Speaker 1: Pushkin. Hi, It's Jacob Goldstein and I'm here today with
Speaker 1: another podcast I think you might like. The show is
Speaker 1: called Unsung Science and it's hosted by David Pogue. You
Speaker 1: might know David from CBS Sunday Morning, where he's a
Speaker 1: correspondent covering topics like science, tech, and innovation, topics like
Speaker 1: the ones we talk about here on What's Your Problem.
Speaker 1: In the episode You're about to hear, David chats with
Speaker 1: Luis Vonon, the founder and CEO of the popular language
Speaker 1: app Duolingo. You might recall I talked with Louise earlier
Speaker 1: this year about Duolingo and language and the current limits
Speaker 1: of artificial intelligence. But the show you're about to hear
Speaker 1: is about what Louise did before he started Duolingo. He
Speaker 1: invented this thing called Capsha. Capsha is that test that
Speaker 1: you have to take all the time on the Internet
Speaker 1: to prove that you're not a robot. And yes, Louise
Speaker 1: knows that the test is super annoying. But the story
Speaker 1: of capsha and what happened with it is really interesting.
Speaker 1: It's got some great twists. By the year two thousand,
Speaker 1: the Internet was already becoming a cesspool software bots were
Speaker 1: signing up for millions of fake email accounts for sending
Speaker 1: out spam. Luis Vaughan stopped them. He invented the CAPTA,
Speaker 1: the website login test where you have to decipher the
Speaker 1: distorted image of a word, where you have to find
Speaker 1: the traffic lights in a grid of nine blurry photos.
Speaker 1: The only problem we hate that test. I would be
Speaker 1: at a party and you know, people would ask me
Speaker 1: what I did, and I would tell them that I
Speaker 1: helped invent that thing, and people would tell me, oh,
Speaker 1: I hate you. I'm David Pogue And this is Unsung Science,
Speaker 1: Season one, episode fourteen, The man who stopped the spammers.
Speaker 1: In his forty three years on this earth, so far,
Speaker 1: Luis vonn has had three ingenious innovative world changing ideas.
Speaker 1: I guarantee that you've encountered his second one, probably hundreds
Speaker 1: of times. Actually, most of us have zero world changing ideas.
Speaker 1: Occasionally somebody has won, but three times. His first idea
Speaker 1: came to him in Guatemala, where he grew up. They
Speaker 1: wanted to start a gym where instead of charging people
Speaker 1: to show up, let people just show up for free.
Speaker 1: We're going to connect all the machines to kind of
Speaker 1: the power grid, and we're going to use the kinetic
Speaker 1: energy that people had whenever they were exercising to generate power.
Speaker 1: And I thought we could make a lot of money
Speaker 1: from that. Now you will note that I did not
Speaker 1: say that all three of his world changing ideas actually
Speaker 1: succeeded in changing the world. I thought I was the
Speaker 1: first person to have this idea. It turns out it's
Speaker 1: a very old idea. It also turns out it doesn't work.
Speaker 1: That's right, the pedal power Jim idea flopped. It turns
Speaker 1: out this is not a good idea for many reasons,
Speaker 1: the biggest one of which is that humans are just
Speaker 1: not very good at creating energy. Oh, you just just
Speaker 1: don't make a lot of money from this. There's another
Speaker 1: reason why this doesn't work a lot. It turns out
Speaker 1: Jim's make most of their money from people who don't
Speaker 1: show up. Of course, here you kind of need people
Speaker 1: to show up to be fair. He was pretty new
Speaker 1: at the game when he had this first idea. And
Speaker 1: how old were you at this point, twelve years old,
Speaker 1: eleven years old. Things started going better six years later,
Speaker 1: when he came to the United States to attend Duke University.
Speaker 1: As the year two thousand dawned. Luis was at Carnegie
Speaker 1: Mellon in his first year of working toward a PhD
Speaker 1: in computer science, and one fateful day he went to
Speaker 1: a talk by an Israeli computer scientist named Udi Manber,
Speaker 1: who at this point was the chief scientist at Yahoo.
Speaker 1: By the way, at end the year two thousand, Yahoo
Speaker 1: was the biggest biggest tech company in the words like
Speaker 1: the Google of today. And you know, he was giving
Speaker 1: a talk about ten problems that they didn't know how
Speaker 1: to solve inside inside the company. And one of those
Speaker 1: ten problems that the greatest minds at Yahoo could not
Speaker 1: solve was automated software spam bots signing up for free
Speaker 1: Yahoo mail accounts. By the millions. Yahoo gave up free
Speaker 1: email accounts, and there were people who wanted to send
Speaker 1: spam from Yahoo accounts. But each Yahoo account only allowed
Speaker 1: you to send like five hundred messages a day. If
Speaker 1: you wanted to send millions of emails spam emails per day,
Speaker 1: then what these people did is they wrote programs to
Speaker 1: obtain millions of Yahoo accounts every day, and they didn't
Speaker 1: know how to solve that problem, how to stop that.
Speaker 1: So I started talking about it with a person who
Speaker 1: had just become my PhD advisor. His name was Manuel
Speaker 1: Blum or is Manolum. He's still he's most definitely still alive.
Speaker 1: And you know, we started thinking, and this is where
Speaker 1: this idea of a capture came up. The idea was this,
Speaker 1: anytime you tried to sign up for a Yahoo Mail account,
Speaker 1: you'd encounter a little puzzle, something easy for a person
Speaker 1: to solve, but hard for a spambot. The way to
Speaker 1: stop these spammers was to have a test that can
Speaker 1: distinguish between whether you're a human or a computer. If
Speaker 1: you are a human, then presumably you can't get millions
Speaker 1: of email accounts because you get bored, whereas if you're
Speaker 1: a computer, you can get millions. So if the only
Speaker 1: entity is that we were giving email accounts to where humans,
Speaker 1: then that would stop the spam. KAPTA, the name he
Speaker 1: gave his online mini puzzle, is an acronym. It stands
Speaker 1: for completely automated public touring tests to tell computers and
Speaker 1: humans apart more or less. Not sure if you've heard
Speaker 1: of the touring test, but it is incredibly famous among
Speaker 1: computer scientists. It's this experiment proposed by British mathematician and
Speaker 1: computer scientist Alan Turing, who's known as the father of
Speaker 1: artificial intelligence. There was actually a movie about him called
Speaker 1: The Imitation Game, where Benedict's Cumberbatch played Alan Tour. Would
Speaker 1: you like to play play? It's a game, a test
Speaker 1: of songs for determining whether something is machine or a
Speaker 1: human being. Anyway, the Touring test is intended to set
Speaker 1: a standard for determining if a computer has achieved true
Speaker 1: artificial intelligence. When can we tell that a computer is
Speaker 1: actually intelligent. This is kind of like a philosophical test
Speaker 1: that said, like, look, we're going to have a human
Speaker 1: judge ask questions to two entities. One is the computer,
Speaker 1: one is the human. The computer and the human are
Speaker 1: hidden behind two curtains. The judge can't see them. The
Speaker 1: judge types in questions and then looks at the text
Speaker 1: of the responses. If it's impossible to tell which answer
Speaker 1: came from the person in which from the computer, the
Speaker 1: computer has passed the Turing test. The judge can just
Speaker 1: ask whatever questions they want, and if we really can't
Speaker 1: distinguish them, we'll say the computer is really intelligent. To
Speaker 1: this day, we have not made a computer that can
Speaker 1: actually pass the turning test successfully. It's just it's just
Speaker 1: too hard. The funny thing is, if you really think
Speaker 1: about it, the capture problem is the opposite of the
Speaker 1: touring test. The touring test is successful if the judge
Speaker 1: can't tell the difference between a person and a machine.
Speaker 1: The whole point of Louis Vanan's project was to create
Speaker 1: a test that can tell the difference. There's another difference
Speaker 1: between the two tests too. Here's the key. In this case,
Speaker 1: the judge was a human. In our case for the capture,
Speaker 1: what we needed to do is we needed the judge
Speaker 1: to be a computer because we need we need the
Speaker 1: computer to determine whether it's talking to a human our computer,
Speaker 1: which is which is much harder in some sense, at
Speaker 1: least for to grade it. So I think the hardest
Speaker 1: thing was just coming up with this general idea that like, okay,
Speaker 1: what we need is a test that can assume shroom
Speaker 1: some computers, but that computers need to be able to grade.
Speaker 1: Then after that we started coming up with like, okay,
Speaker 1: what I think the computers are not very good at.
Speaker 1: In the year two thousand, the answer was obvious, computers
Speaker 1: are not very good at identifying what's in pictures. We
Speaker 1: quickly owned in on images and just doing you know,
Speaker 1: images of text, images of flowers, images of stuff. And
Speaker 1: then after a while, the images of text were the
Speaker 1: ones that seemed like the best idea. And then I
Speaker 1: just went and developed a program that distorted random text
Speaker 1: and that was the first version of a cap chow.
Speaker 1: That's right. The test they came up with presents you
Speaker 1: with the image of a typed word, but the letters
Speaker 1: are all like twisted, bent and distorted, as though the
Speaker 1: typist were severely drunk and typing on saran wrap. You
Speaker 1: are supposed to interpret what that word is and type
Speaker 1: it into a box on the website. Actually, computers in
Speaker 1: the early two thousands were pretty good at OCR. That's
Speaker 1: optical character recognition, meaning looking at a picture of text
Speaker 1: and figuring out what the letters are. But the added
Speaker 1: challenge of the twisty distortion really threw those OCR programs
Speaker 1: off the track. Behind the scenes, I mean, what is it.
Speaker 1: I mean there's got to be some I don't know,
Speaker 1: sequel database or massive bank of little images. I mean,
Speaker 1: actually there was no database at first. We would just
Speaker 1: write a program that what it would do is it
Speaker 1: would pick some first random characters, would put them on
Speaker 1: an image, then it would distore him, and then we
Speaker 1: would save that image. And then we just had I
Speaker 1: don't know, a couple of million of those saved, not
Speaker 1: even in a sequel database. Is just they were there,
Speaker 1: so save as files. It worked brilliantly. The spambots didn't
Speaker 1: have a chance. At the time. Vonn had no idea
Speaker 1: if his invention would be of any commercial use. But
Speaker 1: one guy he knew would be interested, Oodi Manber, that
Speaker 1: Yahoo chief scientist who'd given the talk that started this
Speaker 1: whole affair. We sent them an email saying, hey, we
Speaker 1: think we can solve your problem, and he said, oh,
Speaker 1: that that seems like it solves the problem. And then
Speaker 1: in fact, pretty soon after that it was being used
Speaker 1: by Yahoo, and then basically every website started using it,
Speaker 1: and you know, there was millions of websites out there.
Speaker 1: We're using it. Well, how wonderful Luis Spawn's ingenuity. One spammers,
Speaker 1: zero Internet saved. And at first I was very kind
Speaker 1: of proud of myself because, okay, look at the impact
Speaker 1: that my work has had. Basically we stopped spams being
Speaker 1: used by a lot of people. There was only one problem. Now,
Speaker 1: people hated his invention. How many of you have had
Speaker 1: to fill out some sort of web form where even
Speaker 1: has to read a distorted sequence of characters like this, Yeah,
Speaker 1: how many of you found it really really annoying. Okay,
Speaker 1: that's standing. So I invented that. That's how he introduces
Speaker 1: himself in a twenty eleven TEDx talk at Carnegie Mellon.
Speaker 1: I would be at a party, and you know, people
Speaker 1: would ask me what I did, and I would tell
Speaker 1: them that I helped invent that thing. And people would
Speaker 1: tell me, oh, I hate you. That's right. The inventor
Speaker 1: of kapta is fully aware that people hate the thing.
Speaker 1: I say, either well, I'm sorry, or I find it
Speaker 1: annoying too. You've heard it right here, folks. Even he
Speaker 1: finds them annoying. In fact, Louise can tell you exactly
Speaker 1: how much of your time they waste. I did a
Speaker 1: little back of the envelope calculation at the time, about
Speaker 1: two hundred million times a day somebody type one of
Speaker 1: these captures two hundred million times times ten seconds, which
Speaker 1: is how long it takes to type one of these.
Speaker 1: Humanity as a whole was wasting about five hundred thousand
Speaker 1: hours every day typing these annoying captures. Great. So I
Speaker 1: started feeling bad about that, and that's when I started thinking, Okay,
Speaker 1: can we do something good with that time? See, the
Speaker 1: thing is kind of similar to the gym idea. Can
Speaker 1: we get millions of people to do something during that
Speaker 1: time that is actually valuable. I'll give you a hint.
Speaker 1: We're only at the halfway point in this story. After
Speaker 1: the break, we'll tell you what he came up with
Speaker 1: to make those half a million hours every day useful
Speaker 1: to humanity. And one more plug here. I'm the author
Speaker 1: of a book called How to Prepare for Climate Change.
Speaker 1: It's a six hundred page paperback that's designed to be
Speaker 1: a field guide to the new climate. It tells you
Speaker 1: where to live, where to invest, what to grow, how
Speaker 1: to reinforce your home, how to insure, how to talk
Speaker 1: to your kids, and how to ride out wildfires, hurricanes, heatwaves,
Speaker 1: and so on. If you live in a state whose
Speaker 1: name contains a vowel, then you've been affected by climate
Speaker 1: change already, and you should check out this book to
Speaker 1: protect your health, your family, your home, and your finances.
Speaker 1: It's How to Prepare for Climate Change. The book that's
Speaker 1: exactly what it sounds like. Welcome back. By two thousand
Speaker 1: and five, Louis vaughan An's invention the captcha test was
Speaker 1: a huge hit. It reduced the world scumbag spammers to
Speaker 1: blubbering losers. No longer could they bombard websites with phony
Speaker 1: sign ups for the purpose of pursuing their pathetic spanny schemes. Unfortunately,
Speaker 1: he had achieved this success by transferring the burden onto us,
Speaker 1: treating us as though we were guilty until proven innocent.
Speaker 1: Now we were the ones being challenged. We were losing
Speaker 1: ten seconds per website typing in those stupid distorted letters. Now.
Speaker 1: To be fair, history is full of examples like that,
Speaker 1: where the actions of a few selfish, greedy idiots wind
Speaker 1: up inconveniencing billions of innocent people for the rest of
Speaker 1: our lives. You know, some dirtbag tries to put poison
Speaker 1: into drug store tile on all bottles, and now the
Speaker 1: rest of us are stuck with frustrating, plastic, wasteful bottle
Speaker 1: lids forever. Some delinquent tries to blow up a plane
Speaker 1: with a shoe bomb, and now we all have to
Speaker 1: walk through the TSA scanners in our socks. Louise felt
Speaker 1: bad that his hacker blockade wasted everybody's time, but at
Speaker 1: least he could do something about it. So that's a
Speaker 1: very valuable time, So can we use it for something?
Speaker 1: And then I ended up coming up with this idea
Speaker 1: that while you were typing a capture, you could be
Speaker 1: helping digitize books. And here's here's kind of how that works.
Speaker 1: So at the time, this is the year maybe two
Speaker 1: thousand and five, two thousand and six, there were a
Speaker 1: lot of projects trying to digitize all of the world's
Speaker 1: books where where you know. The way that worked is
Speaker 1: you start with a physical book and you want to
Speaker 1: put it on the internet. And the way you do
Speaker 1: that is you basically take a digital photograph of every
Speaker 1: page of the book. Now these are pictures of text.
Speaker 1: The next step in the process is that the computer
Speaker 1: needs to decipher what's the text in there. In other words,
Speaker 1: computers had to perform come on, you know, this term
Speaker 1: ocr optical character recognition, and unfortunately, for books that are
Speaker 1: older where maybe the ink has faded, computers could not
Speaker 1: recognize many of the words. So the thought, the idea was,
Speaker 1: let's take all those words that the computers could not
Speaker 1: recognize while books are being digitized, and let's get people
Speaker 1: to read them for us while they're typing a capture.
Speaker 1: So what we started giving people where these words that
Speaker 1: they con computer was not able to digitize and or
Speaker 1: to recognize. So yeah, all this time you thought you
Speaker 1: were typing random words. In fact, you were helping companies
Speaker 1: digitize old books and articles and, by the way, helping
Speaker 1: Luise's little company make money. The ideas we made a capture,
Speaker 1: a system, a whole system that would help your website
Speaker 1: be protected against BAM, and we gave that away for free.
Speaker 1: And for example, Facebook use our capture and we gave
Speaker 1: it away for free, etc. But always with a caveat
Speaker 1: that if they are going to do that, then we
Speaker 1: can see the answers that users are typing, so that
Speaker 1: we helped digitize something. And the way we made money
Speaker 1: is by charging people who needed digitization stuff. For example,
Speaker 1: the New York Times was our client. The New York
Speaker 1: Times had this old archive of all the editions of
Speaker 1: the New York Times from you know, one hundred and
Speaker 1: thirty years of the New York Times or something like that,
Speaker 1: from the eighteen hundreds, and they needed this to help
Speaker 1: digitize their whole archive. They were sending us all the
Speaker 1: scans they had scanded already and we were sending them.
Speaker 1: We were taking all the words that computer could not recognize,
Speaker 1: and we were getting through the captures people who were,
Speaker 1: for example, signing up for Facebook or Twitter or a
Speaker 1: lot of websites that we're using our capture. They were
Speaker 1: helping us digitize the New York Times, and we would
Speaker 1: make money from The New York Times. It became very successful,
Speaker 1: and then Google bought it to help their book digitization
Speaker 1: whole project. The new system called recapture became an even
Speaker 1: bigger hit. Here's how we described the aftermath in his
Speaker 1: TEDx talk. So every time you buy tickets on Ticketmaster,
Speaker 1: you hope to digitize a book. Facebook, every time you
Speaker 1: add a friend, you help to digitize a book. Twitter,
Speaker 1: and about three hundred and fifty thousand other sites are
Speaker 1: all using recapture. And in fact, the number of sites
Speaker 1: that are using recaptures so high that the number of
Speaker 1: words that we're digitizing per day is really really large.
Speaker 1: It's about one hundred million a day, which is the
Speaker 1: equivalent of about two and a half million books a year.
Speaker 1: And this is all being done one word at a
Speaker 1: time by just people tapping captures on the Internet. There
Speaker 1: are some people who are a little nervous about Google
Speaker 1: being the owner of one of the most widely used
Speaker 1: captive systems. I'm sure you've then asked about that. Yeah,
Speaker 1: there are people who are nervous about that. I mean,
Speaker 1: I understand, I think you know this is these are
Speaker 1: very very tricky questions. I mean, personally, I think the
Speaker 1: privacy fight US is over. I mean I I've given
Speaker 1: up on my privacy against large companies a while ago. Wow.
Speaker 1: Not only that, I also think after having been inside Google,
Speaker 1: I saw with how much respect they treat user data
Speaker 1: because they know that they are, you know, a few
Speaker 1: scandals away from being in deep trouble, so they take
Speaker 1: it with a lot of care, I think. And we
Speaker 1: should point out that Google has said we do not
Speaker 1: use data collected for advertising purposes. Yeah, that's the case,
Speaker 1: and so and I actually believe them. Now. Remember Louise
Speaker 1: said that the hard part was finding a test that
Speaker 1: was too hard for a computer to pass, but easy
Speaker 1: enough for a computer to judge whether the test had
Speaker 1: been passed. That's been bugging me. If the computer chooses
Speaker 1: a word that's so distorted that it itself cannot do
Speaker 1: the ocr then how does it know if we're right. Yeah,
Speaker 1: that's a great question. When we try to digitize books,
Speaker 1: Here's here's what we do. We take a word that
Speaker 1: the computer does not know. We actually pair it with
Speaker 1: another word for which the computer does know the answer,
Speaker 1: and we actually give people both words, and we say
Speaker 1: please type both, and we don't tell them which ones which,
Speaker 1: We just say, hey, please type both. If they type
Speaker 1: the word for which we know the answer, if they
Speaker 1: type that one correctly, we assume that they're human, and
Speaker 1: we also get some confidence that they type the other
Speaker 1: word correctly, and then what we do is okay, so
Speaker 1: now we have a guess for what that other word is.
Speaker 1: We give it to like ten other different people and
Speaker 1: we see if they type the same thing, and if
Speaker 1: they all type the same thing, we get with very
Speaker 1: high accuracy what that word really is, and that works.
Speaker 1: One hallmark of the recapture system in other words, is
Speaker 1: that you have to type in two words. There's sometimes
Speaker 1: also funny words that a funny combinations that happen, especially
Speaker 1: because we are showing two words at a time. Oh boy,
Speaker 1: I mean, you know, there's been all kinds of really
Speaker 1: funny examples where it's just like, you know, a website
Speaker 1: of a church that says like bad Christians and it's
Speaker 1: just but these are just two randomly chosen words, so
Speaker 1: we shouldn't infer any evil on your part. No, they're random. Now,
Speaker 1: a lot has happened since two thousand when capture came along,
Speaker 1: and since two thousand and six when you started unsuspectingly
Speaker 1: helping Google in the New York Times digitize their old pages.
Speaker 1: You know, early on in the first version of a
Speaker 1: cap shop, computers were pretty bad at recognizing distorted text,
Speaker 1: so they didn't have to be that distorted. But you know,
Speaker 1: over time, computers got better and better, and in fact,
Speaker 1: by now computers are in many cases about as good
Speaker 1: as humans. Because of that, we have to make them
Speaker 1: harder and harder. A lot of times, the puzzles are
Speaker 1: so hard that even the human can't pass the challenge.
Speaker 1: I'm sure you've been sent screenshots of words that are
Speaker 1: so much no one can tell where it is. Yes,
Speaker 1: that happens, I mean, it's rare that that happens, and
Speaker 1: that's why the capture itself in true arms race fashion
Speaker 1: has evolved. So what has happened is that for the
Speaker 1: more secure things, the captures have moved away from these
Speaker 1: distorted characters. And what is being used now are these
Speaker 1: the puzzles are now things like you see a bunch
Speaker 1: of pictures and you have to click the ones that
Speaker 1: contain a stop sign right the traffic lights, the fire hydrants. Yeah,
Speaker 1: it's exactly the same idea as recapture, except we're not
Speaker 1: the story. We're not trying to digitize books. This a
Speaker 1: lot of times comes from things like all the all
Speaker 1: the mapping cars or the self driving cars. Basically, these
Speaker 1: are cars that are driving around that are capturing images
Speaker 1: of the whole world. They're trying to figure out what's
Speaker 1: around them. Sometimes they cannot recognize what's in an image.
Speaker 1: So it's a similar case. It takes the things like
Speaker 1: is this a stopting I'm not sure, Okay, send it
Speaker 1: to a human, and then when you get it and
Speaker 1: you click on the store sign, you're actually helping either
Speaker 1: the self driving car or the mapping software or whatever
Speaker 1: know that there is actually a stop sign right here.
Speaker 1: Oh so we're still doing good for the world as
Speaker 1: we do this, still doing good for the world, or
Speaker 1: for a company or for a company, but maybe not
Speaker 1: digitizing books. But it's a similar ideas thing that a
Speaker 1: computer cannot do. You've just solved a mystery for hundreds
Speaker 1: of millions of people. Why it's always traffic lights and
Speaker 1: fire hydrants we're supposed to choose and not bananas and puppies,
Speaker 1: or it has to do with both self driving cars
Speaker 1: and also mapping software. Okay, so now we kind of
Speaker 1: get why we have to put up with these challenges,
Speaker 1: or we did twenty years ago, but really nothing better
Speaker 1: has come along since. Are we sure that there's nothing
Speaker 1: less annoying that we could do to thwart these spammers? Yes,
Speaker 1: there is. By now, it did become a lot less annoying.
Speaker 1: I don't know if you've seen that of late, where
Speaker 1: you know, there's a thing that's us recapture. We're just
Speaker 1: trying to figure out whether you're a human, and they
Speaker 1: just ask you to click somewhere, just click on this box.
Speaker 1: That is much less annoying. So sometimes you don't see
Speaker 1: anything except I'm not a robot by yeah, yeah, yeah,
Speaker 1: I'm not a robot. This is something that is that
Speaker 1: is done by Google. This actually comes from you know,
Speaker 1: the original team, that is the company that they bought
Speaker 1: from me. When you get that one, that means that,
Speaker 1: in this particular case, probably means Google has figured out that, yeah,
Speaker 1: you know what, we know you because you've been around
Speaker 1: since twenty sixteen in this computer, and yeah, you have
Speaker 1: a lot of Gmail emails, and you've done a lot
Speaker 1: of Google search queries. You're a normal person, You're not
Speaker 1: a spammer. So they just do a little thing that
Speaker 1: just tries to double check that, you know, I can
Speaker 1: move the mouse or whatever. So one thing that has
Speaker 1: changed from the year two thousand and five to now
Speaker 1: is that there are companies like Google or like Facebook
Speaker 1: that for the majority of people on the Internet, they
Speaker 1: kind of know who you are. If you have a
Speaker 1: fresh computer that you've never used before, then you would
Speaker 1: have to do the annoying capture. But for most of us,
Speaker 1: you're unlikely to have to type these as much as
Speaker 1: you you were back in say the year two thousand
Speaker 1: and five. It has become a lot better, you know,
Speaker 1: probably a little bit at the cost of your privacy. Okay,
Speaker 1: but wait a minute, we now know that computers eventually
Speaker 1: got too smart for the distorted text reading touring tests.
Speaker 1: Won't they eventually get good enough to identify a few
Speaker 1: stupid stop signs in a photo grid? It is, it's
Speaker 1: a cat of mouse game. Now. Probably there's a bunch
Speaker 1: of people working on making better recognition of stop signs
Speaker 1: or something like that eventually. But eventually computers are going
Speaker 1: to be able to do everything humans can, and so
Speaker 1: at some point there won't be a test that kind
Speaker 1: distinguished humans going to computer. Well wait a minute, does
Speaker 1: that mean the end of the internet? I mean, what
Speaker 1: happens if that, If there's no sort of touring tests
Speaker 1: that works anymore. I don't think it's the end of
Speaker 1: the internet, particularly because, like I said, more and more
Speaker 1: these companies are going to know more and more about you,
Speaker 1: and I just don't think there will be a humans problem. Okay, well,
Speaker 1: whatever the end game is, why can't we do today,
Speaker 1: Since we know it's an arms race, Since we know
Speaker 1: that eventually we'll lose it to AI and computers, why
Speaker 1: can't we jump to whatever we'll follow it? Now? I'll
Speaker 1: tell you why this. By the way, it's like ninety
Speaker 1: five percent of the way there. I mean, really, for
Speaker 1: most of us, you know, Facebook knows who we are,
Speaker 1: and Google knows who we are, So it's ninety percent
Speaker 1: of the way there. The reason is not one hundred
Speaker 1: percent of the way there is because there are some
Speaker 1: people who really care about privacy, and you know, there's
Speaker 1: there's always going to be a kind of a way
Speaker 1: to browse privately. So for example, there's a chrome has
Speaker 1: private browsing. So it's all the stuff when people care
Speaker 1: about privacy, I mean there's there's a trade off here, right. Well,
Speaker 1: the irony is it seems like most of the websites
Speaker 1: to present me with a capture I'm trying to get
Speaker 1: to in order to supply my name and address, like
Speaker 1: like I'm signing up for something. Yes, it's funny. Why
Speaker 1: do I need privacy when the whole purpose is to
Speaker 1: supply my information? Yeah, that's funny. Now. I mentioned at
Speaker 1: the beginning that Louis has had three world changing ideas.
Speaker 1: You've heard about the gym membership that powers the grid,
Speaker 1: and you now know about kapture. But what about his
Speaker 1: third creation. It's Duo Lingo, the language training app. At
Speaker 1: this moment, it has half a billion registered users learning
Speaker 1: forty different languages, all for free. And from the very
Speaker 1: beginning you could see the fingerprints of Luis Vaughnan, master
Speaker 1: of crowdsourcing all over it. In early due Lingo, as
Speaker 1: you were learning a language on dual Lingo, you're actually
Speaker 1: helping us to translate stuff that computers could not translate.
Speaker 1: In fact, CNN was a client, so CNN would send
Speaker 1: us their news in English. We would then give it
Speaker 1: to people who were Spanish speakers who were learning English,
Speaker 1: and we would say, hey, you want to practice your English,
Speaker 1: help us translate this CNN article into your native language
Speaker 1: of Spanish. And so they would do it, and they
Speaker 1: would be learning English and then we would get that translation,
Speaker 1: and then we would send it back to CNN and
Speaker 1: they would pay us for the translation. That was the
Speaker 1: very first version of due Lingo. It turned out that,
Speaker 1: just like the gym, it ends up being that it
Speaker 1: just can't make much money from this, and so we decided, okay,
Speaker 1: just go go to a business model where we actually
Speaker 1: give you ads, and the way we make money is by,
Speaker 1: you know, showing your ads. The dude just keeps doing that.
Speaker 1: He keeps coming up with ideas that make the world
Speaker 1: a better place, thwart the bad guys, and make a
Speaker 1: lot of money. It's really a shame he gave up
Speaker 1: that electrical grid gym thing. Are there ever things that
Speaker 1: come to you in the shower that might be your
Speaker 1: big third act? I mean, honestly, to have the impact
Speaker 1: you've had twice is astonishing, But it makes me think
Speaker 1: there's something in you that just has great ideas that
Speaker 1: can go really wide. You know, as time passes, I
Speaker 1: am a lot more interested in literacy and teaching people
Speaker 1: how to read. I think with a computer, we should
Speaker 1: be able to teach the whole world how to read
Speaker 1: significantly better than humans can teach you how to read.
Speaker 1: You know, the US, The US is fine, most adults
Speaker 1: Indias know how to read, but many countries in the
Speaker 1: world there's a significant fraction of people who don't know
Speaker 1: how to read. In fact, there's about a billion adults
Speaker 1: in the world that are illiterate. And I think we
Speaker 1: can I think we can make a big dent, you know,
Speaker 1: with a system to teach people how to read. So
Speaker 1: we're working on that in the meantime. Now you know
Speaker 1: why you have to encounter those infernal website challenges. You
Speaker 1: know how they came about, and you now consider them
Speaker 1: unnecessary evil. Well, maybe you do just for people who
Speaker 1: are like, I don't know what it is. I just
Speaker 1: don't like doing it. I can't even tell what's a
Speaker 1: freaking draffic. Like, let's just lay out what would happen
Speaker 1: if all these challenges went away tomorrow. What would happen
Speaker 1: to the Internet. Most likely, you would get a lot
Speaker 1: more spam in either your email spam or you'd get
Speaker 1: a more kind of random Facebook follow words that are
Speaker 1: not you know, real people. These fake accounts can start
Speaker 1: boosting up that political messages. There would be probably more
Speaker 1: fake news. They would probably be you know, more spam, right,
Speaker 1: and from spam fishing and spywear and yeah, more spywear. Yeah.
Speaker 1: The web would be a less safe place, all right.
Speaker 1: So when you do explain this to someone at the
Speaker 1: proverbial party, are they generally satisfied with the notion that? Yeah,
Speaker 1: I think most people. I think most people realize that
Speaker 1: it's like, you know, these things are kind of like
Speaker 1: a like a key nobody nobody likes. It's not like
Speaker 1: I love opening my door with the key. It's kind
Speaker 1: of annoying, but yeah, that's there, and I understand it
Speaker 1: just makes it makes my house safer. In this case,
Speaker 1: it's kind of just makes the whole Internet safer. I
Speaker 1: kind of gotta do it. Thanks for listening to this
Speaker 1: second to last episode of the season. If you have
Speaker 1: any interest in a second season, please spread the word,
Speaker 1: subscribe to this podcast, leave a review on Apple Podcasts,
Speaker 1: or a rating on Spotify. Unsung Science with David Pogue
Speaker 1: is presented by Simon and Schuster and CBS News and
Speaker 1: produced by PRX Productions. The executive producers for Simon and
Speaker 1: Schuster are Richard Rohrer and Chris Lynch. The PRX production
Speaker 1: team is Jocelyn Gonzalez, Morgan Flannery, Pedro, Raphael Rosatto, and
Speaker 1: Ian Fox. Project manager Jesse Nelson composed the Unsung Science
Speaker 1: theme music and Christina Robello fact checked my script. At
Speaker 1: Unsung Science dot com, you can listen to every episode
Speaker 1: we've ever made and read complete transcripts. For more of
Speaker 1: my stuff, visit David Pogue dot com or follow me
Speaker 1: on Twitter at Pogue Pogue. Thanks for the scene that
Speaker 1: was an episode of Unsung Science from our friends at
Speaker 1: CBS News. You can find more episodes of Unsung Science
Speaker 1: wherever you get your podcasts.