Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts. And how the
Speaker 1: tech are you. I thought today we could do a
Speaker 1: real quickie, because you know, sometimes it's nice just to
Speaker 1: do a short thing to talk about a subject in tech.
Speaker 1: And there are a whole bunch of tech Stuff episodes
Speaker 1: in which I have talked about the Turing test. So
Speaker 1: there are a lot of different variations of the Turing test.
Speaker 1: It's based off a thought experiment from Alan Turing, the
Speaker 1: computer scientist, very influential, very important in World War Two.
Speaker 1: He helped crack the Enigma code. And actually the movie
Speaker 1: that sort of depicted his efforts in cracking Enigma is
Speaker 1: called the Imitation Game. Well, the Imitation Game makes reference
Speaker 1: that is like part of the Turing test. So he
Speaker 1: kind of proposed this test when people would ask him
Speaker 1: if he thought machines would be capable of thought. Now
Speaker 1: keep in mind, like this is back in the forties
Speaker 1: and fifties, do you think that machines will be capable
Speaker 1: of thinking? And he said, I don't really think that's
Speaker 1: a very interesting question. For one thing, I don't know
Speaker 1: that there's any meaningful way to answer it. However, I
Speaker 1: do think we can be a little more precise if
Speaker 1: we think about it in terms of kind of a
Speaker 1: thought experiment, a test. So imagine this is the situation
Speaker 1: you find yourself in. You go into a room and
Speaker 1: there's a computer terminal there, and that's it. You know,
Speaker 1: you can't see into any other rooms or anything. It's
Speaker 1: just a desk with a computer terminal. And you sit
Speaker 1: down at this terminal and there's a little prompt there
Speaker 1: that lets you get into a chat session. And you
Speaker 1: enter into this chat session, and you have five minutes
Speaker 1: and you can ask the person on the other end
Speaker 1: of the chat session any questions you want within that
Speaker 1: five minute time frame. And once those five minutes are up,
Speaker 1: you're asked to determine was the person on the other
Speaker 1: end of the chat an actual human being or was
Speaker 1: it a computer program? Was it some form of artificial intelligence?
Speaker 1: A bot is what we would call it today. And
Speaker 1: if you are unable to determine whether the subject on
Speaker 1: the other end of the chat is human or a
Speaker 1: bot to any reliable degree, then you could say that, oh,
Speaker 1: that that program passed the Turing test, I could find
Speaker 1: no way of telling the difference between that computer program
Speaker 1: and an actual person. Touring suggests that due to advancements
Speaker 1: in computer science, he suspected that people would have at
Speaker 1: best a success rate of around seventy percent to be
Speaker 1: able to tell whether or not the quote unquote person
Speaker 1: on the other end of the church chat was a
Speaker 1: human being or a computer program, And he said he
Speaker 1: expected that to be capable in just a few years time.
Speaker 1: It took a little bit longer than that, but I
Speaker 1: would say that with the sophistication we've reached with chatbots
Speaker 1: these days, I think you could fairly conclusively say that
Speaker 1: we've got programs out there that can quote unquote beat
Speaker 1: the Turing test. Part of the problem is that Turing
Speaker 1: was saying that in the future, these programs are going
Speaker 1: to be sophisticated enough that they will fool people into
Speaker 1: thinking it's another person. He wasn't saying, oh, you have
Speaker 1: to meet this specific threshold for your system to have
Speaker 1: achieved beating the Turing test. That would come afterward. Other
Speaker 1: people would kind of create the criteria. But since then
Speaker 1: people have subsequently used the phrase Turing test to reference.
Speaker 1: Essentially any kind of task designed to determine if a
Speaker 1: machine has or at least appears to have the property
Speaker 1: of intell and when I say that, I mean really
Speaker 1: general intelligence. But there's another specific use of Turing tests
Speaker 1: that I would like to bring up today, and that
Speaker 1: is the completely automated public Turing test to tell computers
Speaker 1: and humans apart that once you turn it into an acronym,
Speaker 1: becomes cap chop. So these are those little tasks that
Speaker 1: you occasionally encounter on certain websites, and they require you
Speaker 1: to do something like you type in a string of
Speaker 1: characters that are displayed on screen. They're usually deformed in
Speaker 1: some way and up against a crazy background. Or you
Speaker 1: might be given a big selection of images and told
Speaker 1: to pick out all the ones that have a cat
Speaker 1: in them or something. Or you might have to drag
Speaker 1: a little picture of a puzzle piece into an image
Speaker 1: where it fits into a very specific spot. All of
Speaker 1: these are meant to separate out actual human visitors to
Speaker 1: a website service versus all the automated programs or bots
Speaker 1: or whatever you might want to call it. So I
Speaker 1: thought it would be fun to do a quick episode
Speaker 1: on where captures came from and what purpose they serve
Speaker 1: kind of touched on that already, but also how they
Speaker 1: fit into the grand picture of artificial intelligence, because interestingly
Speaker 1: they play a pretty important part. They have helped drive
Speaker 1: the development and advancement of artificial intelligence, not necessarily in
Speaker 1: a way that is helpful to everybody out there, but
Speaker 1: it certainly has served as a way to get people
Speaker 1: thinking about how to tackle certain AI problems. So our
Speaker 1: story actually begins with the good old website Yahoo. Y'all
Speaker 1: remember Yahoo. I mean, it's still a thing, but I
Speaker 1: remember a time where Yahoo was practically synonymous with Internet
Speaker 1: for a lot of folks. You may not even remember
Speaker 1: this if you haven't been on Yahoo in ages, but
Speaker 1: once upon a time, Yahoo was sort of an ordal
Speaker 1: to the rest of the Internet. Yahoo was kind of
Speaker 1: like a landing page. A lot of people had it
Speaker 1: set as their homepage, so when they would go into
Speaker 1: a web browser, they'd go right into Yahoo and you
Speaker 1: would find articles there and all sorts of other links,
Speaker 1: as well as chat rooms and of course the search
Speaker 1: engine where you could search for other stuff online besides
Speaker 1: the stuff that just popped up on Yahoo. Well, in
Speaker 1: those chat rooms, moderators were running into a really serious problem.
Speaker 1: The chat spaces were becoming invaded by bots posing as people.
Speaker 1: Now this is in two thousand. The bots were not
Speaker 1: particularly sophisticated, but they were creating a lot of spam, Like,
Speaker 1: they were jamming up chat spaces with just spam messages
Speaker 1: while people are trying to chat. In some cases, they
Speaker 1: were gathering personal information of users in an effort to
Speaker 1: exploit those users in some way or another. So Yahoo
Speaker 1: didn't want this to keep going. It wasn't reflecting well
Speaker 1: on the companies. So they turned to the computer science
Speaker 1: department at Carnegie Mellon University in order to see, Hey,
Speaker 1: is there some way that we could, you know, kind
Speaker 1: of like have a bouncer out front, a gate keeper
Speaker 1: if you will, that would allow humans into the various
Speaker 1: systems so that they can make use of them the
Speaker 1: way they were intended, but prevent all the robots, all
Speaker 1: the AI programs, all the computer software or algorithms, however
Speaker 1: you want to define it, keep them from getting access.
Speaker 1: So a team led by Manuel Bloom and including folks
Speaker 1: or Blum, I suppose, and including folks like John Langford,
Speaker 1: Louis von On, Nicholas Hopper, and others tackled this challenge,
Speaker 1: so they needed to come up with a test. Now,
Speaker 1: in an ideal world, the test would be a cinch
Speaker 1: for a human being to complete, but it would be
Speaker 1: a real stumper for algorithmically driven by And that is
Speaker 1: the basic philosophy of capture. Make a test that humans
Speaker 1: find really easy to complete, perhaps even trivial, like it's
Speaker 1: just a mild inconvenience, as they say, but for bots
Speaker 1: it's like a turn away. You're never going to be
Speaker 1: able to get this. Now, some of y'all might be
Speaker 1: saying something along the lines of but Jonathan, whenever I
Speaker 1: run into captures these days, they're sometimes really hard, Like
Speaker 1: it's hard to see what they spell out. I'll try
Speaker 1: and type things in three four times and get kicked out.
Speaker 1: And you're right, that is a problem. It is something
Speaker 1: that actually is happening. It doesn't mean that you're not human.
Speaker 1: If you're having like existential crises. I would like to
Speaker 1: set your mind at ease by saying you're probably human.
Speaker 1: I mean, I don't think I could say anything for certain,
Speaker 1: but I feel fairly confidence saying you're probably human. But
Speaker 1: the reason why captures have become really difficult in some
Speaker 1: way cases anyway with some specific types of captures is
Speaker 1: largely because other programmers figured out how to make better
Speaker 1: automated programs that can parse and respond to captures. So
Speaker 1: as one group of programmers figured out how to design
Speaker 1: tools to defeat a capture, the captured designers would go
Speaker 1: back to the drawing board to create new tests to
Speaker 1: be more challenging for those bots, to say, well, they
Speaker 1: got good at this, let's change these things and reintroduce
Speaker 1: the capture so that this will trip up those systems
Speaker 1: because while they're good at what we used to use
Speaker 1: for gatekeeping, they've never run into this before, and unfortunately
Speaker 1: that sometimes means that the tests become more challenging for
Speaker 1: human beings as well. It no longer is a case
Speaker 1: where something is trivial for a human but difficult for robots,
Speaker 1: at least for certain types of captures. And that's particularly
Speaker 1: true so if the human has some impairments like if
Speaker 1: they have color blindness for example, or some other visual
Speaker 1: or impairment like there are real issues in making captures
Speaker 1: that do what they're supposed to do, that is weed
Speaker 1: out all the non humans but also be accessible to
Speaker 1: all humans, even those who might have impairments that would
Speaker 1: otherwise make it difficult or challenging to complete a capture.
Speaker 1: It is not an easy path to walk. We're going
Speaker 1: to take a quick break. When we come back, i'll
Speaker 1: talk more about the capture story we're back. So in
Speaker 1: the early days of captures, they mostly took on the
Speaker 1: form of distorted text that was printed over a busy background.
Speaker 1: And the idea was that most automated programs would not
Speaker 1: be able to recognize distorted texts like it would be
Speaker 1: an image, not just text letters where it would be
Speaker 1: able to read like the code used to generate the
Speaker 1: letters and then say, oh, well that's these letters that
Speaker 1: can replicate that and get through no problem. You had
Speaker 1: to have something that was going to really stump them. Now,
Speaker 1: image recognition is a pretty tricky science. I've talked about
Speaker 1: it on this show before, Like, training computer systems to
Speaker 1: recognize images takes a lot of time and effort and
Speaker 1: lots and lots and lots of samples so that the
Speaker 1: computer system can quote unquote learn what those images represent. Now,
Speaker 1: it's one thing to teach a computer how to recognize
Speaker 1: standard letters that are in a recognizable font. So if
Speaker 1: the Internet only ever used one font and only used
Speaker 1: one size of that font. Then it would be relatively
Speaker 1: trivial for those who want to defeat captures, because once
Speaker 1: you train a computer vision system on what a lower
Speaker 1: case T looks like, for example, then the system would
Speaker 1: recognize a lowercase tea every time one popped up. But
Speaker 1: of course, there are lots of different fonts and typefaces
Speaker 1: on the Internet, and they come in different sizes and
Speaker 1: colors and on different backgrounds. So teaching a computer system
Speaker 1: what a times new Roman lowercase tea looks like against
Speaker 1: a blank background doesn't mean it's also going to recognize
Speaker 1: a lowercase tea and some other font on some crazy background.
Speaker 1: Plus maybe the tea is a little wavy, a little distorted,
Speaker 1: so distorting that text makes it more challenging for image
Speaker 1: recognition systems, like they're looking for defining features to be
Speaker 1: able to recognize the image of a letter with the
Speaker 1: actual letter. You see, humans, when we teach a human
Speaker 1: what something looks like, it's a lot easier for humans
Speaker 1: to associate other things that look kind of the way
Speaker 1: the first example did, but maybe not exactly the same.
Speaker 1: So in other words, like the example, I always use
Speaker 1: our coffee mugs right. If I show you a coffee mugdug,
Speaker 1: and I say this is a coffee mug, and then
Speaker 1: I show you a second kind that looks totally different,
Speaker 1: different color, different size, you know, whatever, maybe has different
Speaker 1: writing on it, whatever it might be. And I say,
Speaker 1: this is also a coffee mug. And then I show
Speaker 1: you a third example that looks unlike the first two.
Speaker 1: You could say, oh, okay, I get the idea. I
Speaker 1: get the different features that make up what a coffee
Speaker 1: mug is. I understand now. And now when I encounter
Speaker 1: different types of coffee mugs, even though they might not
Speaker 1: look anything like any of the other ones I've encountered,
Speaker 1: I know, Okay, that's probably a coffee mug. Until someone says, no,
Speaker 1: that's a teacup, and then your world is turned upside down.
Speaker 1: But you get what I'm saying. Computers don't work that way.
Speaker 1: Computers like, if you teach it an example is a thing,
Speaker 1: it doesn't necessarily understand that similar but distinctly different versions
Speaker 1: of that same thing fall into the same category. That
Speaker 1: takes lots and lots of training. So the whole idea
Speaker 1: of distortion was that this would make it very tricky
Speaker 1: for most systems to be able to parse that information
Speaker 1: and be able to put it in reliably and to
Speaker 1: fool the capture system doesn't mean that it was fool proof.
Speaker 1: Over time, those systems did get better at being able
Speaker 1: to recognize those figures that were on screen, even better
Speaker 1: than humans could in some cases, which is obviously a problem. Now,
Speaker 1: there have been lots of other capture systems, not just Capture.
Speaker 1: For example, there's one called Asira Asira. Asira did something
Speaker 1: I mentioned earlier in the episode. It would present the
Speaker 1: visitor with a collection of photographs and they would include
Speaker 1: cats and dogs, and it would ask you, okay, identify
Speaker 1: the pictures that have cats in them. So that was
Speaker 1: one way to get around this was that it wasn't
Speaker 1: just figuring out text. It was differentiating between cats and dogs,
Speaker 1: something that again computer systems couldn't do just natively. They
Speaker 1: had to be taught how to recognize the features that
Speaker 1: belonged to a cat versus those that belonged to a dog,
Speaker 1: just the same as all other image recognition software. The
Speaker 1: folks over at Google developed Recapture, and that actually served
Speaker 1: a dual purpose. It was kind of sneaky. So with Recapture,
Speaker 1: you would go to a website and you would be
Speaker 1: greeted by some you know, kind of grainy text, and
Speaker 1: you'd be asked to type it out. You'd actually get
Speaker 1: a couple of different ones, not just one. And this
Speaker 1: text was from scans made of physical digitized books, so
Speaker 1: in other words, books where they had put the page
Speaker 1: down on a scanner and created a scan. So some
Speaker 1: of these books were in you know, pretty bad shape.
Speaker 1: They were at all crisp, clear images. So your first
Speaker 1: capture you'd be presented with, Google actually knew the answer
Speaker 1: to whatever the word was. So let's say the word
Speaker 1: is salamander and you type in salamander, and so Google says,
Speaker 1: all right, I already knew that this scanned word is salamander.
Speaker 1: This is obviously a person who has typed this in.
Speaker 1: But the second image would be a scan from a book.
Speaker 1: Maybe it'd be a really smudged one, like one that's
Speaker 1: harder to read, and it would ask you, okay, was
Speaker 1: this word. Let's say it's surgeon and you type insurgeon. Well,
Speaker 1: the secret sauce here is that Google didn't know that
Speaker 1: that scanned word was surgeon. What Google was doing was
Speaker 1: crowdsourcing crowdsourcing the effort to figure out what the text
Speaker 1: in this scanned image actually said. So if you and
Speaker 1: thousands of other people all put the same word in
Speaker 1: when you were encountering this particular scan, Google would say,
Speaker 1: all right, that word is very likely surgeon. Because you know,
Speaker 1: ninety eight percent of the people who were shown this
Speaker 1: recapture typed surgeon in. So now we know that that
Speaker 1: word is which meant that they could essentially transcribe these
Speaker 1: digitized texts by using the crowd to do the work
Speaker 1: for them. And that is kind of the heart of
Speaker 1: where capture and AI meet. That captures have been used
Speaker 1: one to help train AI so that it's more effective.
Speaker 1: Like if you've encountered other Google ones where it's like
Speaker 1: pick all the images here that have motorcycles in them
Speaker 1: or stairs. Well, part of that is training Google's image
Speaker 1: recognition systems so that they're more accurate. Right, Like an
Speaker 1: image recognition system might have trouble differentiating an actual like
Speaker 1: stone staircase out in front of a building with a
Speaker 1: pedestrian crosswalk, because you know you've got those those broken
Speaker 1: lines on a crosswalk, those could look like stairs to
Speaker 1: a computer image recognition system. So by giving users the
Speaker 1: task of hey, identify all the excit samples in this
Speaker 1: list that have stairs in them, Google starts to train
Speaker 1: its own image recognition algorithms to be more effective and
Speaker 1: more accurate. So in a way, we were essentially being
Speaker 1: used as free labor to make these AI systems more accurate,
Speaker 1: just so that we could get access to whatever it
Speaker 1: was we were trying to visit, whether that was an
Speaker 1: online shop or a chat room, or you know, whatever
Speaker 1: it might be. So, yeah, we we've been working for free, y'all.
Speaker 1: Actually it's in some cases we've been working for free
Speaker 1: and denied access to tools that we wanted to use
Speaker 1: because the captures were too hard for us to be
Speaker 1: able to solve. But yeah, that's that's the quick story
Speaker 1: about the history and evolution of captures. Clearly they're still
Speaker 1: used today. Sometimes it's something simple like click this box
Speaker 1: to prove your human that kind of thing where it
Speaker 1: requires you to take an action. Those obviously are much
Speaker 1: more simple for humans to comp than for robots, so
Speaker 1: those still follow the philosophy of the original captions. A
Speaker 1: lot of other ones, though they get pretty tricky, to
Speaker 1: the point where sometimes I'm discouraged from even going further
Speaker 1: and visiting the website in particular, just like, you know what,
Speaker 1: I don't need to feel stupid because I couldn't find
Speaker 1: all the fire hydrants in these photographs, so I'm just out.
Speaker 1: But yeah, that's it. And like I said, it plays
Speaker 1: a really important part with AI. It's kind of a
Speaker 1: seesaw effect, right, Like you create a barrier that AI
Speaker 1: can't get over until it can, and then you have
Speaker 1: to go back and create a harder barrier. And meanwhile,
Speaker 1: the folks developing the AI keep making advancements that the
Speaker 1: AI gets more sophisticated and powerful over time. So yeah,
Speaker 1: delicate balance and not everybody benefits. As I said, hope
Speaker 1: that that was interesting and informative to y'all. I hope
Speaker 1: you're all doing well, and I'll talk to you again
Speaker 1: really soon. Tech Stuff is an iHeartRadio production. For more
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