Duolingo — the next chapter in human computation | Luis von Ahn | TEDxCMU 2011

Reviewer: Anny Chung Ok, so I want to start
by asking a question: How many of you have had
to fill out some sort of web form or even has to read a distorted
sequence of characters like this? How many of you found it
really annoying? Ok, I understand. I invented that. (Laughter) Well, I was one of the people who did it. That thing is called a captcha. And the reason it’s there
is to make sure that you, the entity filling out the form,
are actually a human, and not a computer program written to submit
the form millions of times. The reason it works
is because humans, at least, non-visually-impaired humans, have no trouble reading
these distorted characters, whereas computer programs
simply can’t do it as well yet. For example,
in the case of Ticket Master the reason you have to type
these distorted characters is to prevent scalpers to write
a program that can buy millions of ticket at a time. Captchas are used all over the Internet and they are used so often
that a lot of times the precise sequence of random characters
that are shown to the user is not so fortunate. This is an example from
the Yahoo registration page. [W A I T] Random characters
that happen to be shown to the user were W, A, I, T,
which of course spell a word. But the best part is the message that the Yahoo Help got
about 20 minutes later. [“Help! I’ve been waiting
for 20 minutes, and nothing happens!”] (Laughter) This of course is not as bad
as this poor person who… [R E S T A R T] (Laughter) Ok, now, this Captcha project
was something that we did here at Carnegie Mellon about ten years ago
and is used everywhere. Let me now tell you about a project
that we did a few years later, which is sort of the next evolution
of captchas. This is the project
that we called Recaptcha, which is something that we started
here at Carnegie Melon, then we turned it into a startup company, and then about a year and a half ago Google actually acquired this company. So, let me tell you
[how] this project started. So this project started
from the following realization: it turns out that approximately
200 million captchas are typed every day
by people around the world. When I first heard this
I was quite proud of myself I thought, “Look at the impact
that my research has had.” But then I started feeling bad, see here is the thing:
each time you type a captcha, essentially, you waste
ten seconds of your time. Because it takes
ten seconds to type a captcha — and if you multiply that by 200 million
you get that humanity as a whole is wasting about 500
thousand hours every day typing these annoying captchas.
(Laughter) So then I started feeling bad, (Laughter) and I started thinking,
“Well, of course we can’t just get rid of captchas,
because the security of the Web sort of depends on them,
but then I started thinking, “Is there any way we can use this effort for something that is good for humanity?” Here is the thing,
while you’re typing a captcha, during those ten seconds, your brain is doing something amazing. It is doing something
that computers cannot yet do. So, can we get you to do useful work
for those ten seconds? Another way of putting it is: Is there some humongous problem
that we cannot get yet computers to solve, that somehow we can split
into tiny ten second chunks such that each time
somebody solves a captcha they solve a little bit of this problem? The answer is yes,
and this is what we are doing now. What you may not know
is that nowadays, while you’re typing a captcha not only are you
authenticating yourself as a human but in addition you are actually
helping us to digitize books. Let me explain how it works. A lot of projects
are trying to digitize books. Google has one,
the Internet Archive has one, Amazon, with the Kindle,
is trying to digitize books. The way is works is, you start
with an old book, a physical thing, you’ve seen those things right?
Like a… book… (Laughter) So, you start with a book,
and then you scan it. Scanning a book is like
taking a digital photograph of every page of the book. It gives you an image
for every page of the book. This is an image with text
for every page of the book. The next step in the process
is that the computer needs to be able to decipher all of the words
in this image. That is done using a tecnology called OCR, Optical Character Recognition, which takes a picture of text and tries to figure out
what text is in there. Now, the problem is
that OCR is not perfect, especially for older books,
where the ink has faded, and the pages have turned yellow, OCR cannot recognize a lot of the words. For things that were written
more than fifty years ago, the computer cannot recognize about
thirty percent of the words. We are now taking all of the words
that the computer cannot recognize and we are getting people
to read them for us, while they are typing
a captcha on the Internet. So next time you type a captcha, these words that you’re typing
are actually words that are coming from
books that are being digitized that the computer could not recognize. The reason we have two words nowadays
instead of one, is because one of the words is a word that the system
just got out of a book, it didn’t know what it was and it’s going to present it to you, but since it doesn’t know
the answer for it, it cannot grade it for you, so we give you another word, one for which the system
does know the answer, we don’t tell you which one is which,
and we say, please type both. If you type the correct word for the one which the system
already knows, it assumes you are a human, and it also gets some confidence
that you typed the other word correctly. And if we repeat this process
to ten different people, and all of them
agree on what the new word is, then we have one more word
digitized accurately. This is how the system works, and since we released it
about 3 or 4 years ago, a lot of websites started switching from the old captcha,
where people wasted their time, to the new captcha, where people
are helping to digitize books. So every time you
buy tickets on Ticket Master, you help to digitize a book. Facebook, every time you add a friend, or poke somebody,
you help digitize a book. Twitter, and about
350 thousand other sites are all using recaptcha.
And in fact, the number of sites that are using recaptcha is so high that the number of words
that we’re digitizing per day is really large.
It’s about 100 million a day which is the equivalent of about 2.5 million books a year. And this is all being done
one word at a time by just people typing captchas
on the Internet. (Applause) Of course, since we are doing
so many words per day funny things can happen,
and this is especially true because now we’re giving people two randomly-chosen
English words next to each other. So funny things can happen,
for example, we presented this word,
it’s the word “christians”, there’s nothing wrong with it, but if you present it along
with another randomly chosen word, bad things can happen: [“bad christians”]
(Laughter) But it’s even worse
because the particular website where we showed this
actually happened to be called The Embassy of the Kingdom of God. (Laughter) Here is another really bad one: [“damn liberal”] (Laughter) So we keep on insulting people
left and right every day. Of course we are not just
insulting people, see, here is the thing,
since we’re presenting two randomly chosen words, just interesting things can happen. So this actually has given rise to a really big Internet meme that tens of thousands of people
have participated in, which is called Captcha Art. I’m sure some of you have heard about it, here’s how it works:
imagine you’re using the Internet and you see a captcha that you think is somewhat peculiar. Like this captcha:
[“invisible toaster”] Then you take a screenshot of it, — of course you fill out the captcha because it helps us digitize a book — but then, first you take a screenshot and then you draw something
that is related to it. (Laughter) That’s how it works. There are tens of thousands of these, some of them are very cute, [clenched it] some of them are funnier, [stoned founders] (Laughter) and some of them,
like “paleontological shvisle”, they contain Snoop Dog. (Laughter) Ok, so this is my
favorite number of recaptchas this is the favorite thing that I like
about this whole project. This is the number
of distinct people that have helped us digitize
at least one word out of a book through recaptcha:
750 million, which is a little over than ten percent of the world’s population has helped us digitize human knowledge. And it is numbers like these that motivate
my research agenda. The question that motivates
my research is the following: if you look at humanity’s
large scale achievements these really big things that humanity has gotten together and done historically, like for example,
building the pyramids of Egypt, or the Panama canal,
or putting a man on the moon, there’s a curious fact about them. And it is that they were all done with about the same number of people. It’s weird. They were all done with about
100 thousand people And the reason for that is because before the Internet, coordinating more than 100 thousand people let alone paying them,
was essentially impossible. But see now with the Internet, I’ve just shown you a project
where we’ve gotten 750 million people
to help us digitize human knowledge. So the question that
motivates my research is if we can put a man on the moon
with 100 thousand what can we do with 100 million? So based on this question
we’ve had a lot of different projects
that we’ve been working on, let me tell you about one
that I’m most excited about this is something that we’ve been
sort of semi-quietly working on for the last year and a half or so. It hasn’t yet been launched,
it’s called Duolingo, since it hasn’t been launched, shhh! (Laughter) Ah, yeah, I can trust you…. Ok, so here is how it started: it started with me posing a question
to my graduate student, Severin Hacker
— this clicker is not working so well — ok that’s Severin Hacker, so I posed a question
to my graduate student by the way, you did hear me correctly, his last name is Hacker, so I posed this question to him: how can we get 100 million people translating the Web
into every major language for free? There’s a lot of things to say
about this question. First of all, translating the Web. So, right now the Web is partitioned
into multiple languages, a large fraction of it is in English, if you don’t know any English
you can’t access it, but there’s large fractions
in other different languages, and if you don’t know those languages
you can’t access it. So I would like to translate
all of the web or at least, most of it
into every major language. So that’s what I would like to do. Now, some of you may say, why can’t we use
computers to translate, why can’t we use machine translation, which nowadays is starting to translate
some sentences here and there, why can’t we use it
to translate the whole Web? Well, the problem is,
it’s not yet good enough, and it probably won’t be
for the next 15 to 20 years. It makes a lot of mistakes,
and even when it doesn’t since it makes so many mistakes
you don’t know whether to trust it or not. Let me show you an example of something that was
translated with a machine, it was actually a forum post with someone who was trying to ask
a question about Javascript. It was translated
from Japanese into English. Let’s see if this works. I’ll just let you read,
this person starts apologizing for the fact that it’s translated
with the computer. [This is question, English is faulty.
Thank computer to translate. Sorry!] The next sentence is going to be
the preamble to the question he’s just explaining something, remember, it’s a question
about Javascript: [At often, the goat-time
install a error is vomit.] (Laughter) Then comes the first part of the question: [How many times like the wind,
a pole, and the dragon?] (Laughter) Then comes my favorite
part of the question: [This insult to father’s stones?] (Laughter) And then comes the ending, which is my favorite part
of the whole thing: [Please apologize for your stupidity.
There are a many thank you.] (Laughter) Ok, so computer translation,
not yet good enough. Ok? So back to the question,
we need people to translate the whole Web. Now the next question you may have is “Why can’t we just
pay people to do this, we could pay professional
language translators to translate the whole Web”. We could do that. Unfortunately,
it’d be extremely expensive. For example,
translating a tiny tiny fraction of the whole Web, Wikipedia, into one other language, Spanish. You know, Wikipedia exists in Spanish, but it is very small
compared to the size of English, it’s about 20% the size of English. If you want to translate
the other 80% into Spanish it would cost at least 50 million dollars. This is even at the most exploited
outsourcing country out there. (Laughter) So, it’d very expensive.
What we want to do is to get 100 million people
translating the Web into every major language for free. Now, if this is what you want to do you pretty quickly realize
you’re going to run into two pretty big hurdles, two big obstacles. The first one is a lack of bilinguals. I don’t even know if there exists
100 million people out there using the Web who are bilingual enough
to help us translate. That’s a big problem. The other problem
that you’re going to run into is the lack of motivation. How are we going to motivate people to actually translate the Web for free? Normally, you have to pay people
to do this, so how are you going to motivate them
to do it for free? Now, when we were
starting to think about this we were blocked by these two things but then we realized
that there’s actually a way to solve both these problems
with the same solution. There’s a way to kill two birds
with one stone. And that is to transform
language translation into something that millions
of people want to do and that also helps with the problem of a lack of bilinguals. And that is language education. So, it turns out that, today,
there are over 1.2 billion people learning a foreign language. People really want to learn
a foreign language, and it’s not just because they’re being
forced to do so in school. For example, in the United States alone there are 5 million people
who have paid over 500 dollars for software to learn a new language. So people really want
to learn a new language. What we’ve been working on
for the last year and half is a new website. It’s called Duolingo. Where the basic idea is as people use Duolingo,
people learn a new language for free while simultaneously translating the Web. Basically they are learning by doing. The way this works is whenever you’re just a beginner
we give you very simple sentences, there’s of course a lot of
very simple sentences on the Web. We give you very simple sentences along with what each word means. And as you translate them, and as you see how
other people translate them, you start learning the language,
as you get more advanced, we give you more
complex sentences to translate. But at all times,
you are learning by doing. The crazy thing about this method is that it actually really works. First of all, people are
really learning a language we’re mostly done building,
and we are now testing it people really can learn
a language with it. And they learn it about as well as
the leading language learning software. So people really do learn a language. And not only do they learn it as well,
but actually it’s way more interesting. Because, you see, with Duolingo people are actually learning
with real content. As opposed to learning
with made-up sentences people are learning with real content, which is inherently interesting. So people really do learn a language, but perhaps, more surprisingly, the translations that we get from people using the site,
even though they’re just beginners, the translations that we get
are as accurate as those of professional
language translators, which is very surprising. Let me show you one example,
this is a sentence that was translated
from German into English the top is the German, the middle is an English translation that was done by somebody who was
a professional language translator, who we’ve paid 20 cents a word
for this translation, and the bottom is a translation
by users of Duolingo, none of whom knew any German before they started using the site. As you can see, it’s pretty much perfect. Now, of course we played a trick here to make the translations as good as professional language translators. We combined the translations
of multiple beginners to get the quality
of a single professional translator. Even though we’re
combining the translations the site actually
can translate pretty fast. Let me show you, these are estimates of how fast we could translate Wikipedia from English into Spanish. Remember, this is
50 million dollars worth of value. If we wanted to
translate Wikipedia into Spanish we can do it in five weeks
with 100 thousand active users, and we can do it in about 80 hours
with 1 million active users. Since all of the projects
my group has worked on so far have gotten millions of users, we’re hopeful that we would be able to translate extremely fast
with this project. Now, the thing that
I am most excited about Duolingo is that I think this provides
a fair business model for language education. So here’s the thing: the current business model
for language education is: the student pays. And, particularly, the student
pays Rosetta Stone 500 dollars. That’s the current business model. The problem with this business model is that 95% of the world’s population doesn’t have 500 dollars. So it’s extremely unfair towards the poor, this is totally biased towards the rich. In Duolingo, because while you learn you’re actually creating value, you are translating stuff
which, for example, we could charge somebody for translation — this is how we could monetize this. Since people are
creating value while learning, they don’t have to pay with their money,
they pay with their time. But the magical thing here is that
they’re paying with their time but that is time that would
have to be spent anyway, learning the language. The nice thing about Duolingo is I think it provides a fair business model, one that doesn’t discriminate
against the poor people. So, here is the site:
[] (Applause) Thank you. (Applause) Here’s the site.
We haven’t yet launched but if you go there you can sign up
to be part of our private beta which we’re probably going to start
in about three or four weeks. By the way, I’m the one talking here, but actually Duolingo is the work
of a really awesome team some of whom are here. So, thank you. (Applause)

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