Speaker 1: Technology with tech Stuff from works dot com. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm a senior writer for how stuff works dot com
Speaker 1: focusing on all things technological, and recently I did an
Speaker 1: episode about artificial intelligence and how Mark Zuckerberg and Elon
Speaker 1: Musk had kind of a public disagreement about the direction
Speaker 1: of AI, and how other people have weighed in. Some
Speaker 1: people have said that perhaps Musk and Zuckerberg are arguing
Speaker 1: about something that isn't really relevant right now, and that
Speaker 1: there are in fact other elements of artificial intelligence that
Speaker 1: we should be focusing on instead of whether or not
Speaker 1: it is certain to make our lives better or worse
Speaker 1: or rule over us. But it got me to thinking
Speaker 1: about a related topic, and I touched on it all
Speaker 1: little bit in that episode, and that was all about
Speaker 1: how do you tell when a a an entity that
Speaker 1: is communicating with you is in fact a person or
Speaker 1: it is a computer program that is mimicking a person.
Speaker 1: So we're going to look at that, And honestly, I
Speaker 1: was inspired a lot also by the fact that we've
Speaker 1: got a new Blade Runner movie coming out. It's Blade
Speaker 1: Runner two thousand, forty nine, which has no connection to
Speaker 1: this show. By the way, they are not sponsoring us.
Speaker 1: I'm pretty sure no one connected to Blade Runner two
Speaker 1: thousand forty nine even is aware that I exist. But
Speaker 1: I'm a fan of the original Blade Runner film and
Speaker 1: I'm looking forward to seeing what happens in Blade Runner
Speaker 1: two thousand forty nine. I'm a little hesitant because it
Speaker 1: depends upon which interpretation of the original film they decided
Speaker 1: to ultimately go with. If they went with the director's vision,
Speaker 1: might not want to see two thousand forty nine, but
Speaker 1: I wanted to kind of talk about the difference between
Speaker 1: communicating with a person and a synthetic being. Now in
Speaker 1: Blade Runner, the synthetic beings are called replicants, and they
Speaker 1: are not exactly robots. They're often referred to as androids,
Speaker 1: but I don't really think that's terribly accurate either. They're
Speaker 1: more like genetically engineered human simulations. Like they're not fully human. Uh.
Speaker 1: They have other elements that either augmented abilities and intelligence,
Speaker 1: but a lower lifespan that sort of stuffy. They tend
Speaker 1: to be born in the adult stage of their lives
Speaker 1: and implanted with false memories, but they're meant to do
Speaker 1: jobs that humans can't or won't do, and they do
Speaker 1: have a tendency to resent their lot in life, seeing
Speaker 1: as how in the original film they did just have
Speaker 1: that built an expiration data just a few years. They
Speaker 1: don't live for a few years, and then they would
Speaker 1: their bodies would break down and blade Runner the story
Speaker 1: follows an investigator who is seeking out specific replicants that
Speaker 1: are on the run in order to quote unquote retire
Speaker 1: them with extreme prejudice. So this is all set up
Speaker 1: at the beginning of the movie. Now, one thing those
Speaker 1: investigators or blade Runners do is ask questions of suspects,
Speaker 1: suspected replicants. You know, they find someone they think that
Speaker 1: might be a replicant, and then they interview that person
Speaker 1: and they look for signs that that is not actually
Speaker 1: a real human being, because replicants are not exactly human.
Speaker 1: They're human like, but they do not process emotions the
Speaker 1: same way that humans do. So blade Runners can look
Speaker 1: for indications that the suspect is actually a replicant and
Speaker 1: they use what is called the void comp test in
Speaker 1: the movie. This is a test that includes the hypothetical
Speaker 1: situation you're in a desert walking along in the sand
Speaker 1: when all of a sudden, you look down and see
Speaker 1: it towards it. This you reach down and flip the
Speaker 1: tortoise on its back. The tortoise lays on its back.
Speaker 1: It's belly baking in the hot sun, beating its legs,
Speaker 1: trying to turn itself over, but it can't, not without
Speaker 1: your help. But you're not helping. Why is that? Now?
Speaker 1: I kind of paraphrase that scene because it actually happens
Speaker 1: as dialogue between two characters. But that was the attempt
Speaker 1: of an interrogator to figure out whether or not the
Speaker 1: person they were talking to is actually a human being,
Speaker 1: because the emotional responses would indicate whether or not it
Speaker 1: was a human response, or if there was a lack
Speaker 1: of that, that it was perhaps a replicant. Now, that's
Speaker 1: all science fiction, but in the real world there are
Speaker 1: times when we encounter bots or AI constructs and we
Speaker 1: might not know at first, at least they were not
Speaker 1: communicating with a real life person. In fact, the Interactive
Speaker 1: Advertising Bureau reported in two thousand fourteen that thirty six
Speaker 1: per cent of all web traffic is general to buy bots,
Speaker 1: not people, and the security firm Imperva reported in early
Speaker 1: two thousand seventeen that today that figure is now closer
Speaker 1: to fifty, which means that right now there's more traffic
Speaker 1: on the web being generated from bots than actual human beings,
Speaker 1: And that's not exactly great. Much of the web depends
Speaker 1: upon advertising for monetization. But how do you figure out
Speaker 1: what the value of traffic to your website is when
Speaker 1: you know there's a good chance that more than half
Speaker 1: of all those page views were generated from algorithms, not
Speaker 1: from human beings. Now, much of the bot traffic isn't
Speaker 1: meant to be outright malicious. There might be bots that
Speaker 1: are essentially trying to to scour the Internet for data
Speaker 1: for nefarious purposes, but a lot of them are just
Speaker 1: they're gathering information for, you know, completely innocent purposes. Really,
Speaker 1: gathering information on its own is not necessarily a bad thing.
Speaker 1: It's how we use the information that makes it good
Speaker 1: or bad. It's kind of paraphrasing Shakespeare there. But there
Speaker 1: are the various bots on social platforms and websites that
Speaker 1: also interact with people, and some of them are again benign.
Speaker 1: They're meant to be helpful, such as bots that can
Speaker 1: answer basic customer service questions for companies. You've probably encountered
Speaker 1: one of these where you were looking for some information
Speaker 1: about a particular product or service and then a little
Speaker 1: chat window pops up, and you get the feeling that
Speaker 1: the entity you're talking to is not exactly another human
Speaker 1: being on the other end. It may just be a bot.
Speaker 1: Sometimes that's fine. Sometimes it's more frustrating than helpful, because
Speaker 1: you find that you have to word things in a
Speaker 1: very particular way for the bot to comprehend what you mean,
Speaker 1: whereas a human would probably pick it up much faster.
Speaker 1: But you get the idea of why that was employed, right.
Speaker 1: That was meant to make things a little more smooth
Speaker 1: and to remove the necessity of putting a human being
Speaker 1: in charge of that at all hours of the day.
Speaker 1: You can also find these sorts of automated services on
Speaker 1: phone lines, including bots that call you, which is always fun.
Speaker 1: There's nothing like having a conversation with a bot for
Speaker 1: half a minute before you figure out something fishy is
Speaker 1: going on. But other bots are meant to serve the
Speaker 1: purposes of some third party, sometimes with malicious intent, such
Speaker 1: as convincing you to click on a link that leads
Speaker 1: to malware, and that's where we really run into obvious problems.
Speaker 1: Some of the benign ones can run into problems too often.
Speaker 1: There are unintended consequences if you're scouring the web for data.
Speaker 1: Data is valuable and sometimes people will want to get
Speaker 1: hold of it for bad reasons, even if the initial
Speaker 1: approach wasn't to do anything nefarious. Now, some hackers have
Speaker 1: used bots to flood a platform with complaints in an
Speaker 1: effort to silence people that the hackers do not like.
Speaker 1: So let's say there's this jerk face hacker who thinks
Speaker 1: a Facebook page devoted to promoting women in STEM education
Speaker 1: and careers is dumb. So this jerk face then creates
Speaker 1: or more likely purchases bots to flood Facebook with complaint
Speaker 1: reports about that specific page in an attempt to get
Speaker 1: Facebook to shut the page down. Now that's a pretty
Speaker 1: lousy thing to do. And to be clear, some of
Speaker 1: the jerk faces are aiming at pages that the average
Speaker 1: person would say is a bad one. It doesn't have
Speaker 1: to be something that like I feel strongly about and
Speaker 1: and in favor for. I think STEM education and careers
Speaker 1: for women is amazing, and I would be very upset
Speaker 1: to hear about a page that was shut down because
Speaker 1: of one of these attacks. On the other hand, let's
Speaker 1: say that there was a page that was promoting something
Speaker 1: I really do not like. Maybe it was a page
Speaker 1: that was promoting, uh, you know, racial discrimination. I would
Speaker 1: think that was terrible. If someone else were to take
Speaker 1: bots and direct them to that page in order to
Speaker 1: shut it down, I would also think that that's not
Speaker 1: so great. I don't think that a page about racial
Speaker 1: discrimination should be promoted or exist on Facebook. I don't
Speaker 1: think that's appropriate. But at the same time, I don't
Speaker 1: think it's appropriate to use automated systems to bring that down.
Speaker 1: I would rather see an actual ground swell of human
Speaker 1: support for that, not to you know, boost it with
Speaker 1: a bunch of automated scripts. I don't want to give
Speaker 1: the indication that the only people who ever use bots
Speaker 1: are those who want to silence vulnerable or underrepresented populations.
Speaker 1: There are some who use them to attempt to silence
Speaker 1: voices of hate. In either case, it's dirty pool. I
Speaker 1: don't think it's really a legitimate strategy. Uh, it ends
Speaker 1: up hurting everyone in the long run to use bots
Speaker 1: in that specific way. Butts in general, I'm not against.
Speaker 1: I do think there are times when they are incredibly useful,
Speaker 1: but to use them specifically to fool people into think
Speaker 1: ging their actual human beings in order to achieve an
Speaker 1: ulterior motive that sets me on edge that I can't
Speaker 1: really see an upside to that. I can definitely see
Speaker 1: it from the side of customer service or answering general questions,
Speaker 1: maybe even just trying to funnel out people who have
Speaker 1: a very simple issue to resolve versus those who need
Speaker 1: more attention, whereas you know those people would get directed
Speaker 1: towards a pathway that would lead to speaking to an
Speaker 1: actual human being. I get it from that perspective. Now,
Speaker 1: in a recent episode, I explained in brief what the
Speaker 1: Turing test was, or at least how we interpret it.
Speaker 1: The Turing test is sort of the inspiration for the
Speaker 1: Void comp test and Blade Runner. Alan Turing, one of
Speaker 1: the fathers of computer science, proposed the test back in
Speaker 1: nineteen fifty and in the actual thought experiment that he
Speaker 1: was proposing, it was a variation on a parlor game
Speaker 1: called the imitation game. Now, the imitation game is one
Speaker 1: where you have an interrogator that's player, and the player
Speaker 1: is presented with two subjects, neither of whom the interrogator
Speaker 1: can see or talk to directly. One of the two
Speaker 1: subjects is a woman, the other is a man. Both
Speaker 1: of the subjects can communicate with the interrogator in a
Speaker 1: way that does not require face to face contact or
Speaker 1: voice or anything like that. Typically it would be through
Speaker 1: something like typewritten letters, because that would help disguise handwriting
Speaker 1: as well. And the two subjects have the same task.
Speaker 1: They have to convince the interrogator that they are female.
Speaker 1: So the woman will be telling the truth, the man
Speaker 1: will be lying, and it's the interrogator's job to figure
Speaker 1: out who is imitating a woman and who actually is
Speaker 1: a woman. Touring then suggested taking this game a step
Speaker 1: further by replacing the male subject in this thought experiment
Speaker 1: with a computer. The computer would also attempt to convince
Speaker 1: the interrogator that the computer was in fact a woman.
Speaker 1: Now would the interrogator be able to detect the computer's
Speaker 1: ruse if not? Touring suggested that this would indicate some
Speaker 1: form of intelligence, though not necessarily human intelligence. But you
Speaker 1: could say the machine is capable of fooling a human being,
Speaker 1: of of practicing deception, which I think most of us
Speaker 1: would argue. The ability to practice deception does indicate at
Speaker 1: least some form of intelligence. Maybe not the type of
Speaker 1: intelligence that's gonna go out and teach a class on
Speaker 1: quantum mechanics, but the type of intelligence that does understand
Speaker 1: the concept of manipulation at least or at least is
Speaker 1: able to employ the concept of manipulation, if not understand
Speaker 1: it from a truly cognitive point of view. Now, the
Speaker 1: other variations and refinements to the Touring test followed after
Speaker 1: Touring's death in nineteen fifty four, and Touring's life was
Speaker 1: very tragic. We've done an episode on Alan Touring, so
Speaker 1: if you want to go back and find that in
Speaker 1: our archives, you can learn all about his his death
Speaker 1: and why some people rule it a suicide. I think
Speaker 1: most people do, and some people say it was accidental.
Speaker 1: But it is an interesting and tragic tale. Today, the
Speaker 1: general interpretation of the Turing test is that if a
Speaker 1: certain threshold is met, such as a greater than thirty
Speaker 1: percent success rate of a computer convincing interrogators that's actually
Speaker 1: a human, it has passed the Turing test. So, in
Speaker 1: other words, if you're an interrogator and you've got a
Speaker 1: computer terminal in front of you, and you're typing messages
Speaker 1: and the the response is coming back to you. And
Speaker 1: if more than thirty percent of the time you cannot
Speaker 1: tell if that actually is a computer or a person,
Speaker 1: maybe you misidentified as a person more than thirty percent
Speaker 1: of the time, and it's actually the computer. That computer
Speaker 1: is said to pass the Turing test, and that it
Speaker 1: is capable of fooling you into thinking it's an actual
Speaker 1: human being. Now, there was a case in two thousand
Speaker 1: and four in which a chat bought called Eugene seemed
Speaker 1: to accomplish this. Eugene's persona was that of a thirteen
Speaker 1: year old Ukrainian boy. Critics pointed out that Eugene's limitations
Speaker 1: as a non native English speaker with a limited knowledge
Speaker 1: of the world due to his age and the fact
Speaker 1: that he was from the Ukraine, meant that people were
Speaker 1: lowering their expectations on his performance when they were chatting
Speaker 1: with him over a computer. In other words, critics were
Speaker 1: saying that Eugene was gaming the system by making people think, oh, well,
Speaker 1: non native English speaker, so if the responses come back
Speaker 1: a little weird, that explains that. And being young means
Speaker 1: that they this kid doesn't have that much knowledge about
Speaker 1: a lot of things in the world, pop culture, politics,
Speaker 1: lots of stuff, so your expectations are set low, and
Speaker 1: then you just think, all right, well, are the messages
Speaker 1: I'm getting Are those in line with what I would
Speaker 1: expect a thirteen year old non native English speaker to
Speaker 1: say to me, or do they stand out as being artificial?
Speaker 1: And a lot of this ends up being deflection as well,
Speaker 1: where if you ask somebody a question and the computer
Speaker 1: program doesn't have a way of responding, it will try
Speaker 1: to deflect the question so that it doesn't indicate that
Speaker 1: and in fact is a computer program. Well, Eugene managed
Speaker 1: to to fool a lot of people, But again the
Speaker 1: critics were saying, well, Eugene was kind of an outlier
Speaker 1: in the sense that you didn't really think of Eugene
Speaker 1: as being a native speaker with a lifetime of experience
Speaker 1: where you could really quiz the the entity and find out, Okay,
Speaker 1: is this actually a person or is it a computer program.
Speaker 1: It's sort of beside the point. I'm not here to
Speaker 1: argue about whether or not machines possess intelligence if they
Speaker 1: passed the Turing test, because I did that recently already. Instead,
Speaker 1: let's focus on the flip side of the scenario, we're
Speaker 1: human at least I'm assuming you're a human. You might
Speaker 1: be a bot who subscribed to text stuff. Apparently of
Speaker 1: you out there are in that case. Thanks. I hope
Speaker 1: you like the show. But this is all for the
Speaker 1: humans here, this bit out here, so you bots out
Speaker 1: there can take a break. How can we humans tell
Speaker 1: if we're dealing with an actual person or if it
Speaker 1: is a bot. Well, one of the ways that we
Speaker 1: have created a means of separating bots from humans is capture.
Speaker 1: Capture is an acronym that stands for a completely automated
Speaker 1: public touring test to tell computers and humans apart. That
Speaker 1: pretty much sums it up when you break it down.
Speaker 1: It's completely automated, meaning there's no human oversight necessary for
Speaker 1: any given implementation of the technology. It's public. It's pretty
Speaker 1: self explanatory. It's a test that's out there in the public.
Speaker 1: I guess I explained it even though it wasn't necessary.
Speaker 1: That's my bad, y'all. Now, it's said to be a
Speaker 1: turing test because it's meant to detect human versus automated
Speaker 1: agents operating on a given web page. We talked about
Speaker 1: the touring test just now. But h so we're not
Speaker 1: gonna go over that again. But you know, again, it's
Speaker 1: just just this indicator. Is there something there that implicates
Speaker 1: this as being a computer agent not a human being?
Speaker 1: And if it is in fact a computer agent, then
Speaker 1: you have a gate up saying all right, you don't
Speaker 1: get to participate in this because it's not meant for you.
Speaker 1: When you have of your web traffic out there generated
Speaker 1: by bots and you're trying to collect meaningful data about
Speaker 1: real human being users, you have to have a way
Speaker 1: to separate the two. Right. So, if I'm a web
Speaker 1: administrator and let's say that I've got let's just say
Speaker 1: that I'm running a sweepstakes, have created an online entry form.
Speaker 1: I don't want someone flooding my sweepstakes with bots in
Speaker 1: an effort to try and game the system and win
Speaker 1: by submitting more entries than anybody else. I want to
Speaker 1: be able to control that. So I want to have
Speaker 1: some sort of element on there that can weed out
Speaker 1: the automated agents out there versus the actual human beings. Now,
Speaker 1: that last bit and capture to tell computers and humans
Speaker 1: apart is the key to all of this. Capture is
Speaker 1: a Guardian right, Like I was just saying, it's meant
Speaker 1: to keep people from just writing a script to fill
Speaker 1: out a form or make a comment on forums, really
Speaker 1: complete any interaction on the web in an automated way.
Speaker 1: As someone who creates content online and I get lots
Speaker 1: of comments on various platforms, I don't want a whole
Speaker 1: bunch of automated gobbledygook showing up under my various podcasts
Speaker 1: and videos because then I can't tell where the actual
Speaker 1: signal is. All I'm seeing is noise. So you want
Speaker 1: to have some way of controlling that, and you might
Speaker 1: use it to limit spam in the message board, or
Speaker 1: to stop people from abusing the format of an online mistakes,
Speaker 1: or or again to stop people from harassing others on
Speaker 1: social platforms. Now, the necessity for cap chub is due
Speaker 1: to a fundamental flaw of the Internet, and that flaw
Speaker 1: is this, it doesn't take very many people to make
Speaker 1: using the Internet a total drag. You don't want some
Speaker 1: jerk face to use a script to create thousands of
Speaker 1: email addresses from a web based email provider and then
Speaker 1: use those email addresses for spam purposes or for someone
Speaker 1: to gain the system. In other ways a single person
Speaker 1: has the potential to impact lots of other people. So
Speaker 1: everything's out of balance, and the force demands a Jedi
Speaker 1: to right the wrongs or something. Now, the ideal application
Speaker 1: of capture is some sort of test that is very
Speaker 1: easy for humans to complete, but very difficult for computers
Speaker 1: to complete. And that requires some creative thinking. So what
Speaker 1: are some things that people are really good at but
Speaker 1: computers are aren't so great at. Over time this changes.
Speaker 1: Computer programmers get better at designing software that allows computers
Speaker 1: to simulate more of what humans can do. And that's
Speaker 1: not a bad thing necessarily because it pushes our development
Speaker 1: of artificial intelligence. But for the purposes of gate keeping,
Speaker 1: it does make it more tricky. You've gotta figure out
Speaker 1: a new way to be able to prevent people from
Speaker 1: abusing the system. Now, the idea for capture came from
Speaker 1: a couple of different teams. One team was at Alta Vista,
Speaker 1: which started to work on ways to cut down on
Speaker 1: online abuse way back in the Ulta Vista team was
Speaker 1: trying to find a way to prevent bots or scripts
Speaker 1: from adding active u r l s to the search
Speaker 1: engine platform. Meanwhile, the other team was at Carnegie Mellon University,
Speaker 1: And actually this happened a couple of years after Alta
Speaker 1: Vista's work, and they included some researchers who were really
Speaker 1: eager to try and find a solution to this problem,
Speaker 1: and they included Louis on On, Manuel Bloom, Nicholas Hopper,
Speaker 1: and John Langford. It was the Carnegie Melon team that
Speaker 1: coined the term capture back in two thousand three, and
Speaker 1: it worked pretty well. Humans could get a capture right
Speaker 1: more often than not, and computers weren't nearly as good
Speaker 1: at it, at least not at first. Now we'll talk
Speaker 1: a lot about captures in just a minute and get
Speaker 1: into some more elements about telling the difference between butts
Speaker 1: and humans, but right now let's take a quick break
Speaker 1: to thank our sponsor. So with early capture implementations, things
Speaker 1: were pretty simple. The capture would take on a pretty
Speaker 1: universal form. You'd have a little box and inside that
Speaker 1: box you would see a couple of different words or
Speaker 1: collections of letters or other characters, often distorted in some way,
Speaker 1: and a little field beneath it telling you, hey, tie
Speaker 1: been what you see here? And it was your job,
Speaker 1: as a human being type person to type in the
Speaker 1: correct characters, and that would allow you to gain access
Speaker 1: to whatever it was that the capture was guarding. And
Speaker 1: the thought was that computers just weren't as good at
Speaker 1: recognizing those characters as humans are. That if you distort them,
Speaker 1: then the character recognition software couldn't put piece that altogether.
Speaker 1: The weird shapes would be too far outside the norm
Speaker 1: for the computer model. So if you had a one,
Speaker 1: but that number one, the numeral one, it was all
Speaker 1: wavy and staticky or something like you were, uh, you
Speaker 1: were breaking up the shape a bit by changing it.
Speaker 1: Computers can't really see that and conceptualize that's a one,
Speaker 1: or at least not in the early days, so it
Speaker 1: would just look like a weird squiggle to them, and
Speaker 1: they wouldn't be able to complete the capture. Whereas we
Speaker 1: human being type people, we'd look and think, that's the
Speaker 1: worst number one I've ever seen. Some kid must have
Speaker 1: drawn that, but we understand it is, we recognize it,
Speaker 1: so we would type that in. That was the basis
Speaker 1: for capture. Create a test that's relatively easy for humans,
Speaker 1: very difficult for computers. Now, not everyone was capable of
Speaker 1: seeing these captures. Clearly, some people have visual impair impairment,
Speaker 1: and so they need to have some other element to
Speaker 1: captures in order to be able to access that same content.
Speaker 1: So there are also audible captures, which is pretty important
Speaker 1: option to get around those visual impairments that some people have.
Speaker 1: And uh, you might get a distorted voice being repeating
Speaker 1: out the same sort of letters and numbers that you
Speaker 1: would encounter with a capture. There might also be some
Speaker 1: background noise that would include some other elements that would
Speaker 1: make it hard for a computer program to analyze the
Speaker 1: audio and figure out what was being said, but hopefully
Speaker 1: humans would be able to make it out. So again,
Speaker 1: it was all about making it more challenging for a
Speaker 1: computer while not making it too challenging for human beings.
Speaker 1: And sometimes that works great, and sometimes that doesn't work
Speaker 1: so great. There are plenty of examples of human beings
Speaker 1: who could not get through a capture because the distortion
Speaker 1: was so great that it made the made it almost
Speaker 1: impossible to recognize what the actual capture was supposed to be.
Speaker 1: But the first counter to capture wasn't an advance in
Speaker 1: computational analysis of visual or audible data. You know, there
Speaker 1: are a lot of tricks that people figured out later
Speaker 1: down the line, to make these visual captures easier to analyze,
Speaker 1: things like switching all the images gray scales so that
Speaker 1: you take out the different color gradations that could fool
Speaker 1: a computer, and other elements along those lines. But at
Speaker 1: first those weren't even really necessary because the people who
Speaker 1: really wanted to get access to those systems didn't bother
Speaker 1: programming better AI. They just went and started paying people
Speaker 1: to fill out capture forms. Those who wanted to continue
Speaker 1: the game giving the systems, they created a new industry.
Speaker 1: They'd pay the people to fill out all these capture fields.
Speaker 1: There was no need to develop any sort of AI.
Speaker 1: People were doing what people were supposed to be doing easily.
Speaker 1: They were solving captures. Now, the pay was super low
Speaker 1: and the output was super high, and it posed a
Speaker 1: threat to the capture system. Now, as an analogy, amount
Speaker 1: imagine that you build a big fence strong enough to
Speaker 1: keep bears out. No bears will get in this fence,
Speaker 1: you say, and you go on your married a little way.
Speaker 1: What you didn't notice is that there were gaps in
Speaker 1: the fence that while the bears are far too big
Speaker 1: to fit through the gaps, the gaps are big enough
Speaker 1: to let rabid I don't know possums through, And so
Speaker 1: the bears who go to employ rabid possums, paying them handsomely,
Speaker 1: are able to access the stuff behind your fence anyway,
Speaker 1: because the rapid possums pass right through the security. They
Speaker 1: weren't intended to be kept out. Of course, in the
Speaker 1: case the captures, we are talking about people accessing the system.
Speaker 1: They were just doing so in massive numbers and for
Speaker 1: less than ethical reasons. So the Carnegie Melon team began
Speaker 1: to consider a new approach. That's when they developed recapture.
Speaker 1: This tech used images of real words and numbers taken
Speaker 1: from old documents. The original run was of New York
Speaker 1: Times archival texts, but eventually the teams sold this technology
Speaker 1: to Google, which began to use it on lots and
Speaker 1: lots of scanned books. They were trying to transcribe those
Speaker 1: old books. The company used recapture to display scanned words
Speaker 1: or numbers from the texts, and as more people filled
Speaker 1: out the recaptures, Google began to use that data to
Speaker 1: transcribe these old works, which meant that they had a
Speaker 1: digital copy of these books that they had come into
Speaker 1: possession of, which means anyone filling those fields out was
Speaker 1: actually technically doing real work for Google, including all those
Speaker 1: folks who were being employed to write out captures. Meanwhile,
Speaker 1: bot developers were making better bots, and character recognition and
Speaker 1: analysis software was getting better at increasing success rates with
Speaker 1: visual captures. Now that would prompt capture designers to make
Speaker 1: more challenging captures, and soon we reach a real problem.
Speaker 1: The whole point of capture was that it was supposed
Speaker 1: to be easy for a human to complete, but difficult
Speaker 1: for a bot to complete. If it becomes tricky for humans,
Speaker 1: you've defeated its original purpose. Now Google updated capture to
Speaker 1: the familiar I'm not a robot check box that you
Speaker 1: can still find on some online forms. They call it
Speaker 1: the no capture recapture catchy. It wasn't just a check
Speaker 1: box that needed checking. Behind the scenes, back if you
Speaker 1: were able to stare at the back side of the
Speaker 1: website that you're on, software was analyzing your clicking style
Speaker 1: all so it would look for stuff like was the
Speaker 1: box clicked right away, perhaps before or at the same
Speaker 1: time as fields were being filled in. If so, that
Speaker 1: indicates a bot rather than a human being. But this
Speaker 1: approach also doesn't get around the fact that you could
Speaker 1: employ real human beings to do this same work. So well,
Speaker 1: it's an effective way to tell the difference between a
Speaker 1: bot and a person. It's not necessarily effective in keeping
Speaker 1: spam traffic away from a site if people are willing
Speaker 1: to employ actual human beings to do it. In sen
Speaker 1: Google killed off this version of capture on its own services.
Speaker 1: You can still find it everywhere else, but these days
Speaker 1: Google uses invisible recaptures. Now, this version analyzes your browsing behavior,
Speaker 1: and there aren't a lot of details released about it yet,
Speaker 1: but presumably Google is looking at how any given agent
Speaker 1: on a website uses a web page to determine if,
Speaker 1: in fact, that is an honest to goodness human being,
Speaker 1: or if the terminator has the said to pop over
Speaker 1: to Zeppos to look for some new kicks. So this
Speaker 1: is still in a way of being able to differentiate
Speaker 1: humans from machines based solely upon behavior, just analyzing the
Speaker 1: behavior and thinking all right, well, this indicates a human being.
Speaker 1: This is this person, This entity is navigating a web
Speaker 1: page the way a human would versus this is really
Speaker 1: efficient and formulaic and repetitive, and that tells me that's
Speaker 1: possibly a machine. So let's switch over to Twitter. Twitter
Speaker 1: has got a lot of bots on it. Twitter and
Speaker 1: the follower numbers are kind of a type of status online.
Speaker 1: If you have more followers than the general implication is
Speaker 1: that you must be more important than someone who has
Speaker 1: fewer followers, and so there's a healthy market for purchased followers.
Speaker 1: On Twitter. You can go to several different companies and
Speaker 1: stores and buy followers by the hundreds or thousands. So
Speaker 1: if you're desperate to boost that number, you can pay
Speaker 1: a service that will link accounts to your account. Now,
Speaker 1: most of those probably do not have real, live human
Speaker 1: beings behind those accounts, and so a visit to any
Speaker 1: of those accounts will show you that they never seem
Speaker 1: to say anything themselves. They'll retweet what lots of other
Speaker 1: people are saying, but they don't actually, you know, tweet
Speaker 1: anything of their own, or if they do, it makes
Speaker 1: little to no sense. It might just be kind of
Speaker 1: like garbled general you know, new a g kind of stuff,
Speaker 1: the things that that sound like they might have some
Speaker 1: sort of deep meaning, but if you think about it,
Speaker 1: you realize, no, that really doesn't mean anything at all. Now,
Speaker 1: on a one on one basis, Twitter bots are pretty
Speaker 1: easy to spot. So let's say your you tweet about
Speaker 1: something important going on, such as you know something's going
Speaker 1: on in politics or whether John Snow is going to
Speaker 1: win the Game of Thrones, and almost immediately after you tweet,
Speaker 1: you notice a new followed notification and if it popped
Speaker 1: up sue pretty quickly, like instantly after you posted a tweet.
Speaker 1: That might very well be a bot running on a
Speaker 1: script that is searching for instances of specific keywords, and
Speaker 1: when it finds those keywords, it then prompts the bot
Speaker 1: account to follow the account that generated the keywords, assuming
Speaker 1: that hasn't already followed that account. And some butts do
Speaker 1: this in order to convince people to follow them back
Speaker 1: because lots of folks on Twitter have a follow back
Speaker 1: policy which helps them boost up their own follower numbers.
Speaker 1: You know, it's the whole hey, if you follow me,
Speaker 1: I'll follow you quit bro quo kind of approach. But
Speaker 1: in this case, one of the two parties is a bot,
Speaker 1: at least one of them. Anyway, maybe they both are,
Speaker 1: which is kind of funny and pointless. Now, once you
Speaker 1: follow the bot, you may start seeing spam messages from
Speaker 1: that bot pop up in your feed. Whenever it occasionally
Speaker 1: posts two followers, it's likely trying to get you to
Speaker 1: engage in a particular behavior. Now that behavior might be
Speaker 1: more or less benign, such as convincing you to shop
Speaker 1: a certain brand which is obnoxious but not you know, malicious,
Speaker 1: Or it might be more sinisters, such as trying to
Speaker 1: get you to do something foolish that will compromise your
Speaker 1: computer and allow it to join like a hacker's bot
Speaker 1: net army or something. And there's a lot of reasons,
Speaker 1: most of them annoying, that a bot programmer would want
Speaker 1: you to follow their butt. According to a study conducted
Speaker 1: by researchers at Indiana University and the University of Southern California,
Speaker 1: somewhere between nine and fifteen percent of all active Twitter
Speaker 1: accounts are actually bots. It usually doesn't require a lot
Speaker 1: of work to determine if a single account is the
Speaker 1: work of an actual human being, but if you have
Speaker 1: a lot of them, that can be a challenge. I mean,
Speaker 1: if you've got thousands of followers, sorting through all of
Speaker 1: those would take a real long time. So that's what
Speaker 1: prompted developers to create apps like butt or Not, which
Speaker 1: scour Twitter followers and look for signs of butts, returning
Speaker 1: a report to the user to let him or her
Speaker 1: know how many legitimate followers they have. Those apps, which
Speaker 1: you can argue are are kind of bots themselves, look
Speaker 1: for indicators such as each followers Twitter description uh the
Speaker 1: u r L field, the number of tweets the account
Speaker 1: has generated of its own, the number of followers the
Speaker 1: account has, and so on. So if you come across
Speaker 1: an account that follows thousands of other accounts but only
Speaker 1: as a few followers of its own, that's a red flag.
Speaker 1: That's saying, well, this account is following lots of people,
Speaker 1: not a lot of people follow it. That tells me
Speaker 1: something hinky might be going on. If the description or
Speaker 1: you are l are empty, that's another indicator because it
Speaker 1: shows maybe someone didn't want to take the time to
Speaker 1: try and fool people by creating a bogus description and
Speaker 1: a bogus u r L. There are several other criteria
Speaker 1: that the apps look for, and depending upon how many
Speaker 1: red flag boxes get checked, the app determines if the
Speaker 1: account is the work of a script or if it's
Speaker 1: an actual person. Now, on the one hand, we can
Speaker 1: look at all these stories about bots, and think of
Speaker 1: how irritating they are because they generate spam content, they
Speaker 1: clog up actual communication. They create deception, whether it's an
Speaker 1: attempt to trick you into following a malicious link or
Speaker 1: to think someone is particularly notable due to the enormous
Speaker 1: number of Twitter followers they have. But on the other hand,
Speaker 1: we can think about how these examples show how we're
Speaker 1: getting better at creating more human like agents. Now that's
Speaker 1: not to say these agents possess intelligence, only that they
Speaker 1: can imitate human interactions enough to raise the question could
Speaker 1: this be a bot I'm talking to? If you have
Speaker 1: to ask that question, then that indicates programmers are getting
Speaker 1: better at designing bots, or that you're getting pretty bad
Speaker 1: at recognizing humans. Some days, I certainly have that problem.
Speaker 1: We'll talk a little bit more about machine intelligence and
Speaker 1: communication in just a minute and kind of layout why
Speaker 1: it's so difficult to really create a truly compelling butt
Speaker 1: that can fool people into thinking it's a human. But
Speaker 1: first let's take another quick break to think our sponsor. Now,
Speaker 1: they're just elements to human communication that bots are not
Speaker 1: great at handling, or they need a huge amount of
Speaker 1: help in order to pull it off. So let's take
Speaker 1: IBM S Watson for example. Now, Watson is the interface
Speaker 1: that made the news when it competed against two former
Speaker 1: Jeopardy champions on a special edition of Jeopardy. Watson beat
Speaker 1: the opponents, which is pretty impressive when you consider that
Speaker 1: Jeopardys format includes elements of wordplay includes and machines are
Speaker 1: typically not very good at interpreting word play and subtext
Speaker 1: and that sort of thing and getting at what the
Speaker 1: actual meaning to a sentence is. Watson even attempted a
Speaker 1: couple of jokes throughout the course of the game, but
Speaker 1: they weren't really spontaneous bumb malls designed to get a
Speaker 1: chuckle of, you know, Alex Trebek. Humor is just one
Speaker 1: of those aspects of human communication that is difficult to
Speaker 1: quantify and implement with machines. Typically, it requires programmers to
Speaker 1: think ahead and imagine specific scenarios and queries to build
Speaker 1: out appropriate or, depending upon the context, inappropriate responses. So,
Speaker 1: for example, when Apple's personal assistant Sirie debuted, people immediately
Speaker 1: began to test Sirie. They began to ask the digital
Speaker 1: personal assistant all sorts of odd things and sharing the results.
Speaker 1: If you create any sort of system. One of the
Speaker 1: first things you're going to find when you allow people
Speaker 1: to access that system is they're going to try and
Speaker 1: break it, or they're at least going to try and
Speaker 1: explore what the limitations are within that system. And they're
Speaker 1: not necessarily doing this malicious with malicious intent, but rather
Speaker 1: that you know, we're humans, were curious. We want to
Speaker 1: know how how far do things go? Are they really
Speaker 1: limitless or are you going to run up against an
Speaker 1: invisible all if you keep going in one direction long enough.
Speaker 1: The same thing is true about personal digital assistance. So
Speaker 1: in some cases where people were asking weird things of SIRIE,
Speaker 1: serious responses were particularly hilarious, indicating that someone over at
Speaker 1: Apple had anticipated some of those shenanigans because SIRIE wasn't
Speaker 1: coming up with these wacky responses on its own account.
Speaker 1: It was referring to a database of responses that people
Speaker 1: had been compiling ever since they started working on the project. So,
Speaker 1: if you are working on a personal Digital assistant project
Speaker 1: and you think, oh, someone's gonna say I love you
Speaker 1: eventually to this, I want to have a response to
Speaker 1: come back that isn't just I'm sorry. I don't understand
Speaker 1: that every time the digital assistant says, I'm sorry, I
Speaker 1: don't understand that is an overall, like outright admission of limitations.
Speaker 1: So you will try avoid that as much as you can.
Speaker 1: Make it kind of a joke instead. But it means
Speaker 1: thinking ahead, and it means the humans are thinking ahead.
Speaker 1: It's not a machine. Uh So, here's an example. One
Speaker 1: of the early queries that got widespread traction was I
Speaker 1: need to hide a body, and Siri would respond originally
Speaker 1: with various sites where you could, you know, possibly dump
Speaker 1: a body and get away with it, like nearby reservoirs
Speaker 1: or quarries. It's pretty grim, but darkly humorous, and it
Speaker 1: showed that someone had been thinking those things through by
Speaker 1: the way, that joke became very serious. In two thousand twelve,
Speaker 1: a Florida man stood accused of murdering a friend of his,
Speaker 1: a roommate, and on his phone the suspects phone was
Speaker 1: a screenshot of a query to Sirie, the one about
Speaker 1: where to hide his roommate, and prosecutors used it as
Speaker 1: evidence in the trial. But it turned out the screenshot
Speaker 1: that used wasn't really a query that the man had
Speaker 1: made himself, because his iPhone was as an iPhone that
Speaker 1: was running on Verizons service, and the screenshot was from
Speaker 1: an iPhone that was running on a T and T
Speaker 1: S service. Uh. Also, it turned out that the phone
Speaker 1: he was using, the suspect was an older model of
Speaker 1: iPhone that didn't even support SIRIE. However, he was later
Speaker 1: found guilty of his crime, though the Serie connection was
Speaker 1: again dismissed for those multiple reasons. Later on, Apple would
Speaker 1: replace that joking response with a referential but less morbid joke,
Speaker 1: which was quote I used to know the answer to
Speaker 1: this question end quote, So, in other words, acknowledging that,
Speaker 1: in fact, there used to be another response without actually
Speaker 1: giving it because of you know, these very grim, macabre
Speaker 1: reasons in real life. But let's say you wanted to
Speaker 1: create an artificial entity that could respond with humor dynamically.
Speaker 1: It wouldn't require you to pre program in responses to
Speaker 1: different questions you'd have to anticipate. This would let you
Speaker 1: have a bot that could convincingly stand in as a
Speaker 1: human without the danger of the bot encountering something you
Speaker 1: didn't expect and having no response to it, or to
Speaker 1: misinterpreting the interaction with an actual human being, or if
Speaker 1: it did misinterpret it that it could follow up in
Speaker 1: a very human way. So if I make a joke
Speaker 1: to my coworkers and I do it well, my coworkers
Speaker 1: understand what the meaning of the joke was, what the
Speaker 1: intended meaning of the joke was, and there you get
Speaker 1: that response. If the joke doesn't go well, I can
Speaker 1: follow it up by explaining the joke or explaining what
Speaker 1: I had tried to do with the joke, which doesn't
Speaker 1: make the joke funny, but at least informs the audience
Speaker 1: as to what it was I was thinking. Machines would
Speaker 1: have to be able to do that too, and this
Speaker 1: is hard to do. Machines would need to be able
Speaker 1: to interpret not only the literal meaning of any statement,
Speaker 1: but the potential intended meanings as well. So I would
Speaker 1: have to incorporate the concept of novelties, introducing something new
Speaker 1: and unexpected into the interaction. It's a subversion of our
Speaker 1: expectations that tends to lead to humor. So, for example,
Speaker 1: Douglas Adams, who is one of my favorite authors, once
Speaker 1: wrote a sentence describing a fleet of spaceships, and the
Speaker 1: sentence goes like this, The ships hung in the sky
Speaker 1: in much the same way that bricks didn't. Now that's
Speaker 1: a great sentence. It gets across the humor and intent
Speaker 1: to the reader. You know that if you were to
Speaker 1: see these spaceships in the sky, they would look completely
Speaker 1: out of place. They might even be remotely brick shaped.
Speaker 1: But mostly it's the idea that if bricks could hang
Speaker 1: in the air, those ships would look like that, except
Speaker 1: obviously bricks can't hang in the air, And in one sentence,
Speaker 1: Adams is able to convey with humor the mind bendingly
Speaker 1: weird experience of seeing these spaceships in the Earth's sky.
Speaker 1: Computers would have a real hard time replicating that, at
Speaker 1: least on purpose. A computer program that put rough sentences
Speaker 1: together using a basic syntax and vocabulary could potentially make
Speaker 1: weird and funny sentences, but these would be mostly random
Speaker 1: and frequently meaningless, and you wouldn't be able to hold
Speaker 1: a context from sentence to sentence. To make something that
Speaker 1: has meaning requires aspects of intelligence that computers don't yet possess. Watson,
Speaker 1: with its jokes, was running on a massively powerful computer
Speaker 1: system with two thousand, eight hundred eighty processing cores, and
Speaker 1: that doesn't even approach the power necessary to create real
Speaker 1: humor spontaneously. To detect and generate sarcasm, and entity must
Speaker 1: understand context and other cues and machines aren't very good
Speaker 1: at this, though we've seen some advances in contextual tracking.
Speaker 1: For example, Google's Personal Assistant can follow a line of
Speaker 1: questions about the same subject without you having to restate
Speaker 1: the subject with each question. If I asked my Google
Speaker 1: Home what when the next Braves game is, it would
Speaker 1: give me an answer. Let's say it's day after tomorrow. Well,
Speaker 1: I could follow that up with what will the weather
Speaker 1: be like then? And the system would understand that by
Speaker 1: then I mean the day of the game, So the
Speaker 1: day after tomorrow. I might also ask what's the fastest
Speaker 1: way there, and it will know that by there I
Speaker 1: mean the stadium, and that I am probably am asking
Speaker 1: how to get from my current location to that stadium
Speaker 1: and the most efficient way possible. The subject is stored
Speaker 1: in temporary memory, I don't have to keep asking specific
Speaker 1: questions about the game or the stadium. But that's still
Speaker 1: a long way off from actually understanding context. So one
Speaker 1: test for bots might be for us to have it
Speaker 1: tell us a joke. If it's clear that the bot
Speaker 1: can create a brand new joke, one that has not
Speaker 1: been pre programmed, one that is spontaneous and novel and
Speaker 1: created by the bot itself, and it makes sense and
Speaker 1: it is funny. We've reached a point where telling bots
Speaker 1: and humans apart is going to be tremendously complicated, but
Speaker 1: right now we're nowhere near that. The jokes that we
Speaker 1: hear bots tell, for the most part, are ones that
Speaker 1: have been created by human beings and just stored in
Speaker 1: a database, and the body just pulls them out and
Speaker 1: then recites them. It's not creating them. It's just pulling
Speaker 1: a massive data from a cell in a giant spreadsheet
Speaker 1: and saying, all right, this is the joke I'm gonna
Speaker 1: tell us. The joke that's in sell see four and seventeen.
Speaker 1: That's the joke for today. That's not creating a joke,
Speaker 1: it's just reciting one. If we can get to a
Speaker 1: point where they can create jokes, that's a big jump
Speaker 1: in computer intelligence and maybe a brand new audience from
Speaker 1: my type of humor I'm always looking. Well, that pretty
Speaker 1: much wraps up this episode. Really, the key to determining
Speaker 1: whether or not it's a bot or a hu Man
Speaker 1: is testing whether or not it's capable of handling novelty.
Speaker 1: Most bots are fairly limited in the scope of things
Speaker 1: they can handle, and if you step outside of that,
Speaker 1: you see those limitations pretty quickly, and that then it
Speaker 1: becomes apparent. But every year we're getting a little bit
Speaker 1: better at handling wider spectrums of experiences with bots, so
Speaker 1: that it becomes more and more complicated to tell them
Speaker 1: apart from human beings. Uh. In most cases it's probably
Speaker 1: a moot point. It's not really necessary depending upon what
Speaker 1: it is you're trying to do, But in some cases
Speaker 1: you really do want to know whether or not that's
Speaker 1: a human being or a machine. On the other end,
Speaker 1: if you guys have any stories about funny times where
Speaker 1: you were chatting with something that you thought was a
Speaker 1: human and turned out to be a bot, Like I've
Speaker 1: got friends who have received robo calls and didn't know
Speaker 1: until about half a minute in or maybe a minute in,
Speaker 1: that it was a robot. Those are great stories. I
Speaker 1: have specifically love the ones where if you ask the
Speaker 1: entity are you a robot? It tries to deflect but
Speaker 1: does not actually answer the question. Those are the best.
Speaker 1: But you can get in touch with me, let me
Speaker 1: know your experiences. The email address is tech stuff at
Speaker 1: how stuff works dot com, or you can drop me
Speaker 1: a line on Twitter or Facebook. The handle for the
Speaker 1: show at both of those is tech stuff hs W. Remember,
Speaker 1: normally you can watch me record shows live at twitch
Speaker 1: dot tv slash tech stuff. I record on Wednesdays and Friday's.
Speaker 1: Today's episode is a little bit outside the norm. There
Speaker 1: is no one currently watching me live, so when I'm
Speaker 1: doing my dance like I am right now, no one
Speaker 1: can see. But most days you can see, and I
Speaker 1: do the dance then too. So join me at twitch
Speaker 1: dot tv slash tech stuff to watch the show live.
Speaker 1: You get to see all the elements of the show
Speaker 1: come together, and I will talk to you again. Really
Speaker 1: for more on this and thousands of other topics, is
Speaker 1: that how stuff works dot com.