a posebno Jon Orwant,
03:48
told us a little equation that we should learn.
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nam je pokazao jednu jednačinu koju trebamo znati.
03:50
So you have five million, that is, five million authors
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Ako imate 5 miliona, tj., 5 miliona autora,
03:53
and five million plaintiffs is a massive lawsuit.
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to znači 5 miliona tužilaca.
03:56
So, although that would be really, really awesome,
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Iako bi to bilo veoma, veoma fenomenalno,
03:58
again, that's extremely, extremely impractical.
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ipak je jako nepraktično.
04:01
(Laughter)
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(Smijeh)
04:03
Now again, we kind of caved in,
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Nekako smo popustili,
04:05
and we did the very practical approach, which was a bit less awesome.
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i krenuli smo praktični pristup, koji je bio malo manje fenomenalan.
04:08
We said, well instead of releasing the full text,
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Umjesto da objavljujemo cijeli tekst,
04:10
we're going to release statistics about the books.
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objavit ćemo statistiku o knjigama.
04:12
So take for instance "A gleam of happiness."
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Uzmite naprimjer "Tračak sreće."
04:14
It's four words; we call that a four-gram.
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Ima četiri riječi; zovemo je četiri-grama.
04:16
We're going to tell you how many times a particular four-gram
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Pokazat ćemo vam koliko puta se ona
04:18
appeared in books in 1801, 1802, 1803,
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pojavila u knjigama u 1801, 1802, 1803,
04:20
all the way up to 2008.
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sve do 2008.
04:22
That gives us a time series
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Tako znamo
04:24
of how frequently this particular sentence was used over time.
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koliko često se neka rečenica ponavljala tokom vremena.
04:26
We do that for all the words and phrases that appear in those books,
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Uradili smo to za sve riječi i fraze koje se pojavljuju u ovim knjigama,
04:29
and that gives us a big table of two billion lines
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i tako imamo tabelu od 2 milijarde redova
04:32
that tell us about the way culture has been changing.
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koji nam govore kako se kultura mijenjala.
04:34
ELA: So those two billion lines,
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ELA: Te redove
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we call them two billion n-grams.
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zovemo 2 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 određuju kulturalne trendove.
04:42
Let me give you an example.
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Evo primjera.
04:44
Let's suppose that I am thriving,
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Pretpostavimo da napredujem,
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then tomorrow I want to tell you about how well I did.
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i sutra vam želim ispričati kako sam uradio.
04:48
And so I might say, "Yesterday, I throve."
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Mogu reći, "Jučer sam napredovao."
04:51
Alternatively, I could say, "Yesterday, I thrived."
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Umjesto toga, mogu reći, "Jučer napredovah."
04:54
Well which one should I use?
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Koju riječ da koristim?
04:57
How to know?
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Kako da znam?
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As of about six months ago,
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Od prije šest mjeseci,
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 mogli, naprimjer,
05:05
go up to the following psychologist with fabulous hair,
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otići psihologu sa odličnom kosom,
05:07
and you'd say,
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i reći,
05:09
"Steve, you're an expert on the irregular verbs.
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"Steve, ti si ekspert u nepravilnim glagolima.
05:12
What should I do?"
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Šta trebam uraditi?"
05:14
And he'd tell you, "Well most people say thrived,
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A on bi ti rekao, "Većina ljudi kaže napredova,
05:16
but some people say throve."
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ali neki kažu napredovah."
05:19
And you also knew, more or less,
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Takođe ste znali, manje ili više,
05:21
that if you were to go back in time 200 years
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da ako se vratite 200 godina unazad
05:24
and ask the following statesman with equally fabulous hair,
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i pitate državnika sa jednako dobrom kosom
05:27
(Laughter)
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(Smijeh)
05:30
"Tom, what should I say?"
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"Tom, šta da kažem?"
05:32
He'd say, "Well, in my day, most people throve,
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On bi rekao, "Pa, u moje vrijeme, većina ljudi kaže napredovao,
05:34
but some thrived."
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a neki kažu napredovah."
05:37
So now what I'm just going to show you is raw data.
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Sada ću vam pokazati nepripremljene podatke.
05:39
Two rows from this table of two billion entries.
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Dvije kolone u tabeli sa 2 milijarde unosa.
05:43
What you're seeing is year by year frequency
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Možete vidjeti frekvenciju godinu za godinom
05:45
of "thrived" and "throve" over time.
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za riječi "napredovao" i "napredovah".
05:49
Now this is just two
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Ovo je samo 2
05:51
out of two billion rows.
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od 2 milijarde kolona.
05:54
So the entire data set
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Čitav set podataka
05:56
is a billion times more awesome than this slide.
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je milijardu puta fenomenalniji od ovog slajda.
05:59
(Laughter)
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(Smijeh)
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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JM: Ima drugih slika koje vrijede 500 milijardi riječi.
06:07
For instance, this one.
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Naprimjer, ova.
06:09
If you just take influenza,
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Ako uzmemo gripu,
06:11
you will see peaks at the time where you knew
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vidjećete razdoblja kada je poznato
06:13
big flu epidemics were killing people around the globe.
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da je epidemija gripe ubijala ljude širom planete.
06:16
ELA: If you were not yet convinced,
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ELA: Ako još niste uvjereni,
06:19
sea levels are rising,
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nivo mora se povećava,
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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JM: Pogledajte ovaj n-gram,
06:27
and that's to tell Nietzsche that God is not dead,
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koji pokazuje Nietzscheu da Bog nije mrtav,
06:30
although you might agree that he might need a better publicist.
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iako se morate složiti da on bi mu dobro došao bolji publicist.
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: Možete posmatrati neke vrlo abstraktne koncepte.
06:38
For instance, let me tell you the history
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Naprimjer, dopustite da vam kažem nešto
06:40
of the year 1950.
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o godini 1950-toj.
06:42
Pretty much for the vast majority of history,
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Tokom čitave prošlosti, poprilično
06:44
no one gave a damn about 1950.
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nikome nije bilo stalo do godine 1950.
06:46
In 1700, in 1800, in 1900,
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U 1700, 1800, i 1900.
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no one cared.
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nikome nije bilo stalo.
06:52
Through the 30s and 40s,
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Kroz 30-te i 40-te,
06:54
no one cared.
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nikome nije bilo stalo.
06:56
Suddenly, in the mid-40s,
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Najednom, sredinom 40-tih,
06:58
there started to be a buzz.
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počela je galama.
07:00
People realized that 1950 was going to happen,
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Ljudi su shvatili da će se desiti 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 nikada se ljudi nisu više zainteresirali za godinu 1950.
07:10
like the year 1950.
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kao u godini 1950.
07:13
(Laughter)
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(Smijeh)
07:16
People were walking around obsessed.
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Ljudi su opsjednuto hodali uokolo.
07:18
They couldn't stop talking
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Nisu mogli prestati pričati
07:20
about all the things they did in 1950,
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o stvarima koje su radili godine 1050.,
07:23
all the things they were planning to do in 1950,
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i o stvarima koje su planirali raditi godine 1950.
07:26
all the dreams of what they wanted to accomplish in 1950.
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o snovima koje su htjeli ostvariti godine 1950.
07:31
In fact, 1950 was so fascinating
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Zapravo, godina 1950 bila je tako fascinantna
07:33
that for years thereafter,
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da su godinama nakon,
07:35
people just kept talking about all the amazing things that happened,
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ljudi nastavili pričati o svim zapanjujućim stvarima koje su se desile,
07:38
in '51, '52, '53.
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godine 1951, '52, '53.
07:40
Finally in 1954,
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Napokon 1954.,
07:42
someone woke up and realized
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neko je shvatio
07:44
that 1950 had gotten somewhat passé.
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da je 1950. nekako zastarijela.
07:48
(Laughter)
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(Smijeh)
07:50
And just like that, the bubble burst.
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I samo tako, balon je pukao.
07:52
(Laughter)
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(Smijeh)
07:54
And the story of 1950
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Priča o godini 1950.
07:56
is the story of every year that we have on record,
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je priča o svakoj godini koju smo zabilježili,
07:58
with a little twist, because now we've got these nice charts.
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a malim preokretom, jer sada imamo ove lijepe grafikone.
08:01
And because we have these nice charts, we can measure things.
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I zbog toga što imamo ove grafikone, možemo da mjerimo stvari.
08:04
We can say, "Well how fast does the bubble burst?"
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Možemo reći, "Kako brzo balon može da pukne?"
08:06
And it turns out that we can measure that very precisely.
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Ispostavilo se da to možemo veoma precizno da izmjerimo.
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 jednostavan rezultat
08:14
is that we find that the bubble bursts faster and faster
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je taj da balon buca sve brže
08:17
with each passing year.
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kako godine prolaze.
08:19
We are losing interest in the past more rapidly.
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Sve brže gubimo interes za prošlost.
08:24
JM: Now a little piece of career advice.
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JM: Sada ću vam dati jedan mali savjet u vezi odabira karijere.
08:26
So for those of you who seek to be famous,
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Za one koji žele postati poznati,
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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Ako želite rano postati poznat, trebali ste biti glumac,
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 krajem vaših 20-tih godina --
08:37
you're still young, it's really great.
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još uvijek ste mladi, što je sjajno.
08:39
Now if you can wait a little bit, you should be an author,
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Ako možete čekati još malo, onda bi ste trebali biti 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 Twain, naprimjer: on je veoma poznat.
08:45
But if you want to reach the very top,
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Ali ako želite doseći sam vrh,
08:47
you should delay gratification
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trebali bi ste odgoditi slavu
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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Ovako ćete postati popularni krajem vaših 50-tih godina,
08:53
and become very, very famous afterward.
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i ostati veoma, veoma, poznati i nakon.
08:55
So scientists also tend to get famous when they're much older.
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I naučnici postaju slavni kako stare.
08:58
Like for instance, biologists and physics
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Naprimejr, biolozi i fizičari
09:00
tend to be almost as famous as actors.
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su obično poznati kao i glumci.
09:02
One mistake you should not do is become a mathematician.
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Jedina greška koju ne smijete napraviti jeste da postanete matematičar.
09:05
(Laughter)
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(Smijeh)
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, "Super. Objavit ću najbolji rad u svojim 20-tim."
09:12
But guess what, nobody will really care.
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Ali pogodite, nikome zaista neće biti stalo.
09:14
(Laughter)
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(Smijeh)
09:17
ELA: There are more sobering notes
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ELA: Ima i nešto trezvenih bilješki
09:19
among the n-grams.
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mešu n-gramima.
09:21
For instance, here's the trajectory of Marc Chagall,
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Naprimjer, ovo je put Marca Chagalla,
09:23
an artist born in 1887.
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umjetnika rođenog 1887.
09:25
And this looks like the normal trajectory of a famous person.
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I ovo izgleda kao normalan put poznate osobe.
09:28
He gets more and more and more famous,
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On postaje sve poznatiji,
09:32
except if you look in German.
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osim ako gledate na njemačkom.
09:34
If you look in German, you see something completely bizarre,
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Na njemačkom, postoji nešto veoma bizarno,
09:36
something you pretty much never see,
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nešto što se skoro nikada ne može vidjeti,
09:38
which is he becomes extremely famous
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a to je da on postaje strašno poznat
09:40
and then all of a sudden plummets,
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i onda najednom njegova popularnost snažno se penje,
09:42
going through a nadir between 1933 and 1945,
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i doseže nebeske visine između 1933 i 1945.,
09:45
before rebounding afterward.
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prije se ponovo vraća.
09:48
And of course, what we're seeing
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Naravno, vidimo
09:50
is the fact Marc Chagall was a Jewish artist
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da je Marc Chagall bio jevrejski umjetnih
09:53
in Nazi Germany.
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u nacističkoj Njemačkoj.
09:55
Now these signals
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Ovi signali
09:57
are actually so strong
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su zapravo tako jaki
09:59
that we don't need to know that someone was censored.
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da ne moramo znati da je neko cenzurisan.
10:02
We can actually figure it out
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Možemo zapravo shvatiti
10:04
using really basic signal processing.
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procesuirajući jednostavne signale.
10:06
Here's a simple way to do it.
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Evo jednostavnog načina za to.
10:08
Well, a reasonable expectation
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Logično je očekivati
10:10
is that somebody's fame in a given period of time
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da nečija slava u datom preiodu
10:12
should be roughly the average of their fame before
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bi trebala otprilike biti prosjek njihove slave prije
10:14
and their fame after.
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i slave poslije.
10:16
So that's sort of what we expect.
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Takvo nešto mi očekujemo.
10:18
And we compare that to the fame that we observe.
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I poredimo to sa slavom koju mi posmatramo.
10:21
And we just divide one by the other
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I jednostavno podijelimo jedno sa drugim
10:23
to produce something we call a suppression index.
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da bismo dobili nešto što nazivamo indeks zabrane.
10:25
If the suppression index is very, very, very small,
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Ako je indeks veoma, veoma, veoma mali,
10:28
then you very well might be being suppressed.
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onda možda ste zabranjeni.
10:30
If it's very large, maybe you're benefiting from propaganda.
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Ako je veoma veliki, onda možda imate korist od propagande.
10:34
JM: Now you can actually look at
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JM: Možete zapravo posmatrati
10:36
the distribution of suppression indexes over whole populations.
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distribuciju indeksa zabrane čitave populacije.
10:39
So for instance, here --
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Naprimjer, ovdje --
10:41
this suppression index is for 5,000 people
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indeks zabrane za 5,000 ljudi
10:43
picked in English books where there's no known suppression --
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odabranih iz engleskih udžbenika gdje nema zabrana --
10:45
it would be like this, basically tightly centered on one.
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izgledalo bi ovako, usko centrirani na jedan.
10:47
What you expect is basically what you observe.
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Ono što očekujete je jednostavno ono što posmatrate.
10:49
This is distribution as seen in Germany --
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Ovo je rasprostranjenost posmatrana u Njemačkoj --
10:51
very different, it's shifted to the left.
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veoma različita, pomjerena u lijevo.
10:53
People talked about it twice less as it should have been.
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Ljudi su o tome govorili dva puta manje nego što je trebalo.
10:56
But much more importantly, the distribution is much wider.
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Ali što je najvažnije, rasprostranjenost je mnogo šira.
10:58
There are many people who end up on the far left on this distribution
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Mnogo je ljudi koji završe na krajnje lijevoj tački rasprostranjenosti
11:01
who are talked about 10 times fewer than they should have been.
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o kojima se govori 10 puta manje nego što bi trebalo.
11:04
But then also many people on the far right
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Ali i mnogi ljudi na krajnje desnoj tački
11:06
who seem to benefit from propaganda.
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očigledno imaju korist od propadande.
11:08
This picture is the hallmark of censorship in the book record.
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Ova slika je znak cenzure.
11:11
ELA: So culturomics
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ELA: Kulturomija
11:13
is what we call this method.
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je naziv ove naše metode.
11:15
It's kind of like genomics.
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Nešto je nalik genomiji.
11:17
Except genomics is a lens on biology
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Osim što je genomija uvid u bilogiju
11:19
through the window of the sequence of bases in the human genome.
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kroz prozor slijeda baza u ljudskom genomu.
11:22
Culturomics is similar.
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Kulturomija je slična.
11:24
It's the application of massive-scale data collection analysis
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To je primjena skupljanja podataka velikog uzorka
11:27
to the study of human culture.
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na ljudsku kulturu.
11:29
Here, instead of through the lens of a genome,
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Umjesto kroz ljudski genom,
11:31
through the lens of digitized pieces of the historical record.
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gleda se kroz digitalizirane historijske zapise.
11:34
The great thing about culturomics
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Odlična stvar u vezi kulturonomije
11:36
is that everyone can do it.
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je da svako to može uraditi.
11:38
Why can everyone do it?
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Zašto je dostupna svima?
11:40
Everyone can do it because three guys,
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Zato što su tri čovjeka,
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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su vidjeli prototip Ngram VIewera,
11:47
and they said, "This is so fun.
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i rekli su, "Ovo je tako zabavno.
11:49
We have to make this available for people."
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Moramo ovo pružiti ljudima."
11:52
So in two weeks flat -- the two weeks before our paper came out --
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Za ravno dvije sedmice -- dvije sedmice prije nego naš rad objavljen --
11:54
they coded up a version of the Ngram Viewer for the general public.
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napravili su verziju Ngram Viewera 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 sada možete ukucati bilo koju riječ ili frazu koja vas zanima
12:00
and see its n-gram immediately --
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i odmah vidjeti njen n-gram --
12:02
also browse examples of all the various books
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i naći primjere iz ranih knjiga
12:04
in which your n-gram appears.
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u kojima se vaš n-gram spominje.
12:06
JM: Now this was used over a million times on the first day,
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JM: Ngram Viewer
12:08
and this is really the best of all the queries.
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i ovo je najbolje od svih upita.
12:10
So people want to be their best, put their best foot forward.
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Ljudi žele najbolje, da urade najbolje što mogu.
12:13
But it turns out in the 18th century, people didn't really care about that at all.
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Ali izgleda da ljudi u 18-tom stoljeću o tome nisu marili.
12:16
They didn't want to be their best, they wanted to be their beft.
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Nisu željeli uraditi najbolje, željeli su najbolje.
12:19
So what happened is, of course, this is just a mistake.
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Desilo se, naravno, ovo je samo pogreška.
12:22
It's not that strove for mediocrity,
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Nije da su težili prosjećnosti,
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 pisalo drugačije, slično F.
12:27
Now of course, Google didn't pick this up at the time,
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Naravno, Google nije ovo izdvojio,
12:30
so we reported this in the science article that we wrote.
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tako da smo ovo naveli u naučnom članku.
12:33
But it turns out this is just a reminder
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Ali ovo je ispalo kao podsjetnik
12:35
that, although this is a lot of fun,
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da, iako je ovo veoma zabavno,
12:37
when you interpret these graphs, you have to be very careful,
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kada tumačite ove grafikone, morate biti veoma pažljivi,
12:39
and you have to adopt the base standards in the sciences.
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i morati primijeniti ove standarde u nauci.
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 svrhe.
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 pričati,
12:54
we're just going to show you all the slides and remain silent.
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samo ćemo vam u tišini pokazati sve slajdove.
12:57
This person was interested in the history of frustration.
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Ovu osobu je interesovala historija frustracije.
13:00
There's various types of frustration.
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Postoje razni tipovi fustracija.
13:03
If you stub your toe, that's a one A "argh."
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Ako se udarite u nožni prst, to je jedno A "argh".
13:06
If the planet Earth is annihilated by the Vogons
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Ako planetu Zemlju nasele Vogonci
13:08
to make room for an interstellar bypass,
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da naprave međuzvjezdanu zaobliaznicu,
13:10
that's an eight A "aaaaaaaargh."
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to je osam A "aaaaaaaargh."
13:12
This person studies all the "arghs,"
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Ova osoba je istražila sve "arghove,"
13:14
from one through eight A's.
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od jednog pa do osam slova A.
13:16
And it turns out
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I ispada
13:18
that the less-frequent "arghs"
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najrjeđi "arghovi"
13:20
are, of course, the ones that correspond to things that are more frustrating --
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su, naravno, oni koji se odnose na stvari koji više frustrirajuće --
13:23
except, oddly, in the early 80s.
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osim, začudo, početkom 80-tih.
13:26
We think that might have something to do with Reagan.
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Možda to ima neke veze sa 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 podaci se koriste u razne svrhe,
13:33
but the bottom line is that the historical record is being digitized.
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ali historijski zapisi se digitalizuju.
13:36
Google has started to digitize 15 million books.
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Google je počeo sa digitalizacijom 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 posto svih knjiga koje su izdate.
13:40
It's a sizable chunk of human culture.
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To je veliki dio ljudske kulture.
13:43
There's much more in culture: there's manuscripts, there newspapers,
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Tu su i rukopisi, novine,
13:46
there's things that are not text, like art and paintings.
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tu su i materijali bez teksta, kao umjetnost i slike.
13:48
These all happen to be on our computers,
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To je sve u našim kompjuterima,
13:50
on computers across the world.
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i kompjuterima širom svijeta.
13:52
And when that happens, that will transform the way we have
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Kada se to desi, to će promijeniti način na koji
13:55
to understand our past, our present and human culture.
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mi shvatamo prošlost, sadašnjost i ljudsku 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