TEDxCMU
Luis von Ahn: Massive-scale online collaboration
Luis von Ahn: Bashkëpunimi masive online
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Pasi ri-propozimit CAPTCHA ashtuqë secila përgjigjëje e secilit njeri do te ndihmonte për të digjitalizuar libra, Luis von Ahn dëshironte diqka tjeter për një kontribut të vogel nga shum njerëz ne internet for një qellim te mirë. Tek TEDxCMU, ai ndanë me ne se si ambicja e tije për një projekt të ri, do ti ndihmoj miliona njerëzve të mësojn një gjuh të re duke përkthyer web-in me shpejt dhe më sakët -- dhe e gjitha pa pages.
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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Sa prej jush i'u eshte dashur te mbushonje ndonje form ne web ( faqe interneti )
00:17
where you've been asked to read a distorted sequence of characters like this?
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ku ju eshte kerkuar te lexoni nje sekuenc te qrregullt te nje karakterit sikurse ky ?
00:19
How many of you found it really, really annoying?
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Sa prej jush e keni gjetur verte te tepruar?
00:21
Okay, outstanding. So I invented that.
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Ne rregull, dalluar. Pra e kam ftuar ate.
00:24
(Laughter)
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(Qeshen)
00:26
Or I was one of the people who did it.
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ndoshta une isha njeri nga njerzit qe e bera ate.
00:28
That thing is called a CAPTCHA.
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Ajo gje eshte quajtur nje CAPTACHA
00:30
And the reason it is there is to make sure you, the entity filling out the form,
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dhe arsyja eshte atje per te bere te sigurt juve , etnitetit per te mbushur forma.
00:32
are actually a human and not some sort of computer program
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jane ne te vertet njerzore jo ndonje lloj programi kompjuterik
00:35
that was written to submit the form millions and millions of times.
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atje ishte shkruar per ta derguar formen, miliona dhe miliona herë.
00:37
The reason it works is because humans,
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Arsyeja perse funksionon eshte sepse njerezit,
00:39
at least non-visually-impaired humans,
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te pakten jo-vizualisht jo njeri i bashkangjitur,
00:41
have no trouble reading these distorted squiggly characters,
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ska probleme leximi keto karaktere te qorjentuara,
00:43
whereas computer programs simply can't do it as well yet.
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ku programet kompjuterike thjesht nuk mund ti bejen ato deri me tani.
00:46
So for example, in the case of Ticketmaster,
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per shembull ne rastin e nje perdoruesi te tiketave
00:48
the reason you have to type these distorted characters
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arsyje se ju duhet ti shenoni gjith ato karaktere te qorjentuara
00:50
is to prevent scalpers from writing a program
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eshte per te mbrojtur skulptoret per te shkruar nje program
00:52
that can buy millions of tickets, two at a time.
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i cili mund te blen miliona tiketa, dy me njeher.
00:54
CAPTCHAs are used all over the Internet.
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CAPTCHA perdoret neper te gjith internetin.
00:56
And since they're used so often,
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gjersa jan duke u perdorur kaq shume,
00:58
a lot of times the precise sequence of random characters that is shown to the user
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shum her sekuencat precize jan te karaktereve te rendomta te cilat u shfaqen shfrytzuesve
01:00
is not so fortunate.
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nuk eshte sa me fat.
01:02
So this is an example from the Yahoo registration page.
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pra ky eshte nje shembulle from regjistrimit në Yahoo.
01:05
The random characters that happened to be shown to the user
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Karakteri i rendomt i cili ndodh te shfaqet tek nje perdorues
01:07
were W, A, I, T, which, of course, spell a word.
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ku W,A,I,T te cilat sigurisht shqiptojne nje fjali.
01:10
But the best part is the message
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mirpo pjesa me e mir eshte porosia
01:13
that the Yahoo help desk got about 20 minutes later.
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qe Yahoo ndihmuesi ka rreth 20 minuta me vone
01:16
Text: "Help! I've been waiting for over 20 minutes, and nothing happens."
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Teksti "Ndihm! Kam mbi 20 minute qe pres dhe asgje nuk ka ndodhur"
01:19
(Laughter)
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(Qeshen)
01:23
This person thought they needed to wait.
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Ky person mendon qe ata duhet te presin.
01:25
This of course, is not as bad as this poor person.
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Ky sigurisht qe nuk eshte aq keq sa nje person i varfer.
01:28
(Laughter)
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(Qeshen)
01:30
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago,
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CAPTCHA projekti eshte diqka qe e kemi bere ketu ne CARNEIGE MELLON para 10 viteve
01:33
and it's been used everywhere.
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dhe eshte perdorur gjdokunde.
01:35
Let me now tell you about a project that we did a few years later,
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Me lejoni t'ju tregoj tashi per nje projekt qe e kemi bere ketyre viteve te fundit,
01:37
which is sort of the next evolution of CAPTCHA.
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i cili eshte nje lloje i evulucionit te se ardhmes
01:40
This is a project that we call reCAPTCHA,
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Kete projekt e kemi quajtur reCAPTCHA,
01:42
which is something that we started here at Carnegie Mellon,
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i cili eshte diqka qe kemi filluar kete tek Carneigie Mellon,
01:44
then we turned it into a startup company.
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dhe me pas e kthyem ne nje kompani
01:46
And then about a year and a half ago,
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dhe pastaj rreth nje viti e gjysme me vone,
01:48
Google actually acquired this company.
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Google bleu kete kompani.
01:50
So let me tell you what this project started.
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Pra me lejoni t'ju them se qfar ishte projekti i filluar
01:52
So this project started from the following realization:
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pra projekti filloj nga realizimet siq vijojnë:
01:55
It turns out that approximately 200 million CAPTCHAs
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u kthye qe parafersisht rreth 200 milion CAPTCHA-ne
01:57
are typed everyday by people around the world.
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jan duke shkruar gjdo dite ne tere boten.
02:00
When I first heard this, I was quite proud of myself.
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Kur e negjova kete, me te vertet isha krenar me veten time
02:02
I thought, look at the impact that my research has had.
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Mendova shiqoni impaktin qe kishte kerkimi ime
02:04
But then I started feeling bad.
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mirpo me pas fillova te ndihem keq/
02:06
See here's the thing, each time you type a CAPTCHA,
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Pra ketu eshte gjeja, gjdoher qe e shkruan CAPTCHA,
02:08
essentially you waste 10 seconds of your time.
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esencialisht ju keni humbur 10 sekonda te kohes suaj
02:11
And if you multiply that by 200 million,
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dhe po te shumzonit me 200 milion
02:13
you get that humanity as a whole is wasting about 500,000 hours every day
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ju mund te merrni kete humanitet si pergjethsi jane duke humbur rreth 500,000 ore gjdo dite
02:16
typing these annoying CAPTCHAs.
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duke shenuar keto CAPTACHA te merzitshme.
02:18
So then I started feeling bad.
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dhe me pas fillova te nidhem keq.
02:20
(Laughter)
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(Qeshen)
02:22
And then I started thinking, well, of course, we can't just get rid of CAPTCHAs,
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dhe me pas fillova te mendoj, mire sigurisht ne nuk mundemi te heqim qafesh Captchas-it
02:25
because the security of the Web sort of depends on them.
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sepse siguria e web-it mvaret nga ato.
02:27
But then I started thinking, is there any way we can use this effort
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pastaj fillova te mendoj, a ka ndonje menyr tjeter qe mundemi te perdorim ket mundesi
02:30
for something that is good for humanity?
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per diqka qe eshte ne te miren e njerezimit?
02:32
So see, here's the thing.
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pra shiq e shihni ketu eshte gjeja.
02:34
While you're typing a CAPTCHA, during those 10 seconds,
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Gjersa jemi duke shenuar CAPTCHA, gjat atyre 10 sekondave
02:36
your brain is doing something amazing.
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truri juaj eshte duke bere diqka te mahniteshme
02:38
Your brain is doing something that computers cannot yet do.
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truri juaj eshte duke bere diqka qe kompjuteri deri tani nuk munden te bejne.
02:40
So can we get you to do useful work for those 10 seconds?
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pra e mundemi te bejme ndonje pune te dobishme per ato 10 sekonda?
02:43
Another way of putting it is,
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nje menyr tjeter e vendosjes se kesaj eshte
02:45
is there some humongous problem that we cannot yet get computers to solve,
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a ka ndonje problem tjeter homogjem te cilin kompjuteret e tanishem nuk mund ta zgjedhin,
02:47
yet we can split into tiny 10-second chunks
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deri tani mundemi te ndajme keto 10 sekonda te kushtueshme
02:50
such that each time somebody solves a CAPTCHA
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saqe secilen koh kur dikush zgjedh nje CAPTCHA
02:52
they solve a little bit of this problem?
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ata zgjedhin ngapak te ketij problemi?
02:54
And the answer to that is "yes," and this is what we're doing now.
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dhe pergjigjeja per ate eshte "PO" dhe kjo eshte ajo se qfar ne po e bejme tani.
02:56
So what you may not know is that nowadays while you're typing a CAPTCHA,
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pra ate qe ju ndoshta nuk mundeni ta dini ketyre ditve eshte se kur ju jeni duke shenuar nje CAPTCHA,
02:59
not only are you authenticating yourself as a human,
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jo qe vetem ju jeni duke e autorizuar vetveten tuaj si njeri,
03:01
but in addition you're actually helping us to digitize books.
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por ne perparsi ju jeni duke i ndihmuar digjitalizimit te nje libri.
03:03
So let me explain how this works.
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pra me lejoni t'ju shpjegoj se si funksionon.
03:05
So there's a lot of projects out there trying to digitize books.
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Jan shum projekte te cilat jan duke u munduar per te digjitalizuar libra.
03:07
Google has one. The Internet Archive has one.
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Google ka nje. Arhivi i interneti ka poashtu.
03:10
Amazon, now with the Kindle, is trying to digitize books.
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Amazoni tani me Kindle (lexuesi i librave elektronik) provon te digjitalizoj libra.
03:12
Basically the way this works
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Faktikisht menyra se si funksionon
03:14
is you start with an old book.
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eshte ju filloni me nje liber te vjeter.
03:16
You've seen those things, right? Like a book?
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I keni pare keto gjere mire? sikurse nje liber ?
03:18
(Laughter)
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(Qeshen)
03:20
So you start with a book, and then you scan it.
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Pra filloni me nje liber, dhe e skanoni ate
03:22
Now scanning a book
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Tashi skanimi i nje libre
03:24
is like taking a digital photograph of every page of the book.
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eshte sikurse te marrim nje fotografi digjitale te seciles faqe te nje libri
03:26
It gives you an image for every page of the book.
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te jep nje imazh per secilen faqe te librit
03:28
This is an image with text for every page of the book.
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Ky eshte nje imazh me tekst per secilen faqe
03:30
The next step in the process
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Hapi tjeter eshte proces.
03:32
is that the computer needs to be able to decipher all of the words in this image.
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te cilin kompjuterit i nevoitet te jet te ngjendje ti nxierr te gjitha fjalet nga ky imazh.
03:35
That's using a technology called OCR,
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Ata e perdorin nje teknologji te quajtur OCR,
03:37
for optical character recognition,
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per karakte optice te nxierruar,
03:39
which takes a picture of text
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te cilat i merr si fotografi nga teksti
03:41
and tries to figure out what text is in there.
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dhe fillon ta gjeje se qfar teksti eshte atje.
03:43
Now the problem is that OCR is not perfect.
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Tashi problemi eshte se OCR-ja nuk eshte perfekt
03:45
Especially for older books
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Ne veqanti per libra te vjeter
03:47
where the ink has faded and the pages have turned yellow,
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ku ngjyra ka filluar te bie dhe faqet jan bere ne te verdh,
03:50
OCR cannot recognize a lot of the words.
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OCR-ja nuk mundet ti nxierr te gjitha fjalet.
03:52
For example, for things that were written more than 50 years ago,
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Per shembull, per gjerat te cilat ishin shkruar para 50 viteve
03:54
the computer cannot recognize about 30 percent of the words.
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kompjuteri nuk mundet ti nxierr rreth 30 perqind te fjalve.
03:57
So what we're doing now
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Pra qfar jemi duke bere tani
03:59
is we're taking all of the words that the computer cannot recognize
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eshte ne po i nxierrim te gjitha fjalit te cilat kompjuteret nuk munden
04:01
and we're getting people to read them for us
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dhe po i'a japim njerzve qe ti lexojne ato per neve
04:03
while they're typing a CAPTCHA on the Internet.
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gjersa ata jan duke shenuar nje CAPTCHA ne internet
04:05
So the next time you type a CAPTCHA, these words that you're typing
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pra heren tjeter kur ju jeni duke shenuar nje CAPTCHA, keto fjali qe jeni duke i shenuar
04:08
are actually words that are coming from books that are being digitized
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jan ne fakt fjalit te cilat jane duke ardhur nga librat te cilat po digjitalizohen
04:11
that the computer could not recognize.
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qe kompjuteri nuk mundnte ti nxierrte.
04:13
And now the reason we have two words nowadays instead of one
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dhe arsyeja perse ne kemi dy fjali koheve te fundit dhe jo nje
04:15
is because, you see, one of the words
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eshte sepse ju shihni, njeren nga fjalet
04:17
is a word that the system just got out of a book,
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eshte fjale te cilen sistemi sapo e ka nxierr nga nje liber,
04:19
it didn't know what it was, and it's going to present it to you.
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nuk e dinte se qfar ishte, vetem do t'ua prezentoj para jush.
04:22
But since it doesn't know the answer for it, it cannot grade it for you.
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meqe nuk e din pergjigjejen e tij, smundet ta noton per ty.
04:25
So what we do is we give you another word,
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qfar ne bejme eshte ju japi nje fjali tjeter,
04:27
one for which the system does know the answer.
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nje nga te cilen systemi e di pergjigjejen.
04:29
We don't tell you which one's which, and we say, please type both.
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ne nuk ju themi se cila eshte cila, dhe ne ju themi ju lutem shtypni te dyja.
04:31
And if you type the correct word
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nese e keni shkruar fjalin korrekte
04:33
for the one for which the system already knows the answer,
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per te cilen sistemi e din pergjigjejen
04:35
it assumes you are human,
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e pranon se je njeri,
04:37
and it also gets some confidence that you typed the other word correctly.
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dhe gjithashtu pranon nje konfidenc qe ju jeni duke shtypur fjalin tjeter te saket.
04:39
And if we repeat this process to like 10 different people
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dhe nese e perseritim kete proces tek 10 njerez te ndryshum
04:42
and all of them agree on what the new word is,
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dhe te gjith prej tyre pajtohen se qfar eshte fjalia e re
04:44
then we get one more word digitized accurately.
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ateher ne kemi digjitalizuar nje fjali te re saktesisht.
04:46
So this is how the system works.
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pra ky keshtu funksionon sistemi.
04:48
And basically, since we released it about three or four years ago,
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faktikisth, qe nga data e leshimit qe ka qen rreth 3 apo kater vite me pare
04:51
a lot of websites have started switching
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shum web faqe kan filluar ti ndrrojne
04:53
from the old CAPTCHA where people wasted their time
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nga CAPTCHA e vjeter ku njerzit humbsin kohen e tyre
04:55
to the new CAPTCHA where people are helping to digitize books.
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tek CATPCHA e re ku njerzit jane duke ndihmuar digjitalizimin e librave.
04:57
So for example, Ticketmaster.
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pershembull, Tiketuesi master
04:59
So every time you buy tickets on Ticketmaster, you help to digitize a book.
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Pra gjodher kur ju blenje nje tiket, ju i ndihmoni digjitalizimit te nje libri.
05:02
Facebook: Every time you add a friend or poke somebody,
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Facebook: gjdoher kur ju shtoni nje shok ose pokoni dike,
05:04
you help to digitize a book.
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ju jeni duke i ndihmuar digjitalizimit.
05:06
Twitter and about 350,000 other sites are all using reCAPTCHA.
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Twitter dhe rreth 350,000 faqe tjera jan duke perdorur reCAPTCHA
05:09
And in fact, the number of sites that are using reCAPTCHA is so high
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dhe ne fakt, numri i web faqeve qe jan duke perdorur eshte shum i larte
05:11
that the number of words that we're digitizing per day is really, really large.
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dhe numri i fjalive te cilat jane duke u digjitalizuar per nje dite eshte shum shum i madh.
05:14
It's about 100 million a day,
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eshte rreth 100 milion per nje dite,
05:16
which is the equivalent of about two and a half million books a year.
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e cila eshte ekuivalente rreth dy e gjysme milioni libra per nje vite.
05:20
And this is all being done one word at a time
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dhe e gjith kjo eshte duke u ber me nga nje fjali per njeher
05:22
by just people typing CAPTCHAs on the Internet.
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me njerzit duke shtypur CAPTCHA ne internet.
05:24
(Applause)
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(Duartrokitje)
05:32
Now of course,
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Tani sigurisht,
05:34
since we're doing so many words per day,
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meqe jemi duke i ber shum fjali per nje dit,
05:36
funny things can happen.
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gjera te quditshme munden te ndodhin.
05:38
And this is especially true because now we're giving people
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Dhe tani me te vertet eshte sepse ne jemi duke i dhen njerezve
05:40
two randomly chosen English words next to each other.
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dy zgjidhje te rendomta ne anglisht fjali ngjitur me njera tjetren.
05:42
So funny things can happen.
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pra gjera qesharake munden te ndodhin.
05:44
For example, we presented this word.
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pershembull, ne prezentuam ket fjali.
05:46
It's the word "Christians"; there's nothing wrong with it.
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eshte fjali "Kristianet" nuk ka asgje te keqe me te.
05:48
But if you present it along with another randomly chosen word,
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mirpo nese e prezentoni se bashku me nje fjali tjeter te rendomt,
05:51
bad things can happen.
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gjera te keqija mund te ndodhin.
05:53
So we get this. (Text: bad christians)
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pra kemi kete tekst keq Krishtianisum
05:55
But it's even worse, because the particular website where we showed this
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madje edhe me keq, sepse disa web faqe ishin duke e shfaqur kete
05:58
actually happened to be called The Embassy of the Kingdom of God.
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faktikisht ndodhi te jen quajtur Ambasada e zotit te britanis se madhe.
06:01
(Laughter)
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(Qeshen)
06:03
Oops.
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Opps.
06:05
(Laughter)
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(Qeshen)
06:08
Here's another really bad one.
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Ketu eshte nje tjeter shum e keqe
06:10
JohnEdwards.com
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JohnEdwards.com
06:12
(Text: Damn liberal)
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(Teksti: libreralet e kqninje)
06:15
(Laughter)
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(Qeshen)
06:17
So we keep on insulting people left and right everyday.
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pra e mbajke kete majtas dhe djathtas gjdo dite.
06:20
Now, of course, we're not just insulting people.
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Tani, sigurisht ne nuk jemi duke i inslutura njerzit.
06:22
See here's the thing, since we're presenting two randomly chosen words,
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shiqoni ketu, meqe ne jemi duke ju prezentuar dy fjalo te rendomta zgjedhore,
06:25
interesting things can happen.
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gjera interesante munden te ndodhin.
06:27
So this actually has given rise
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pra ne fakt ka nje rritje
06:29
to a really big Internet meme
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tek nje gje shum e madhe ne internet
06:32
that tens of thousands of people have participated in,
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ku qindra dhe mijera njerz kan participuar ne te,
06:34
which is called CAPTCHA art.
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e cila eshte quajtur arti i CAPTCHA.
06:36
I'm sure some of you have heard about it.
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Jam shum i sigurt qe disa nga ju keni degjuar per kete.
06:38
Here's how it works.
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ketu eshte se si punon.
06:40
Imagine you're using the Internet and you see a CAPTCHA
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Imagjinoni ju jeni duke perdorur internetin dhe e shifni nje CAPTCHA
06:42
that you think is somewhat peculiar,
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dhe ju mendoni se eshte diqka e njohur nga diku,
06:44
like this CAPTCHA. (Text: invisible toaster)
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sikurse kjo CAPTCHA
06:46
Then what you're supposed to do is you take a screen shot of it.
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Pastaj qfar ju duhet te beni eshte te shtypni butnin per te marruar ekranin tuaj.
06:48
Then of course, you fill out the CAPTCHA
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pastaj sigurisht qe do te mbushni formen e CAPTCHA
06:50
because you help us digitize a book.
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sepse ju ndihmoni te digjitalizoni nje liber.
06:52
But then, first you take a screen shot,
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mirpo se pari shtypni butonin per printimin e kranit,
06:54
and then you draw something that is related to it.
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dhe pastaj ju vizatoni diqka qe lidhet me te.
06:56
(Laughter)
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(Qeshen)
06:58
That's how it works.
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Pra keshtu punon.
07:01
There are tens of thousands of these.
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Jan dhjetra dhe mira nga keto.
07:04
Some of them are very cute. (Text: clenched it)
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disa nga to jan shum te lezetshme
07:06
(Laughter)
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(Qeshen)
07:08
Some of them are funnier.
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disa nga to jane qesharake
07:10
(Text: stoned founders)
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(Tekst: ideatoret)
07:13
(Laughter)
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(Qeshen)
07:16
And some of them,
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dhe disa nga ta,
07:18
like paleontological shvisle,
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sikurse gurdhendesit,
07:21
they contain Snoop Dogg.
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ata permbajn snoop dogg
07:23
(Laughter)
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(Qeshen)
07:26
Okay, so this is my favorite number of reCAPTCHA.
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Ne rregull, pra ky eshte numri im i preferuar i re-CAPTCHA
07:28
So this is the favorite thing that I like about this whole project.
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pra ky eshte pjesa dhe gjeja kryesore qe u dua me se shumti mbi te gjith ket projekt.
07:31
This is the number of distinct people
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My eshte numri i dalluar i njerezve
07:33
that have helped us digitize at least one word out of a book through reCAPTCHA:
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te cilet na kan ndihmuar neve per te digjitalizuar te pakten nje nga fjalit e librave nepermjet reCAPTCHA:
07:36
750 million,
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750 milion,
07:38
which is a little over 10 percent of the world's population,
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i cili eshte me pak se 10 perqindshi i popllsis botrore,
07:40
has helped us digitize human knowledge.
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te cilet na kane ndihmuar neve te digjitalizojme njohurit e njerzimit.
07:42
And it is numbers like these that motivate my research agenda.
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dhe numrat sikurse ky te cilet me motivojne ne agjenden e kerkimit tim.
07:45
So the question that motivates my research is the following:
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Pra pyetja e cila me motivon kerkimin tim eshte:
07:48
If you look at humanity's large-scale achievements,
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Po ta shiqoni te arrituart e njerezimit ne nje mas te madhe,
07:50
these really big things
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jane me te vertet gjera shum te mdha
07:52
that humanity has gotten together and done historically --
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qe humaniteti i ka bere se bashku dhe i ka krijuar neper histori --
07:55
like for example, building the pyramids of Egypt
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sirkuse per shembull, ndertimi i piradimave te Egjiptit
07:57
or the Panama Canal
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apo Kanalin e panamase
07:59
or putting a man on the Moon --
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ose vendosja e njeriut ne Hene-
08:01
there is a curious fact about them,
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eshte nje fakt kurioz rreth tyre,
08:03
and it is that they were all done with about the same number off people.
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dhe e gjitha eshte bere pothuajse me te njejtin numer te njerezve
08:05
It's weird; they were all done with about 100,000 people.
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eshte e quditshme; te gjitha keto ishin bere me rreth 100,000 njerez
08:08
And the reason for that is because, before the Internet,
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dhe arsyeja per ate eshte sepse , perpara internetit,
08:11
coordinating more than 100,000 people,
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te kordinosh me teper se 100,000 njerez
08:13
let alone paying them, was essentially impossible.
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te paguash veqmash per secili ishte pothuajse e pamundur.
08:16
But now with the Internet, I've just shown you a project
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mirpo tani me internetin, Ju kam treguar nje projekt
08:18
where we've gotten 750 million people
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ku patem 750 milion njerez
08:20
to help us digitize human knowledge.
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te cilet na ndihmuan te digjitalizojme njohurit e njerezimit.
08:22
So the question that motivates my research is,
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pra pyetja e cila me motivon kerkimin tim eshte,
08:24
if we can put a man on the Moon with 100,000,
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Nese ne mundemi ta vendosim nje njeri ne hene me 100,000
08:27
what can we do with 100 million?
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qfar mundemi te bejeme me 100 milion?
08:29
So based on this question,
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pra bazuar ne kete pyetje,
08:31
we've had a lot of different projects that we've been working on.
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ne kemi pasur shum lloje projektesh te cilat kemi punuar ne.
08:33
Let me tell you about one that I'm most excited about.
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Me lejoni t'ju them rreth nje projekti te cilin jam shum i knaqur me.
08:36
This is something that we've been semi-quietly working on
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Kjo eshte diqka qe kemi punuar koh-pas-kohe
08:38
for the last year and a half or so.
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vitin e fundit apo me teper.
08:40
It hasn't yet been launched. It's called Duolingo.
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akoma nuk eshte lansuar. E quajme Duolingo.
08:42
Since it hasn't been launched, shhhhh!
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me qe nuk eshte lancuar akoma , shhhhh!
08:44
(Laughter)
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(Qeshen)
08:46
Yeah, I can trust you'll do that.
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po, Une mundem t'ju besoj juve ate.
08:48
So this is the project. Here's how it started.
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Pra ky eshte projekti. Ja se si filloi.
08:50
It started with me posing a question to my graduate student,
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Filloj me mua duke postuar nje pyetje tek studentat e mi.
08:52
Severin Hacker.
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Severin hacker.
08:54
Okay, that's Severin Hacker.
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Ne rregull, ai eshte Severn Hacker.
08:56
So I posed the question to my graduate student.
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Pra e postova pyetjen tek studentat e mi
08:58
By the way, you did hear me correctly;
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mirpo menyra se si ju me degjuat mua saktesisht;
09:00
his last name is Hacker.
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mbiemri i tij eshte Hacker.
09:02
So I posed this question to him:
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Pra i postova pyetjen atij:
09:04
How can we get 100 million people
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Si mund ti marrim 100 milion njerez
09:06
translating the Web into every major language for free?
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ta perkthejm web-in ne secilen gjuh kryesore pa pages?
09:09
Okay, so there's a lot of things to say about this question.
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Ne rregull, jane shum gjera per te thene rreth kesaj pyetje.
09:11
First of all, translating the Web.
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Se pari , per ta perkthyer web-in
09:13
So right now the Web is partitioned into multiple languages.
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Per momentin web-i eshte i pozicionuar ne shumicen e gjuheve.
09:16
A large fraction of it is in English.
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Nje shum e madhe e tij eshte ne Anglisht.
09:18
If you don't know any English, you can't access it.
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Nese nuk e di fare Anglishten, ju smuneni te keni akces tek ai.
09:20
But there's large fractions in other different languages,
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mirpo jan edhe shum pjes tjera ne gjuh te ndryshme,
09:22
and if you don't know those languages, you can't access it.
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dhe nese nuk i din ato gjuh nuk mundesh te kesh akcez tek ai.
09:25
So I would like to translate all of the Web, or at least most of the Web,
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Pra une deshiroj te perkthej te gjith web-in apo pjesen me te madhe te tij,
09:28
into every major language.
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ne te gjitha gjuhet kryesore.
09:30
So that's what I would like to do.
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pra kjo eshte ajo qe une dua te beje.
09:32
Now some of you may say, why can't we use computers to translate?
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Tani disa nga ju mundeni te thoni , perse nuk mundemi te perdorim kompjuterin per ta perkthyer ate?
09:35
Why can't we use machine translation?
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perse nuk mundemi te perdorim perkthyesin makin?
09:37
Machine translation nowadays is starting to translate some sentences here and there.
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Perkthyesit makin koheve te fundit jane duke perkthyer disa fjali ketu atu.
09:39
Why can't we use it to translate the whole Web?
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perse nuk mundemi te perdorim ta perkthejme te gjith web-in?
09:41
Well the problem with that is that it's not yet good enough
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Mire por problemi eshte sepse nuk mundemi te jemi shum te mire per ta bere kete
09:43
and it probably won't be for the next 15 to 20 years.
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dhe perafersisht nuk do te mund ta bejem kete edhe per 15 apo 20 vitet e ardheshme.
09:45
It makes a lot of mistakes.
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Ben shume gabime.
09:47
Even when it doesn't make a mistake,
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Madje edhe kur nuk bene ndonje gabim,
09:49
since it makes so many mistakes, you don't know whether to trust it or not.
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meqe bene shume gabime ne nuk e dim se a ti besoj apo jo.
09:52
So let me show you an example
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Pra me lejoni t'ju jap nje shembull
09:54
of something that was translated with a machine.
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apo diqka qe ishte perkthyer me nje makineri.
09:56
Actually it was a forum post.
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Faktikisht ishte nje postim ne forum.
09:58
It was somebody who was trying to ask a question about JavaScript.
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Ishte dikush qe kishte provuar te beje nje pyetje rreth JavaScritp-it (program per programera)
10:01
It was translated from Japanese into English.
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Ishte perkthyer nga Japonishtja ne Anglisht.
10:04
So I'll just let you read.
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Pra vetem do t'ju le te lexoni.
10:06
This person starts apologizing
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Personi filloj te kerkoj falje
10:08
for the fact that it's translated with a computer.
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per faktin qe ishte perkthyer me nje kompjuer.
10:10
So the next sentence is is going to be the preamble to the question.
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Pra fjalia tjeter do te jet direkt e marrur nga pyetja.
10:13
So he's just explaining something.
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Pra ai vetem po sqaron diqka.
10:15
Remember, it's a question about JavaScript.
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Kujtoni, eshte pyetja rreth JavaScritp-it.
10:19
(Text: At often, the goat-time install a error is vomit.)
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(Teksti: shpesh, koha e instalimit gabim kaniher)
10:23
(Laughter)
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(Qeshen)
10:27
Then comes the first part of the question.
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Pastaj vjen pjesa e pare e pyetjes.
10:30
(Text: How many times like the wind, a pole, and the dragon?)
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Tekst: Sa herë si era, një shtyllë, dhe dragon
10:34
(Laughter)
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(Qeshen)
10:36
Then comes my favorite part of the question.
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Pastaj vjen pjesa ime e preferuar e pyetjes.
10:39
(Text: This insult to father's stones?)
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(Tekst: ky ka instulim per baban e gurve?)
10:42
(Laughter)
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(Qeshen)
10:44
And then comes the ending, which is my favorite part of the whole thing.
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dhe pastaj vjen pjesa e fundit,e cila eshte me e preferuar e te gjith kesaj gjeje.
10:47
(Text: Please apologize for your stupidity. There are a many thank you.)
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(Tekst: Ju lutem me falni per marrezin tuaj. Jan shum faliminderit.)
10:51
(Laughter)
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(Qeshen)
10:53
Okay, so computer translation, not yet good enough.
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Ne rregull, pra perkthimi me kompjuteri, nuk eshte akom i mire.
10:55
So back to the question.
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Pra kthehemi tek pyetja.
10:57
So we need people to translate the whole Web.
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Pra na nevoiten njerez per ta perkthyer te gjithe web-in.
11:00
So now the next question you may have is,
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Tani pyetja tjeter te cilin ju mund ta keni eshte,
11:02
well why can't we just pay people to do this?
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mire, perse nuk mundemi te paguaj njerez per ta bere kete?
11:04
We could pay professional language translators to translate the whole Web.
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Ne mundemi te paguaj perkthyer profesional per ta perkthyer te gjith web-in.
11:07
We could do that.
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Ne mundemi te ta beje kete.
11:09
Unfortunately, it would be extremely expensive.
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Fatkeqesisht, do te ishte ekstremisht e shtrenjet.
11:11
For example, translating a tiny, tiny fraction of the whole Web, Wikipedia,
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Per shembull, perkthimi ne pjes te vogla fraksion te te gjithe web-it, Wikipedia,
11:14
into one other language, Spanish.
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ne nje gjuh tjeter, Spanjollisht.
11:17
Wikipedia exists in Spanish,
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Wikipedia eksiston ne Spanjollisht,
11:19
but it's very small compared to the size of English.
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mirpoo eshte shum e vogel nese e krahazojme me madhesin e Anglishtes.
11:21
It's about 20 percent of the size of English.
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eshte rreth 20 perqind te madhesis se Anglishtes.
11:23
If we wanted to translate the other 80 percent into Spanish,
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nese deshirojme te perkthejme pjesen e 80 perqindeshit ne Spanjollisht,
11:26
it would cost at least 50 million dollars --
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do te kushtonte se paku 50 milion dollare --
11:28
and this is at even the most exploited, outsourcing country out there.
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dhe ky eshte sepaku shteti me outsort jasht.
11:31
So it would be very expensive.
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pra do te jet shume e shtrejte.
11:33
So what we want to do is we want to get 100 million people
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Pra qfar deshirojme te bejme eshte deshirojme ti marrim 100 milion njerez
11:35
translating the Web into every major language
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perkthimin e te gjith web-it ne shumicen e gjuheve
11:37
for free.
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pa pages.
11:39
Now if this is what you want to do,
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Tashi nese kjo eshte ajo se qfar deshirojme te bejme,
11:41
you pretty quickly realize you're going to run into two pretty big hurdles,
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shum shpejt do te zbulonin qe ju jeni duke diqka shum te madhe,
11:43
two big obstacles.
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dy pengesa te medhaja.
11:45
The first one is a lack of bilinguals.
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E para eshte mungesa e pageses.
11:48
So I don't even know
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Pra une nuk e di
11:50
if there exists 100 million people out there using the Web
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nese eksistojne 100 milion njerez atje te cilet e perdorin web-in
11:53
who are bilingual enough to help us translate.
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kush eshte i interesuar te na ndihmoje neve per ta perkthyer.
11:55
That's a big problem.
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Ky eshte nje problem i madh.
11:57
The other problem you're going to run into is a lack of motivation.
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Problemi tjeter ne te cilin do te hasim eshte mungesa e motivimit.
11:59
How are we going to motivate people
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Si do ti motivojme njerezit
12:01
to actually translate the Web for free?
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per ta perkthyer Webin pa pages?
12:03
Normally, you have to pay people to do this.
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Normalisht, ju duhet te paguni njerezit per ta bere kete.
12:06
So how are we going to motivate them to do it for free?
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pra si do ti motivojme ata per ta bere kete pa pages?
12:08
Now when we were starting to think about this, we were blocked by these two things.
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Tashi kur ne filluam te mendojme per kete, ne ishim te bllokuar nga keto dy gjera.
12:11
But then we realized, there's actually a way
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Mirpo ne e gjetem, se ne te vertet eshte nje menyr
12:13
to solve both these problems with the same solution.
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per ti zgjidhur te dyja keto probleme me te njejten menyre
12:15
There's a way to kill two birds with one stone.
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Eshte nje menyre per te vrare dy zogj me te njejtin guri.
12:17
And that is to transform language translation
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dhe kjo eshte per ta transpormuar gjuhen ne nje perkthim
12:20
into something that millions of people want to do,
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ne diqka qe miliona njere deshirojne te bejne,
12:23
and that also helps with the problem of lack of bilinguals,
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dhe ajo gjithashtu na ndihmon neve me mungeses se pagesave.
12:26
and that is language education.
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dhe kjo eshe edukimi gjuhesore.
12:29
So it turns out that today,
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pra u kthye qe sot,
12:31
there are over 1.2 billion people learning a foreign language.
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jan mbi 1.2 miljard njereze te cilet mesoje nje gjuh te huaje.
12:34
People really, really want to learn a foreign language.
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Njerezit me te vertet deshirojne te mesojne nje gjuh te huaje.
12:36
And it's not just because they're being forced to do so in school.
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dhe nuk eshte sepse jan te detyruar me force per te mesuar kete ne shkolle.
12:39
For example, in the United States alone,
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Pershembull, ne ShBA vetem,
12:41
there are over five million people who have paid over $500
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jane me teper se 5 milion njereze te cilet kan paguar mbi 500 dollar
12:43
for software to learn a new language.
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per nje softuer per te mesuar nje gjuhe te re.
12:45
So people really, really want to learn a new language.
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pra njerezit me te vertet deshirojne te mesojne nje gjuhe te re.
12:47
So what we've been working on for the last year and a half is a new website --
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pra ne qfar kemi punuar ne vitin e fundit eshte nje web i ri --
12:50
it's called Duolingo --
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i quajtur Duolingo --
12:52
where the basic idea is people learn a new language for free
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idea eshte qe njerezit te mesojne nje gjuh te re pa pages
12:55
while simultaneously translating the Web.
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e cila stimulon perkthimin e web-it
12:57
And so basically they're learning by doing.
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dhe faktikisht eshte e thjesht sikurse te mesuarit duke e bere.
12:59
So the way this works
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Pra menyra se si funksionon
13:01
is whenever you're a just a beginner, we give you very, very simple sentences.
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kurdohere qe ju jeni nje fillestar, ne ju japim fjali shum shum te thjeshta.
13:04
There's, of course, a lot of very simple sentences on the Web.
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Sigurisht qe ka shum shum fjali te thjeshta ne web.
13:06
We give you very, very simple sentences
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Ne ju japim shum shum fjali te thjeshta
13:08
along with what each word means.
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sebashku me te dhe kuptimin e fjalise.
13:10
And as you translate them, and as you see how other people translate them,
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dhe duke i perkthyer ato, dhe duke i pare njerezit duke i perkthyer ato
13:13
you start learning the language.
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ju filloni te mesoni gjuhen.
13:15
And as you get more and more advanced,
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dhe sa me shum qe ju profesionalizoheni,
13:17
we give you more and more complex sentences to translate.
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ne ju japim me shum fjali te veshtira per ti perkthyer.
13:19
But at all times, you're learning by doing.
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mirpo gjat gjith kohes, ju jeni duke mesuar duke e bere.
13:21
Now the crazy thing about this method
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Tashi gjeja e qmenduar rreth kesaj metode
13:23
is that it actually really works.
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eshte sa ne fakt funksionon per mrekulli.
13:25
First of all, people are really, really learning a language.
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se pari njerezit jane me te vertet per te mesuar nje gjuhe.
13:27
We're mostly done building it, and now we're testing it.
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Jemi pothuajse te perfunduar, te ndertuari ate, tashi jemi duke e testuar ate.
13:29
People really can learn a language with it.
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Njerezit munden te mesojne nje gjuh me te.
13:31
And they learn it about as well as the leading language learning software.
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Dhe ata e mesojne ate sikurse programi per mesimin e gjuheve.
13:34
So people really do learn a language.
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Pra njerezit me te vertet mesojne nje gjuhe.
13:36
And not only do they learn it as well,
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dhe jo vetem qe e mesojne,
13:38
but actually it's way more interesting.
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por eshte shum me interesante.
13:40
Because you see with Duolingo, people are actually learning with real content.
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Sepse ju e shihni me Duolingo, njerezit e mesojne kontentin real.
13:43
As opposed to learning with made-up sentences,
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Jo sikurse te mesuarit me fjali te bera gati,
13:45
people are learning with real content, which is inherently interesting.
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njerezit jane duke mesuar me kontentin reale, i cili eshte me te vertet interesante.
13:48
So people really do learn a language.
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Pra njerezit me te vertet mesojnje nje gjuhe.
13:50
But perhaps more surprisingly,
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Miporo ndoshta me surprizuese,
13:52
the translations that we get from people using the site,
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perkthimi qe marrim nga njerezit duke perkthyer web-in
13:55
even though they're just beginners,
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madje edhe pse jan vetem fillestar,
13:57
the translations that we get are as accurate as those of professional language translators,
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perkthimi qe ne marrim eshte aq i saket sikruse ai i marr nga ndonje perkthyer profesionale.
14:00
which is very surprising.
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i cili eshte shum suprizues.
14:02
So let me show you one example.
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pra me lejoni te marr nje shembull.
14:04
This is a sentence that was translated from German into English.
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Kjo eshte nje fjali e cila eshte perkthyer nga Gjermanishtja ne Anglisht.
14:06
The top is the German.
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Fillimi eshte Gjermanisht
14:08
The middle is an English translation
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Mesi eshte perkthim ne Anglisht
14:10
that was done by somebody who was a professional English translator
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kjo eshte kryer nga dikush qe ishte nje perkthyer profesional Englisht
14:12
who we paid 20 cents a word for this translation.
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te cilin e paguam 20 cent per nje fjal per perkthim.
14:14
And the bottom is a translation by users of Duolingo,
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dhe ne fund eshte nje perkthim nga nje shfrytzues i Duolingo,
14:17
none of whom knew any German
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qe nuk dinte asnje copez te Gjermanishtes
14:19
before they started using the site.
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para se te fillonin te perdorin sajtin.
14:21
You can see, it's pretty much perfect.
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Mund ta shihni eshe shum perfekt.
14:23
Now of course, we play a trick here
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Tani sirusiht,ne bejme nje trik ketu
14:25
to make the translations as good as professional language translators.
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per te bere perkthimin aq te mri sa nje perkthyer profesional mund te beje.
14:27
We combine the translations of multiple beginners
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Ne i kombinojme disa perkthyes fillestare
14:30
to get the quality of a single professional translator.
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per te fituar kualitetin e nje profesionalisti te vetem.
14:33
Now even though we're combining the translations,
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Tani edhe pese ne jemi duke i kombinuar keto perkthime,
14:38
the site actually can translate pretty fast.
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sajti mundet te perkthej shum shpejt.
14:40
So let me show you,
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Ja t'ju tregoj,
14:42
this is our estimates of how fast we could translate Wikipedia
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kjo eshte perllogaritje se sa shpejt mundemi ta perkthejme Wikipedia-në
14:44
from English into Spanish.
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Nga Anglishtja ne Spanjollisht.
14:46
Remember, this is 50 million dollars-worth of value.
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Kujdoni, ky eshte 50 milion dollar vler
14:49
So if we wanted to translate Wikipedia into Spanish,
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Pra nese deshirojme ta perkthejme Wikipedian ne Spanjollisht
14:51
we could do it in five weeks with 100,000 active users.
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mundemi ta bejme kete ne pese jave me 100,000 shfrytzues aktive
14:54
And we could do it in about 80 hours with a million active users.
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dhe mundemi ta bejme kete me rreth 80 ore me 1 milion shfrytzues aktive
14:57
Since all the projects that my group has worked on so far have gotten millions of users,
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Meqe te gjitha projektet qe grupi ime ka punuar deri me tani kan marrur mbi 100 milion shfrytzues
15:00
we're hopeful that we'll be able to translate
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jemi te kenaqur qe do te jemi ne gjendje ta perkthejme
15:02
extremely fast with this project.
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ekstremisht shpejt me kete projekt.
15:04
Now the thing that I'm most excited about with Duolingo
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Tani gjeja qe une jam i gezuar rreth Duolingo
15:07
is I think this provides a fair business model for language education.
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eshte se mendoj qe ne jep nje model te biznesit te drejt per edukimin gjuhesore.
15:10
So here's the thing:
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Ja ku eshte gjeja:
15:12
The current business model for language education
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Modeli i bizesit per edukimin gjuhesore
15:14
is the student pays,
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eshte studenti paguane,
15:16
and in particular, the student pays Rosetta Stone 500 dollars.
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dhe ne pergjigjethsi studentet kan paguar Rosetta Stone 500 dollar.
15:18
(Laughter)
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(Qeshen)
15:20
That's the current business model.
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Ky eshte modeli i tanishum i binesit !
15:22
The problem with this business model
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Problemi me modelin e biznesit eshte
15:24
is that 95 percent of the world's population doesn't have 500 dollars.
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se 95 perqind e popullsis botrore nuk ka 500 dollar.
15:27
So it's extremely unfair towards the poor.
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Pra nuk eshte e drejt per njerezit e varfer
15:30
This is totally biased towards the rich.
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Totalisht e drejt per te pasurit.
15:32
Now see, in Duolingo,
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Tashi me Duolingo,
15:34
because while you learn
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gjersa ju jeni duke mesuar
15:36
you're actually creating value, you're translating stuff --
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ju faktikisht jeni duke krijuar vlera, ju jeni duke perkthyer gjera --
15:39
which for example, we could charge somebody for translations.
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te cilat pershembull, ne mundemi ta ngrakojme dike per te perkthyer.
15:42
So this is how we could monetize this.
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Pra kjo eshte menyra se si mund ta bejeme te gjith kete.
15:44
Since people are creating value while they're learning,
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Meqe njerezit jane duke krijuar vlera dhe gjithashtu duke mesuar,
15:46
they don't have to pay their money, they pay with their time.
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Ata nuk duhet te paguajne me parat e tyre, ata paguajne me kohen e tyre.
15:49
But the magical thing here is that they're paying with their time,
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Mirpo gjeja magjike ketu eshte sepse ata po paguajne me kohen e tyre,
15:52
but that is time that would have had to have been spent anyways
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mirpo kjo eshte koha te cilen ata eshte dashur qe ta hargjojne sidoqoft
15:54
learning the language.
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duke mesuar gjuhen.
15:56
So the nice thing about Duolingo is I think it provides a fair business model --
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pra gjeja me e mire rreth Duolingo-it eshte sepse une mendoj se eshte nje model i biznesit te drejt --
15:59
one that doesn't discriminate against poor people.
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nje i cili nuk eshte diskriminues kunder njerezve te varfer.
16:01
So here's the site. Thank you.
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Pra ketu eshte sajti. Faliminderit
16:03
(Applause)
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(Duartrokitje)
16:11
So here's the site.
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Ketu eshte sajti.
16:13
We haven't yet launched,
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Akoma nuk e kemi lansuar,
16:15
but if you go there, you can sign up to be part of our private beta,
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mirpo nese shkon atje, mundesh te kyqesh te jesh pjes private e versioni te pa leshuar per publikun ( beta)
16:18
which is probably going to start in about three or four weeks.
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i cili do te filloj se shpejti besoj ne 3 apo kater jave.
16:20
We haven't yet launched this Duolingo.
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Akoma nuk e kemi lancuar kete Duolingo.
16:22
By the way, I'm the one talking here,
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Me qe ra fjala.Une jam duke folur ketu,
16:24
but actually Duolingo is the work of a really awesome team, some of whom are here.
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faktikisht Duolingo eshte nje pune e mrekullueshme e nje ekimi shum te mir, ku pjes e saj jane edhe ketu.
16:27
So thank you.
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Pra faliminderit shume.
16:29
(Applause)
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(Duartrokitje)
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