March 31, 2022 • 19 mins
Luis von Ahn is the founder and CEO of the language app DuoLingo. His problem: How do you teach people to speak a language -- really speak it -- using only an iPhone app?
On the surface, DuoLingo looks warm and fuzzy. Underneath the hood, it's a serious tech company built on artificial intelligence. But the best machine learning in the world still isn't good enough to really teach people how to fluently speak in a new language. Luis is trying to change that.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Pushkin. Artificial intelligence is this weird, big phrase that suddenly
seems to be everywhere, and it can be hard to
know exactly what it means. But when businesses say they're
using artificial intelligence, they usually mean one particular thing. They
mean automated systems that can take in lots and lots
(00:37):
of data and use the data to make predictions. This
is called machine learning, and it's spreading everywhere. Drug companies
use it to predict which molecules are likely to work
as medicines. Hedge funds use it to predict which stocks
are going to go up or down. Instagram uses it
to predict which adds I'm most likely to click on
For the record, the machine has learned that I will
(00:58):
often click on ads for overpriced workout clothes. Anyway, if
you want to understand what's happening with business and technology today,
you really have to understand machine learning. I'm Jacob Goldstein.
This is What's Your Problem, the show where entrepreneurs and
engineers talk about how they're going to change the world
(01:18):
once they've solved a few problems. My guest today is
Luis Vonon, the founder and CEO of Duolingo. Duolingo is
both a wildly popular language app and also a hardcore
tech company built on machine learning. Luis used to be
a professor of computer science at Carnegie Mellon, and in
(01:39):
our conversation he was really candid about the technical limits
of what Duolingo can do today. The app is good
at teaching people to read and to understand, he said,
but Duolingo is not as good at teaching people to
speak a new language. And solving that problem turns out
to be part of this great, big, interesting frontier problem
(01:59):
that is relevant not just for Duolingo, but for the
whole field of artificial intelligence. We started out talking about
the origins of Duolingo, which go back to a different problem,
one that Louis discovered before he'd ever heard of machine learning.
It was a problem he saw all around him when
he was growing up in Guatemala. I was fortunate that
(02:19):
my mother basically spent essentially her entire net worth on
my education, and so I was fortunately that I got
a good education. But then I could see the people
who would get public education barely learn how to read
and write. This is just what would happen, And you
cannot expect that these people are going to become, you know,
the CEO of a public company or anything like that,
because they kind of wont. Often people talk about education
(02:43):
as an engine for reducing inequality, but what you're describing
is the exact opposite, and it's education, when you have
to pay for it, is a mechanism for perpetuating inequality.
And I really believe that, and I believe that's true
in most countries in the world. There may be some countries,
you know, like the Scandinavian countries, where pretty much everybody
gets the same education everything, right, Yeah, there may be
(03:03):
some countries like that, But in the vast majority of countries,
if you have money, you can get a much better education.
So I wanted to do something that would give equal
access to education to everybody, and so we started with that.
But then we started thinking, Okay, if education is pretty general,
let's start by teaching one thing. Eventually we settled on
teaching languages for a number of reasons, the biggest one
(03:26):
of which is that learning English in particular can completely
change people's lives. If you know English, you can double
your incompotential in most countries. Wow, it's just as simple
as that. And so it's it's why why is that.
Basically it opens up for almost any job. You can
get a better version of that job. For example, you
could be a waiter, or you could be a waiter
(03:48):
at the five star hotel. You could be an executive assistant,
or you could be an executive assistant for a multinational
ceoyeah yeah, okay, So really teaching English the core, the
core sort of reducing inequality dream of a language is
really teaching English to people in largely in poorer countries. Yes,
(04:10):
so what we wanted to do was teach teach English.
But you know, if you're going to teach English, we
may as well teach other languages, So teach those and
do so for free. So that's what Luis did and
it worked. Today, tens of millions of people use Dual
Lingo every month to learn English and dozens of other
languages for free. The company makes money by selling ads
(04:31):
and premium subscriptions. It went public in twenty twenty one
and is currently worth billions of dollars. And the company
really is built on machine learning. Luis gave me a
few key examples of the way the company uses the technology.
So let me tell you a few of the things
that we do. One of the things that we do
we like very much is we have data on whenever
(04:53):
people use dual Lingo. We record every exercise that they
do and whether they got it right or wrong, and
if they got it wrong, why they got it wrong.
With all of this data, we're able to do certain
things with artificial intelligence. For examples, for every exercise that
we're about to give you able to predict what is
the probability that you're going to get this exercise right
or wrong. So, in a sense, that is a thing
(05:14):
that a teacher in a classroom could do fairly easily,
right a teacher with twenty students, But you're able to
do it with whatever how many people use your app
actively forty two million per month, so the machine can
do that for all forty two million people at the
same time. More or less, yes, and very accurately. Part
(05:36):
of the secret source of Duelingo is that we realized
if we were to only give you things that you're
not very good at, we'd basically be giving you lessons
from hell every time. So we can't do that because
that frustrates users. So what we do is, whenever you
start a listening and doing we're actually trying to optimize
for two things at the same time. We're trying to
teach you things you know that you don't not very
(05:56):
good at, but also we're trying to keep you motivated
and engaged. Yea. And the way we do that is
we try to give you exercises for which we know
you have about an eighty percent chance of getting them right. Huh.
And have you found that to be the sweet spot?
I mean, have you done like experiments and sort of
turn the dial. We've done that, and we're you know,
we're not the first to figure this out. I mean,
(06:17):
there's a lot of literature and psychology, etc. Just and
the number is not exactly eighty percent. It's a little
higher than that. It's like eighty three percent or something.
But there's there's a number, and it really is the
case that if that number is higher, that means these
things are a little easier for you. Then you get
a little bored. You feel like you're not learning right.
If I'm getting ninety five percent right, I'm like, what,
I'm just wasting my time? And you feel bored because
it's like it's like a game that you always win.
(06:38):
I mean, that's that's nice. At the very beginning, but
then you're just not going to play it, and then
if it's lower than that, that means that things are
too hard for you. You get very frustrated and you
go away. And there's a lot of tricks that you know,
certainly app developers play, and you know we play as well.
So I'll tell you another kind of similar trick. You know,
we end up applying it to language. But the easiest
way to understand this trick is with a slot machine.
(07:02):
When you get two out of three, it's you almost
got it, you gotta do one more, you gotta do
one more. You just gotta do one more because you
got it. So there's this, there's this you're so close
to psychological trick that we played. It's like, oh, there's
two out of three, almost got it, But you knew
I was going to get two out of three. Yes, sure,
you gave me two easy ones and when there was
super hard that's exactly right. So so we we played
(07:22):
this type of trick where just people are like, almost
got it, and that gets them to do another one.
So you know, in our case, we just we basically
spend a lot of time training computers to figure out
what it is that makes people use Duolingo for longer,
and also that we teach them more so that that's
a major use for artificial intelligence. Main use is just
in teaching better. After the break a big problem, Louise
(07:47):
and dual Lingo are still trying to solve a problem
that turns out to be a big frontier problem for
all of artificial intelligence. That's the end of the ads.
Now we're going back to the show. So let's talk
now about problems you haven't solved yet. You know, like,
(08:08):
what are you what are you trying to figure out?
What are you working on that that isn't quite working yet.
So dual linguals is very effective at teaching you all
kinds of things. But if you go look under the
hood or you know, what is it that you're learning.
You're learning reading really well. You're learning writing pretty well,
but not as well as reading. You're learning listening pretty well,
but you're not learning spontaneous speaking very well. This, by
(08:31):
the way, is also you're not something you're not learning
very well in university semesters, Like you're basically not learning
that well either in due lingo or in university semesters. Okay,
it's just harder to teach in a sort of classroom.
What you need to do to teach that is basically,
have you really interact with wealth? For now another human
and just you just practice that a lot. Now, here's
(08:55):
the here's the thing about that. I know how to
get you to interact with another human. Just put another
human there. The problem is about eighty percent of our
users just does not want to talk to a stranger
in a language that they're not very good. So the
problem that we're trying to solve here is how do
we practice kind of spontaneous conversation but without having a
(09:18):
human on the other side. And we've been working on that,
and you know we're not there yet. Can I just interrupt?
Because I mean we were talking about artificial intelligence? Right?
The most famous test that I know of, the most
famous idea I know of of artificial intelligence in a
computer is can a computer hold one end of a conversation? Right? Like?
That's the classic touring test is like you're going to
(09:41):
have this like chat conversation and can you tell if
the person on the other end is a person or
a machine? Like? That's the og artificial intelligence idea, right,
I mean, are you telling me, that's what you're trying
to solve. Not quite. I mean, it would be awesome
if we would solve that. I mean, but that's the dream,
right solution to that, that is the dream. But notice
in our case, we don't actually care if the human
(10:03):
can tell that there's a computer on the other side. Okay,
it's okay. As long as it practices thing and as
long as you're able to carry on a conversation in
a way that seems a little natural or something, it's okay.
If if it, you know, goes off the rails every
now and then, So tell me, what is it that
you're trying to build. This is exciting, Like what are
you trying to do? We're starting with text, by the way,
(10:25):
so either just basically a texting conversation. So think of
it as like a chat bot in uh, you know,
in Spanish, where it just you're just having a real
a little conversation. You've had lots of people have had
experience with chat bots. Yes, they're right, Like you go
to whatever, cancel your cable and they want you to text,
and then you realize you're texting with the machine. So
(10:46):
like that's the that's step one. So that's the idea.
That's step one. We of course, I mean a lot
of those experiences with chatbots are are very um, they're
just very geared at whatever it is you're trying to do. So,
for example, that chapel maybe very good at at canceling
your cable, but only that in my experience, they're not
even good at that. No, they're not that great. So
(11:09):
we're trying to do that, and you know, we're not
there yet. I don't think so you're like out on
the frontier. We are. We are like you're trying to, yes,
and we're not there yet. I mean, this is something
that's going to take us, not just us, I mean
the whole academic community and technology just a few more years.
But so let me ask you this. Can we talk
about that in a way that would be like, can
(11:30):
we try and just go one level into sort of
what you're trying to do and like what works and
what doesn't work, and like why it's hard? Yeah, I mean,
you know, the first, by the ways, the first way
you think of if you're trying to make a chap
the first thing you think of is, okay, I'm just
going to program the computer. Forget about artificial intelligence. I'm
just going to program the computer to respond to specific questions,
(11:55):
and how many possible questions could there be? You start thinking, okay, well,
when the person says high, we're going to program a
think to say hi back. When the person says, how
are you doing, We're going to program I think to
say I'm doing pretty well? How about you? Yeah? V
zero of a chatbot. And this, you know, this comes
from you know, fifty years ago. This is what you
start doing. The problem is there's billions of things that
(12:15):
people can say, and so we may have programmed the
thing of what to say, but you know how you're doing,
and we can respond. But if instead of asking that,
they may ask like, hey, did you watch the game
last night? And we just have no idea how to
respond to that. About a decade ago, Louis says Ai,
researchers started trying a really different approach. Rather than trying
(12:36):
to teach computers every rule, they started throwing massive amounts
of documents and texts at computers and essentially telling the
computers figure out the patterns in all these documents. So
when somebody writes something like did you watch the game
last night? The computers should be able to predict what
kinds of answers might follow. This strategy clearly has not
(12:57):
entirely worked yet. That's why it's still a problem solving.
It will take both more text and more clever algorithms
to help computers make sense of that text. But Louis says,
you can see progress every time you open your Gmail
or a Google Doc. And I don't know if you've used,
for example, you use Google Docs lately or Gmail like
it finishes off your sentences now. And basically the way
(13:20):
this works is, you know, this system has looked at
a ton ton of text that has been written by
a lot of people. In the case of Google Docs,
I actually don't know what they look at, but I
wouldn't be surprised if they look at everything that has
ever been written in Google Docs. I'm going to tell
you one that happened to me in Google Docs today
when I was typing notes for this interview, I typed
zone of pr and then you know what, you know
(13:43):
how it completed it proximal development. Yes, it knew I
was going to write zone of proximal development. Yep. No,
this is amazing, And they just see that if you'd
write the zone of per there's like a ninety five
percent chance that it ends in proximal development. What is
the zone of proximal development? You know? In teaching, you know,
(14:05):
there's this concept of just keeping you at this zone
of proximal development, which is always kind of challenging you,
giving you things that you don't know. But but there
are all things that are fair to give you. Proximal
means like close to or next two. Right. So it's
the idea is like you know a thing, yes, then
like what you want to teach the person is the
very next thing, right, It's like that's right, It's like
the frontier of your knowledge. I like it because it
(14:26):
applies to like the way you teach, but also to
your work, right, And like it's just a nice life idea, right,
It's like the next thing you want, the next thing.
And I feel like the chat bot is maybe a
version of that at the level of your company. Yeah, yeah,
it really is. It really is a nice idea, and
it is I mean, and if you think about it,
this is what a great teacher does. You know. I've
said this inside the company at due Lingo. UM. All
(14:48):
we need to do is first figure out what you know,
by the way, not that easy to figure out what
you know. But let's first figure out what you know
and then just take you to that zone of proximal development.
Because now we know what you know, just take you
to the frontier and then just keep expanding it as
fast as possible. That's all we need to do. Of course,
this is easily said, hard to do. Yeah. Um, And
(15:09):
is there a limit to what you can do with
a computer? Is there anything a teacher can do? Is
that a computer will never be able to do? You know?
Of course I do lingual We love teachers. If they
are a good teacher and also have the time, they
are much more able to adapt to their students than
a computer is. Um. But I don't believe that will
always be the case. I mean, I think at some
(15:30):
point it's not just teachers. I mean teachers, this is
one thing. I mean, at some point my belief and
this is of course just my belief. People, not everybody agrees.
They believe that computers will be able to do every
single thing that humans can. Now you may start asking
really tough questions like can they love? Yeah, I don't know,
I don't know what they can love or not but
from the outside it will look just as if they
(15:51):
love so who knows who knows what's going on inside?
Who knows that they that's like a big yeah, we're
big philosophical questions that I'm not here today, and that's right,
nor am I. But I do think from input output behavior,
I don't see why. I don't see any reason why
computers won't be able to do everything that humans can.
So they can teach, but they can also write a
computer code. They can also run companies, they can also
(16:13):
make podcasts, they can do everything. Should be able to
do that. I think they should be able to do that.
I don't know when that'll happen, but they should be
able to do that. In a minute, the lightning round, well,
hear what job Luise would love to do but thinks
he wouldn't be very good at. And the real reason
treasure Chess keep showing up in duo lingo. And now
(16:40):
back to the show. We're going to finish with a
lightning round, not counting duo Lingo. What's your favorite app
on your phone? Spotify? What have you been listening to
on Spotify? I'm always a huge fan of the band
called Churches with a v to Virchase. So that's what
I was listening to this morning on my work at work.
If you have a ten minute break in the middle
(17:00):
of the day, what do you do to relax? Played
this game called Class Royale. We are a lot of
the gaming mechanics that we use for duel and will
come from gaming companies, like the treasure chests, exactly right,
the treasure chests. If you ever played Class Royale, they
have the treasure chests. If somebody's going to go to
(17:20):
visit Guatemala for the first time, what's one thing they
should definitely do? Oh? Um, Decal is the Mayan ruins. Um.
You know, if I feel very strong, I've been to
southern Mexico where they have chi Chenitsa. It's a joke
compared to the Mayan ruins in Guada. There's there's one
pyramid in Chichenitsa. There are four hundred in Guatemala in Decal,
(17:42):
So yeah, they should like I like that. Not only
are you recommending Tikal, you're also taking as I have
no trouble with Chichenitsa. It's just they are very good
at marketing. Amazing. What would you do if you couldn't
do the job you do. Now, well there's what what
would I actually do? On? What would I'd like to do?
(18:03):
I would love to be a writer. I don't think
i'd be a very good one. Um So if I
if I wasn't doing the job that I'm doing right now,
you know, I'd probably be back to being a professor.
How will you know when it's time to retire? I'm
never retiring, That's what everybody says. Well, maybe I will,
but I mean right now, I don't. I don't want
(18:24):
to do that. Luis Vaughan is the founder and CEO
of Duelingo. Today's show was produced by Edith Russelo. It
was edited by Kate Parkinson Morgan and Robert Smith, and
it was engineered by Amanda kay Wong. Theme music by
Louis Kara. Our development team is Lee, Tom Mulad and
Justine Lang. A huge team of people makes What's Your
(18:45):
Problem possible. That team includes, but is not limited to,
Jacob Weisberg, Mia Lobel, Heather Fain, John Schnars, Kerry Brodie,
Carli mcgleory, Christina Sullivan, Jason Gambrell, Brand Hayes, Eric Sandler,
Maggie Taylor, Morgan Ratner, Nicolemrano, Mary Beth Smith, Royston Deserve,
Maya Kanig, Daniello, Lakhan, Kazia Tan and David Clever. What's
Your Problem is a co production of Pushkin Industries and iHeartMedia.
(19:07):
To find more Pushkin podcast Us, listen on the iHeartRadio app,
Apple Podcasts, or wherever. I'm Jacob Goldstein and I'll be
back next week with another episode of What's Your Problem.
Pushkin. Artificial intelligence is this weird, big phrase that suddenly
seems to be everywhere, and it can be hard to
know exactly what it means. But when businesses say they're
using artificial intelligence, they usually mean one particular thing. They
mean automated systems that can take in lots and lots
(00:37):
of data and use the data to make predictions. This
is called machine learning, and it's spreading everywhere. Drug companies
use it to predict which molecules are likely to work
as medicines. Hedge funds use it to predict which stocks
are going to go up or down. Instagram uses it
to predict which adds I'm most likely to click on
For the record, the machine has learned that I will
(00:58):
often click on ads for overpriced workout clothes. Anyway, if
you want to understand what's happening with business and technology today,
you really have to understand machine learning. I'm Jacob Goldstein.
This is What's Your Problem, the show where entrepreneurs and
engineers talk about how they're going to change the world
(01:18):
once they've solved a few problems. My guest today is
Luis Vonon, the founder and CEO of Duolingo. Duolingo is
both a wildly popular language app and also a hardcore
tech company built on machine learning. Luis used to be
a professor of computer science at Carnegie Mellon, and in
(01:39):
our conversation he was really candid about the technical limits
of what Duolingo can do today. The app is good
at teaching people to read and to understand, he said,
but Duolingo is not as good at teaching people to
speak a new language. And solving that problem turns out
to be part of this great, big, interesting frontier problem
(01:59):
that is relevant not just for Duolingo, but for the
whole field of artificial intelligence. We started out talking about
the origins of Duolingo, which go back to a different problem,
one that Louis discovered before he'd ever heard of machine learning.
It was a problem he saw all around him when
he was growing up in Guatemala. I was fortunate that
(02:19):
my mother basically spent essentially her entire net worth on
my education, and so I was fortunately that I got
a good education. But then I could see the people
who would get public education barely learn how to read
and write. This is just what would happen, And you
cannot expect that these people are going to become, you know,
the CEO of a public company or anything like that,
because they kind of wont. Often people talk about education
(02:43):
as an engine for reducing inequality, but what you're describing
is the exact opposite, and it's education, when you have
to pay for it, is a mechanism for perpetuating inequality.
And I really believe that, and I believe that's true
in most countries in the world. There may be some countries,
you know, like the Scandinavian countries, where pretty much everybody
gets the same education everything, right, Yeah, there may be
(03:03):
some countries like that, But in the vast majority of countries,
if you have money, you can get a much better education.
So I wanted to do something that would give equal
access to education to everybody, and so we started with that.
But then we started thinking, Okay, if education is pretty general,
let's start by teaching one thing. Eventually we settled on
teaching languages for a number of reasons, the biggest one
(03:26):
of which is that learning English in particular can completely
change people's lives. If you know English, you can double
your incompotential in most countries. Wow, it's just as simple
as that. And so it's it's why why is that.
Basically it opens up for almost any job. You can
get a better version of that job. For example, you
could be a waiter, or you could be a waiter
(03:48):
at the five star hotel. You could be an executive assistant,
or you could be an executive assistant for a multinational
ceoyeah yeah, okay, So really teaching English the core, the
core sort of reducing inequality dream of a language is
really teaching English to people in largely in poorer countries. Yes,
(04:10):
so what we wanted to do was teach teach English.
But you know, if you're going to teach English, we
may as well teach other languages, So teach those and
do so for free. So that's what Luis did and
it worked. Today, tens of millions of people use Dual
Lingo every month to learn English and dozens of other
languages for free. The company makes money by selling ads
(04:31):
and premium subscriptions. It went public in twenty twenty one
and is currently worth billions of dollars. And the company
really is built on machine learning. Luis gave me a
few key examples of the way the company uses the technology.
So let me tell you a few of the things
that we do. One of the things that we do
we like very much is we have data on whenever
(04:53):
people use dual Lingo. We record every exercise that they
do and whether they got it right or wrong, and
if they got it wrong, why they got it wrong.
With all of this data, we're able to do certain
things with artificial intelligence. For examples, for every exercise that
we're about to give you able to predict what is
the probability that you're going to get this exercise right
or wrong. So, in a sense, that is a thing
(05:14):
that a teacher in a classroom could do fairly easily,
right a teacher with twenty students, But you're able to
do it with whatever how many people use your app
actively forty two million per month, so the machine can
do that for all forty two million people at the
same time. More or less, yes, and very accurately. Part
(05:36):
of the secret source of Duelingo is that we realized
if we were to only give you things that you're
not very good at, we'd basically be giving you lessons
from hell every time. So we can't do that because
that frustrates users. So what we do is, whenever you
start a listening and doing we're actually trying to optimize
for two things at the same time. We're trying to
teach you things you know that you don't not very
(05:56):
good at, but also we're trying to keep you motivated
and engaged. Yea. And the way we do that is
we try to give you exercises for which we know
you have about an eighty percent chance of getting them right. Huh.
And have you found that to be the sweet spot?
I mean, have you done like experiments and sort of
turn the dial. We've done that, and we're you know,
we're not the first to figure this out. I mean,
(06:17):
there's a lot of literature and psychology, etc. Just and
the number is not exactly eighty percent. It's a little
higher than that. It's like eighty three percent or something.
But there's there's a number, and it really is the
case that if that number is higher, that means these
things are a little easier for you. Then you get
a little bored. You feel like you're not learning right.
If I'm getting ninety five percent right, I'm like, what,
I'm just wasting my time? And you feel bored because
it's like it's like a game that you always win.
(06:38):
I mean, that's that's nice. At the very beginning, but
then you're just not going to play it, and then
if it's lower than that, that means that things are
too hard for you. You get very frustrated and you
go away. And there's a lot of tricks that you know,
certainly app developers play, and you know we play as well.
So I'll tell you another kind of similar trick. You know,
we end up applying it to language. But the easiest
way to understand this trick is with a slot machine.
(07:02):
When you get two out of three, it's you almost
got it, you gotta do one more, you gotta do
one more. You just gotta do one more because you
got it. So there's this, there's this you're so close
to psychological trick that we played. It's like, oh, there's
two out of three, almost got it, But you knew
I was going to get two out of three. Yes, sure,
you gave me two easy ones and when there was
super hard that's exactly right. So so we we played
(07:22):
this type of trick where just people are like, almost
got it, and that gets them to do another one.
So you know, in our case, we just we basically
spend a lot of time training computers to figure out
what it is that makes people use Duolingo for longer,
and also that we teach them more so that that's
a major use for artificial intelligence. Main use is just
in teaching better. After the break a big problem, Louise
(07:47):
and dual Lingo are still trying to solve a problem
that turns out to be a big frontier problem for
all of artificial intelligence. That's the end of the ads.
Now we're going back to the show. So let's talk
now about problems you haven't solved yet. You know, like,
(08:08):
what are you what are you trying to figure out?
What are you working on that that isn't quite working yet.
So dual linguals is very effective at teaching you all
kinds of things. But if you go look under the
hood or you know, what is it that you're learning.
You're learning reading really well. You're learning writing pretty well,
but not as well as reading. You're learning listening pretty well,
but you're not learning spontaneous speaking very well. This, by
(08:31):
the way, is also you're not something you're not learning
very well in university semesters, Like you're basically not learning
that well either in due lingo or in university semesters. Okay,
it's just harder to teach in a sort of classroom.
What you need to do to teach that is basically,
have you really interact with wealth? For now another human
and just you just practice that a lot. Now, here's
(08:55):
the here's the thing about that. I know how to
get you to interact with another human. Just put another
human there. The problem is about eighty percent of our
users just does not want to talk to a stranger
in a language that they're not very good. So the
problem that we're trying to solve here is how do
we practice kind of spontaneous conversation but without having a
(09:18):
human on the other side. And we've been working on that,
and you know we're not there yet. Can I just interrupt?
Because I mean we were talking about artificial intelligence? Right?
The most famous test that I know of, the most
famous idea I know of of artificial intelligence in a
computer is can a computer hold one end of a conversation? Right? Like?
That's the classic touring test is like you're going to
(09:41):
have this like chat conversation and can you tell if
the person on the other end is a person or
a machine? Like? That's the og artificial intelligence idea, right,
I mean, are you telling me, that's what you're trying
to solve. Not quite. I mean, it would be awesome
if we would solve that. I mean, but that's the dream,
right solution to that, that is the dream. But notice
in our case, we don't actually care if the human
(10:03):
can tell that there's a computer on the other side. Okay,
it's okay. As long as it practices thing and as
long as you're able to carry on a conversation in
a way that seems a little natural or something, it's okay.
If if it, you know, goes off the rails every
now and then, So tell me, what is it that
you're trying to build. This is exciting, Like what are
you trying to do? We're starting with text, by the way,
(10:25):
so either just basically a texting conversation. So think of
it as like a chat bot in uh, you know,
in Spanish, where it just you're just having a real
a little conversation. You've had lots of people have had
experience with chat bots. Yes, they're right, Like you go
to whatever, cancel your cable and they want you to text,
and then you realize you're texting with the machine. So
(10:46):
like that's the that's step one. So that's the idea.
That's step one. We of course, I mean a lot
of those experiences with chatbots are are very um, they're
just very geared at whatever it is you're trying to do. So,
for example, that chapel maybe very good at at canceling
your cable, but only that in my experience, they're not
even good at that. No, they're not that great. So
(11:09):
we're trying to do that, and you know, we're not
there yet. I don't think so you're like out on
the frontier. We are. We are like you're trying to, yes,
and we're not there yet. I mean, this is something
that's going to take us, not just us, I mean
the whole academic community and technology just a few more years.
But so let me ask you this. Can we talk
about that in a way that would be like, can
(11:30):
we try and just go one level into sort of
what you're trying to do and like what works and
what doesn't work, and like why it's hard? Yeah, I mean,
you know, the first, by the ways, the first way
you think of if you're trying to make a chap
the first thing you think of is, okay, I'm just
going to program the computer. Forget about artificial intelligence. I'm
just going to program the computer to respond to specific questions,
(11:55):
and how many possible questions could there be? You start thinking, okay, well,
when the person says high, we're going to program a
think to say hi back. When the person says, how
are you doing, We're going to program I think to
say I'm doing pretty well? How about you? Yeah? V
zero of a chatbot. And this, you know, this comes
from you know, fifty years ago. This is what you
start doing. The problem is there's billions of things that
(12:15):
people can say, and so we may have programmed the
thing of what to say, but you know how you're doing,
and we can respond. But if instead of asking that,
they may ask like, hey, did you watch the game
last night? And we just have no idea how to
respond to that. About a decade ago, Louis says Ai,
researchers started trying a really different approach. Rather than trying
(12:36):
to teach computers every rule, they started throwing massive amounts
of documents and texts at computers and essentially telling the
computers figure out the patterns in all these documents. So
when somebody writes something like did you watch the game
last night? The computers should be able to predict what
kinds of answers might follow. This strategy clearly has not
(12:57):
entirely worked yet. That's why it's still a problem solving.
It will take both more text and more clever algorithms
to help computers make sense of that text. But Louis says,
you can see progress every time you open your Gmail
or a Google Doc. And I don't know if you've used,
for example, you use Google Docs lately or Gmail like
it finishes off your sentences now. And basically the way
(13:20):
this works is, you know, this system has looked at
a ton ton of text that has been written by
a lot of people. In the case of Google Docs,
I actually don't know what they look at, but I
wouldn't be surprised if they look at everything that has
ever been written in Google Docs. I'm going to tell
you one that happened to me in Google Docs today
when I was typing notes for this interview, I typed
zone of pr and then you know what, you know
(13:43):
how it completed it proximal development. Yes, it knew I
was going to write zone of proximal development. Yep. No,
this is amazing, And they just see that if you'd
write the zone of per there's like a ninety five
percent chance that it ends in proximal development. What is
the zone of proximal development? You know? In teaching, you know,
(14:05):
there's this concept of just keeping you at this zone
of proximal development, which is always kind of challenging you,
giving you things that you don't know. But but there
are all things that are fair to give you. Proximal
means like close to or next two. Right. So it's
the idea is like you know a thing, yes, then
like what you want to teach the person is the
very next thing, right, It's like that's right, It's like
the frontier of your knowledge. I like it because it
(14:26):
applies to like the way you teach, but also to
your work, right, And like it's just a nice life idea, right,
It's like the next thing you want, the next thing.
And I feel like the chat bot is maybe a
version of that at the level of your company. Yeah, yeah,
it really is. It really is a nice idea, and
it is I mean, and if you think about it,
this is what a great teacher does. You know. I've
said this inside the company at due Lingo. UM. All
(14:48):
we need to do is first figure out what you know,
by the way, not that easy to figure out what
you know. But let's first figure out what you know
and then just take you to that zone of proximal development.
Because now we know what you know, just take you
to the frontier and then just keep expanding it as
fast as possible. That's all we need to do. Of course,
this is easily said, hard to do. Yeah. Um, And
(15:09):
is there a limit to what you can do with
a computer? Is there anything a teacher can do? Is
that a computer will never be able to do? You know?
Of course I do lingual We love teachers. If they
are a good teacher and also have the time, they
are much more able to adapt to their students than
a computer is. Um. But I don't believe that will
always be the case. I mean, I think at some
(15:30):
point it's not just teachers. I mean teachers, this is
one thing. I mean, at some point my belief and
this is of course just my belief. People, not everybody agrees.
They believe that computers will be able to do every
single thing that humans can. Now you may start asking
really tough questions like can they love? Yeah, I don't know,
I don't know what they can love or not but
from the outside it will look just as if they
(15:51):
love so who knows who knows what's going on inside?
Who knows that they that's like a big yeah, we're
big philosophical questions that I'm not here today, and that's right,
nor am I. But I do think from input output behavior,
I don't see why. I don't see any reason why
computers won't be able to do everything that humans can.
So they can teach, but they can also write a
computer code. They can also run companies, they can also
(16:13):
make podcasts, they can do everything. Should be able to
do that. I think they should be able to do that.
I don't know when that'll happen, but they should be
able to do that. In a minute, the lightning round, well,
hear what job Luise would love to do but thinks
he wouldn't be very good at. And the real reason
treasure Chess keep showing up in duo lingo. And now
(16:40):
back to the show. We're going to finish with a
lightning round, not counting duo Lingo. What's your favorite app
on your phone? Spotify? What have you been listening to
on Spotify? I'm always a huge fan of the band
called Churches with a v to Virchase. So that's what
I was listening to this morning on my work at work.
If you have a ten minute break in the middle
(17:00):
of the day, what do you do to relax? Played
this game called Class Royale. We are a lot of
the gaming mechanics that we use for duel and will
come from gaming companies, like the treasure chests, exactly right,
the treasure chests. If you ever played Class Royale, they
have the treasure chests. If somebody's going to go to
(17:20):
visit Guatemala for the first time, what's one thing they
should definitely do? Oh? Um, Decal is the Mayan ruins. Um.
You know, if I feel very strong, I've been to
southern Mexico where they have chi Chenitsa. It's a joke
compared to the Mayan ruins in Guada. There's there's one
pyramid in Chichenitsa. There are four hundred in Guatemala in Decal,
(17:42):
So yeah, they should like I like that. Not only
are you recommending Tikal, you're also taking as I have
no trouble with Chichenitsa. It's just they are very good
at marketing. Amazing. What would you do if you couldn't
do the job you do. Now, well there's what what
would I actually do? On? What would I'd like to do?
(18:03):
I would love to be a writer. I don't think
i'd be a very good one. Um So if I
if I wasn't doing the job that I'm doing right now,
you know, I'd probably be back to being a professor.
How will you know when it's time to retire? I'm
never retiring, That's what everybody says. Well, maybe I will,
but I mean right now, I don't. I don't want
(18:24):
to do that. Luis Vaughan is the founder and CEO
of Duelingo. Today's show was produced by Edith Russelo. It
was edited by Kate Parkinson Morgan and Robert Smith, and
it was engineered by Amanda kay Wong. Theme music by
Louis Kara. Our development team is Lee, Tom Mulad and
Justine Lang. A huge team of people makes What's Your
(18:45):
Problem possible. That team includes, but is not limited to,
Jacob Weisberg, Mia Lobel, Heather Fain, John Schnars, Kerry Brodie,
Carli mcgleory, Christina Sullivan, Jason Gambrell, Brand Hayes, Eric Sandler,
Maggie Taylor, Morgan Ratner, Nicolemrano, Mary Beth Smith, Royston Deserve,
Maya Kanig, Daniello, Lakhan, Kazia Tan and David Clever. What's
Your Problem is a co production of Pushkin Industries and iHeartMedia.
(19:07):
To find more Pushkin podcast Us, listen on the iHeartRadio app,
Apple Podcasts, or wherever. I'm Jacob Goldstein and I'll be
back next week with another episode of What's Your Problem.
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