TEDxCMU
Luis von Ahn: Massive-scale online collaboration
Luis von Ahn: Masivna umrežena suradnja
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Nakon što je prenamijenio Captchu na način da svaki odgovor koji utipka čovjek pomogne digitalizirati knjige, Luis von Ahn se pitao kako još upotrijebiti male doprinose mnogih na Internetu za veće dobro. Na TEDxCMU-u govori kako će njegov ambiciozni novi projekt, Duolingo, pomoći milijunima da nauče novi jezik dok prevode Mrežu brzo i točno -- i to besplatno.
Luis von Ahn - Computer scientist
Luis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone. Full bio
Luis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone. Full bio
Double-click the English transcript below to play the video.
00:15
How many of you had to fill out some sort of web form
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Koliko vas je moralo ispuniti neku vrstu mrežnog obrasca
00:17
where you've been asked to read a distorted sequence of characters like this?
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gdje su vas tražili da čitate iskrivljeni niz znakova poput ovog?
00:19
How many of you found it really, really annoying?
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Koliko vas smatra ovo stvarno, stvarno dosadnim?
00:21
Okay, outstanding. So I invented that.
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U redu, izvanredno. Ja sam to izmislio.
00:24
(Laughter)
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(Smijeh)
00:26
Or I was one of the people who did it.
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Ili sam bio jedan od ljudi koji su to napravili.
00:28
That thing is called a CAPTCHA.
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Ta se stvar zove Captcha.
00:30
And the reason it is there is to make sure you, the entity filling out the form,
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I razlog zbog kojeg je tu jest da ste vi, entitet koji ispunjava obrazac,
00:32
are actually a human and not some sort of computer program
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doista čovjek, a ne neka vrsta kompjuterskog programa
00:35
that was written to submit the form millions and millions of times.
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napisana da podnosi obrazac milijune i milijune puta.
00:37
The reason it works is because humans,
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Ona djeluje zato što ljudi,
00:39
at least non-visually-impaired humans,
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barem oni koji nemaju oštećen vid,
00:41
have no trouble reading these distorted squiggly characters,
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nemaju problema s čitanjem tih iskrivljenih vijugavih znakova,
00:43
whereas computer programs simply can't do it as well yet.
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dok to kompjutorski programi jednostavno još ne mogu tako dobro.
00:46
So for example, in the case of Ticketmaster,
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Npr., u slučaju Ticketmastera,
00:48
the reason you have to type these distorted characters
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razlog zbog kojeg morate tipkati te iskrivljene znakove
00:50
is to prevent scalpers from writing a program
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jest da se spriječe preprodavači da napišu program
00:52
that can buy millions of tickets, two at a time.
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koji može kupiti milijune ulaznica, dvije odjednom.
00:54
CAPTCHAs are used all over the Internet.
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Captche se koriste na cijelom Internetu.
00:56
And since they're used so often,
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I budući da se koriste tako često,
00:58
a lot of times the precise sequence of random characters that is shown to the user
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puno puta precizan niz nasumičnih znakova koje vidi korisnik
01:00
is not so fortunate.
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nije tako sretan.
01:02
So this is an example from the Yahoo registration page.
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Ovo je primjer Yahooove stranice za registraciju.
01:05
The random characters that happened to be shown to the user
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Nasumični znakovi koji se pokazuju korisniku
01:07
were W, A, I, T, which, of course, spell a word.
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bili su W, A, I, T, koji, naravno, sriču riječ (čekaj).
01:10
But the best part is the message
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No najbolji dio je poruka
01:13
that the Yahoo help desk got about 20 minutes later.
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koju je Yahooov odjel za pomoć dobio oko 20 minuta kasnije.
01:16
Text: "Help! I've been waiting for over 20 minutes, and nothing happens."
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Tekst: "Upomoć! Čekam već 20 minuta, i ništa se ne događa."
01:19
(Laughter)
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(Smijeh)
01:23
This person thought they needed to wait.
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Ovaj je čovjek mislio da mora čekati.
01:25
This of course, is not as bad as this poor person.
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Ovo, naravno, nije tako loše kao ovaj jadnik.
01:28
(Laughter)
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(Smijeh)
01:30
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago,
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Projekt Captcha je nešto što smo napravili ovdje na sveučilištu Carnegie Mellon prije 10 godina,
01:33
and it's been used everywhere.
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i otada se koristi posvuda.
01:35
Let me now tell you about a project that we did a few years later,
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Sad ću govoriti o projektu koji smo napravili prije nekoliko godina,
01:37
which is sort of the next evolution of CAPTCHA.
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nešto nalik sljedećoj evoluciji Captche.
01:40
This is a project that we call reCAPTCHA,
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Ovo je projekt koji nazivamo reCaptcha,
01:42
which is something that we started here at Carnegie Mellon,
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što je nešto što smo započeli ovdje na Carnegie Mellonu,
01:44
then we turned it into a startup company.
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zatim smo to pretvorili u malu tvrtku.
01:46
And then about a year and a half ago,
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I nakon godine i pol,
01:48
Google actually acquired this company.
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Google je zapravo kupio ovu tvrtku.
01:50
So let me tell you what this project started.
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Reći ću vam što je pokrenuo ovaj projekt.
01:52
So this project started from the following realization:
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Dakle, ovaj projekt je krenuo od sljedeće realizacije:
01:55
It turns out that approximately 200 million CAPTCHAs
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Ispada da otprilike 200 milijuna Captcha
01:57
are typed everyday by people around the world.
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tipkaju ljudi svaki dan diljem svijeta.
02:00
When I first heard this, I was quite proud of myself.
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Kad sam prvi put čuo za to, bio sam poprilično ponosan na sebe.
02:02
I thought, look at the impact that my research has had.
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Mislio sam, pogledaj na utjecaj koje je imalo moje istraživanje.
02:04
But then I started feeling bad.
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Ali zatim sam se počeo osjećati loše.
02:06
See here's the thing, each time you type a CAPTCHA,
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Evo o čemu se radi, svaki put kad tipkate Captchu,
02:08
essentially you waste 10 seconds of your time.
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u biti gubite 10 sekundi svog vremena.
02:11
And if you multiply that by 200 million,
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I ako to umnožite s 200 milijuna,
02:13
you get that humanity as a whole is wasting about 500,000 hours every day
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dobivate da čovječanstvo u cijelom gubi oko 500.000 sati svaki dan
02:16
typing these annoying CAPTCHAs.
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tipkajući te dosadne Captche.
02:18
So then I started feeling bad.
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I tad sam se počeo osjećati loše.
02:20
(Laughter)
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(Smijeh)
02:22
And then I started thinking, well, of course, we can't just get rid of CAPTCHAs,
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I tad sam počeo razmišljati, pa, naravno, ne možemo se samo riješiti Captchi,
02:25
because the security of the Web sort of depends on them.
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zato što sigurnost Mreže na neki način ovisi o njima.
02:27
But then I started thinking, is there any way we can use this effort
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Ali zatim sam počeo razmišljati, postoji li bilo koji način na koji možemo upotrijebiti ovaj trud
02:30
for something that is good for humanity?
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za nešto što je dobro za čovječanstvo?
02:32
So see, here's the thing.
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Gledajte, evo o čemu se radi.
02:34
While you're typing a CAPTCHA, during those 10 seconds,
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Dok tipkate Captchu, tijekom tih 10 sekundi,
02:36
your brain is doing something amazing.
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vaš mozak radi nešto nevjerojatno.
02:38
Your brain is doing something that computers cannot yet do.
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Vaš mozak radi nešto što kompjuteri još ne mogu.
02:40
So can we get you to do useful work for those 10 seconds?
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Pa, možete li napraviti koristan posao za tih 10 sekundi?
02:43
Another way of putting it is,
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Drugim riječima,
02:45
is there some humongous problem that we cannot yet get computers to solve,
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postoji li neki grdan problem koji kompjuteri još ne mogu riješiti,
02:47
yet we can split into tiny 10-second chunks
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ali možemo podijeliti na sićušne dijelove od 10 sekundi
02:50
such that each time somebody solves a CAPTCHA
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tako da svaki put kad netko rješava Captchu
02:52
they solve a little bit of this problem?
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riješi i mali dio ovog problema?
02:54
And the answer to that is "yes," and this is what we're doing now.
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I odgovor na to je "da", i to upravo radimo.
02:56
So what you may not know is that nowadays while you're typing a CAPTCHA,
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Ono što možda ne znate je da danas, dok tipkate Captchu,
02:59
not only are you authenticating yourself as a human,
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ne samo da se identificirate kao čovjek,
03:01
but in addition you're actually helping us to digitize books.
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nego nam također zapravo pomažete digitalizirati knjige.
03:03
So let me explain how this works.
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Objasnit ću vam kako to funkcionira.
03:05
So there's a lot of projects out there trying to digitize books.
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Postoji puno projekata koji pokušavaju digitalizirati knjige.
03:07
Google has one. The Internet Archive has one.
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Google ima jedan. Internetski arhiv ima jedan.
03:10
Amazon, now with the Kindle, is trying to digitize books.
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Amazon, sad s Kindleom, pokušava digitalizirati knjige.
03:12
Basically the way this works
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U osnovi to funkcionira na način da
03:14
is you start with an old book.
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započinjete sa starom knjigom.
03:16
You've seen those things, right? Like a book?
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Vidjeli ste te stvari, točno? Poput knjige?
03:18
(Laughter)
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(Smijeh)
03:20
So you start with a book, and then you scan it.
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Započinjete s knjigom, zatim ju skenirate.
03:22
Now scanning a book
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Sad, skeniranje knjige je
03:24
is like taking a digital photograph of every page of the book.
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kao da snimate digitalnu fotografiju svake stranice knjige.
03:26
It gives you an image for every page of the book.
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Daje vam sliku za svaku stranicu knjige.
03:28
This is an image with text for every page of the book.
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Ovo je slika s tekstom za svaku stranicu knjige.
03:30
The next step in the process
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Slijedeći je korak u postupku
03:32
is that the computer needs to be able to decipher all of the words in this image.
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taj da kompjuter mora biti kadar dešifrirati sve riječi u toj slici.
03:35
That's using a technology called OCR,
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Koristeći tehnologiju OCR,
03:37
for optical character recognition,
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optičko prepoznavanje znakova,
03:39
which takes a picture of text
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koja snima sliku teksta
03:41
and tries to figure out what text is in there.
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i pokušava odgonetnuti koji je tekst unutra.
03:43
Now the problem is that OCR is not perfect.
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Sad, OCR nije savršen.
03:45
Especially for older books
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Posebno za starije knjige
03:47
where the ink has faded and the pages have turned yellow,
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gdje je tinta izblijedjela i stranice su požutjele,
03:50
OCR cannot recognize a lot of the words.
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OCR ne može prepoznati puno riječi.
03:52
For example, for things that were written more than 50 years ago,
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Npr., stvari koje su bile napisane prije više od 50 godina,
03:54
the computer cannot recognize about 30 percent of the words.
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kompjuter ne može prepoznati oko 30 posto riječi.
03:57
So what we're doing now
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Ono što sad radimo jest
03:59
is we're taking all of the words that the computer cannot recognize
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uzimanje svih riječi koje kompjuter ne može prepoznati
04:01
and we're getting people to read them for us
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i davanje ljudima da ih čitaju za nas
04:03
while they're typing a CAPTCHA on the Internet.
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dok tipkaju Captchu na Internetu.
04:05
So the next time you type a CAPTCHA, these words that you're typing
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Sljedeći put kad tipkate Captchu, te riječi koje tipkate
04:08
are actually words that are coming from books that are being digitized
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su zapravo riječi koje dolaze iz knjiga koje se digitaliziraju
04:11
that the computer could not recognize.
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koje kompjuter nije mogao prepoznati.
04:13
And now the reason we have two words nowadays instead of one
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I sad, razlog zbog kojeg danas imamo dvije riječi umjesto jedne
04:15
is because, you see, one of the words
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jest taj što je, vidite, jedna od riječi
04:17
is a word that the system just got out of a book,
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riječ koju je sustav tek dobio iz knjige,
04:19
it didn't know what it was, and it's going to present it to you.
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nije znao što je, i namjerava je prikazati vama.
04:22
But since it doesn't know the answer for it, it cannot grade it for you.
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Ali kako ne zna odgovor na nju, ne može ju ocijeniti za vas.
04:25
So what we do is we give you another word,
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Dakle, dajemo vam drugu riječ,
04:27
one for which the system does know the answer.
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za koju sustav zna odgovor.
04:29
We don't tell you which one's which, and we say, please type both.
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Ne govorimo vam koja je koja, i kažemo da utipkate obje.
04:31
And if you type the correct word
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I ako utipkate pravilnu riječ
04:33
for the one for which the system already knows the answer,
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za onu koju sustav već zna odgovor,
04:35
it assumes you are human,
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pretpostavlja da ste čovjek,
04:37
and it also gets some confidence that you typed the other word correctly.
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i također dobiva neku sigurnost da ste utipkali drugu riječ pravilno.
04:39
And if we repeat this process to like 10 different people
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I ako ponovimo taj proces s 10 različitih ljudi
04:42
and all of them agree on what the new word is,
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i svi se slože oko toga što je nova riječ,
04:44
then we get one more word digitized accurately.
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tad mi dobivamo još jednu riječ točno digitaliziranu.
04:46
So this is how the system works.
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Evo kako sustav funkcionira.
04:48
And basically, since we released it about three or four years ago,
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I u osnovi, otkad smo ga objavili prije tri ili četiri godine,
04:51
a lot of websites have started switching
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puno se mrežnih mjesta počelo prebacivati
04:53
from the old CAPTCHA where people wasted their time
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sa stare Captche gdje su ljudi gubili vrijeme
04:55
to the new CAPTCHA where people are helping to digitize books.
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na novu Captchu gdje ljudi pomažu digitalizirati knjige.
04:57
So for example, Ticketmaster.
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Npr., Ticketmaster.
04:59
So every time you buy tickets on Ticketmaster, you help to digitize a book.
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Svaki put kad kupite ulaznicu na Ticketmasteru, pomažete digitalizirati knjigu.
05:02
Facebook: Every time you add a friend or poke somebody,
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Facebook: Svaki put kad dodate prijatelja ili bocnete nekog,
05:04
you help to digitize a book.
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pomažete digitalizirati knjigu.
05:06
Twitter and about 350,000 other sites are all using reCAPTCHA.
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Twitter i oko 350.000 drugih mjesta koriste reCaptchu.
05:09
And in fact, the number of sites that are using reCAPTCHA is so high
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I u stvari, broj mjesta koji koriste reCaptchu je tako visok
05:11
that the number of words that we're digitizing per day is really, really large.
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da je broj riječi koje digitaliziramo na dan, stvarno, stvarno velik.
05:14
It's about 100 million a day,
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To je oko 100 milijuna na dan,
05:16
which is the equivalent of about two and a half million books a year.
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što je ekvivalent oko dva i pol milijuna knjiga godišnje.
05:20
And this is all being done one word at a time
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I sve rade s jednom po jednom riječju
05:22
by just people typing CAPTCHAs on the Internet.
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samo ljudi koji tipkaju Captchu na Internetu.
05:24
(Applause)
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(Pljesak)
05:32
Now of course,
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Naravno,
05:34
since we're doing so many words per day,
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budući da obrađujemo toliko riječi na dan,
05:36
funny things can happen.
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mogu se dogoditi smiješne stvari.
05:38
And this is especially true because now we're giving people
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I to je posebno istinito zato što sada dajemo ljudima
05:40
two randomly chosen English words next to each other.
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dvije nasumično izabrane engleske riječi jednu pokraj druge.
05:42
So funny things can happen.
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Pa se mogu dogoditi smiješne stvari.
05:44
For example, we presented this word.
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Npr., prikazali smo ovu riječ.
05:46
It's the word "Christians"; there's nothing wrong with it.
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To je riječ "Christians" (kršćani); sve je u redu s njom.
05:48
But if you present it along with another randomly chosen word,
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Ali ako ju prikažete zajedno s drugom nasumično odabranom riječi,
05:51
bad things can happen.
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loše se stvari mogu dogoditi.
05:53
So we get this. (Text: bad christians)
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Pa dobivamo ovo. (Tekst: loši kršćani)
05:55
But it's even worse, because the particular website where we showed this
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Ali još je gore, zato što se određena mrežna mjesta gdje smo ovo pokazali
05:58
actually happened to be called The Embassy of the Kingdom of God.
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zapravo nazivaju Poslanstvo Kraljevstva Božjeg.
06:01
(Laughter)
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(Smijeh)
06:03
Oops.
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Joj.
06:05
(Laughter)
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(Smijeh)
06:08
Here's another really bad one.
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Evo još jednog stvarno lošeg.
06:10
JohnEdwards.com
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JohnEdwards.com
06:12
(Text: Damn liberal)
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(Tekst: Damn liberal - prokleti liberal)
06:15
(Laughter)
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(Smijeh)
06:17
So we keep on insulting people left and right everyday.
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Nastavljamo vrijeđati ljude na sve strane svaki dan.
06:20
Now, of course, we're not just insulting people.
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Naravno, ne vrijeđamo samo ljude.
06:22
See here's the thing, since we're presenting two randomly chosen words,
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Evo o čemu se radi, budući da prikazujemo dvije nasumično izabrane riječi,
06:25
interesting things can happen.
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zanimljive se stvari mogu dogoditi.
06:27
So this actually has given rise
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Ovo je zapravo dovelo do
06:29
to a really big Internet meme
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stvarno velikog internetskog fenomena
06:32
that tens of thousands of people have participated in,
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u kojem su sudjelovali deseci tisuća ljudi,
06:34
which is called CAPTCHA art.
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koji se naziva umjetnost Captcha.
06:36
I'm sure some of you have heard about it.
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Siguran sam da su neki od vas čuli za to.
06:38
Here's how it works.
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Evo kako funkcionira.
06:40
Imagine you're using the Internet and you see a CAPTCHA
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Zamislite da ste na Internetu i vidite Captchu
06:42
that you think is somewhat peculiar,
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za koju mislite da je pomalo neobična,
06:44
like this CAPTCHA. (Text: invisible toaster)
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kao ova Captcha. (Tekst: nevidljivi toster)
06:46
Then what you're supposed to do is you take a screen shot of it.
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Ono što bi trebali učiniti jest snimiti sadržaj ekrana.
06:48
Then of course, you fill out the CAPTCHA
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Zatim, naravno, ispunite Captchu
06:50
because you help us digitize a book.
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zato što nam pomažete digitalizirati knjige.
06:52
But then, first you take a screen shot,
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Ali zatim, prvo snimite sadržaj ekrana,
06:54
and then you draw something that is related to it.
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i zatim nacrtate nešto što ima veze s tim.
06:56
(Laughter)
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(Smijeh)
06:58
That's how it works.
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Tako to funkcionira.
07:01
There are tens of thousands of these.
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Postoje deseci tisuća takvih.
07:04
Some of them are very cute. (Text: clenched it)
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Neke od njih su stvarno ljupke. (Tekst: stisnuti ga)
07:06
(Laughter)
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(Smijeh)
07:08
Some of them are funnier.
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Neke od njih su smiješnije.
07:10
(Text: stoned founders)
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(Tekst: napušeni osnivači)
07:13
(Laughter)
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(Smijeh)
07:16
And some of them,
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A neki od njih,
07:18
like paleontological shvisle,
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kao "paleontološki shvisle",
07:21
they contain Snoop Dogg.
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imaju Snoop Dogga.
07:23
(Laughter)
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(Smijeh)
07:26
Okay, so this is my favorite number of reCAPTCHA.
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U redu, ovo je moj najdraži broj reCaptche.
07:28
So this is the favorite thing that I like about this whole project.
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Ovo je najdraža stvar koju volim u cijelom ovom projektu.
07:31
This is the number of distinct people
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Ovo je broj određenih ljudi
07:33
that have helped us digitize at least one word out of a book through reCAPTCHA:
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koji su nam pomogli digitalizirati najmanje jednu riječ iz knjige putem reCaptche.
07:36
750 million,
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750 milijuna,
07:38
which is a little over 10 percent of the world's population,
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što je malo preko 10 posto svjetske populacije,
07:40
has helped us digitize human knowledge.
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pomoglo nam je digitalizirati ljudsko znanje.
07:42
And it is numbers like these that motivate my research agenda.
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Upravo brojevi poput ovih motiviraju moju istraživačku agendu.
07:45
So the question that motivates my research is the following:
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Dakle, pitanje koje motivira moje istraživanje glasi:
07:48
If you look at humanity's large-scale achievements,
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ako pogledate velika ljudska dostignuća,
07:50
these really big things
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te doista velike stvari
07:52
that humanity has gotten together and done historically --
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koje je čovječanstvo steklo zajedno i učinilo povijesno --
07:55
like for example, building the pyramids of Egypt
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kao npr., gradnja egipatskih piramida
07:57
or the Panama Canal
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ili Panamskog kanala
07:59
or putting a man on the Moon --
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ili slanje čovjeka na Mjesec --
08:01
there is a curious fact about them,
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zanimljiva je činjenica
08:03
and it is that they were all done with about the same number off people.
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da su sve napravljene s otprilike istim brojem ljudi.
08:05
It's weird; they were all done with about 100,000 people.
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Čudno je; sve su napravljene s oko 100.000 ljudi.
08:08
And the reason for that is because, before the Internet,
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I razlog je tomu taj što je, prije Interneta,
08:11
coordinating more than 100,000 people,
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koordinacija više od 100.000 ljudi,
08:13
let alone paying them, was essentially impossible.
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a da i ne spominjem njihovo plaćanje, bilo u osnovi nemoguće.
08:16
But now with the Internet, I've just shown you a project
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Ali sad s Internetom, upravo sam vam pokazao projekt
08:18
where we've gotten 750 million people
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gdje nam je 750 milijuna ljudi
08:20
to help us digitize human knowledge.
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pomoglo digitalizirati ljudsko znanje.
08:22
So the question that motivates my research is,
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Pa pitanje koje motivira moje istraživanje glasi,
08:24
if we can put a man on the Moon with 100,000,
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ako možemo poslati čovjeka na Mjesec sa 100.000,
08:27
what can we do with 100 million?
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što možemo učiniti sa 100 milijuna?
08:29
So based on this question,
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Na temelju ovog pitanja,
08:31
we've had a lot of different projects that we've been working on.
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imamo puno različitih projekata na kojima radimo.
08:33
Let me tell you about one that I'm most excited about.
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Reći ću vam nešto o jednom zbog kojeg sam najviše uzbuđen.
08:36
This is something that we've been semi-quietly working on
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To je nešto na čemu smo napola tiho radili
08:38
for the last year and a half or so.
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zadnju godinu i pol.
08:40
It hasn't yet been launched. It's called Duolingo.
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Još nije pokrenut. Naziva se Duolingo.
08:42
Since it hasn't been launched, shhhhh!
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Budući da još nije pokrenut, pssst!
08:44
(Laughter)
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(Smijeh)
08:46
Yeah, I can trust you'll do that.
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Da, vjerujem vam da ćete to učiniti.
08:48
So this is the project. Here's how it started.
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Ovo je projekt. Evo kako je počeo.
08:50
It started with me posing a question to my graduate student,
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Počeo je s pitanjem koje sam postavio svojem diplomiranom studentu,
08:52
Severin Hacker.
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Severinu Hackeru.
08:54
Okay, that's Severin Hacker.
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Ok, ovo je Severin Hacker.
08:56
So I posed the question to my graduate student.
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Dakle, postavio sam pitanje svojem studentu.
08:58
By the way, you did hear me correctly;
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Uzgred budi rečeno, dobro ste me čuli;
09:00
his last name is Hacker.
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njegovo je prezime Hacker.
09:02
So I posed this question to him:
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Postavio sam mu pitanje:
09:04
How can we get 100 million people
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Kako pridobiti 100 milijuna ljudi
09:06
translating the Web into every major language for free?
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da prevode Mrežu na sve veće jezike besplatno?
09:09
Okay, so there's a lot of things to say about this question.
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Ok, ima puno stvari koje se mogu reći o ovom pitanju.
09:11
First of all, translating the Web.
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Prvo, prevoditi Mrežu.
09:13
So right now the Web is partitioned into multiple languages.
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Trenutno je Mreža podijeljena na brojne jezike.
09:16
A large fraction of it is in English.
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Velik dio je na engleskom.
09:18
If you don't know any English, you can't access it.
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Ako imalo ne znaš engleski, ne možeš joj pristupiti.
09:20
But there's large fractions in other different languages,
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Ali postoje veliki dijelovi na drugim jezicima,
09:22
and if you don't know those languages, you can't access it.
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i ako ne znaš te jezike, ne možeš joj pristupiti.
09:25
So I would like to translate all of the Web, or at least most of the Web,
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Želio bih prevesti svu Mrežu, ili barem većinu Mreže,
09:28
into every major language.
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na svaki veći jezik.
09:30
So that's what I would like to do.
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Dakle, to bih želio napraviti.
09:32
Now some of you may say, why can't we use computers to translate?
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Sad, neki od vas bi mogli reći, zašto ne možemo upotrijebiti kompjutore da prevode?
09:35
Why can't we use machine translation?
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Zašto ne možemo upotrijebiti automatski prijevod?
09:37
Machine translation nowadays is starting to translate some sentences here and there.
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Automatski prijevod danas počinje prevoditi tu i tamo neke rečenice.
09:39
Why can't we use it to translate the whole Web?
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Zašto ga ne možemo upotrijebiti da prevedemo cijelu Mrežu?
09:41
Well the problem with that is that it's not yet good enough
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Pa, problem je u tome da još uvijek nije dovoljno dobar
09:43
and it probably won't be for the next 15 to 20 years.
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i najvjerojatnije neće biti 15 do 20 godina.
09:45
It makes a lot of mistakes.
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Radi puno grešaka.
09:47
Even when it doesn't make a mistake,
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Čak i kad ne griješi,
09:49
since it makes so many mistakes, you don't know whether to trust it or not.
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budući da radi tako puno grešaka, ne znate da li da mu vjerujete ili ne.
09:52
So let me show you an example
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Pokazat ću vam jedan primjer
09:54
of something that was translated with a machine.
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nečega što je bilo prevedeno sa strojem.
09:56
Actually it was a forum post.
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Zapravo je to bio post na forumu.
09:58
It was somebody who was trying to ask a question about JavaScript.
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Netko je pokušavao postaviti pitanje o JavaScriptu.
10:01
It was translated from Japanese into English.
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Preveden je s japanskog na engleski.
10:04
So I'll just let you read.
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Pustit ću vas da pročitate.
10:06
This person starts apologizing
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Ova osoba počinje se ispričavati
10:08
for the fact that it's translated with a computer.
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zbog činjenice da je prevedeno kompjuterom.
10:10
So the next sentence is is going to be the preamble to the question.
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Pa će sljedeća rečenica biti uvod u pitanje.
10:13
So he's just explaining something.
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On samo objašnjava nešto.
10:15
Remember, it's a question about JavaScript.
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Zapamtite, ovo je pitanje o JavaScriptu.
10:19
(Text: At often, the goat-time install a error is vomit.)
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(Tekst: Na često, jarac-vrijeme instalira greška je izbljuvak.)
10:23
(Laughter)
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(Smijeh)
10:27
Then comes the first part of the question.
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Zatim dolazi prvi dio pitanja.
10:30
(Text: How many times like the wind, a pole, and the dragon?)
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(Tekst: Koliko puta kao vjetar, štap, i zmaj?)
10:34
(Laughter)
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(Smijeh)
10:36
Then comes my favorite part of the question.
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Zatim dolazi moj omiljeni dio pitanja.
10:39
(Text: This insult to father's stones?)
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(Tekst: Ovo uvreda očevom kamenju?)
10:42
(Laughter)
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(Smijeh)
10:44
And then comes the ending, which is my favorite part of the whole thing.
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Zatim dolazi kraj, moj najdraži dio cijele stvari.
10:47
(Text: Please apologize for your stupidity. There are a many thank you.)
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(Tekst: Molim ispričati se za vašu glupost. Ima mnogo hvala vam.)
10:51
(Laughter)
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(Smijeh)
10:53
Okay, so computer translation, not yet good enough.
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Ok, kompjuterski prijevod, još nije dovoljno dobar.
10:55
So back to the question.
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Nazad k pitanju.
10:57
So we need people to translate the whole Web.
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Trebamo ljude da prevode cijelu Mrežu.
11:00
So now the next question you may have is,
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Dakle, sljedeće pitanje koje bi mogli imati glasi,
11:02
well why can't we just pay people to do this?
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pa zašto ne možemo samo platiti ljudima da to učine?
11:04
We could pay professional language translators to translate the whole Web.
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Možemo platiti profesionalnim prevoditeljima da prevedu cijelu Mrežu.
11:07
We could do that.
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Mogli bi to učiniti.
11:09
Unfortunately, it would be extremely expensive.
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Na žalost, bilo bi ekstremno skupo.
11:11
For example, translating a tiny, tiny fraction of the whole Web, Wikipedia,
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Npr., prijevod malenog, malenog dijela cijele Mreže, Wikipedie,
11:14
into one other language, Spanish.
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na jedan drugi jezik, španjolski.
11:17
Wikipedia exists in Spanish,
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Wikipedia postoji na španjolskom,
11:19
but it's very small compared to the size of English.
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ali je jako mala u usporedbi s veličinom na engleskom.
11:21
It's about 20 percent of the size of English.
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Iznosi oko 20 posto veličine na engleskom.
11:23
If we wanted to translate the other 80 percent into Spanish,
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Da smo željeli prevesti ostalih 80 posto na španjolski,
11:26
it would cost at least 50 million dollars --
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koštalo bi nas najmanje 50 milijuna dolara --
11:28
and this is at even the most exploited, outsourcing country out there.
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i to kod najiskorištenije, najiznajmljivačke zemlje koja postoji.
11:31
So it would be very expensive.
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Bilo bi vrlo skupo.
11:33
So what we want to do is we want to get 100 million people
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Dakle, ono što želimo jest pridobiti 100 milijuna ljudi
11:35
translating the Web into every major language
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da prevode Mrežu na svaki veći jezik
11:37
for free.
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besplatno.
11:39
Now if this is what you want to do,
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Ako je ovo to što želite učiniti,
11:41
you pretty quickly realize you're going to run into two pretty big hurdles,
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vrlo brzo ćete shvatiti da ćete naletjeti na dvije prilično velike prepreke,
11:43
two big obstacles.
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dvije velike poteškoće.
11:45
The first one is a lack of bilinguals.
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Prva je nedostatak bilingvista.
11:48
So I don't even know
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Pa čak ni ne znam
11:50
if there exists 100 million people out there using the Web
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postoji li 100 milijuna ljudi koji koriste Mrežu
11:53
who are bilingual enough to help us translate.
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koji su dovoljno bilingvalni da nam pomognu prevoditi.
11:55
That's a big problem.
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To je velik problem.
11:57
The other problem you're going to run into is a lack of motivation.
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Drugi problem na koji ćete naići jest nedostatak motivacije.
11:59
How are we going to motivate people
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Kako ćemo motivirati ljude
12:01
to actually translate the Web for free?
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da ustvari prevode Mrežu besplatno?
12:03
Normally, you have to pay people to do this.
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Inače moraš platiti ljudima da to naprave.
12:06
So how are we going to motivate them to do it for free?
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Kako ćemo ih motivirati da to naprave besplatno?
12:08
Now when we were starting to think about this, we were blocked by these two things.
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Kad smo započinjali razmišljati o tome, zaustavile su nas ove dvije stvari.
12:11
But then we realized, there's actually a way
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Ali tad smo shvatili da ustvari postoji način
12:13
to solve both these problems with the same solution.
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da riješimo oba problema istom solucijom.
12:15
There's a way to kill two birds with one stone.
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Postoji način da se ubiju dvije muhe jednim udarcem.
12:17
And that is to transform language translation
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A to je da se transformira prevođenje
12:20
into something that millions of people want to do,
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na nešto što milijuni ljudi žele učiniti,
12:23
and that also helps with the problem of lack of bilinguals,
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što također pomaže kod problema s nedostatkom bilingvista,
12:26
and that is language education.
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a to je učenje jezika.
12:29
So it turns out that today,
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Ispada da danas,
12:31
there are over 1.2 billion people learning a foreign language.
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postoji preko 1,2 milijarde ljudi koji uče strani jezik.
12:34
People really, really want to learn a foreign language.
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Ljudi doista, doista žele učiti strani jezik.
12:36
And it's not just because they're being forced to do so in school.
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I to nije samo zato što ih se tjera na to u školi.
12:39
For example, in the United States alone,
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Npr., samo u Sjedinjenim Državama,
12:41
there are over five million people who have paid over $500
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ima preko pet milijuna ljudi koji su platili više od 500 dolara
12:43
for software to learn a new language.
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za softver za učenje novog jezika.
12:45
So people really, really want to learn a new language.
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Pa ljudi doista, doista žele naučiti novi jezik.
12:47
So what we've been working on for the last year and a half is a new website --
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Dakle, ono na čemu smo radili zadnju godinu i pol je novo mrežno mjesto --
12:50
it's called Duolingo --
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Duolingo --
12:52
where the basic idea is people learn a new language for free
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gdje je osnovna ideja da ljudi uče novi jezik besplatno
12:55
while simultaneously translating the Web.
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dok istovremeno prevode Mrežu.
12:57
And so basically they're learning by doing.
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I u osnovi uče dok prevode.
12:59
So the way this works
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Način na koji ovo funkcionira
13:01
is whenever you're a just a beginner, we give you very, very simple sentences.
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jest da kad god ste samo početnik, dajemo vam vrlo, vrlo jednostavne rečenice.
13:04
There's, of course, a lot of very simple sentences on the Web.
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Postoji, naravno, puno vrlo jednostavnih rečenica na Mreži.
13:06
We give you very, very simple sentences
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Dajemo vam vrlo, vrlo jednostavne rečenice
13:08
along with what each word means.
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zajedno s onim što svaka riječ znači.
13:10
And as you translate them, and as you see how other people translate them,
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I kako ih prevodite, i dok gledate kako ih drugi ljudi prevode,
13:13
you start learning the language.
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počinjete učiti jezik.
13:15
And as you get more and more advanced,
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I kako postajete sve više napredni,
13:17
we give you more and more complex sentences to translate.
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dajemo vam sve složenije rečenice na prijevod.
13:19
But at all times, you're learning by doing.
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Ali cijelo vrijeme, učite dok prevodite.
13:21
Now the crazy thing about this method
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Sad, luda stvar u svezi s ovom metodom
13:23
is that it actually really works.
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jest da ona doista funkcionira.
13:25
First of all, people are really, really learning a language.
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Prvo, ljudi doista, doista uče jezik.
13:27
We're mostly done building it, and now we're testing it.
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Uglavnom smo završili s izradom, sada ga testiramo.
13:29
People really can learn a language with it.
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Ljudi doista mogu naučiti jezik s njim.
13:31
And they learn it about as well as the leading language learning software.
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I uče ga gotovo jednako dobro kao s vodećim softverom za učenje jezika.
13:34
So people really do learn a language.
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Pa ljudi doista nauče jezik.
13:36
And not only do they learn it as well,
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I ne samo da ga nauče jednako dobro,
13:38
but actually it's way more interesting.
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zapravo je puno zanimljivije.
13:40
Because you see with Duolingo, people are actually learning with real content.
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Zato što s Duolingom ljudi zapravo uče sa stvarnim sadržajem.
13:43
As opposed to learning with made-up sentences,
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U suprotnosti s učenjem s izmišljenim rečenicama,
13:45
people are learning with real content, which is inherently interesting.
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ljudi uče sa stvarnim sadržajem koji je po sebi zanimljiv.
13:48
So people really do learn a language.
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Pa ljudi stvarno uče jezik.
13:50
But perhaps more surprisingly,
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Ali možda što je još veće iznenađenje,
13:52
the translations that we get from people using the site,
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prijevodi koje dobivamo od ljudi koji koriste mjesto,
13:55
even though they're just beginners,
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iako su samo početnici,
13:57
the translations that we get are as accurate as those of professional language translators,
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prijevodi koje dobivamo su jednako točni kao oni profesionalnih prevoditelja,
14:00
which is very surprising.
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što vrlo iznenađuje.
14:02
So let me show you one example.
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Pokazat će vam jedan primjer.
14:04
This is a sentence that was translated from German into English.
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Ovo je rečenica prevedena s njemačkog na engleski.
14:06
The top is the German.
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Na vrhu je njemački.
14:08
The middle is an English translation
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U sredini je prijevod na engleski
14:10
that was done by somebody who was a professional English translator
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profesionalnog prevoditelja engleskog jezika
14:12
who we paid 20 cents a word for this translation.
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kojeg smo platili 20 centi za riječ za ovaj prijevod.
14:14
And the bottom is a translation by users of Duolingo,
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Na dnu je prijevod korisnika Duolinga,
14:17
none of whom knew any German
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od kojih ni jedan nije znao njemački
14:19
before they started using the site.
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prije nego što su počeli koristiti mjesto.
14:21
You can see, it's pretty much perfect.
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Možete vidjeti da je prilično savršen.
14:23
Now of course, we play a trick here
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Naravno, izveli smo trik
14:25
to make the translations as good as professional language translators.
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da napravimo prijevode jednako dobrima kao one profesionalnih prevoditelja.
14:27
We combine the translations of multiple beginners
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Kombinirali smo prijevode više početnika
14:30
to get the quality of a single professional translator.
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da dobijemo kvalitetu jednog profesionalnog prevoditelja.
14:33
Now even though we're combining the translations,
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Iako kombiniramo prijevode,
14:38
the site actually can translate pretty fast.
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mjesto zapravo može prevoditi prilično brzo.
14:40
So let me show you,
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Pokazat ću vam
14:42
this is our estimates of how fast we could translate Wikipedia
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koje su naše procjene koliko brzo možemo prevesti Wikipediu
14:44
from English into Spanish.
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s engleskog na španjolski.
14:46
Remember, this is 50 million dollars-worth of value.
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Zapamtite, to je vrijednost od 50 milijuna dolara.
14:49
So if we wanted to translate Wikipedia into Spanish,
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Da smo htjeli prevesti Wikipediu na španjolski,
14:51
we could do it in five weeks with 100,000 active users.
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napravili bismo to u pet tjedana sa 100.000 aktivnih korisnika.
14:54
And we could do it in about 80 hours with a million active users.
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I mogli smo to napraviti za otprilike 80 sati s milijun aktivnih korisnika.
14:57
Since all the projects that my group has worked on so far have gotten millions of users,
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Budući da su svi projekti na kojima je radila moja grupa dosad pridobili milijune korisnika,
15:00
we're hopeful that we'll be able to translate
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nadamo se da ćemo biti u mogućnosti prevoditi
15:02
extremely fast with this project.
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ekstremno brzo s ovim projektom.
15:04
Now the thing that I'm most excited about with Duolingo
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Ono što me najviše uzbuđuju kod Duolinga
15:07
is I think this provides a fair business model for language education.
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jest to da omogućuje pošteni poslovni model za učenje jezika.
15:10
So here's the thing:
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Evo o čemu se radi:
15:12
The current business model for language education
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Sadašnji poslovni model za učenje jezika sastoji se u tome da
15:14
is the student pays,
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učenik plaća,
15:16
and in particular, the student pays Rosetta Stone 500 dollars.
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i posebno, učenik plaća Rosetti Stone 500 dolara.
15:18
(Laughter)
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(Smijeh)
15:20
That's the current business model.
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To je sadašnji poslovni model.
15:22
The problem with this business model
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Problem je s tim poslovnim modelom u tome
15:24
is that 95 percent of the world's population doesn't have 500 dollars.
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da 95 posto svjetske populacije nema 500 dolara.
15:27
So it's extremely unfair towards the poor.
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To je krajnje nepošteno prema siromašnima.
15:30
This is totally biased towards the rich.
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Ovo je u potpunosti podređeno bogatima.
15:32
Now see, in Duolingo,
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Sad, u Duolingu,
15:34
because while you learn
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zato što, dok učite
15:36
you're actually creating value, you're translating stuff --
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vi zapravo stvarate vrijednost, prevodite stvari --
15:39
which for example, we could charge somebody for translations.
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za koje bi npr.mogli platiti nekoga za prijevod.
15:42
So this is how we could monetize this.
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Ovo je način na koji bi to mogli unovčiti.
15:44
Since people are creating value while they're learning,
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Budući da ljudi stvaraju vrijednost dok uče,
15:46
they don't have to pay their money, they pay with their time.
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ne moraju plaćati novcem, oni plaćaju svojim vremenom.
15:49
But the magical thing here is that they're paying with their time,
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Magična je stvar u tome da plaćaju svojim vremenom,
15:52
but that is time that would have had to have been spent anyways
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ali to je vrijeme koje bi ionako potrošili
15:54
learning the language.
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učeći jezik.
15:56
So the nice thing about Duolingo is I think it provides a fair business model --
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Dakle, lijepa stvar u svezi s Duolingom je u tom što, mislim, omogućuje pošten poslovni model --
15:59
one that doesn't discriminate against poor people.
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onaj koji ne diskriminira siromašne ljude.
16:01
So here's the site. Thank you.
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Ovo je stranica. Hvala vam.
16:03
(Applause)
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(Pljesak)
16:11
So here's the site.
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Ovo je stranica.
16:13
We haven't yet launched,
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Još nije pokrenuto,
16:15
but if you go there, you can sign up to be part of our private beta,
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ali ako odete tamo, možete se upisati da budete dio naše probne verzije,
16:18
which is probably going to start in about three or four weeks.
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koja će najvjerojatnije početi za tri do četiri tjedna.
16:20
We haven't yet launched this Duolingo.
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Još uvijek nismo pokrenuli Duolingo.
16:22
By the way, I'm the one talking here,
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Uzgred budi rečeno, ja sam taj koji govori,
16:24
but actually Duolingo is the work of a really awesome team, some of whom are here.
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ali zapravo Duolingo je rad stvarno fenomenalnog tima, od kojeg su neki ovdje.
16:27
So thank you.
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Hvala vam.
16:29
(Applause)
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(Pljesak)
ABOUT THE SPEAKER
Luis von Ahn - Computer scientistLuis von Ahn builds systems that combine humans and computers to solve large-scale problems that neither can solve alone.
Why you should listen
Louis von Ahn is an associate professor of Computer Science at Carnegie Mellon University, and he's at the forefront of the crowdsourcing craze. His work takes advantage of the evergrowing Web-connected population to acheive collaboration in unprecedented numbers. His projects aim to leverage the crowd for human good. His company reCAPTCHA, sold to Google in 2009, digitizes human knowledge (books), one word at a time. His new project is Duolingo, which aims to get 100 million people translating the Web in every major language.
Luis von Ahn | Speaker | TED.com