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From Unsung Science with David Pogue: The Man Who Stopped the Spammers · What's Your Problem?

0:15
Speaker 1: Pushkin. Hi, It's Jacob Goldstein and I'm here today with
0:20
Speaker 1: another podcast I think you might like. The show is
0:23
Speaker 1: called Unsung Science and it's hosted by David Pogue. You
0:27
Speaker 1: might know David from CBS Sunday Morning, where he's a
0:31
Speaker 1: correspondent covering topics like science, tech, and innovation, topics like
0:35
Speaker 1: the ones we talk about here on What's Your Problem.
0:37
Speaker 1: In the episode You're about to hear, David chats with
0:40
Speaker 1: Luis Vonon, the founder and CEO of the popular language
0:44
Speaker 1: app Duolingo. You might recall I talked with Louise earlier
0:47
Speaker 1: this year about Duolingo and language and the current limits
0:50
Speaker 1: of artificial intelligence. But the show you're about to hear
0:53
Speaker 1: is about what Louise did before he started Duolingo. He
0:57
Speaker 1: invented this thing called Capsha. Capsha is that test that
1:00
Speaker 1: you have to take all the time on the Internet
1:02
Speaker 1: to prove that you're not a robot. And yes, Louise
1:05
Speaker 1: knows that the test is super annoying. But the story
1:08
Speaker 1: of capsha and what happened with it is really interesting.
1:11
Speaker 1: It's got some great twists. By the year two thousand,
1:18
Speaker 1: the Internet was already becoming a cesspool software bots were
1:22
Speaker 1: signing up for millions of fake email accounts for sending
1:26
Speaker 1: out spam. Luis Vaughan stopped them. He invented the CAPTA,
1:31
Speaker 1: the website login test where you have to decipher the
1:35
Speaker 1: distorted image of a word, where you have to find
1:38
Speaker 1: the traffic lights in a grid of nine blurry photos.
1:42
Speaker 1: The only problem we hate that test. I would be
1:45
Speaker 1: at a party and you know, people would ask me
1:47
Speaker 1: what I did, and I would tell them that I
1:48
Speaker 1: helped invent that thing, and people would tell me, oh,
1:50
Speaker 1: I hate you. I'm David Pogue And this is Unsung Science,
2:01
Speaker 1: Season one, episode fourteen, The man who stopped the spammers.
2:08
Speaker 1: In his forty three years on this earth, so far,
2:11
Speaker 1: Luis vonn has had three ingenious innovative world changing ideas.
2:18
Speaker 1: I guarantee that you've encountered his second one, probably hundreds
2:22
Speaker 1: of times. Actually, most of us have zero world changing ideas.
2:27
Speaker 1: Occasionally somebody has won, but three times. His first idea
2:34
Speaker 1: came to him in Guatemala, where he grew up. They
2:37
Speaker 1: wanted to start a gym where instead of charging people
2:41
Speaker 1: to show up, let people just show up for free.
2:44
Speaker 1: We're going to connect all the machines to kind of
2:46
Speaker 1: the power grid, and we're going to use the kinetic
2:48
Speaker 1: energy that people had whenever they were exercising to generate power.
2:53
Speaker 1: And I thought we could make a lot of money
2:55
Speaker 1: from that. Now you will note that I did not
2:57
Speaker 1: say that all three of his world changing ideas actually
3:00
Speaker 1: succeeded in changing the world. I thought I was the
3:03
Speaker 1: first person to have this idea. It turns out it's
3:05
Speaker 1: a very old idea. It also turns out it doesn't work.
3:07
Speaker 1: That's right, the pedal power Jim idea flopped. It turns
3:11
Speaker 1: out this is not a good idea for many reasons,
3:13
Speaker 1: the biggest one of which is that humans are just
3:15
Speaker 1: not very good at creating energy. Oh, you just just
3:18
Speaker 1: don't make a lot of money from this. There's another
3:20
Speaker 1: reason why this doesn't work a lot. It turns out
3:22
Speaker 1: Jim's make most of their money from people who don't
3:24
Speaker 1: show up. Of course, here you kind of need people
3:26
Speaker 1: to show up to be fair. He was pretty new
3:30
Speaker 1: at the game when he had this first idea. And
3:33
Speaker 1: how old were you at this point, twelve years old,
3:36
Speaker 1: eleven years old. Things started going better six years later,
3:39
Speaker 1: when he came to the United States to attend Duke University.
3:43
Speaker 1: As the year two thousand dawned. Luis was at Carnegie
3:46
Speaker 1: Mellon in his first year of working toward a PhD
3:49
Speaker 1: in computer science, and one fateful day he went to
3:53
Speaker 1: a talk by an Israeli computer scientist named Udi Manber,
3:58
Speaker 1: who at this point was the chief scientist at Yahoo.
4:02
Speaker 1: By the way, at end the year two thousand, Yahoo
4:03
Speaker 1: was the biggest biggest tech company in the words like
4:07
Speaker 1: the Google of today. And you know, he was giving
4:10
Speaker 1: a talk about ten problems that they didn't know how
4:12
Speaker 1: to solve inside inside the company. And one of those
4:16
Speaker 1: ten problems that the greatest minds at Yahoo could not
4:19
Speaker 1: solve was automated software spam bots signing up for free
4:24
Speaker 1: Yahoo mail accounts. By the millions. Yahoo gave up free
4:28
Speaker 1: email accounts, and there were people who wanted to send
4:30
Speaker 1: spam from Yahoo accounts. But each Yahoo account only allowed
4:34
Speaker 1: you to send like five hundred messages a day. If
4:37
Speaker 1: you wanted to send millions of emails spam emails per day,
4:40
Speaker 1: then what these people did is they wrote programs to
4:43
Speaker 1: obtain millions of Yahoo accounts every day, and they didn't
4:46
Speaker 1: know how to solve that problem, how to stop that.
4:48
Speaker 1: So I started talking about it with a person who
4:51
Speaker 1: had just become my PhD advisor. His name was Manuel
4:54
Speaker 1: Blum or is Manolum. He's still he's most definitely still alive.
4:57
Speaker 1: And you know, we started thinking, and this is where
5:00
Speaker 1: this idea of a capture came up. The idea was this,
5:03
Speaker 1: anytime you tried to sign up for a Yahoo Mail account,
5:06
Speaker 1: you'd encounter a little puzzle, something easy for a person
5:10
Speaker 1: to solve, but hard for a spambot. The way to
5:13
Speaker 1: stop these spammers was to have a test that can
5:16
Speaker 1: distinguish between whether you're a human or a computer. If
5:20
Speaker 1: you are a human, then presumably you can't get millions
5:23
Speaker 1: of email accounts because you get bored, whereas if you're
5:26
Speaker 1: a computer, you can get millions. So if the only
5:28
Speaker 1: entity is that we were giving email accounts to where humans,
5:31
Speaker 1: then that would stop the spam. KAPTA, the name he
5:34
Speaker 1: gave his online mini puzzle, is an acronym. It stands
5:38
Speaker 1: for completely automated public touring tests to tell computers and
5:44
Speaker 1: humans apart more or less. Not sure if you've heard
5:49
Speaker 1: of the touring test, but it is incredibly famous among
5:52
Speaker 1: computer scientists. It's this experiment proposed by British mathematician and
5:57
Speaker 1: computer scientist Alan Turing, who's known as the father of
6:01
Speaker 1: artificial intelligence. There was actually a movie about him called
6:04
Speaker 1: The Imitation Game, where Benedict's Cumberbatch played Alan Tour. Would
6:09
Speaker 1: you like to play play? It's a game, a test
6:15
Speaker 1: of songs for determining whether something is machine or a
6:20
Speaker 1: human being. Anyway, the Touring test is intended to set
6:24
Speaker 1: a standard for determining if a computer has achieved true
6:29
Speaker 1: artificial intelligence. When can we tell that a computer is
6:33
Speaker 1: actually intelligent. This is kind of like a philosophical test
6:35
Speaker 1: that said, like, look, we're going to have a human
6:38
Speaker 1: judge ask questions to two entities. One is the computer,
6:42
Speaker 1: one is the human. The computer and the human are
6:44
Speaker 1: hidden behind two curtains. The judge can't see them. The
6:48
Speaker 1: judge types in questions and then looks at the text
6:51
Speaker 1: of the responses. If it's impossible to tell which answer
6:55
Speaker 1: came from the person in which from the computer, the
6:58
Speaker 1: computer has passed the Turing test. The judge can just
7:02
Speaker 1: ask whatever questions they want, and if we really can't
7:04
Speaker 1: distinguish them, we'll say the computer is really intelligent. To
7:07
Speaker 1: this day, we have not made a computer that can
7:09
Speaker 1: actually pass the turning test successfully. It's just it's just
7:13
Speaker 1: too hard. The funny thing is, if you really think
7:15
Speaker 1: about it, the capture problem is the opposite of the
7:19
Speaker 1: touring test. The touring test is successful if the judge
7:23
Speaker 1: can't tell the difference between a person and a machine.
7:27
Speaker 1: The whole point of Louis Vanan's project was to create
7:29
Speaker 1: a test that can tell the difference. There's another difference
7:33
Speaker 1: between the two tests too. Here's the key. In this case,
7:36
Speaker 1: the judge was a human. In our case for the capture,
7:40
Speaker 1: what we needed to do is we needed the judge
7:41
Speaker 1: to be a computer because we need we need the
7:44
Speaker 1: computer to determine whether it's talking to a human our computer,
7:46
Speaker 1: which is which is much harder in some sense, at
7:49
Speaker 1: least for to grade it. So I think the hardest
7:51
Speaker 1: thing was just coming up with this general idea that like, okay,
7:54
Speaker 1: what we need is a test that can assume shroom
7:56
Speaker 1: some computers, but that computers need to be able to grade.
7:59
Speaker 1: Then after that we started coming up with like, okay,
8:02
Speaker 1: what I think the computers are not very good at.
8:04
Speaker 1: In the year two thousand, the answer was obvious, computers
8:08
Speaker 1: are not very good at identifying what's in pictures. We
8:12
Speaker 1: quickly owned in on images and just doing you know,
8:15
Speaker 1: images of text, images of flowers, images of stuff. And
8:19
Speaker 1: then after a while, the images of text were the
8:21
Speaker 1: ones that seemed like the best idea. And then I
8:23
Speaker 1: just went and developed a program that distorted random text
8:27
Speaker 1: and that was the first version of a cap chow.
8:30
Speaker 1: That's right. The test they came up with presents you
8:33
Speaker 1: with the image of a typed word, but the letters
8:36
Speaker 1: are all like twisted, bent and distorted, as though the
8:40
Speaker 1: typist were severely drunk and typing on saran wrap. You
8:44
Speaker 1: are supposed to interpret what that word is and type
8:47
Speaker 1: it into a box on the website. Actually, computers in
8:52
Speaker 1: the early two thousands were pretty good at OCR. That's
8:56
Speaker 1: optical character recognition, meaning looking at a picture of text
9:00
Speaker 1: and figuring out what the letters are. But the added
9:03
Speaker 1: challenge of the twisty distortion really threw those OCR programs
9:07
Speaker 1: off the track. Behind the scenes, I mean, what is it.
9:11
Speaker 1: I mean there's got to be some I don't know,
9:13
Speaker 1: sequel database or massive bank of little images. I mean,
9:18
Speaker 1: actually there was no database at first. We would just
9:21
Speaker 1: write a program that what it would do is it
9:23
Speaker 1: would pick some first random characters, would put them on
9:26
Speaker 1: an image, then it would distore him, and then we
9:28
Speaker 1: would save that image. And then we just had I
9:31
Speaker 1: don't know, a couple of million of those saved, not
9:32
Speaker 1: even in a sequel database. Is just they were there,
9:34
Speaker 1: so save as files. It worked brilliantly. The spambots didn't
9:39
Speaker 1: have a chance. At the time. Vonn had no idea
9:43
Speaker 1: if his invention would be of any commercial use. But
9:46
Speaker 1: one guy he knew would be interested, Oodi Manber, that
9:51
Speaker 1: Yahoo chief scientist who'd given the talk that started this
9:54
Speaker 1: whole affair. We sent them an email saying, hey, we
9:57
Speaker 1: think we can solve your problem, and he said, oh,
10:00
Speaker 1: that that seems like it solves the problem. And then
10:02
Speaker 1: in fact, pretty soon after that it was being used
10:03
Speaker 1: by Yahoo, and then basically every website started using it,
10:07
Speaker 1: and you know, there was millions of websites out there.
10:11
Speaker 1: We're using it. Well, how wonderful Luis Spawn's ingenuity. One spammers,
10:18
Speaker 1: zero Internet saved. And at first I was very kind
10:23
Speaker 1: of proud of myself because, okay, look at the impact
10:25
Speaker 1: that my work has had. Basically we stopped spams being
10:28
Speaker 1: used by a lot of people. There was only one problem. Now,
10:32
Speaker 1: people hated his invention. How many of you have had
10:36
Speaker 1: to fill out some sort of web form where even
10:38
Speaker 1: has to read a distorted sequence of characters like this, Yeah,
10:40
Speaker 1: how many of you found it really really annoying. Okay,
10:43
Speaker 1: that's standing. So I invented that. That's how he introduces
10:48
Speaker 1: himself in a twenty eleven TEDx talk at Carnegie Mellon.
10:52
Speaker 1: I would be at a party, and you know, people
10:54
Speaker 1: would ask me what I did, and I would tell
10:55
Speaker 1: them that I helped invent that thing. And people would
10:57
Speaker 1: tell me, oh, I hate you. That's right. The inventor
10:59
Speaker 1: of kapta is fully aware that people hate the thing.
11:03
Speaker 1: I say, either well, I'm sorry, or I find it
11:08
Speaker 1: annoying too. You've heard it right here, folks. Even he
11:12
Speaker 1: finds them annoying. In fact, Louise can tell you exactly
11:16
Speaker 1: how much of your time they waste. I did a
11:18
Speaker 1: little back of the envelope calculation at the time, about
11:21
Speaker 1: two hundred million times a day somebody type one of
11:23
Speaker 1: these captures two hundred million times times ten seconds, which
11:28
Speaker 1: is how long it takes to type one of these.
11:30
Speaker 1: Humanity as a whole was wasting about five hundred thousand
11:32
Speaker 1: hours every day typing these annoying captures. Great. So I
11:36
Speaker 1: started feeling bad about that, and that's when I started thinking, Okay,
11:39
Speaker 1: can we do something good with that time? See, the
11:41
Speaker 1: thing is kind of similar to the gym idea. Can
11:44
Speaker 1: we get millions of people to do something during that
11:48
Speaker 1: time that is actually valuable. I'll give you a hint.
11:51
Speaker 1: We're only at the halfway point in this story. After
11:54
Speaker 1: the break, we'll tell you what he came up with
11:57
Speaker 1: to make those half a million hours every day useful
12:00
Speaker 1: to humanity. And one more plug here. I'm the author
12:11
Speaker 1: of a book called How to Prepare for Climate Change.
12:14
Speaker 1: It's a six hundred page paperback that's designed to be
12:17
Speaker 1: a field guide to the new climate. It tells you
12:20
Speaker 1: where to live, where to invest, what to grow, how
12:23
Speaker 1: to reinforce your home, how to insure, how to talk
12:26
Speaker 1: to your kids, and how to ride out wildfires, hurricanes, heatwaves,
12:30
Speaker 1: and so on. If you live in a state whose
12:33
Speaker 1: name contains a vowel, then you've been affected by climate
12:37
Speaker 1: change already, and you should check out this book to
12:39
Speaker 1: protect your health, your family, your home, and your finances.
12:43
Speaker 1: It's How to Prepare for Climate Change. The book that's
12:46
Speaker 1: exactly what it sounds like. Welcome back. By two thousand
12:54
Speaker 1: and five, Louis vaughan An's invention the captcha test was
12:57
Speaker 1: a huge hit. It reduced the world scumbag spammers to
13:02
Speaker 1: blubbering losers. No longer could they bombard websites with phony
13:06
Speaker 1: sign ups for the purpose of pursuing their pathetic spanny schemes. Unfortunately,
13:13
Speaker 1: he had achieved this success by transferring the burden onto us,
13:18
Speaker 1: treating us as though we were guilty until proven innocent.
13:21
Speaker 1: Now we were the ones being challenged. We were losing
13:25
Speaker 1: ten seconds per website typing in those stupid distorted letters. Now.
13:30
Speaker 1: To be fair, history is full of examples like that,
13:34
Speaker 1: where the actions of a few selfish, greedy idiots wind
13:37
Speaker 1: up inconveniencing billions of innocent people for the rest of
13:41
Speaker 1: our lives. You know, some dirtbag tries to put poison
13:44
Speaker 1: into drug store tile on all bottles, and now the
13:47
Speaker 1: rest of us are stuck with frustrating, plastic, wasteful bottle
13:51
Speaker 1: lids forever. Some delinquent tries to blow up a plane
13:54
Speaker 1: with a shoe bomb, and now we all have to
13:56
Speaker 1: walk through the TSA scanners in our socks. Louise felt
14:00
Speaker 1: bad that his hacker blockade wasted everybody's time, but at
14:04
Speaker 1: least he could do something about it. So that's a
14:06
Speaker 1: very valuable time, So can we use it for something?
14:09
Speaker 1: And then I ended up coming up with this idea
14:11
Speaker 1: that while you were typing a capture, you could be
14:14
Speaker 1: helping digitize books. And here's here's kind of how that works.
14:19
Speaker 1: So at the time, this is the year maybe two
14:20
Speaker 1: thousand and five, two thousand and six, there were a
14:22
Speaker 1: lot of projects trying to digitize all of the world's
14:24
Speaker 1: books where where you know. The way that worked is
14:26
Speaker 1: you start with a physical book and you want to
14:28
Speaker 1: put it on the internet. And the way you do
14:30
Speaker 1: that is you basically take a digital photograph of every
14:33
Speaker 1: page of the book. Now these are pictures of text.
14:37
Speaker 1: The next step in the process is that the computer
14:38
Speaker 1: needs to decipher what's the text in there. In other words,
14:42
Speaker 1: computers had to perform come on, you know, this term
14:45
Speaker 1: ocr optical character recognition, and unfortunately, for books that are
14:52
Speaker 1: older where maybe the ink has faded, computers could not
14:55
Speaker 1: recognize many of the words. So the thought, the idea was,
14:59
Speaker 1: let's take all those words that the computers could not
15:01
Speaker 1: recognize while books are being digitized, and let's get people
15:04
Speaker 1: to read them for us while they're typing a capture.
15:06
Speaker 1: So what we started giving people where these words that
15:08
Speaker 1: they con computer was not able to digitize and or
15:12
Speaker 1: to recognize. So yeah, all this time you thought you
15:15
Speaker 1: were typing random words. In fact, you were helping companies
15:20
Speaker 1: digitize old books and articles and, by the way, helping
15:24
Speaker 1: Luise's little company make money. The ideas we made a capture,
15:28
Speaker 1: a system, a whole system that would help your website
15:30
Speaker 1: be protected against BAM, and we gave that away for free.
15:33
Speaker 1: And for example, Facebook use our capture and we gave
15:36
Speaker 1: it away for free, etc. But always with a caveat
15:39
Speaker 1: that if they are going to do that, then we
15:42
Speaker 1: can see the answers that users are typing, so that
15:44
Speaker 1: we helped digitize something. And the way we made money
15:48
Speaker 1: is by charging people who needed digitization stuff. For example,
15:52
Speaker 1: the New York Times was our client. The New York
15:54
Speaker 1: Times had this old archive of all the editions of
15:57
Speaker 1: the New York Times from you know, one hundred and
15:59
Speaker 1: thirty years of the New York Times or something like that,
16:00
Speaker 1: from the eighteen hundreds, and they needed this to help
16:05
Speaker 1: digitize their whole archive. They were sending us all the
16:07
Speaker 1: scans they had scanded already and we were sending them.
16:10
Speaker 1: We were taking all the words that computer could not recognize,
16:12
Speaker 1: and we were getting through the captures people who were,
16:15
Speaker 1: for example, signing up for Facebook or Twitter or a
16:18
Speaker 1: lot of websites that we're using our capture. They were
16:19
Speaker 1: helping us digitize the New York Times, and we would
16:21
Speaker 1: make money from The New York Times. It became very successful,
16:24
Speaker 1: and then Google bought it to help their book digitization
16:27
Speaker 1: whole project. The new system called recapture became an even
16:31
Speaker 1: bigger hit. Here's how we described the aftermath in his
16:34
Speaker 1: TEDx talk. So every time you buy tickets on Ticketmaster,
16:37
Speaker 1: you hope to digitize a book. Facebook, every time you
16:39
Speaker 1: add a friend, you help to digitize a book. Twitter,
16:42
Speaker 1: and about three hundred and fifty thousand other sites are
16:44
Speaker 1: all using recapture. And in fact, the number of sites
16:46
Speaker 1: that are using recaptures so high that the number of
16:48
Speaker 1: words that we're digitizing per day is really really large.
16:51
Speaker 1: It's about one hundred million a day, which is the
16:53
Speaker 1: equivalent of about two and a half million books a year.
16:56
Speaker 1: And this is all being done one word at a
16:58
Speaker 1: time by just people tapping captures on the Internet. There
17:05
Speaker 1: are some people who are a little nervous about Google
17:07
Speaker 1: being the owner of one of the most widely used
17:12
Speaker 1: captive systems. I'm sure you've then asked about that. Yeah,
17:15
Speaker 1: there are people who are nervous about that. I mean,
17:17
Speaker 1: I understand, I think you know this is these are
17:20
Speaker 1: very very tricky questions. I mean, personally, I think the
17:25
Speaker 1: privacy fight US is over. I mean I I've given
17:28
Speaker 1: up on my privacy against large companies a while ago. Wow.
17:31
Speaker 1: Not only that, I also think after having been inside Google,
17:35
Speaker 1: I saw with how much respect they treat user data
17:37
Speaker 1: because they know that they are, you know, a few
17:40
Speaker 1: scandals away from being in deep trouble, so they take
17:45
Speaker 1: it with a lot of care, I think. And we
17:47
Speaker 1: should point out that Google has said we do not
17:50
Speaker 1: use data collected for advertising purposes. Yeah, that's the case,
17:54
Speaker 1: and so and I actually believe them. Now. Remember Louise
17:57
Speaker 1: said that the hard part was finding a test that
18:00
Speaker 1: was too hard for a computer to pass, but easy
18:03
Speaker 1: enough for a computer to judge whether the test had
18:07
Speaker 1: been passed. That's been bugging me. If the computer chooses
18:12
Speaker 1: a word that's so distorted that it itself cannot do
18:15
Speaker 1: the ocr then how does it know if we're right. Yeah,
18:19
Speaker 1: that's a great question. When we try to digitize books,
18:22
Speaker 1: Here's here's what we do. We take a word that
18:24
Speaker 1: the computer does not know. We actually pair it with
18:28
Speaker 1: another word for which the computer does know the answer,
18:30
Speaker 1: and we actually give people both words, and we say
18:33
Speaker 1: please type both, and we don't tell them which ones which,
18:35
Speaker 1: We just say, hey, please type both. If they type
18:37
Speaker 1: the word for which we know the answer, if they
18:39
Speaker 1: type that one correctly, we assume that they're human, and
18:42
Speaker 1: we also get some confidence that they type the other
18:44
Speaker 1: word correctly, and then what we do is okay, so
18:47
Speaker 1: now we have a guess for what that other word is.
18:49
Speaker 1: We give it to like ten other different people and
18:51
Speaker 1: we see if they type the same thing, and if
18:53
Speaker 1: they all type the same thing, we get with very
18:55
Speaker 1: high accuracy what that word really is, and that works.
19:00
Speaker 1: One hallmark of the recapture system in other words, is
19:03
Speaker 1: that you have to type in two words. There's sometimes
19:07
Speaker 1: also funny words that a funny combinations that happen, especially
19:11
Speaker 1: because we are showing two words at a time. Oh boy,
19:14
Speaker 1: I mean, you know, there's been all kinds of really
19:16
Speaker 1: funny examples where it's just like, you know, a website
19:19
Speaker 1: of a church that says like bad Christians and it's
19:23
Speaker 1: just but these are just two randomly chosen words, so
19:26
Speaker 1: we shouldn't infer any evil on your part. No, they're random. Now,
19:30
Speaker 1: a lot has happened since two thousand when capture came along,
19:34
Speaker 1: and since two thousand and six when you started unsuspectingly
19:37
Speaker 1: helping Google in the New York Times digitize their old pages.
19:41
Speaker 1: You know, early on in the first version of a
19:43
Speaker 1: cap shop, computers were pretty bad at recognizing distorted text,
19:47
Speaker 1: so they didn't have to be that distorted. But you know,
19:50
Speaker 1: over time, computers got better and better, and in fact,
19:52
Speaker 1: by now computers are in many cases about as good
19:55
Speaker 1: as humans. Because of that, we have to make them
19:58
Speaker 1: harder and harder. A lot of times, the puzzles are
20:01
Speaker 1: so hard that even the human can't pass the challenge.
20:05
Speaker 1: I'm sure you've been sent screenshots of words that are
20:08
Speaker 1: so much no one can tell where it is. Yes,
20:12
Speaker 1: that happens, I mean, it's rare that that happens, and
20:17
Speaker 1: that's why the capture itself in true arms race fashion
20:22
Speaker 1: has evolved. So what has happened is that for the
20:26
Speaker 1: more secure things, the captures have moved away from these
20:30
Speaker 1: distorted characters. And what is being used now are these
20:33
Speaker 1: the puzzles are now things like you see a bunch
20:35
Speaker 1: of pictures and you have to click the ones that
20:37
Speaker 1: contain a stop sign right the traffic lights, the fire hydrants. Yeah,
20:42
Speaker 1: it's exactly the same idea as recapture, except we're not
20:45
Speaker 1: the story. We're not trying to digitize books. This a
20:47
Speaker 1: lot of times comes from things like all the all
20:49
Speaker 1: the mapping cars or the self driving cars. Basically, these
20:54
Speaker 1: are cars that are driving around that are capturing images
20:56
Speaker 1: of the whole world. They're trying to figure out what's
20:58
Speaker 1: around them. Sometimes they cannot recognize what's in an image.
21:01
Speaker 1: So it's a similar case. It takes the things like
21:04
Speaker 1: is this a stopting I'm not sure, Okay, send it
21:06
Speaker 1: to a human, and then when you get it and
21:08
Speaker 1: you click on the store sign, you're actually helping either
21:10
Speaker 1: the self driving car or the mapping software or whatever
21:14
Speaker 1: know that there is actually a stop sign right here.
21:16
Speaker 1: Oh so we're still doing good for the world as
21:18
Speaker 1: we do this, still doing good for the world, or
21:21
Speaker 1: for a company or for a company, but maybe not
21:23
Speaker 1: digitizing books. But it's a similar ideas thing that a
21:26
Speaker 1: computer cannot do. You've just solved a mystery for hundreds
21:30
Speaker 1: of millions of people. Why it's always traffic lights and
21:33
Speaker 1: fire hydrants we're supposed to choose and not bananas and puppies,
21:37
Speaker 1: or it has to do with both self driving cars
21:40
Speaker 1: and also mapping software. Okay, so now we kind of
21:43
Speaker 1: get why we have to put up with these challenges,
21:46
Speaker 1: or we did twenty years ago, but really nothing better
21:50
Speaker 1: has come along since. Are we sure that there's nothing
21:54
Speaker 1: less annoying that we could do to thwart these spammers? Yes,
21:58
Speaker 1: there is. By now, it did become a lot less annoying.
22:00
Speaker 1: I don't know if you've seen that of late, where
22:03
Speaker 1: you know, there's a thing that's us recapture. We're just
22:05
Speaker 1: trying to figure out whether you're a human, and they
22:07
Speaker 1: just ask you to click somewhere, just click on this box.
22:11
Speaker 1: That is much less annoying. So sometimes you don't see
22:13
Speaker 1: anything except I'm not a robot by yeah, yeah, yeah,
22:17
Speaker 1: I'm not a robot. This is something that is that
22:19
Speaker 1: is done by Google. This actually comes from you know,
22:22
Speaker 1: the original team, that is the company that they bought
22:24
Speaker 1: from me. When you get that one, that means that,
22:27
Speaker 1: in this particular case, probably means Google has figured out that, yeah,
22:31
Speaker 1: you know what, we know you because you've been around
22:33
Speaker 1: since twenty sixteen in this computer, and yeah, you have
22:37
Speaker 1: a lot of Gmail emails, and you've done a lot
22:40
Speaker 1: of Google search queries. You're a normal person, You're not
22:43
Speaker 1: a spammer. So they just do a little thing that
22:46
Speaker 1: just tries to double check that, you know, I can
22:48
Speaker 1: move the mouse or whatever. So one thing that has
22:51
Speaker 1: changed from the year two thousand and five to now
22:53
Speaker 1: is that there are companies like Google or like Facebook
22:56
Speaker 1: that for the majority of people on the Internet, they
22:59
Speaker 1: kind of know who you are. If you have a
23:01
Speaker 1: fresh computer that you've never used before, then you would
23:05
Speaker 1: have to do the annoying capture. But for most of us,
23:09
Speaker 1: you're unlikely to have to type these as much as
23:11
Speaker 1: you you were back in say the year two thousand
23:13
Speaker 1: and five. It has become a lot better, you know,
23:16
Speaker 1: probably a little bit at the cost of your privacy. Okay,
23:18
Speaker 1: but wait a minute, we now know that computers eventually
23:21
Speaker 1: got too smart for the distorted text reading touring tests.
23:26
Speaker 1: Won't they eventually get good enough to identify a few
23:28
Speaker 1: stupid stop signs in a photo grid? It is, it's
23:31
Speaker 1: a cat of mouse game. Now. Probably there's a bunch
23:34
Speaker 1: of people working on making better recognition of stop signs
23:37
Speaker 1: or something like that eventually. But eventually computers are going
23:40
Speaker 1: to be able to do everything humans can, and so
23:43
Speaker 1: at some point there won't be a test that kind
23:45
Speaker 1: distinguished humans going to computer. Well wait a minute, does
23:47
Speaker 1: that mean the end of the internet? I mean, what
23:50
Speaker 1: happens if that, If there's no sort of touring tests
23:53
Speaker 1: that works anymore. I don't think it's the end of
23:56
Speaker 1: the internet, particularly because, like I said, more and more
24:00
Speaker 1: these companies are going to know more and more about you,
24:02
Speaker 1: and I just don't think there will be a humans problem. Okay, well,
24:06
Speaker 1: whatever the end game is, why can't we do today,
24:10
Speaker 1: Since we know it's an arms race, Since we know
24:12
Speaker 1: that eventually we'll lose it to AI and computers, why
24:17
Speaker 1: can't we jump to whatever we'll follow it? Now? I'll
24:22
Speaker 1: tell you why this. By the way, it's like ninety
24:23
Speaker 1: five percent of the way there. I mean, really, for
24:26
Speaker 1: most of us, you know, Facebook knows who we are,
24:28
Speaker 1: and Google knows who we are, So it's ninety percent
24:30
Speaker 1: of the way there. The reason is not one hundred
24:32
Speaker 1: percent of the way there is because there are some
24:33
Speaker 1: people who really care about privacy, and you know, there's
24:37
Speaker 1: there's always going to be a kind of a way
24:39
Speaker 1: to browse privately. So for example, there's a chrome has
24:42
Speaker 1: private browsing. So it's all the stuff when people care
24:45
Speaker 1: about privacy, I mean there's there's a trade off here, right. Well,
24:48
Speaker 1: the irony is it seems like most of the websites
24:52
Speaker 1: to present me with a capture I'm trying to get
24:54
Speaker 1: to in order to supply my name and address, like
24:57
Speaker 1: like I'm signing up for something. Yes, it's funny. Why
25:00
Speaker 1: do I need privacy when the whole purpose is to
25:02
Speaker 1: supply my information? Yeah, that's funny. Now. I mentioned at
25:07
Speaker 1: the beginning that Louis has had three world changing ideas.
25:12
Speaker 1: You've heard about the gym membership that powers the grid,
25:16
Speaker 1: and you now know about kapture. But what about his
25:19
Speaker 1: third creation. It's Duo Lingo, the language training app. At
25:24
Speaker 1: this moment, it has half a billion registered users learning
25:29
Speaker 1: forty different languages, all for free. And from the very
25:38
Speaker 1: beginning you could see the fingerprints of Luis Vaughnan, master
25:42
Speaker 1: of crowdsourcing all over it. In early due Lingo, as
25:46
Speaker 1: you were learning a language on dual Lingo, you're actually
25:49
Speaker 1: helping us to translate stuff that computers could not translate.
25:52
Speaker 1: In fact, CNN was a client, so CNN would send
25:55
Speaker 1: us their news in English. We would then give it
25:58
Speaker 1: to people who were Spanish speakers who were learning English,
26:00
Speaker 1: and we would say, hey, you want to practice your English,
26:01
Speaker 1: help us translate this CNN article into your native language
26:05
Speaker 1: of Spanish. And so they would do it, and they
26:08
Speaker 1: would be learning English and then we would get that translation,
26:11
Speaker 1: and then we would send it back to CNN and
26:13
Speaker 1: they would pay us for the translation. That was the
26:16
Speaker 1: very first version of due Lingo. It turned out that,
26:20
Speaker 1: just like the gym, it ends up being that it
26:22
Speaker 1: just can't make much money from this, and so we decided, okay,
26:25
Speaker 1: just go go to a business model where we actually
26:28
Speaker 1: give you ads, and the way we make money is by,
26:30
Speaker 1: you know, showing your ads. The dude just keeps doing that.
26:34
Speaker 1: He keeps coming up with ideas that make the world
26:36
Speaker 1: a better place, thwart the bad guys, and make a
26:40
Speaker 1: lot of money. It's really a shame he gave up
26:43
Speaker 1: that electrical grid gym thing. Are there ever things that
26:47
Speaker 1: come to you in the shower that might be your
26:49
Speaker 1: big third act? I mean, honestly, to have the impact
26:53
Speaker 1: you've had twice is astonishing, But it makes me think
26:57
Speaker 1: there's something in you that just has great ideas that
27:01
Speaker 1: can go really wide. You know, as time passes, I
27:05
Speaker 1: am a lot more interested in literacy and teaching people
27:08
Speaker 1: how to read. I think with a computer, we should
27:11
Speaker 1: be able to teach the whole world how to read
27:12
Speaker 1: significantly better than humans can teach you how to read.
27:15
Speaker 1: You know, the US, The US is fine, most adults
27:17
Speaker 1: Indias know how to read, but many countries in the
27:20
Speaker 1: world there's a significant fraction of people who don't know
27:22
Speaker 1: how to read. In fact, there's about a billion adults
27:24
Speaker 1: in the world that are illiterate. And I think we
27:27
Speaker 1: can I think we can make a big dent, you know,
27:29
Speaker 1: with a system to teach people how to read. So
27:31
Speaker 1: we're working on that in the meantime. Now you know
27:34
Speaker 1: why you have to encounter those infernal website challenges. You
27:38
Speaker 1: know how they came about, and you now consider them
27:41
Speaker 1: unnecessary evil. Well, maybe you do just for people who
27:45
Speaker 1: are like, I don't know what it is. I just
27:47
Speaker 1: don't like doing it. I can't even tell what's a
27:49
Speaker 1: freaking draffic. Like, let's just lay out what would happen
27:53
Speaker 1: if all these challenges went away tomorrow. What would happen
27:56
Speaker 1: to the Internet. Most likely, you would get a lot
28:00
Speaker 1: more spam in either your email spam or you'd get
28:06
Speaker 1: a more kind of random Facebook follow words that are
28:09
Speaker 1: not you know, real people. These fake accounts can start
28:13
Speaker 1: boosting up that political messages. There would be probably more
28:17
Speaker 1: fake news. They would probably be you know, more spam, right,
28:21
Speaker 1: and from spam fishing and spywear and yeah, more spywear. Yeah.
28:28
Speaker 1: The web would be a less safe place, all right.
28:30
Speaker 1: So when you do explain this to someone at the
28:33
Speaker 1: proverbial party, are they generally satisfied with the notion that? Yeah,
28:38
Speaker 1: I think most people. I think most people realize that
28:41
Speaker 1: it's like, you know, these things are kind of like
28:43
Speaker 1: a like a key nobody nobody likes. It's not like
28:46
Speaker 1: I love opening my door with the key. It's kind
28:48
Speaker 1: of annoying, but yeah, that's there, and I understand it
28:51
Speaker 1: just makes it makes my house safer. In this case,
28:54
Speaker 1: it's kind of just makes the whole Internet safer. I
28:56
Speaker 1: kind of gotta do it. Thanks for listening to this
29:00
Speaker 1: second to last episode of the season. If you have
29:03
Speaker 1: any interest in a second season, please spread the word,
29:06
Speaker 1: subscribe to this podcast, leave a review on Apple Podcasts,
29:11
Speaker 1: or a rating on Spotify. Unsung Science with David Pogue
29:14
Speaker 1: is presented by Simon and Schuster and CBS News and
29:18
Speaker 1: produced by PRX Productions. The executive producers for Simon and
29:22
Speaker 1: Schuster are Richard Rohrer and Chris Lynch. The PRX production
29:26
Speaker 1: team is Jocelyn Gonzalez, Morgan Flannery, Pedro, Raphael Rosatto, and
29:31
Speaker 1: Ian Fox. Project manager Jesse Nelson composed the Unsung Science
29:35
Speaker 1: theme music and Christina Robello fact checked my script. At
29:39
Speaker 1: Unsung Science dot com, you can listen to every episode
29:43
Speaker 1: we've ever made and read complete transcripts. For more of
29:47
Speaker 1: my stuff, visit David Pogue dot com or follow me
29:50
Speaker 1: on Twitter at Pogue Pogue. Thanks for the scene that
29:58
Speaker 1: was an episode of Unsung Science from our friends at
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