Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of The Odd Lots podcast.
Speaker 2: I'm Tracy Alloway and I'm Joe Wisenthal. Joe, you know,
Speaker 2: I had a life realization recently.
Speaker 3: Okay, this should be good, go on.
Speaker 2: It struck me that I am spending a non negligible
Speaker 2: amount of my time proving that I am in fact
Speaker 2: a human being.
Speaker 3: It's getting harder and harder. I know what you're talking about.
Speaker 3: So we're talking. You know, you go to a website
Speaker 3: and you have to enter in the captcha and it's
Speaker 3: like click all these squares that has like a crosswalk
Speaker 3: on them or a truck, and like it feels like
Speaker 3: it's just getting harder. And sometimes I'm like, no, trust me,
Speaker 3: I'm a human.
Speaker 2: This is it. And every time it happens, I kind
Speaker 2: of have a moment of self doubt whether or not
Speaker 2: is it just me? Am I particularly bad at picking
Speaker 2: out all the motorcycles in a set of pictures? Or
Speaker 2: are they just becoming increasingly weird or perhaps increasingly sophisticated
Speaker 2: in the face of new types of technology.
Speaker 3: It's not just you. I've heard this from multiple people
Speaker 3: in fact, prepping for this episode, I heard people talking
Speaker 3: about exactly this, But you know, it's like a big problem.
Speaker 3: You know, we did that world Coin episode, like everyone
Speaker 3: is trying to figure out, like how in a world
Speaker 3: of AI and bods and artificial intelligence all that stuff,
Speaker 3: how do you know whether someone you're interacting with is
Speaker 3: in fact a person.
Speaker 2: Yeah, and I'm glad you mentioned AI because obviously part
Speaker 2: of this dynamic is AI seems to be getting better
Speaker 2: at solving these particular types of problems, but also they're
Speaker 2: being used more right to train AI models. So at
Speaker 2: this point, I think we all know why we're constantly
Speaker 2: trying to identify bikes and a bunch of photos. But
Speaker 2: the whole idea behind captures is or was that humans
Speaker 2: still have an edge. So there are some things that
Speaker 2: humans are better able to do versus machines. And one
Speaker 2: of the things that we used to talk about humans
Speaker 2: having an edge in was linguistics. So there is this
Speaker 2: idea that human language was so complex, so nuanced, that
Speaker 2: machines would maybe never be able to fully appreciate all
Speaker 2: the intricacies and subtleties of the human language. But obviously,
Speaker 2: since the arrival of generative AI and natural language processing.
Speaker 2: I think there's more of a question mark around that. Yeah.
Speaker 3: I mean, look, I think like a typical chat bot
Speaker 3: right now is probably better than most people at just
Speaker 3: typing out several paragraphs. It's all sort of like seemed
Speaker 3: to sort of as they say on the internet, kind
Speaker 3: of mid curve to me. It never like strikes me
Speaker 3: as like incredibly intelligent, but clearly computers can talk about
Speaker 3: as well as humans, and so it raises all sorts
Speaker 3: of interesting questions. You mentioned that part of capture is
Speaker 3: part of this, like training computers. A big part of
Speaker 3: these chatbots the so called like real life human feedback
Speaker 3: where people say this answer is better then another, this
Speaker 3: answer is better another, is they refine the models, et cetera.
Speaker 4: So I think there's like.
Speaker 3: An interesting moment where like we're learning from computers and
Speaker 3: computers are learning from us, maybe collaboratively, the two sides
Speaker 3: in a carbon and silicon working together.
Speaker 2: I think that's a great way of putting it. Also,
Speaker 2: mid curve is such an underappreciated insult, like calling people
Speaker 2: top of the bell curve is one of my favorite
Speaker 2: things to do online. Anyway, I am very pleased to
Speaker 2: say that today we actually have the perfect guest. We're
Speaker 2: going to be speaking to someone who was very instrumental
Speaker 2: in the development of things like Captcha and someone who
Speaker 2: is doing a lot with AI, particularly in the field
Speaker 2: of linguistics and language. Right now, we're going to be
Speaker 2: speaking with Louis von On. He is, of course the
Speaker 2: CEO and co founder of Duo Lingo. So, Louise, thank
Speaker 2: you so much for coming on.
Speaker 4: On thoughts, Thank you, thank you for having me.
Speaker 2: So maybe to begin with talk to us about the
Speaker 2: idea behind capture and why it seems to have become
Speaker 2: I don't want to say a significant portion of my life,
Speaker 2: but I certainly spend a couple minutes every day doing
Speaker 2: at least one version.
Speaker 4: Yeah. So the original capture, the idea of a capture
Speaker 4: was a test to distinguish humans from computers. The reasons
Speaker 4: why you may want to distinguish whether you're interacting with
Speaker 4: a human or a computer online for example, and this
Speaker 4: is kind of the original motivation for it. Companies offer
Speaker 4: free email services, and you know they have the problem
Speaker 4: that if you allow anything to sign up for a
Speaker 4: freemail service, like either a computer or human, somebody could
Speaker 4: write a program to obtain millions of free email accounts,
Speaker 4: whereas humans, because they are usually not that patient, cannot
Speaker 4: get millions of email accounts for themselves. They can only
Speaker 4: get one or two. So the original motivation for aptual
Speaker 4: was to make a test to make sure that whoever
Speaker 4: is getting a freemail accunt is actually a human and
Speaker 4: not a computer program that was written to obtain millions
Speaker 4: of email accounts, so, you know, and the way it worked,
Speaker 4: there's there's many kind of tests. Originally, the way it
Speaker 4: worked is distorted letters, So you would get a bunch
Speaker 4: of letters that were predistorted and you had to type
Speaker 4: what they were. And the reason that worked is because
Speaker 4: human beings are very good at reindistorted letters. But at
Speaker 4: the time this was, you know, more than twenty years ago,
Speaker 4: computers just could not recognize distorted letters very well. So
Speaker 4: that was a great test to determine whether you were
Speaker 4: talking to a human or a computer. But what happened
Speaker 4: is over time, computers got quite good at this trying
Speaker 4: to deciphering distorted text, so it was no longer possible
Speaker 4: to give an image with distorted text and distinguish a
Speaker 4: human from a computer, because computers pretty much got as
Speaker 4: good as a human at that point, these tests started
Speaker 4: changing to other things. I mean, one of the more
Speaker 4: popular ones that you see nowadays is kind of clicking
Speaker 4: on the images of something. So you can see a grid,
Speaker 4: like a four by four grid, and it may say
Speaker 4: click on all the traffic lights, or click on all
Speaker 4: the bicycles, et cetera. And by clicking on them, you know,
Speaker 4: you're you're showing that you can actually recognize these things.
Speaker 4: And the reason they're getting harder is because computers are
Speaker 4: getting better and better at deciphering which ones are traffic lights,
Speaker 4: et cetera. And by now, what you're getting here are
Speaker 4: the things that we still think computers are not very
Speaker 4: good at. So the image may be very blurry, or
Speaker 4: you know, you may just get a tiny little corner
Speaker 4: of it and things like that. So that's why they're
Speaker 4: getting harder, and I expect that to continue happening.
Speaker 3: So you are the found You founded a company called
Speaker 3: recap Show, which you sold to Google and several years ago.
Speaker 3: Is there gonna be a point where I mean, I
Speaker 3: assume computer vision and their ability to decode images or
Speaker 3: recognize images is not done improving. I assume it's going
Speaker 3: to get better, whereas humans' ability to decode images. I
Speaker 3: doubt it's really getting any better. We've probably been about
Speaker 3: the same for a couple thousand years now. Like, is
Speaker 3: there going to be a point in which it's impossible
Speaker 3: to create a visual test that humans are better at
Speaker 3: than computers?
Speaker 4: I believe that will happen at some point. Yeah, it's
Speaker 4: very hard to say when exactly, but you know, you
Speaker 4: can just see at this point it's getting you know,
Speaker 4: computers are getting better and better. And you know, the
Speaker 4: other thing that is important to mention is this type
Speaker 4: of test has extra constraints. It also has to be
Speaker 4: the case that it's not just that humans can do
Speaker 4: it. It's like, really, humans should be able to do it
Speaker 4: pretty quickly and you know, success.
Speaker 3: Quickly, and on a mobile phone and a very small
Speaker 3: screen in which like my thumb is like half the
Speaker 3: size of the screen.
Speaker 4: Yeah. Yeah, And it may not be you know, quickly.
Speaker 4: I mean it may take you, I don't know, thirty
Speaker 4: seconds or a minute. But we cannot make a test
Speaker 4: that takes you an hour. We can't do that. So
Speaker 4: it has to be quick. It has to be done
Speaker 4: on a mobile phone. It has to be the case
Speaker 4: that the computer should be able to grade it. Computer
Speaker 4: should be able to know what the right answer was,
Speaker 4: even though it can't solve it. So because of all
Speaker 4: of these constraints, I mean, my sense is at some
Speaker 4: point this is just going to be impossible. I mean,
Speaker 4: we knew this when we started the original capture that
Speaker 4: at some point computers were going to get good enough,
Speaker 4: but we just had no idea how long it was
Speaker 4: going to take. And I still don't know how long
Speaker 4: it's going to take. But you know, I would not
Speaker 4: be surprised if in five to ten years there's just
Speaker 4: not much that you can do that is really quick
Speaker 4: online to be able to differentiate humans from computers.
Speaker 2: Yeah, that's when we get the eyeball scanning ORBS. But
Speaker 2: I mean you mentioned that you can't have a test
Speaker 2: that takes an hour or something like that. But this
Speaker 2: kind of begs the question in my mind of why
Speaker 2: are people using these tests at all? So, like, Okay,
Speaker 2: obviously you want to distinguish between humans and robots, but
Speaker 2: I sometimes get the sense that these are basically free
Speaker 2: labor AI training programs, Right, So even if you can
Speaker 2: verify identity in some other way, why not get people
Speaker 2: on a mass scale to spend two minutes training self
Speaker 2: driving cars.
Speaker 4: Yeah, I mean, this is what these things are doing.
Speaker 4: That was the original idea of Recapture, which was my company.
Speaker 4: The idea was that you could, at the same time
Speaker 4: as you were proving that you are a human, you
Speaker 4: could be doing something that computers could not yet do,
Speaker 4: and that data could be used to improve computer programs
Speaker 4: to do it. So certainly, when you're clicking on bicycles
Speaker 4: or when you're clicking on traffic lights or whatever, that
Speaker 4: is likely data that is being used. I say likely
Speaker 4: because you know, I don't know what capture you're using.
Speaker 4: There may be some that are not doing that, but
Speaker 4: overall that data is being used to improve things like
Speaker 4: self driving cars, image recognition programs, et cetera. So that
Speaker 4: is happening, and that's you know, generally a good thing
Speaker 4: because that's making basically AI smarter and smarter. But you know,
Speaker 4: we still needed to be the case that it's a
Speaker 4: good security mechanism. So if at some point just computers
Speaker 4: can do that, then you know, that's just not a
Speaker 4: great security mechanism and it's not going to be used.
Speaker 4: And my sense is if we're gonna want to do something,
Speaker 4: we are going to need something like real identity, Like
Speaker 4: I don't know if it's going to be eyeball scanning
Speaker 4: or whatever, but it's good. We're gonna you know, the
Speaker 4: nice thing about a capture is it doesn't tie you
Speaker 4: to you. It just proves that you're a human. Right,
Speaker 4: We're probably going to need something that ties you to you.
Speaker 4: We're probably going to need something that says, well, I
Speaker 4: just know this is this specific person because you know whatever,
Speaker 4: we're scanning their eyeball, we're looking at their fingerprint, whatever
Speaker 4: it is, and it is actually a real person, and
Speaker 4: it is this person.
Speaker 3: Why don't we sort of zoom out and back up
Speaker 3: for a second. So currently you are the CEO of
Speaker 3: Duo Lingo of the popular language learning app, publicly traded company.
Speaker 3: Done much better sort of stockwise than many companies that
Speaker 3: came public in twenty twenty one. I have expected, you know,
Speaker 3: there was a boom when people a bunch of time
Speaker 3: on their hand gone down. You also sort of one
Speaker 3: of the most respected sort of computer sciences thinkers coming
Speaker 3: out of the Carnegie Mellon University. What is the through
Speaker 3: line of your work or how would you characterize that
Speaker 3: connects something like captures to language learning a dual lingo.
Speaker 4: It's similar to what you were talking about smiling when
Speaker 4: you were mentioning that. I mean, I think the general
Speaker 4: through line is a combination of humans learning from computers
Speaker 4: and computers learning from humans. And you know, capture had
Speaker 4: that while you were typing a capture, computers were learning
Speaker 4: from what you were doing. In the case of duolingo,
Speaker 4: it's really a symbiotic thing that both are learning, in
Speaker 4: that humans are learning a language and in the case
Speaker 4: of due a lingo, due lingos learning how to teach
Speaker 4: humans better by interacting with humans a lot. So you know,
Speaker 4: dual lingo just gets better with time because we figure
Speaker 4: out different ways in which humans are just learning better.
Speaker 4: You know, humans are getting better with a language, and
Speaker 4: do a linguos getting better at teaching you languages.
Speaker 2: Joe, have you used to a lingo?
Speaker 3: I haven't. Well, okay, I hadn't up until recently. So
Speaker 3: last week, as it turns out, I visited my mother
Speaker 3: who lives in Guatemala, which luis I Anderson You're from,
Speaker 3: And oh, wow, yeah, she's she is. Uh, she's not
Speaker 3: from there, but she visited a friend there eight years
Speaker 3: ago and she loved it, and she's like, I'm just
Speaker 3: gonna stay and she has a little never left. She
Speaker 3: loved it so much, and so I visited her for
Speaker 3: the first time at her house near Lake Atitlan, and
Speaker 3: then I was like, oh, there's a great life and
Speaker 3: maybe one day I'll even have that house. And I
Speaker 3: should learn Spanish, And so I did, partly because of
Speaker 3: that trip and partly to prepare for this episode. I
Speaker 3: downloaded it and have started. I know a little bit
Speaker 3: of Spanish, not much like I can, you know, ask
Speaker 3: for the bill and stuff, but it's like, oh, I should,
Speaker 3: I should start to learn it.
Speaker 2: That's funny because I also started learning Spanish right before
Speaker 2: a trip to Guatemala. There you go with Duolingo, and
Speaker 2: I'm not the best advertisement for the app. I'm afraid,
Speaker 2: like the only thing I remember is basically like Kissierra
Speaker 2: una hapatas personas. That's all I remember from.
Speaker 3: It's pretty good.
Speaker 4: Thanks, that's pretty good.
Speaker 2: All right, I need to get back on it. But
Speaker 2: why don't you talk to us a little bit about
Speaker 2: the opportunity with AI in this sort of language learning space,
Speaker 2: because intuitively, it would seem like things like chat bots
Speaker 2: and generative AI and natural language processing and things like
Speaker 2: that would be an amazing fit for this type of business.
Speaker 4: Yeah, it's a really good fit. So okay, So you know,
Speaker 4: we teach languages. We do a lingo. Historically, you know,
Speaker 4: learning a language just has a lot of different components.
Speaker 4: You got to learn how to how to read language.
Speaker 4: You got to learn some vocabulary, you got to learn
Speaker 4: how to listen to it. If there's a different writing system,
Speaker 4: you've got to learn the writing system, you got to
Speaker 4: learn how to have a conversation. There's a lot of
Speaker 4: different skills that are required in learning a language. Historically,
Speaker 4: we have done pretty well in all the skills except
Speaker 4: for one of them, which is having a multi turned
Speaker 4: fluid conversation. So we could teach you, you know, historically, we
Speaker 4: could teach you, We could teach your vocabulary really well.
Speaker 4: We could teach you how to listen to a language.
Speaker 4: It's you know, generally just by just getting you to
Speaker 4: listen a lot to something. So we could teach you
Speaker 4: all the things, but being able to practice actual multi
Speaker 4: turned conversation was not something that we could do with
Speaker 4: just a computer. Historically, that needed us to pair you
Speaker 4: with another human. Now we do a ling We never
Speaker 4: paired people up with other humans, because it turns out
Speaker 4: a very small fraction of people actually want to be
Speaker 4: paired with a random person over the internet who speaks
Speaker 4: a different language. It's just it's kind of too embarrassing
Speaker 4: for most people. I never did that. Well, it may
Speaker 4: be dangerous, yes, but it also it's just it's like
Speaker 4: ninety percent of people just not extroverted enough, yeah to
Speaker 4: do that. I just don't want to do it. So
Speaker 4: we always, you know, kind of we did these kind
Speaker 4: of wonky things to try to emulate short conversations, but
Speaker 4: we could never do anything like what we can do
Speaker 4: now because with large language models, we really can get
Speaker 4: you to practice you know, it may not be a
Speaker 4: three hour conversation, but we can get you to practice
Speaker 4: a multi turn, you know, ten minute conversation and it's
Speaker 4: pretty good. So that's that's what we're doing with du
Speaker 4: A Lingo. We're using it to help you learn conversational
Speaker 4: skills a lot better, and that's helping out quite a bit.
Speaker 3: There are so many questions I have, and I you know,
Speaker 3: I think my mom will rely like this episode because,
Speaker 3: in addition to the Guatemala connection, she is a linguist.
Speaker 3: She speaks like seven languages, including Spanish, and like basically
Speaker 3: you know all the others, not all the others, but
Speaker 3: all the others, many many others. But you know something
Speaker 3: that I was curious about, and maybe this is a
Speaker 3: little bit of random jumping point, you know. I think
Speaker 3: about like chess computers, and originally they were sort of
Speaker 3: trained on a corpus of famous chess games, and then
Speaker 3: with some.
Speaker 4: Computer they got better.
Speaker 3: And then the new generation essentially relearned chess from just
Speaker 3: the rules from first principles, and it turns out that
Speaker 3: they're way better. And I'm wondering, if you're learning through
Speaker 3: the process of building out do a lingo improvement, Like
Speaker 3: are there forms of pedagogy that in language learning, whether
Speaker 3: it's the need for immersion or the need for roat drills,
Speaker 3: or certain things that linguists have always thought were necessary
Speaker 3: components of good language learning that when rebuilding education from
Speaker 3: the ground up, like old dictums just turn out to
Speaker 3: be completely wrong, And when you rebuild the process from
Speaker 3: the beginning, like novel forms of pedagogy emerge.
Speaker 4: It's a great question, and it's a hard question to
Speaker 4: answer for the following reason, at least for us we
Speaker 4: teach a language from an app. Historically, the way people
Speaker 4: learn languages is basically by practicing with another human or
Speaker 4: being in a classroom or whatever. Whereas we teach from
Speaker 4: an app, the setting is just very different for one
Speaker 4: key reason, which is that it is so easy to
Speaker 4: leave the app, whereas leaving a classroom it's just not
Speaker 4: that easy. You kind of have to go. You're usually
Speaker 4: forced by your parents to go to a classroom, and like,
Speaker 4: you know, so generally, the thing about learning something by
Speaker 4: yourself when you're just learning it through a computer is
Speaker 4: that the hardest thing is motivation. It turns out that
Speaker 4: the pedagogy is important, of course it is, but much
Speaker 4: like exercising, what matters the most is that you're actually
Speaker 4: motivated to do it every day. So like, is the
Speaker 4: elliptical better than the step climber or better than the treadmill? Like, yeah,
Speaker 4: they're probably differences, but the reality is what's most important
Speaker 4: is that you kind of do it often. And so
Speaker 4: what we have found with dual linguo is that if
Speaker 4: we're going to teach it with an app, there are
Speaker 4: a lot of things that historically, you know, language teachers
Speaker 4: or linguists didn't think we're the best ways to teach languages,
Speaker 4: but if you're going to do it with an app,
Speaker 4: you have to make it engaging. And we've had to
Speaker 4: do it that way, and we have found that we
Speaker 4: can do some things significantly better than human teachers, and
Speaker 4: something's not as good because it's a very different system.
Speaker 4: But again, the most important thing is just to keep
Speaker 4: you motivated. So examples of things that we've had to
Speaker 4: do to keep people motivated are quote unquote classes, which
Speaker 4: is a lesson undu a lingo. They're not thirty minutes
Speaker 4: or forty five minutes, they're two and a half minutes.
Speaker 4: If they're any longer, we start losing people's attention. So
Speaker 4: stuff like that I think has been really important. Now
Speaker 4: I'll say, related to your question, one thing that has
Speaker 4: been amazing is that, you know, we start out with
Speaker 4: language experts who you know, people with PhDs and second
Speaker 4: language acquisition, who tell us how to best teach something.
Speaker 4: But then it takes it from there and the computer
Speaker 4: optimizes it, and so the computer starts finding different ways.
Speaker 4: There are different orderings of things that are actually better
Speaker 4: than what the people with phg's and second language acquisition thought.
Speaker 4: But it's because they just didn't have the data to
Speaker 4: optimize this, whereas now you know, we do a lingo,
Speaker 4: we have it's something like one billion exercises. Is one
Speaker 4: billion exercises are solved every day by people using dual lingo,
Speaker 4: and that just has a lot of data that helps
Speaker 4: us teach better.
Speaker 2: This is exactly what I wanted to ask you, which
Speaker 2: is how iterative is this technology? So how much is
Speaker 2: it about the AI model sort of developing off the
Speaker 2: data that you feed it, and then the AI model
Speaker 2: improving the outcome for users and thereby generating more data
Speaker 2: from which it can train.
Speaker 4: It's exactly we're exactly doing that, and in particular, one
Speaker 4: of the things that we've been able to optimize a
Speaker 4: lot is which exercise we give to which person. So
Speaker 4: when you start a lesson and do a lingo, you
Speaker 4: may think that all lessons are the same for everybody.
Speaker 4: They're absolutely not. When you use to a lingo, you
Speaker 4: watch what you do, and you know, the computer makes
Speaker 4: a model of you as a student, so it sees
Speaker 4: everything you get right, everything you get wrong, and based
Speaker 4: on that, it starts realizing you're not very good at
Speaker 4: the past tense, or you're not very good at the
Speaker 4: future tens or whatever. And whenever you start a lesson,
Speaker 4: it uses that model specifically for you, and it knows
Speaker 4: that you're not very good at a past tense, so
Speaker 4: it may give you more past tense or it does
Speaker 4: stuff like that. And that definitely gets better with more
Speaker 4: and more data. And I'll say another thing that is
Speaker 4: really important. If we were to give you a lesson
Speaker 4: only with the things that you're not good at, that
Speaker 4: would be a horrible lesson because that would be extremely frustrating.
Speaker 4: It's just basically, here are the things you're bad at,
Speaker 4: just that we do a lot more of that. So
Speaker 4: in addition to that, we have a system that tries
Speaker 4: to and it gets better and better over time. It
Speaker 4: is tuned for every exercise we have on DUELINGO that
Speaker 4: could give you. It knows the probability that you're going
Speaker 4: to get that exercise correct. And whenever we are giving
Speaker 4: you an exercise, we optimize so that we try to
Speaker 4: only give you exercises that you have about an eighty
Speaker 4: percent chance of getting right. And that has been quite
Speaker 4: good because it turns out eighty percent is kind of
Speaker 4: at this zone of maximal development where basically it's not
Speaker 4: too easy because you're not getting Having a one hundred
Speaker 4: percent chance of getting it right if it's too easy
Speaker 4: has two problems. Not only is it boring that it's
Speaker 4: too easy, but also you're probably not learning anything if
Speaker 4: you have a hundred percent chance of getting it right.
Speaker 4: And it's also not too hard because humans get frustrated
Speaker 4: if you're getting things right only thirty percent of the time.
Speaker 4: So it turns out that we should give you things
Speaker 4: that you have an eighty percent chance of getting right,
Speaker 4: and that has been really successful, and you know, we
Speaker 4: keep getting better and better at at finding that exact
Speaker 4: exercise that you have an eighty percent chance of getting right.
Speaker 3: Okay, I have another I guess I would say theory
Speaker 3: of language question, and I think I read in one
Speaker 3: of your interviews. You know, is part of the process
Speaker 3: of making the dual lingo ad better, you're always a
Speaker 3: b testing things like should people learn vocabulary first, should
Speaker 3: people learn adjectives before adverbs or a verbs before verbs,
Speaker 3: whatever it is, and that there's this constant process of
Speaker 3: what is the correct sequence? Do rules about the sequence
Speaker 3: of what you learn differ across languages. So let's say
Speaker 3: someone learning Portuguese may have a different optimal path of
Speaker 3: what to learn first grammatically or vocabulary wise, versus say
Speaker 3: someone learning Chinese or Polish, because I'm curious about whether
Speaker 3: we can undercover deep facts about common grammar and language
Speaker 3: from the sort of learning sequence that is optimal across languages.
Speaker 4: Yes, they definitely vary a lot based on the language
Speaker 4: that you're learning, and even more so, they also vary
Speaker 4: based on your native language. So we actually have a
Speaker 4: different course to learn English for Spanish speakers than the
Speaker 4: course we have to learn English for Chinese speakers. They
Speaker 4: are different courses, and there's a reason for that. It
Speaker 4: turns out that what's hard for Spanish speakers in learning
Speaker 4: English is different than it's hard for Chinese speakers in
Speaker 4: learning English. Typically, you know, the things that are common
Speaker 4: between languages are easy, and the things that are very
Speaker 4: different between languages are hard. So just a stupid example,
Speaker 4: I mean, when you're learning English from Spanish, there's you know,
Speaker 4: a couple of thousand cognates. That's words that are the
Speaker 4: same or very close to the same, so you immediately
Speaker 4: know those We don't even need to teach you those words.
Speaker 4: If you're learning English from Spanish because you already you
Speaker 4: know them automatically because they are the same word. That's
Speaker 4: not quite true from Chinese. Other examples are, you know,
Speaker 4: for me in particular, i started learning German, and for me,
Speaker 4: German was quite hard to learn because Spanish, you know,
Speaker 4: my native language is Spanish. Spanish just does not have
Speaker 4: a very developed concept of grammatical cases, whereas German does.
Speaker 4: But learning German from like from Russian, that's just not
Speaker 4: a very hard concept to grasp. So it kind of
Speaker 4: depends on what concepts your language has, you know, also
Speaker 4: not exactly concepts. But in terms of pronunciation, everybody says
Speaker 4: that Spanish pronunciation is really easy, and it's true. Vowels
Speaker 4: in Spanish are really easy because there's only really about
Speaker 4: five vowel sounds. It's a little more than that, but
Speaker 4: it's about five vowel sounds, whereas you know, there are
Speaker 4: other languages that have, you know, fifteen vowel sounds. So
Speaker 4: learning Spanish is easy, but vice versa. If you're a
Speaker 4: native Spanish speaker, learning the languages that have a lot
Speaker 4: of vowel sounds is really hard because you don't even
Speaker 4: you can't even hear the difference. You know, it's very
Speaker 4: funny when you're learning English from as a native Spanish speaker,
Speaker 4: you cannot hear the difference between beach and bitch. You
Speaker 4: cannot hear that difference, and you know, people make funny
Speaker 4: mistakes because of that.
Speaker 2: But I think there are a lot of T shirts
Speaker 2: that involve that at one point in time.
Speaker 4: Well, because really, if you're a native Spanish speaker, you
Speaker 4: just cannot hear that difference.
Speaker 2: So one thing I wanted to ask you is the
Speaker 2: type of model that you're actually using. So I believe
Speaker 2: you're using GPT four for some things like your premium
Speaker 2: subscription do a Lingo Max, but then you've also developed
Speaker 2: your own proprietary AI model called bird Brain. And I'm
Speaker 2: curious about the decision to both use an off the
Speaker 2: shelf solution or platform and to also develop your own
Speaker 2: model at the same time. How did you end up
Speaker 2: going down that path.
Speaker 4: Yeah, it's a great question. I mean, I think the
Speaker 4: difference is these are just very different the last since
Speaker 4: since I don't know, two years ago, when large language
Speaker 4: models or generative AI became very popular. Before that, there
Speaker 4: were different just different things that AI could be used
Speaker 4: for us. We were not using AI, for example for
Speaker 4: practicing conversation. But we were using AI to determine which
Speaker 4: exercise to give to which person that we built our
Speaker 4: own that is the bird brain model is a model
Speaker 4: that tries to figure out which exercise to give to
Speaker 4: which person, you know, the last two years ago, for
Speaker 4: the last two year stories. When people talk about models,
Speaker 4: they usually mean langue which models, And it's this, it's
Speaker 4: this specific type of AI model that what it does
Speaker 4: is it predicts the next word given the previous words.
Speaker 4: That's what a language model does. The large language models
Speaker 4: are particularly good at doing this, and we did not
Speaker 4: develop our own large language model. We decided it's a
Speaker 4: lot easier to just use something like GPT four. But
Speaker 4: we have our own model for something else that is
Speaker 4: not a language model. That is an but it is
Speaker 4: an AI model to predict what exercise to give to
Speaker 4: which user, which is a pretty pretty different problem.
Speaker 3: Speaking of AI, all these especially the really big companies,
Speaker 3: making an extraordinary show of almost bragging about how much
Speaker 3: money they give to Jensen Wong and in video it's like,
Speaker 3: we just spent you know, we're spending twenty billion dollars
Speaker 3: over the next two years to just acquire h one
Speaker 3: hundred chips or whatever it is, and it almost seems
Speaker 3: like there's like arms race. And then there is also
Speaker 3: this view that actually the best models will not necessarily
Speaker 3: be the ones strictly with the access to the most
Speaker 3: compute but the access to data sets that other models
Speaker 3: simply don't have. And I'm curious sort of like you know,
Speaker 3: you as dual lingo must have an extraordinary amount of
Speaker 3: proprietary data just from all of your user interactions in
Speaker 3: your experience. When you think about who the winners will
Speaker 3: be in this space, is it going to be the
Speaker 3: ones that just have the most electricity and energy and chips,
Speaker 3: or is it going to be who has access to
Speaker 3: some sort of data that they can fine tune their
Speaker 3: model on that the other model can.
Speaker 4: It depends on what you're talking about. You know, certainly
Speaker 4: we a stoolingo have a lot of you know, data
Speaker 4: nobody else has, which is the data on how each
Speaker 4: person's learning language. I mean that's not data you can
Speaker 4: find on the web or anything like that. That is
Speaker 4: just the data that we have that we're generating, and
Speaker 4: we're going to train our own models for that. I
Speaker 4: don't think there's enough electricity to train a model without
Speaker 4: this data to be as good as ours with our data,
Speaker 4: but it is for specifically language learning. If you're talking
Speaker 4: about training a general model, that is going to be something,
Speaker 4: you know, a language model that is general for being
Speaker 4: able to have conversations, et cetera. Usually you can get
Speaker 4: that from there's pretty good data there out there. You know,
Speaker 4: YouTube videos that are free or a lot of kind
Speaker 4: of Reddit conversations or whatever. There's there's a lot of
Speaker 4: data in there. Probably a power is going to matter.
Speaker 4: So it depends on what you're going to use your
Speaker 4: model for. If if you're getting if you're using it
Speaker 4: for a very specific purpose and you have very specific
Speaker 4: data for that that is proprietary, that's going to be
Speaker 4: better for the specific purpose. But my sense is that
Speaker 4: you know both are going to matter. You know what
Speaker 4: data you have and also how much electricity you spend.
Speaker 4: But I also think that over time, hopefully we're going
Speaker 4: to get better and better at these algorithms. And if
Speaker 4: you think about it, the human brain uses something like
Speaker 4: thirty watts for the human brain is pretty good and
Speaker 4: we don't need you know, some of these models. People
Speaker 4: are saying, oh, this is uses the the amount of
Speaker 4: electricity that all of New York City uses. We use
Speaker 4: that to train a model. You know, our brain uses much, much,
Speaker 4: much less electricity than that, and you know, it's pretty good.
Speaker 4: So my sense is that also over time, hopefully we'll
Speaker 4: be able to get to the point where we're not
Speaker 4: as crazy about using electricity as we are today.
Speaker 2: I'm glad our brains are energy efficient. That's nice to know.
Speaker 4: We've been talking a lot better than computers.
Speaker 2: We've been talking a lot about the use of AI
Speaker 2: in the product itself, so improving the experience of learning
Speaker 2: a language. But one of the things that we hear
Speaker 2: a lot about nowadays is also, you know, angst over
Speaker 2: the role of AI in the wider economy in terms
Speaker 2: of the labor force, job security, and stuff like that,
Speaker 2: as companies try to be more efficient. So I guess
Speaker 2: I'm wondering, on the sort of corporate side, how much
Speaker 2: does AI play into the business model right now in
Speaker 2: terms of streamlining things like costs or reducing workforce. And
Speaker 2: I believe there are quite a few headlines around Duo
Speaker 2: Lingo on this exact topic late last year.
Speaker 4: Yeah, first of all, those headlines were upsetting to me.
Speaker 4: Because they were wrong. You know, there were a lot
Speaker 4: of headlines thing that we had done a massive layoff
Speaker 4: that was not actually true. So what is true is that,
Speaker 4: you know, we really are leaning into AI. You know,
Speaker 4: it just it makes sense. This is a very transformative technology,
Speaker 4: so we're leaning into it. And it is also true
Speaker 4: that many workflows are a lot more efficient. And so
Speaker 4: what happened late last year was that we realized we
Speaker 4: have full time employees and but we also have some
Speaker 4: hourly contractors. We realized that we need a fewer hourly
Speaker 4: contractors and so for you know, a small fraction of
Speaker 4: our hourly contracts, we did not renew their contract because
Speaker 4: we realized we need a few of them for doing
Speaker 4: some tests that you know, honestly, computers were just as
Speaker 4: good as as a human and that's you know, that
Speaker 4: may be true for something like a like an hourly
Speaker 4: contractor force that was being asked to do. We were
Speaker 4: basically being asked to do very rote kind of language
Speaker 4: tasks that computers just got very good at. I think
Speaker 4: if you're talking about you know, our full time employees
Speaker 4: and people who are who are not necessarily just doing
Speaker 4: rote repetitive stuff that's going to take a while to replace.
Speaker 4: I don't think, and certainly this is not what we
Speaker 4: want to do as a company. You know, I heard
Speaker 4: a really good saying recently, which is, your job's not
Speaker 4: going to be replaced by AI. It's going to be
Speaker 4: replaced by somebody who knows how to use AI. So
Speaker 4: what we're seeing in the company, at least for our
Speaker 4: full time employees, is not that we're able or even
Speaker 4: want to replace them. What we're seeing is just way
Speaker 4: more productivity, to the point where people are able to
Speaker 4: concentrate on kind of higher level cognitive tasks rather than
Speaker 4: wrote things. I don't know. One hundred years ago, people
Speaker 4: were being hired to add numbers or multiply numbers the
Speaker 4: original quote unquote computers were actually humans who are being
Speaker 4: hired to multiply numbers. We were able to mechanize that
Speaker 4: and use an actual computer to do that so that
Speaker 4: people didn't have to do that. Instead, they spend time,
Speaker 4: you know, planning something at a higher level rather than
Speaker 4: having to do the multiplication. We're seeing something similar to
Speaker 4: that now. And the other thing that we're seeing is
Speaker 4: that is really amazing. So we are saving costs because
Speaker 4: it's a single person can do more, but also we're
Speaker 4: able to do things much much faster, and in particular
Speaker 4: in data creation. I mean, one of the ways in
Speaker 4: which we teach you how to read is even read
Speaker 4: short stories. We used to create and we need to
Speaker 4: create a lot of short stories. We used to be
Speaker 4: able to create short stories, you know, at a certain pace.
Speaker 4: We can now create them like ten times faster. And
Speaker 4: what's beautiful about being able to eat them ten times
Speaker 4: faster is that you can actually make the quality better
Speaker 4: because if you create them once ten times faster and
Speaker 4: you don't like it, you can start over and do
Speaker 4: it again with certain changes and then oh you didn't
Speaker 4: like it, Okay, try it again, So you can you
Speaker 4: can try ten times at this, you know, whereas before
Speaker 4: you can only try once, and generally you don't have
Speaker 4: to try ten times. You have to try a few
Speaker 4: or times. So this is able to at the same
Speaker 4: time lower costs for us, but also make the speed
Speaker 4: faster and the quality better. So I mean, we're very
Speaker 4: happy with that. In terms from the corporate side.
Speaker 3: Could you talk more about benchmarking AI, because there's all
Speaker 3: these tests, right and you see these websites and they're like,
Speaker 3: well this one got this on the l sads, and
Speaker 3: this one got this on the SATs and I can
Speaker 3: never quite tell. And a lot of it seems inscrutable
Speaker 3: to me from your perspective, Like, what are sort of
Speaker 3: your basic approaches to benchmarking different models and determining when
Speaker 3: it like, okay, this makes sense as some sort of
Speaker 3: task to employ AI instead of a person doing it.
Speaker 4: Yeah, I have felt the same as you have. There's
Speaker 4: a lot of my senses and a lot of these
Speaker 4: benchmarks are from marketing teams. You know, what we do
Speaker 4: internally is two things. First of all, we just try stuff,
Speaker 4: and then we look at it, and we look at
Speaker 4: the very specific you know, it's nice that an AI
Speaker 4: can pass the L set or whatever, but we're you know,
Speaker 4: we're not in the business of passing L sets. We're
Speaker 4: in the business of doing whatever it is we're doing,
Speaker 4: you know, creating short stories or whatever. So whatever task,
Speaker 4: we just try it and then we judge the quality ourselves.
Speaker 4: So far, we have found that the quality of the
Speaker 4: open AI models is a little better than everybody else's,
Speaker 4: but not that much better. I mean two years ago
Speaker 4: it was way better. It seems like everybody else is
Speaker 4: catching up. But so far we have found that that's
Speaker 4: just when we do our tests. And again, this is
Speaker 4: you know, just an end of one one company. I'm
Speaker 4: sure that other companies are finding maybe different stuff, but
Speaker 4: for us, for our specific use cases, we find time
Speaker 4: and again the GPT four does better. And I don't know,
Speaker 4: of course, everybody's now announcing like there's going to be
Speaker 4: GPT five et cetera, et cetera. I don't know how
Speaker 4: those will be, but that's what we're finding. You know, generally,
Speaker 4: would just do our own testing.
Speaker 3: Yeah, Tracy, I find that so fascinating, especially, I think
Speaker 3: we've talked about this, like it definitely seems like TBD
Speaker 3: whether like one model would just prove to be head
Speaker 3: and shoulders better than the others, the way that Google
Speaker 3: was just head and shoulders above everyone else for twenty
Speaker 3: years basically and still is kind of like, it's unclear
Speaker 3: to me whether that'll be the.
Speaker 2: Case with they ask right, the idea that we're in
Speaker 2: the I don't know, the bing era of chat models
Speaker 2: and eventually we're all going to migrate to something else. Luise,
Speaker 2: One thing I wanted to ask you, and this is
Speaker 2: sort of going back to the very beginning of the
Speaker 2: conversation and some of the you know, older thoughts around language.
Speaker 2: There used to be I don't want to say a consensus,
Speaker 2: but there used to be some thinking that language was
Speaker 2: very complicated in many ways, and so much of it
Speaker 2: was sort of ambiguous or maybe context dependent, that it
Speaker 2: would be very hard for AI to sort of wrap
Speaker 2: its head around it. And I'm wondering now, with something
Speaker 2: like due lingo, how do your models take into account
Speaker 2: that sort of context dependency? And I'm thinking, you know,
Speaker 2: I'm thinking specifically about things like Mandarin, where the pronunciation
Speaker 2: is kind of tricky and a lot of understanding depends
Speaker 2: on the context in which a particular word is said.
Speaker 2: So how do you sort of deal with that?
Speaker 4: Yeah, I mean it's an interesting thing. You know, when
Speaker 4: you meant when you were asking the question, I thought
Speaker 4: of this thing. You know, I've been around AI since
Speaker 4: the late nineties, and I remember just it's just this
Speaker 4: moving goalpost. I remember. Everybody just kept on saying, look,
Speaker 4: if a computer can play chess, surely we all agree
Speaker 4: it has human level intelligence. This is kind of what
Speaker 4: everybody said. Then it turned out computers could play chess,
Speaker 4: and nobody agreed that I had human level intelligence. It's
Speaker 4: just like, oh, very fine, it can play just next thing.
Speaker 4: And it would just keep coming up with stuff like,
Speaker 4: surely if a computer can you know, play the game
Speaker 4: of go, or if a computer could do this, then
Speaker 4: you know, And one of the last few things was
Speaker 4: if a computer can whatever right poetry so well or
Speaker 4: understand text, then surely is intelligent. And at this point,
Speaker 4: models like GPT four are really good at doing things,
Speaker 4: certainly better than the average human. They may not be
Speaker 4: as good as the best poet in the world, but
Speaker 4: certainly better than the average human writing poetry, certainly better
Speaker 4: than the average human at almost anything with text manipulation. Actually,
Speaker 4: if you look at your average human, they're just not
Speaker 4: particularly good at writing.
Speaker 3: So many professional writers oh yeah, ye.
Speaker 4: Yeah, I mean just these models are excellent. And in fact,
Speaker 4: you can write something that is half well written and
Speaker 4: you can ask the model to make it better and
Speaker 4: it does that. It like makes your text better. So
Speaker 4: it's this funny thing that just AI. We keep coming
Speaker 4: up with things that like if AI can crack that,
Speaker 4: that's it, that's it. You know, I don't know what
Speaker 4: the next one will be, but you know, we gep
Speaker 4: coming up with stuff like that, you know, in terms
Speaker 4: of the language, it just turns out that language can
Speaker 4: be mostly captured by these models. It turns out that
Speaker 4: if you make a neural network architecture and this you know,
Speaker 4: nobody could have guess this, but it just turns out
Speaker 4: that if you make this neural network at architecture that's
Speaker 4: called the transformer, and you train it with a gazillion
Speaker 4: pieces of text, it just turns out it pretty much
Speaker 4: can capture almost any new ones of the language. Again,
Speaker 4: nobody could have figured this out, but it just turns
Speaker 4: out that this is the case. So at this point,
Speaker 4: when you know, when you ask about you know, what
Speaker 4: we do with context or whatever, it just works when
Speaker 4: you're you know, some of it we do with handwritten
Speaker 4: rules because we write the rules. But generally, if you're
Speaker 4: going to use an AI, it just works. And you
Speaker 4: can ask me why it works. And I don't know
Speaker 4: white works. I don't think anybody does. But it turns
Speaker 4: out that the statistics are kind of strong enough there
Speaker 4: that if you train it with a gazillion pieces of text,
Speaker 4: it just works.
Speaker 3: I just want to go back to the sort of
Speaker 3: like you know where AI is going and you mentioned
Speaker 3: that AI can generate thousands or ten, you know, very
Speaker 3: rapidly numerous short stories, and then a human can say, Okay,
Speaker 3: these are the good ones. We can improve and so
Speaker 3: you not only get the efficiency savings, actually can get
Speaker 3: a better higher quality for the lessons and so forth.
Speaker 3: But you know, sort of like I'm moving up the
Speaker 3: abstraction layer, like, will there be a point at some
Speaker 3: point in the future in which the entire concept of
Speaker 3: learning a language or the entire sequence is almost entirely
Speaker 3: something that AI can do from scratch? Again, I'm thinking
Speaker 3: sort of back to that chess analogy of not having
Speaker 3: to use the entire history of games to learn, but
Speaker 3: just knowing the basic rules and then coming up with
Speaker 3: something further like, will AI eventually be able to sort
Speaker 3: of like design the architecture of what it means to
Speaker 3: learn a language?
Speaker 4: I mean sure, I think at some point EI is
Speaker 4: going to be able to do pretty much everything.
Speaker 2: Right.
Speaker 4: It very hard to know how long this will take.
Speaker 4: I mean, it's just very hard, and honestly for our
Speaker 4: own society, I'm hoping that the process is gradual and
Speaker 4: not from one day to the next, because if we
Speaker 4: find that at some point AI really goes from if
Speaker 4: tomorrow somebody announces, okay, I have an AI that can
Speaker 4: pretty much do everything perfectly. I think this will be
Speaker 4: a major societal problem because we won't know what to do.
Speaker 4: But if this process takes twenty thirty years, at least
Speaker 4: we'll be able to as a society figure out what
Speaker 4: to do with ourselves. But generally, I mean, I think
Speaker 4: at some point AI is going to be able to
Speaker 4: do everything we can.
Speaker 2: What's the big challenge when it comes to AI at
Speaker 2: the moment? I realize we've been talking a lot about opportunities,
Speaker 2: but what are some of the issues that you're trying
Speaker 2: to surmount at the moment, Whether it's something like getting
Speaker 2: enough compute or securing the best engineers, or I guess
Speaker 2: being in competition with a number of other companies that
Speaker 2: are also using AI, maybe in the same business.
Speaker 4: I mean, certainly, securing good engineers has been a challenge
Speaker 4: for anything related to engineering for a while. You know,
Speaker 4: you want the best engineers, and there's just not very
Speaker 4: many of them, so there's a lot of competition. So
Speaker 4: that's certainly true in terms of AI in particular, I
Speaker 4: would say that I don't know what depends on what
Speaker 4: you're trying to achieve. These models are getting better and better.
Speaker 4: What they're not yet quite exhibiting is actual kind of
Speaker 4: deduction and understanding as good as we would want them
Speaker 4: to do. I mean, so you still see really because
Speaker 4: of the way they work, I mean, these are just
Speaker 4: predicting the next word. Because of the way they work,
Speaker 4: you can see them do funky stuff like they get
Speaker 4: adding numbers wrong sometimes because they're not actually adding numbers.
Speaker 4: They're just predicting the next word. And it turns out
Speaker 4: you can predict a lot of things you know you
Speaker 4: may not, So it doesn't quite have a concept of addition, doesn't.
Speaker 4: So I think, you know, if what you're looking for
Speaker 4: is kind of general intelligence, I think there's some amount
Speaker 4: that's going to be required in terms of actually understanding
Speaker 4: certain concepts that these models don't yet have. And that's,
Speaker 4: you know, my sense is that new ideas are needed
Speaker 4: for that. I don't know what they are. If I knew,
Speaker 4: I would do them, but new ideas are needed for that.
Speaker 3: Yeah, it's still like mind blowing, Like you see the
Speaker 3: AI produce some sort of amazing output or explanation and
Speaker 3: then it'll like get wrong. Like a question of like
Speaker 3: what weighs more a kilogram of feathers or kill of steel,
Speaker 3: like something really led.
Speaker 4: Or yah because it doesn't Yeah, right, because.
Speaker 3: There's no one, there's no actual intuition. I just have
Speaker 3: one last question, and it's sort of There are not
Speaker 3: many sort of like cutting edge tech companies based in Pittsburgh.
Speaker 3: I understand like CMU has historically been a bastion of
Speaker 3: advanced AI research. I think at one point, like Uber
Speaker 3: bought out like the entire robotics department when it was
Speaker 3: trying to do self driving cars. But how do you
Speaker 3: see that when it comes to this sort of recruiting
Speaker 3: of talent and it's already scarce. What are the advantages
Speaker 3: and disadvantages of being based in Pittsburgh rather than the
Speaker 3: Bay Area or somewhere else.
Speaker 4: Yeah, we that a quarter in Pittsburgh's it's the beginning.
Speaker 4: We've loved being there. There are good things and bad things.
Speaker 4: I mean, certainly a good thing is being close to
Speaker 4: Carnegie Mellon. Carnegie Mellon produces, you know, some of the
Speaker 4: best engineers in the world, and certainly relating to AI.
Speaker 4: Another good thing about being in a city like Pittsburgh
Speaker 4: is that two good things. One of them is that
Speaker 4: people don't leave jobs that easily. And you know, when
Speaker 4: you're in a place like Silicon Valley, you get these
Speaker 4: people that leave jobs every eighteen months. Our average employee
Speaker 4: stays around for a very long time, and that's actually
Speaker 4: a major advantage because you don't have to retrain them.
Speaker 4: They really know how to do the job because they've
Speaker 4: been doing it for the last seven years. So that
Speaker 4: that's been an advantage. And I think another advantage that
Speaker 4: we've had is in terms of Silicon Valley, there's usually
Speaker 4: one or two companies that are kind of the darlings
Speaker 4: of Silicon Valley, and everybody wants to work there, and
Speaker 4: that the Darling Company changes every two three years, and
Speaker 4: the kind of all the good people go there. The
Speaker 4: good news in Pittsburgh is that fad type thing doesn't happen.
Speaker 4: So there have been times. We're lucky that right now
Speaker 4: our stock is doing very well, so we're kind of
Speaker 4: a fad company. But there have been times when we
Speaker 4: just weren't, but we still were able to get really
Speaker 4: good talent. So I think that's been really good. You know.
Speaker 4: On the flip side, of course, there are certain roles
Speaker 4: for which it is hard to hire people in Pittsburgh.
Speaker 4: Particularly product managers are hard to hire in Pittsburgh. So
Speaker 4: because of that, we have an office in New York,
Speaker 4: and we complement that, we have a pretty long our jofice
Speaker 4: in New York, and we compliment that.
Speaker 2: All right, Louise went on from dual LINGO, thank you
Speaker 2: so much for coming on all thoughts. That was great,
Speaker 2: Thank you excellent, Joe. I enjoyed that conversation. You know
Speaker 2: what I was thinking about when Louis was talking about,
Speaker 2: it's not that AI is going to take her job,
Speaker 2: it's someone who knows how to use AI is going
Speaker 2: to take your job. I was thinking about just before
Speaker 2: we came on this recording, you were telling me that
Speaker 2: you used was it Chat, GPT or claude to learn
Speaker 2: something that I normally do.
Speaker 3: Oh yeah. So for those who don't know, we have
Speaker 3: a weekly odd lauged newsletter and we usually comes out
Speaker 3: every Friday. You should go to subscribe and Tracy usually
Speaker 3: sends an email to one of the guests each week
Speaker 3: asking what books they recommend, you know, people like reading books.
Speaker 3: And then she goes into ms paint and then like
Speaker 3: puts the chatbooks of like the four books together, and
Speaker 3: I did add because Tracy was out a couple weeks ago.
Speaker 3: And I am not, like, I've never like learned Photoshop
Speaker 3: or even MS paint, so just like I'm very dumb,
Speaker 3: Like just like the process of putting four images together
Speaker 3: was not something I exactly knew how to do. So
Speaker 3: I went to Claude and I said, I'm putting together
Speaker 3: four book images in an MS paint thing. Please tell
Speaker 3: me how to do it and to walk through the steps.
Speaker 1: And I did it.
Speaker 3: Tracy, you were proud.
Speaker 2: Of me, right, I was very proud. I do think
Speaker 2: it's somewhat ironic that the pinnacle of AI usage is
Speaker 2: teaching someone how to use MS paint, But it's fine,
Speaker 2: I'll take it. Yeah, No, there's so much to pull
Speaker 2: out of that conversation. One thing I'll say, and maybe
Speaker 2: it's a little bit trite, but it does seem like
Speaker 2: language learning is sort of ground zero for the application
Speaker 2: of a lot of this natural language and chat bought technology.
Speaker 2: So it was interesting to come at it from a
Speaker 2: sort of pure language or linguistics perspective.
Speaker 3: Yeah, I mean, I like, I feel like we could
Speaker 3: have talked to Luist for hours, just on like theory
Speaker 3: of language itself, which I find endlessly fascinating, and I
Speaker 3: really I can only speak one language. I used to
Speaker 3: be able to speak French, so I don't know if
Speaker 3: I told you, but I did one semester in Geneva, Switzerland,
Speaker 3: and I lived with a family that only spoke French,
Speaker 3: and I'd never spoken a word of French before I
Speaker 3: got there. And after one semester, I came home and
Speaker 3: I passed out of four years worth of my college
Speaker 3: requirements from that four months living there. And then I
Speaker 3: didn't speak French again for twenty years and I lost
Speaker 3: it all. But I was gonna go somewhere with that.
Speaker 3: I don't really know.
Speaker 2: It's okay I to speak multiple languages poorly.
Speaker 3: But you know the other thing I was thinking about,
Speaker 3: you know, so due LINGO has obviously been around for
Speaker 3: quite a long time before anyone was talking about generative
Speaker 3: AI or anything. And one of the things you hear,
Speaker 3: and it sort of used pejoratively, is like some company
Speaker 3: will be called like a chet GPT rapper, right, so
Speaker 3: basically they're just taking GPT four whatever the latest model is,
Speaker 3: and then building some slick interface to do a specific
Speaker 3: task on top of it. And what's interesting about dual
Speaker 3: Lingo is it feels like it's backwards or going in
Speaker 3: the opposite sequence where they already had this extremely popular
Speaker 3: app for language learning, and then over time they incorporate
Speaker 3: more so rather than being starting off as a rapper
Speaker 3: for someone else's technology, they already have the audience, they
Speaker 3: already have the thing, and then they find more ways
Speaker 3: that the AI can be used to actually like rebuild
Speaker 3: the core app.
Speaker 2: Yeah, that's a really good way of putting it. And
Speaker 2: also just the iterative nature of all of this technology,
Speaker 2: So the idea that you know, you're sort of training it,
Speaker 2: I know, again it's sort of an obvious point, yeah,
Speaker 2: but also I didn't realize how customized a lot of
Speaker 2: the duo lingo stuff is at this point. And the
Speaker 2: idea that if you speak one language, the way you learn,
Speaker 2: say German, is going to be completely different to someone
Speaker 2: who grew up speaking another language. And I'm very intrigued
Speaker 2: by the amount of data that's something like a duolingo
Speaker 2: have at this point, and I guess maybe we should
Speaker 2: have asked Louise about this. But also other business opportunities
Speaker 2: in terms of like licensing that data or maybe I
Speaker 2: don't know. I think they were doing a partnership for
Speaker 2: a while with BuzzFeed where they were where the cap
Speaker 2: show was like actually translating news articles or something.
Speaker 3: Right, there was going to be something like that, I think.
Speaker 3: I recall it didn't really take off, but the idea
Speaker 3: was BuzzFeed would get its news articles translated into Spanish
Speaker 3: and other languages from the process of duo lingo users
Speaker 3: learning that process. I forget why it didn't take off,
Speaker 3: but yeah, absolutely.
Speaker 2: I also I find it funny like in some senses
Speaker 2: that we're sort of I guess the thing that AI
Speaker 2: is feeding off of now right, And like all those
Speaker 2: minutes which I'm sure add up to days eventually of
Speaker 2: going through Capsha, it's all sort of unpaid labor for
Speaker 2: training our future AI overlords.
Speaker 3: So he mentioned that he was upset about headlines last
Speaker 3: year implying that they had laid off a bunch of
Speaker 3: people due to AI. But he did say that there
Speaker 3: are people who they were contractors, so they weren't full
Speaker 3: time employees. But it sounds like a very crisp example
Speaker 3: of AI being able to do a job even if
Speaker 3: they were contractors. That were done by humans. And I'm
Speaker 3: generally skeptical of most articles and that I read where
Speaker 3: a company says, oh, we're getting like cut all this
Speaker 3: labor savings and we're gonna do AI, because I sort
Speaker 3: of think that is often a smokescreen for just like
Speaker 3: a business that wants to cut jobs and make it
Speaker 3: sound like they're progressive. But here did sound like an
Speaker 3: actual example in which there was some form of human
Speaker 3: labor that is no longer needed because it is AI.
Speaker 2: Yes, AI will come for us all. Shall we leave
Speaker 2: it there?
Speaker 4: Let's leave it there.
Speaker 2: This has been another episode of the All Thoughts podcast.
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 3: Follow our guest Louis Vaughan on He's at Louis van On.
Speaker 3: Follow our producers Carman Rodriguez at Herman Ermann dash Ol
Speaker 3: Bennett at Dashbot and kill Brooks at Kilbrooks. Thank you
Speaker 3: to our producer Moses Ondem From our Oddlows content. Go
Speaker 3: to Bloomberg dot com slash odd Lots, where we have transcripts,
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Speaker 3: of these topics twenty four to seven in the Discord.
Speaker 3: In fact, this episode came about because someone in the
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Speaker 4: In