TEDxCMU
Luis von Ahn: Massive-scale online collaboration
Luis von Ahn: Spletno sodelovanje ogromnih razsežnosti
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Po ponovni zasnovi formul CAPTCHA, s katerimi vsak naš vnos pomaga pri digitalizaciji knjig, se je Luis von Ahn spraševal, kako še lahko izkoristimo majcene prispevke spletnih uporabnikov za velike skupne cilje. Na TEDxCMU predstavi nov velikopotezen projekt, Duolingo, ki milijonom po svetu omogoča učenje novega jezika preko prevajanja spleta na hiter, učinkovit in brezplačen način.
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 pri izpolnjevanju spletnih obrazcev
00:17
where you've been asked to read a distorted sequence of characters like this?
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moralo razbrati tak popačen niz znakov?
00:19
How many of you found it really, really annoying?
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Kolikim se zdi to res zoprno?
00:21
Okay, outstanding. So I invented that.
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Prav, krasno. To sem torej izumil jaz.
00:24
(Laughter)
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(Smeh)
00:26
Or I was one of the people who did it.
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Bil sem eden od izumiteljev.
00:28
That thing is called a CAPTCHA.
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Ta reč se imenuje CAPTCHA.
00:30
And the reason it is there is to make sure you, the entity filling out the form,
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Njen namen je, da zagotovi, da obrazec izpolnjuje
00:32
are actually a human and not some sort of computer program
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človek in ne kak računalniški program,
00:35
that was written to submit the form millions and millions of times.
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sprogramiran, da nepretrgoma oddaja obrazce.
00:37
The reason it works is because humans,
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Stvar deluje, ker ljudje,
00:39
at least non-visually-impaired humans,
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vsaj tisti, ki niso slabovidni,
00:41
have no trouble reading these distorted squiggly characters,
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brez težav prepoznajo te popačene znake,
00:43
whereas computer programs simply can't do it as well yet.
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računalniški programi pa tega
še ne zmorejo tako dobro.
še ne zmorejo tako dobro.
00:46
So for example, in the case of Ticketmaster,
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Na strani Ticketmaster
00:48
the reason you have to type these distorted characters
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z vpisom teh skrivljenih znakov preprečite
00:50
is to prevent scalpers from writing a program
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preprodajalcem vstopnic, da bi ustvarili program,
00:52
that can buy millions of tickets, two at a time.
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ki bi jih lahko po dve naenkrat kupil na tisoče.
00:54
CAPTCHAs are used all over the Internet.
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CAPTCHE se uporabljajo širom spleta.
00:56
And since they're used so often,
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Zaradi pogostosti pojavljanja
00:58
a lot of times the precise sequence of random characters that is shown to the user
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je rezultat vrstnega reda znakov,
ki se izpiše,
ki se izpiše,
01:00
is not so fortunate.
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pogosto precej ponesrečen.
01:02
So this is an example from the Yahoo registration page.
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Tu je primer s strani za prijavo na Yahoo.
01:05
The random characters that happened to be shown to the user
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Naključni znaki, ki jih je moral uporabnik vpisati,
01:07
were W, A, I, T, which, of course, spell a word.
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so W, A, I, T, kar seveda tvori besedo (počakaj).
01:10
But the best part is the message
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Še bolj zanimivo pa je sporočilo,
01:13
that the Yahoo help desk got about 20 minutes later.
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ki so ga na Yahoojevi podpori uporabnikom
prejeli 20 minut kasneje.
prejeli 20 minut kasneje.
01:16
Text: "Help! I've been waiting for over 20 minutes, and nothing happens."
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Besedilo: "Na pomoč! Čakam že več kot 20 minut,
a se ne zgodi nič."
a se ne zgodi nič."
01:19
(Laughter)
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(Smeh)
01:23
This person thought they needed to wait.
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Ta uporabnik je torej mislil, da mora čakati.
01:25
This of course, is not as bad as this poor person.
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Še slabše jo je odnesla ta oseba.
("Ponovno zaženi")
("Ponovno zaženi")
01:28
(Laughter)
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(Smeh)
01:30
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago,
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S projektom CAPTCHA smo pri Carnegie Mellon začeli pred dobrim desetletjem
01:33
and it's been used everywhere.
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in odtlej je v splošni uporabi.
01:35
Let me now tell you about a project that we did a few years later,
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Želel pa bi vam predstaviti projekt,
ki smo ga začeli nekaj let kasneje,
ki smo ga začeli nekaj let kasneje,
01:37
which is sort of the next evolution of CAPTCHA.
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neke vrste nadgradnjo CAPTCHE.
01:40
This is a project that we call reCAPTCHA,
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Projekt se imenuje reCAPTCHA,
01:42
which is something that we started here at Carnegie Mellon,
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z njim smo začeli pri Carnegie Mellon,
01:44
then we turned it into a startup company.
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nato pa je prerasel v mlado podjetje.
01:46
And then about a year and a half ago,
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Pred letom in pol
01:48
Google actually acquired this company.
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je Google podjetje tudi prevzel.
01:50
So let me tell you what this project started.
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Naj vam predstavim projekt.
01:52
So this project started from the following realization:
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Osnova je bilo spoznanje,
01:55
It turns out that approximately 200 million CAPTCHAs
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da se približno 200 milijonov CAPTCH
01:57
are typed everyday by people around the world.
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vsakodnevno vtipka po vsem svetu.
02:00
When I first heard this, I was quite proud of myself.
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Ko sem to slišal, sem bil ponosen nase.
02:02
I thought, look at the impact that my research has had.
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Vesel sem bil, da je moje delo imelo tak vpliv.
02:04
But then I started feeling bad.
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Nato pa sem se začel počutiti slabo.
02:06
See here's the thing, each time you type a CAPTCHA,
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Vsakič ko vtipkate CAPTCHO,
02:08
essentially you waste 10 seconds of your time.
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namreč izgubite 10 sekund svojega časa.
02:11
And if you multiply that by 200 million,
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Če to pomnožite s 200 milijoni,
02:13
you get that humanity as a whole is wasting about 500,000 hours every day
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vidite, da človeštvo vsak dan zapravi 500.000 ur
02:16
typing these annoying CAPTCHAs.
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med tipkanjem teh zoprnih CAPTCH.
02:18
So then I started feeling bad.
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Počutil sem se slabo.
02:20
(Laughter)
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(Smeh)
02:22
And then I started thinking, well, of course, we can't just get rid of CAPTCHAs,
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A kljub temu jih ne moremo kar prenehati uporabljati,
02:25
because the security of the Web sort of depends on them.
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saj na njih temelji varnost spleta.
02:27
But then I started thinking, is there any way we can use this effort
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Hotel sem najti način, kako bi ta trud uporabnikov
02:30
for something that is good for humanity?
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spremenili v nekaj dobrega za človeštvo.
02:32
So see, here's the thing.
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Gre namreč za to.
02:34
While you're typing a CAPTCHA, during those 10 seconds,
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Ko tipkate CAPTCHO, vaši možgani teh 10 sekund
02:36
your brain is doing something amazing.
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počnejo nekaj neverjetnega.
02:38
Your brain is doing something that computers cannot yet do.
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Nekaj, česar računalniki še niso sposobni.
02:40
So can we get you to do useful work for those 10 seconds?
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Kako lahko vaš trud teh 10 sekund
uporabimo za nekaj koristnega?
uporabimo za nekaj koristnega?
02:43
Another way of putting it is,
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Z drugimi besedami,
02:45
is there some humongous problem that we cannot yet get computers to solve,
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ali obstaja kakšna ogromna težava,
ki je računalniki še ne morejo rešiti,
ki je računalniki še ne morejo rešiti,
02:47
yet we can split into tiny 10-second chunks
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lahko pa jo razdelimo na 10-sekundne delčke,
02:50
such that each time somebody solves a CAPTCHA
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da lahko vsakdo, ki razbere in vtipka CAPTCHO,
02:52
they solve a little bit of this problem?
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hkrati reši delček te težave?
02:54
And the answer to that is "yes," and this is what we're doing now.
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Odgovor je "da" in s tem se trenutno ukvarjamo.
02:56
So what you may not know is that nowadays while you're typing a CAPTCHA,
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Morda še ne veste, da se dandanes
ob vsakem vnosu CAPTCHE
ob vsakem vnosu CAPTCHE
02:59
not only are you authenticating yourself as a human,
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ne le istovetite kot ljudje,
03:01
but in addition you're actually helping us to digitize books.
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pač pa tudi pomagate pri digitalizaciji knjig.
03:03
So let me explain how this works.
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Naj razložim, kako deluje.
03:05
So there's a lot of projects out there trying to digitize books.
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Veliko projektov se ubada z digitalizacijo knjig.
03:07
Google has one. The Internet Archive has one.
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Taka sta tudi Google ter Internet Archive.
03:10
Amazon, now with the Kindle, is trying to digitize books.
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Tudi Amazon preko Kindla dela na digitalizaciji knjig.
03:12
Basically the way this works
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To poteka tako,
03:14
is you start with an old book.
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da začnemo s staro knjigo.
03:16
You've seen those things, right? Like a book?
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Saj poznate te stvari, kajne? Knjige?
03:18
(Laughter)
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(Smeh)
03:20
So you start with a book, and then you scan it.
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Vzamemo torej knjigo in jo skeniramo.
03:22
Now scanning a book
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To izgleda tako,
03:24
is like taking a digital photograph of every page of the book.
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kot bi posneli digitalno fotografijo
vsake strani v knjigi.
vsake strani v knjigi.
03:26
It gives you an image for every page of the book.
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Dobimo torej podobo vsake strani,
03:28
This is an image with text for every page of the book.
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podobo z besedilom celotne knjige.
03:30
The next step in the process
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Pri naslednjem koraku mora biti računalnik zmožen
03:32
is that the computer needs to be able to decipher all of the words in this image.
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dešifrirati vse besede na tej podobi.
03:35
That's using a technology called OCR,
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Pri tem mu pomaga tehnologija OCR
03:37
for optical character recognition,
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za optično prepoznavanje znakov,
03:39
which takes a picture of text
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ki posname sliko besedila
03:41
and tries to figure out what text is in there.
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in ga poskuša razbrati.
03:43
Now the problem is that OCR is not perfect.
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A tehnologija OCR ni popolna.
03:45
Especially for older books
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Še posebej pri starejših knjigah
03:47
where the ink has faded and the pages have turned yellow,
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z zbledelim črnilom in porumenelimi stranmi
03:50
OCR cannot recognize a lot of the words.
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je mnogo besed, ki jih OCR ne prepozna.
03:52
For example, for things that were written more than 50 years ago,
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Pri besedilih, starejših od 50 let,
03:54
the computer cannot recognize about 30 percent of the words.
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računalnik ne razbere skoraj 30 odstotkov besed.
03:57
So what we're doing now
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Naše delo je,
03:59
is we're taking all of the words that the computer cannot recognize
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da zberemo vse besede,
ki jih računalnik ne prepozna,
ki jih računalnik ne prepozna,
04:01
and we're getting people to read them for us
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in jih posredujemo ljudem, da jih preberejo
04:03
while they're typing a CAPTCHA on the Internet.
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in vtipkajo preko CAPTCHE na spletu.
04:05
So the next time you type a CAPTCHA, these words that you're typing
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Ko boste naslednjič vnašali CAPTCHO,
04:08
are actually words that are coming from books that are being digitized
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bo to beseda iz knjige v postopku digitalizacije,
04:11
that the computer could not recognize.
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ki je računalnik ni uspel razbrati.
04:13
And now the reason we have two words nowadays instead of one
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Dandanes pa CAPTCHE tvorita dve besedi in ne ena,
04:15
is because, you see, one of the words
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in ena od njiju je beseda,
04:17
is a word that the system just got out of a book,
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ki jo je sistem potegnil iz knjige,
04:19
it didn't know what it was, and it's going to present it to you.
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je ni prepoznal in jo posredoval vam.
04:22
But since it doesn't know the answer for it, it cannot grade it for you.
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A ker je ne pozna, ne ve, ali je vaš vnos pravi.
04:25
So what we do is we give you another word,
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Zato vam damo še drugo besedo,
04:27
one for which the system does know the answer.
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za katero sistem pozna odgovor.
04:29
We don't tell you which one's which, and we say, please type both.
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Vi ne veste, katera je katera,
in vnesti je potrebno obe.
in vnesti je potrebno obe.
04:31
And if you type the correct word
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In če prav vnesete besedo,
04:33
for the one for which the system already knows the answer,
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na katero sistem že pozna pravilen odgovor,
04:35
it assumes you are human,
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sklepa, da ste človek,
04:37
and it also gets some confidence that you typed the other word correctly.
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in vam zaupa, da boste pravilno vnesli tudi drugo.
04:39
And if we repeat this process to like 10 different people
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Če ta postopek ponovimo pri 10 različnih ljudeh
04:42
and all of them agree on what the new word is,
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in vsi enako vnesejo pomen nove besede,
04:44
then we get one more word digitized accurately.
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s tem dobimo novo pravilno digitalizirano besedo.
04:46
So this is how the system works.
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Tako torej deluje ta sistem.
04:48
And basically, since we released it about three or four years ago,
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Odkar smo s tem začeli pred tremi ali štirimi leti,
04:51
a lot of websites have started switching
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je mnogo spletnih strani zamenjalo
04:53
from the old CAPTCHA where people wasted their time
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stare CAPTCHE, ki so zapravljale čas ljudi,
04:55
to the new CAPTCHA where people are helping to digitize books.
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za nove CAPTCHE, s katerimi digitaliziramo knjige.
04:57
So for example, Ticketmaster.
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Tak je tudi Ticketmaster.
04:59
So every time you buy tickets on Ticketmaster, you help to digitize a book.
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Vsakič ko kupite vstopnice na Ticketmastru,
pomagate pri digitalizaciji knjig.
pomagate pri digitalizaciji knjig.
05:02
Facebook: Every time you add a friend or poke somebody,
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Vsakič ko na Facebooku dodate
ali dregnete prijatelja,
ali dregnete prijatelja,
05:04
you help to digitize a book.
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digitalizirate knjige.
05:06
Twitter and about 350,000 other sites are all using reCAPTCHA.
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Twitter in še 350.000 drugih spletnih strani
uporablja reCAPTCHE.
uporablja reCAPTCHE.
05:09
And in fact, the number of sites that are using reCAPTCHA is so high
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Število strani z reCAPTCHO je že tako veliko,
05:11
that the number of words that we're digitizing per day is really, really large.
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da je ogromno tudi število besed,
ki se dnevno digitalizirajo,
ki se dnevno digitalizirajo,
05:14
It's about 100 million a day,
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in sicer okrog 100 milijonov na dan,
05:16
which is the equivalent of about two and a half million books a year.
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kar na letni ravni znese okrog 2,5 milijona knjig.
05:20
And this is all being done one word at a time
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Do tega pride le z eno besedo naenkrat,
05:22
by just people typing CAPTCHAs on the Internet.
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z vnašanjem CAPTCH na spletnih straneh.
05:24
(Applause)
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(Aplavz)
05:32
Now of course,
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Seveda pa se s pojavom
05:34
since we're doing so many words per day,
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tolikih besed v enem dnevu
05:36
funny things can happen.
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dogajajo tudi čudne stvari.
05:38
And this is especially true because now we're giving people
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Še posebej zdaj, ko uporabnikom posredujemo
05:40
two randomly chosen English words next to each other.
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dve naključno izbrani angleški besedi hkrati.
05:42
So funny things can happen.
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Včasih so kombinacije zelo zabavne.
05:44
For example, we presented this word.
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Lep primer je ta beseda.
05:46
It's the word "Christians"; there's nothing wrong with it.
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Pomeni "kristjani" in do tu še ni posebnosti.
05:48
But if you present it along with another randomly chosen word,
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Če pa jo postavite zraven
povsem naključno izbrane besede,
povsem naključno izbrane besede,
05:51
bad things can happen.
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je lahko rezultat ponesrečen.
05:53
So we get this. (Text: bad christians)
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Nastane lahko to. (Besedilo: slabi kristjani)
05:55
But it's even worse, because the particular website where we showed this
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Še slabše postane, ko ugotovimo,
da se je bila ta kombinacija pojavila
da se je bila ta kombinacija pojavila
05:58
actually happened to be called The Embassy of the Kingdom of God.
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na strani Ambasade božjega kraljestva.
06:01
(Laughter)
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(Smeh)
06:03
Oops.
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Ups.
06:05
(Laughter)
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(Smeh)
06:08
Here's another really bad one.
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Tu je še en tak primer.
06:10
JohnEdwards.com
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JohnEdwards.com
06:12
(Text: Damn liberal)
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(Besedilo: Prekleti liberalec)
06:15
(Laughter)
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(Smeh)
06:17
So we keep on insulting people left and right everyday.
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Na ta način dnevno žalimo leve in desne.
06:20
Now, of course, we're not just insulting people.
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Seveda pa ne gre le za žalitve.
06:22
See here's the thing, since we're presenting two randomly chosen words,
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Ker gre za kombinacije
dveh naključno izbranih besed,
dveh naključno izbranih besed,
06:25
interesting things can happen.
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se dogajajo zanimive stvari.
06:27
So this actually has given rise
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To je spodbudilo nastanek
06:29
to a really big Internet meme
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obsežnega spletnega fenomena,
06:32
that tens of thousands of people have participated in,
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pri katerem sodeluje na desettisoče ljudi,
06:34
which is called CAPTCHA art.
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imenovanega CAPTCHA umetnost.
06:36
I'm sure some of you have heard about it.
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Gotovo je kdo od vas že slišal zanjo.
06:38
Here's how it works.
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Deluje pa tako.
06:40
Imagine you're using the Internet and you see a CAPTCHA
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Zamislite si, da med uporabo spleta
naletite na CAPTCHO,
naletite na CAPTCHO,
06:42
that you think is somewhat peculiar,
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ki se vam zdi zanimiva,
06:44
like this CAPTCHA. (Text: invisible toaster)
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kot je na primer ta. (Besedilo: nevidni opekač)
06:46
Then what you're supposed to do is you take a screen shot of it.
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Nato naredite posnetek zaslona, ki jo prikazuje.
06:48
Then of course, you fill out the CAPTCHA
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Seveda sledi vnos CAPTCHE,
06:50
because you help us digitize a book.
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saj pomagate digitalizirati knjigo.
06:52
But then, first you take a screen shot,
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Najprej torej naredite posnetek zaslona,
06:54
and then you draw something that is related to it.
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nato pa poskušate narisati njen pomen.
06:56
(Laughter)
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(Smeh)
06:58
That's how it works.
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Tako to poteka.
07:01
There are tens of thousands of these.
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Obstaja na desettisoče takih podob.
07:04
Some of them are very cute. (Text: clenched it)
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Nekatere so zelo ljubke. (Besedilo: stisnil sem)
07:06
(Laughter)
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(Smeh)
07:08
Some of them are funnier.
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Nekatere so bolj zabavne.
07:10
(Text: stoned founders)
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(Besedilo: zadeti ustanovitelji)
07:13
(Laughter)
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(Smeh)
07:16
And some of them,
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Ali pa ta,
07:18
like paleontological shvisle,
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paleontološki shvisle,
07:21
they contain Snoop Dogg.
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kjer nastopa Snoop Dogg.
07:23
(Laughter)
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(Smeh)
07:26
Okay, so this is my favorite number of reCAPTCHA.
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Ta številka mi je pri reCAPTCHI najbolj všeč
07:28
So this is the favorite thing that I like about this whole project.
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in me pri celotnem projektu najbolj navdušuje.
07:31
This is the number of distinct people
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To je število ljudi,
07:33
that have helped us digitize at least one word out of a book through reCAPTCHA:
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ki so nam preko reCAPTCHE pomagali
digitalizirati vsaj eno besedo iz knjige,
digitalizirati vsaj eno besedo iz knjige,
07:36
750 million,
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750 milijonov ljudi,
07:38
which is a little over 10 percent of the world's population,
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dobrih 10 odstotkov svetovnega prebivalstva,
07:40
has helped us digitize human knowledge.
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nam je pomagalo digitalizirati človeško znanje.
07:42
And it is numbers like these that motivate my research agenda.
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Številke, kot je ta, spodbujajo
moje raziskovalne načrte.
moje raziskovalne načrte.
07:45
So the question that motivates my research is the following:
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Vprašanje, ki vodi mojo raziskavo, je sledeče:
07:48
If you look at humanity's large-scale achievements,
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pri velikopoteznih dosežkih človeštva,
07:50
these really big things
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resnično velikih stvareh,
07:52
that humanity has gotten together and done historically --
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ki so v zgodovini združevale človeštvo,
07:55
like for example, building the pyramids of Egypt
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kot je na primer gradnja piramid v Egiptu,
07:57
or the Panama Canal
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Panamskega prekopa,
07:59
or putting a man on the Moon --
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ali človeška odprava na Luno,
08:01
there is a curious fact about them,
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je zanimivo, da je pri vseh sodelovalo
08:03
and it is that they were all done with about the same number off people.
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približno enako število ljudi.
08:05
It's weird; they were all done with about 100,000 people.
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Zanimivo je, da so vse združevale
okrog sto tisoč ljudi.
okrog sto tisoč ljudi.
08:08
And the reason for that is because, before the Internet,
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Razlog za to je, da je bila v času pred spletom
08:11
coordinating more than 100,000 people,
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uskladitev več kot sto tisoč ljudi,
08:13
let alone paying them, was essentially impossible.
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kaj šele plač za njihovo delo, praktično nemogoča.
08:16
But now with the Internet, I've just shown you a project
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Z uporabo spleta pa lahko izvedemo
projekte, kot je ta,
projekte, kot je ta,
08:18
where we've gotten 750 million people
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ki združuje 750 milijonov ljudi
08:20
to help us digitize human knowledge.
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v postopku digitalizacije človeškega znanja.
08:22
So the question that motivates my research is,
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Pri mojem delu me zanima,
08:24
if we can put a man on the Moon with 100,000,
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če lahko sto tisoč ljudi pošlje človeka na Luno,
08:27
what can we do with 100 million?
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kaj šele lahko stori sto milijonov?
08:29
So based on this question,
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Na tem vprašanju temelji še mnogo
08:31
we've had a lot of different projects that we've been working on.
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drugih projektov, s katerimi se ukvarjamo.
08:33
Let me tell you about one that I'm most excited about.
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Predstavil vam bom projekt, ki me najbolj navdušuje.
08:36
This is something that we've been semi-quietly working on
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Na njem smo na pol potiho delali
08:38
for the last year and a half or so.
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zadnje leto in pol.
08:40
It hasn't yet been launched. It's called Duolingo.
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Nismo ga še zagnali. Imenuje se Duolingo.
08:42
Since it hasn't been launched, shhhhh!
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Ker še ni zunaj, pšššt!
08:44
(Laughter)
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(Smeh)
08:46
Yeah, I can trust you'll do that.
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Kot da vam lahko zaupam.
08:48
So this is the project. Here's how it started.
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To je ta projekt. Začel pa se je tako.
08:50
It started with me posing a question to my graduate student,
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Zastavil sem vprašanje svojemu študentu,
08:52
Severin Hacker.
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Severinu Hackerju.
08:54
Okay, that's Severin Hacker.
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To je Severin Hacker.
08:56
So I posed the question to my graduate student.
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Zastavil sem mu torej vprašanje.
08:58
By the way, you did hear me correctly;
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Mimogrede, prav ste slišali,
09:00
his last name is Hacker.
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piše se Hacker (heker).
09:02
So I posed this question to him:
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Vprašanje se je glasilo:
09:04
How can we get 100 million people
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kako pripraviti sto milijonov ljudi,
09:06
translating the Web into every major language for free?
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da prevedejo spletne vsebine v vse velike jezike,
in to brezplačno?
in to brezplačno?
09:09
Okay, so there's a lot of things to say about this question.
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Pri tem vprašanju je treba povedati veliko stvari.
09:11
First of all, translating the Web.
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Prvič, prevajanje spleta.
09:13
So right now the Web is partitioned into multiple languages.
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Trenutno je splet razdeljen na veliko jezikov.
09:16
A large fraction of it is in English.
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Velik del je v angleščini.
09:18
If you don't know any English, you can't access it.
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A če angleščine ne znate, vam ne koristi.
09:20
But there's large fractions in other different languages,
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Tu je še velik del spleta v drugih jezikih,
09:22
and if you don't know those languages, you can't access it.
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a če jih ne poznate, vam ne koristi.
09:25
So I would like to translate all of the Web, or at least most of the Web,
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Želel bi torej prevesti cel ali vsaj večino spleta
09:28
into every major language.
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v vsak veliki jezik.
09:30
So that's what I would like to do.
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To torej želim doseči.
09:32
Now some of you may say, why can't we use computers to translate?
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Nekateri boste rekli,
zakaj ne morejo tega storiti računalniki?
zakaj ne morejo tega storiti računalniki?
09:35
Why can't we use machine translation?
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Zakaj ne moremo uporabiti strojnih prevodov?
09:37
Machine translation nowadays is starting to translate some sentences here and there.
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Strojno prevajanje dandanes že prevaja
določene stavke.
določene stavke.
09:39
Why can't we use it to translate the whole Web?
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Zakaj ne bi mogli strojno prevesti celotnega spleta?
09:41
Well the problem with that is that it's not yet good enough
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Težava je, da je tak način prevajanja
še zelo pomanjkljiv
še zelo pomanjkljiv
09:43
and it probably won't be for the next 15 to 20 years.
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in ne bo zadovoljiv še naslednjih 15 do 20 let.
09:45
It makes a lot of mistakes.
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Dela veliko napak.
09:47
Even when it doesn't make a mistake,
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In tudi če ne naredi napake,
09:49
since it makes so many mistakes, you don't know whether to trust it or not.
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mu zaradi prejšnjih napak
ne zaupamo več popolnoma.
ne zaupamo več popolnoma.
09:52
So let me show you an example
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Naj vam pokažem primer
09:54
of something that was translated with a machine.
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strojnega prevoda.
09:56
Actually it was a forum post.
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Gre za objavo na forumu.
09:58
It was somebody who was trying to ask a question about JavaScript.
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Nekdo je želel postaviti vprašanje o JavaScriptu.
10:01
It was translated from Japanese into English.
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Gre za prevod iz japonščine v angleščino.
10:04
So I'll just let you read.
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Kar preberite.
10:06
This person starts apologizing
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Ta oseba se opraviči,
10:08
for the fact that it's translated with a computer.
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da se je poslužila strojnega prevoda.
10:10
So the next sentence is is going to be the preamble to the question.
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Naslednji stavek bo uvod v vprašanje.
10:13
So he's just explaining something.
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Tu nekaj razlaga.
10:15
Remember, it's a question about JavaScript.
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Ne pozabite, vprašanje zadeva JavaScript.
10:19
(Text: At often, the goat-time install a error is vomit.)
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(Besedilo: Pogosto kozji čas namestitev napake
je bruhanje.)
je bruhanje.)
10:23
(Laughter)
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(Smeh)
10:27
Then comes the first part of the question.
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Nato pride prvi del vprašanja.
10:30
(Text: How many times like the wind, a pole, and the dragon?)
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(Besedilo: Kolikokrat kot veter, drog in zmaj?)
10:34
(Laughter)
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(Smeh)
10:36
Then comes my favorite part of the question.
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Sledi pa meni najljubši del vprašanja.
10:39
(Text: This insult to father's stones?)
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(Besedilo: Ta žalitev očetovih kamnov?)
10:42
(Laughter)
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(Smeh)
10:44
And then comes the ending, which is my favorite part of the whole thing.
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Zadnji del pa je najboljši.
10:47
(Text: Please apologize for your stupidity. There are a many thank you.)
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(Besedilo: Prosim oprosti za tvojo neumnost.
Tam je veliko hvala.)
Tam je veliko hvala.)
10:51
(Laughter)
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(Smeh)
10:53
Okay, so computer translation, not yet good enough.
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Strojno prevajanje torej še ni zadovoljivo.
10:55
So back to the question.
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Vrnimo se k vprašanju.
10:57
So we need people to translate the whole Web.
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Potrebujemo ljudi za prevod celotnega spleta.
11:00
So now the next question you may have is,
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Morda se sprašujete,
11:02
well why can't we just pay people to do this?
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zakaj preprosto ne moremo najeti ljudi za to?
11:04
We could pay professional language translators to translate the whole Web.
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Lahko bi plačali poklicne prevajalce,
da bi prevedli splet.
da bi prevedli splet.
11:07
We could do that.
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Lahko bi.
11:09
Unfortunately, it would be extremely expensive.
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Na žalost bi bilo to nepredstavljivo drago.
11:11
For example, translating a tiny, tiny fraction of the whole Web, Wikipedia,
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Vzemimo prevod majcenega delčka
celotnega spleta, Wikipedije,
celotnega spleta, Wikipedije,
11:14
into one other language, Spanish.
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v drug jezik, španščino.
11:17
Wikipedia exists in Spanish,
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Wikipedija obstaja v španščini,
11:19
but it's very small compared to the size of English.
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a je njen obseg majhen
v primerjavi z angleško različico.
v primerjavi z angleško različico.
11:21
It's about 20 percent of the size of English.
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Predstavlja okrog 20 odstotkov angleške.
11:23
If we wanted to translate the other 80 percent into Spanish,
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Če bi hoteli prevesti
ostalih 80 odstotkov v španščino,
ostalih 80 odstotkov v španščino,
11:26
it would cost at least 50 million dollars --
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bi to stalo najmanj 50 milijonov dolarjev,
11:28
and this is at even the most exploited, outsourcing country out there.
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in še to v najbolj izkoriščevalski in zatiralski državi.
11:31
So it would be very expensive.
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Bilo bi torej izjemno drago.
11:33
So what we want to do is we want to get 100 million people
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Namesto tega želimo dobiti 100 milijonov ljudi,
11:35
translating the Web into every major language
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ki bi splet prevajali v vsak večji jezik
11:37
for free.
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brezplačno.
11:39
Now if this is what you want to do,
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Pri takem cilju se moramo zelo hitro
11:41
you pretty quickly realize you're going to run into two pretty big hurdles,
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soočiti z dvema velikima preprekama,
11:43
two big obstacles.
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dvema velikima ovirama.
11:45
The first one is a lack of bilinguals.
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Prva je pomanjkanje dvojezičnih ljudi.
11:48
So I don't even know
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Nisem prepričan,
11:50
if there exists 100 million people out there using the Web
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da obstaja 100 milijonov uporabnikov spleta
11:53
who are bilingual enough to help us translate.
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z zadostnim dvojezičnim znanjem,
da lahko prevajajo.
da lahko prevajajo.
11:55
That's a big problem.
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To je velika težava.
11:57
The other problem you're going to run into is a lack of motivation.
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Druga pa je pomanjkanje spodbude.
11:59
How are we going to motivate people
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Kako bomo spodbudili ljudi,
12:01
to actually translate the Web for free?
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da brezplačno prevajajo splet?
12:03
Normally, you have to pay people to do this.
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Običajno tako delo zahteva plačilo.
12:06
So how are we going to motivate them to do it for free?
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Kako naj jih torej spodbudimo k neprofitnemu delu?
12:08
Now when we were starting to think about this, we were blocked by these two things.
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Ko smo začeli razmišljati o tem,
sta nas ti težavi ovirali.
sta nas ti težavi ovirali.
12:11
But then we realized, there's actually a way
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Nato pa smo se domislili načina,
12:13
to solve both these problems with the same solution.
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kako ju odpraviti z le eno rešitvijo.
12:15
There's a way to kill two birds with one stone.
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Načinom, kako ubiti dve muhi na en mah.
12:17
And that is to transform language translation
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Prevajanje smo spremenili v nekaj,
12:20
into something that millions of people want to do,
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kar milijoni ljudi želijo delati,
12:23
and that also helps with the problem of lack of bilinguals,
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in kar hkrati odpravlja pomanjkanje dvojezičnih ljudi,
12:26
and that is language education.
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in to je učenje jezika.
12:29
So it turns out that today,
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Izkazalo se je,
12:31
there are over 1.2 billion people learning a foreign language.
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da se danes več kot 1,2 milijarde ljudi
uči tujega jezika.
uči tujega jezika.
12:34
People really, really want to learn a foreign language.
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Ljudje se res želijo naučiti novih jezikov.
12:36
And it's not just because they're being forced to do so in school.
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In ne le zato, ker jih v to silijo v šoli.
12:39
For example, in the United States alone,
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Le v ZDA, na primer,
12:41
there are over five million people who have paid over $500
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je pet milijonov ljudi plačalo več kot 500 dolarjev
12:43
for software to learn a new language.
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za nakup programa za učenje tujega jezika.
12:45
So people really, really want to learn a new language.
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Ljudje se resnično želijo naučiti novega jezika.
12:47
So what we've been working on for the last year and a half is a new website --
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Zadnje leto in pol smo torej delali na
novi spletni strani,
novi spletni strani,
12:50
it's called Duolingo --
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imenovani Duolingo,
12:52
where the basic idea is people learn a new language for free
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vodilna zamisel pa je
brezplačno učenje novega jezika,
brezplačno učenje novega jezika,
12:55
while simultaneously translating the Web.
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ob katerem se hkrati prevaja splet.
12:57
And so basically they're learning by doing.
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Uporabniki se učijo s prakso.
12:59
So the way this works
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Deluje tako,
13:01
is whenever you're a just a beginner, we give you very, very simple sentences.
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da tudi začetniki dobijo izjemno preproste stavke,
13:04
There's, of course, a lot of very simple sentences on the Web.
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ki jih je na spletu seveda obilo.
13:06
We give you very, very simple sentences
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Dobite torej zelo preproste stavke
13:08
along with what each word means.
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skupaj s pomeni za vsako besedo.
13:10
And as you translate them, and as you see how other people translate them,
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Ko jih prevedete in preverite prevod drugih,
13:13
you start learning the language.
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se s tem učite novega jezika.
13:15
And as you get more and more advanced,
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Ko napredujete,
13:17
we give you more and more complex sentences to translate.
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dobite v prevod bolj zapletene stavke.
13:19
But at all times, you're learning by doing.
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V vsakem primeru pa se učite s prakso.
13:21
Now the crazy thing about this method
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Najbolj noro pri tej metodi pa je,
13:23
is that it actually really works.
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da dejansko deluje.
13:25
First of all, people are really, really learning a language.
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Prvič, ljudje se resnično učijo jezika.
13:27
We're mostly done building it, and now we're testing it.
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Program smo že postavili, sedaj ga testiramo.
13:29
People really can learn a language with it.
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Ljudje se z njim resnično lahko naučijo jezika.
13:31
And they learn it about as well as the leading language learning software.
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In uspeh je primerljiv z uporabniki
vodilnih programov za učenje.
vodilnih programov za učenje.
13:34
So people really do learn a language.
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Ljudje resnično osvojijo nov jezik.
13:36
And not only do they learn it as well,
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A ne samo osvojijo,
13:38
but actually it's way more interesting.
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pač pa postane še bolj zanimivo.
13:40
Because you see with Duolingo, people are actually learning with real content.
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Pri Duolingu se namreč učimo
na podlagi resničnih vsebin.
na podlagi resničnih vsebin.
13:43
As opposed to learning with made-up sentences,
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V nasprotju z umetno ustvarjenimi stavki
13:45
people are learning with real content, which is inherently interesting.
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se tu ljudje učijo na resničnih stavkih,
kar je bistveno bolj zanimivo.
kar je bistveno bolj zanimivo.
13:48
So people really do learn a language.
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Jezika se torej uspešno naučijo.
13:50
But perhaps more surprisingly,
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A morda je bolj presenetljivo,
13:52
the translations that we get from people using the site,
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da so prevodi, ki jih dobimo od uporabnikov,
13:55
even though they're just beginners,
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čeprav gre za začetnike,
13:57
the translations that we get are as accurate as those of professional language translators,
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po kakovosti primerljivi s prevodi
poklicnih prevajalcev,
poklicnih prevajalcev,
14:00
which is very surprising.
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kar je res presenetljivo.
14:02
So let me show you one example.
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Naj vam pokažem primer.
14:04
This is a sentence that was translated from German into English.
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Ta stavek je bil preveden iz nemščine v angleščino.
14:06
The top is the German.
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Zgornji odstavek je v nemščini.
14:08
The middle is an English translation
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V sredini je angleški prevod
14:10
that was done by somebody who was a professional English translator
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poklicnega prevajalca za angleščino,
14:12
who we paid 20 cents a word for this translation.
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ki je računal 20 centov na besedo.
14:14
And the bottom is a translation by users of Duolingo,
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Spodaj pa je prevod uporabnikov Duolinga,
14:17
none of whom knew any German
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od katerih nihče ni znal nemško,
14:19
before they started using the site.
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preden so začeli uporabljati to stran.
14:21
You can see, it's pretty much perfect.
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Kot vidite, je praktično popoln.
14:23
Now of course, we play a trick here
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Mi pa z zvijačo naredimo prevode
14:25
to make the translations as good as professional language translators.
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primerljive s prevodi poklicnih prevajalcev.
14:27
We combine the translations of multiple beginners
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Prevode več začetnikov združimo
14:30
to get the quality of a single professional translator.
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in tako dosežemo kakovost
enega poklicnega prevajalca.
enega poklicnega prevajalca.
14:33
Now even though we're combining the translations,
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In čeprav združujemo prevode,
14:38
the site actually can translate pretty fast.
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stran prevaja precej hitro.
14:40
So let me show you,
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Naj vam pokažem naše ocene,
14:42
this is our estimates of how fast we could translate Wikipedia
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kako hitro bi lahko prevedli Wikipedijo
14:44
from English into Spanish.
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iz angleščine v španščino.
14:46
Remember, this is 50 million dollars-worth of value.
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Ne pozabite, da govorimo o vrednosti
50 milijonov dolarjev.
50 milijonov dolarjev.
14:49
So if we wanted to translate Wikipedia into Spanish,
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Če bi torej želeli prevesti Wikipedijo v španščino,
14:51
we could do it in five weeks with 100,000 active users.
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bi v petih tednih to dosegli s 100.000 aktivnih uporabnikov,
14:54
And we could do it in about 80 hours with a million active users.
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oz. v 80 urah z milijonom aktivnih uporabnikov.
14:57
Since all the projects that my group has worked on so far have gotten millions of users,
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Ker so projekti naše ekipe doslej zbrali
že milijone uporabnikov,
že milijone uporabnikov,
15:00
we're hopeful that we'll be able to translate
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upamo, da nam bo ta projekt omogočil
15:02
extremely fast with this project.
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izjemno hitro prevajanje.
15:04
Now the thing that I'm most excited about with Duolingo
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Pri Duolingu me najbolj navdušuje to,
15:07
is I think this provides a fair business model for language education.
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da predstavlja pravičen poslovni način
učenja jezikov.
učenja jezikov.
15:10
So here's the thing:
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Gre namreč za to:
15:12
The current business model for language education
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sedanje poslovne prakse učenja jezikov
delujejo tako,
delujejo tako,
15:14
is the student pays,
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da učenec plača za uporabo,
15:16
and in particular, the student pays Rosetta Stone 500 dollars.
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in sicer 500 dolarjev za program Rosetta Stone.
15:18
(Laughter)
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(Smeh)
15:20
That's the current business model.
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Taka je trenutna poslovna praksa.
15:22
The problem with this business model
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Pri tem pa je težava,
15:24
is that 95 percent of the world's population doesn't have 500 dollars.
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da 95 odstotkov svetovnega prebivalstva
nima 500 dolarjev.
nima 500 dolarjev.
15:27
So it's extremely unfair towards the poor.
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Je torej izjemno nepravična do revnejših
15:30
This is totally biased towards the rich.
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in namenjena bogatim.
15:32
Now see, in Duolingo,
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Duolingo pa je zasnovan tako,
15:34
because while you learn
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da med učenjem pravzaprav
15:36
you're actually creating value, you're translating stuff --
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ne koristite le sebi, pač pa tudi prevajate,
15:39
which for example, we could charge somebody for translations.
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za kar bi nekdo drug računal.
15:42
So this is how we could monetize this.
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S tem ovrednotimo vaše učenje.
15:44
Since people are creating value while they're learning,
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Ker ima rezultat učenja za nas vrednost,
15:46
they don't have to pay their money, they pay with their time.
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jim ne izstavimo računa, saj plačajo s svojim časom.
15:49
But the magical thing here is that they're paying with their time,
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Čudovito pri tem je, da unovčijo čas,
15:52
but that is time that would have had to have been spent anyways
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ki bi ga itak porabili
15:54
learning the language.
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za učenje jezika.
15:56
So the nice thing about Duolingo is I think it provides a fair business model --
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Prednost Duolinga je, da predstavlja
pravičen posloven način,
pravičen posloven način,
15:59
one that doesn't discriminate against poor people.
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ki ne zapostavlja revnejših ljudi.
16:01
So here's the site. Thank you.
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To je ta stran. Hvala vam.
16:03
(Applause)
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(Aplavz)
16:11
So here's the site.
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To je torej naslov strani.
16:13
We haven't yet launched,
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Nismo je še zagnali,
16:15
but if you go there, you can sign up to be part of our private beta,
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a kljub temu se lahko vpišete
in se pridružite naši razvojni različici,
in se pridružite naši razvojni različici,
16:18
which is probably going to start in about three or four weeks.
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ki naj bi se zagnala v treh ali štirih tednih.
16:20
We haven't yet launched this Duolingo.
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Duolingo torej še ni na voljo.
16:22
By the way, I'm the one talking here,
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Mimogrede, o njem govorim le jaz,
16:24
but actually Duolingo is the work of a really awesome team, some of whom are here.
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Duolingo pa je delo izjemne ekipe,
katere del je z nami.
katere del je z nami.
16:27
So thank you.
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Hvala še enkrat.
16:29
(Applause)
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(Aplavz)
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