Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking, either you want and Welcome to Forward Thinking,
Speaker 1: the podcast that looks at the future and says, one
Speaker 1: more robot learns to be something more than a machine.
Speaker 1: I'm Jonathan Strickland and I'm Joe McCormick, and I've got
Speaker 1: a question for y'all. Would you like to ask it?
Speaker 1: How how unusual? Please ask your question? Have you ever
Speaker 1: noticed how any time there's a human being who follows
Speaker 1: you on Twitter or favorites one of your tweets, and
Speaker 1: you click through to their profile, and then you click
Speaker 1: on their website, it's just some spam page with a
Speaker 1: bunch of pop up ads. I will say that I
Speaker 1: am not unfamiliar with what you are saying. I try
Speaker 1: not to click through to the website. Yeah, that's a
Speaker 1: smart move, Lauren, because no, I don't notice the as
Speaker 1: pervasively as I was just leading you to believe. But
Speaker 1: there is a certain phenomenon that I think we are
Speaker 1: all somewhat familiar with now, the fact that there are
Speaker 1: let's call them agents loose on the web that are
Speaker 1: not human beings and and not like subhuman beings. We're
Speaker 1: not being elitist in some kind of way. This isn't
Speaker 1: an episode about the future of trolls. Yeah, there there
Speaker 1: are people who lack humanity, but we we would probably
Speaker 1: say that they possess consciousness and like a brain and
Speaker 1: probably some kind of flesh body. Yeah. Yeah, there are
Speaker 1: many agents indeed on the Internet that are not possessed
Speaker 1: of a flesh body. Yeah. This isn't a new thing either, No, no, no, no.
Speaker 1: And so we've talked plenty of times on this podcast
Speaker 1: about the Turing Test, about artificial intelligence, about ways of
Speaker 1: creating piece of software, robots, computer programs, apps, all kinds
Speaker 1: of things that are designed to try to lull you
Speaker 1: into the sense that you're interacting with another conscious agent,
Speaker 1: like a human, even if you're just interacting with the machine. Yeah,
Speaker 1: and that that might have some very sophisticated kind of
Speaker 1: simulation of intelligence, or it might just have a small
Speaker 1: collection of a few clever tricks, right, And it may
Speaker 1: not even be intended to fool you into thinking you're
Speaker 1: talking to a human. It may just be that this
Speaker 1: was an attempt to create a more naturalistic means of
Speaker 1: of addressing an issue, and it wasn't an attempt to
Speaker 1: you know, deceive you. However, there are some UH agents
Speaker 1: that were specifically engineered as a means of deception in
Speaker 1: one way or another. Yea, So today we want to
Speaker 1: talk about bots, specifically bots on the web, what the
Speaker 1: bots are, how to spot them, what some characteristics are,
Speaker 1: and then maybe have a little discussion about what we
Speaker 1: think about the future of bots. Yeah, so we so
Speaker 1: maybe we should start this off by going back to
Speaker 1: an old favorite of ours, which is the Turing test. Right,
Speaker 1: So the Turing test. This was something that was kind
Speaker 1: of proposed by Alan Turing, and it wasn't really a
Speaker 1: test that he was proposing. Yeah, it was more a
Speaker 1: thought experiment. Yeah. He was just kind of saying that
Speaker 1: if you were to design a computer program that could
Speaker 1: fool a human judge a certain percentage of the time.
Speaker 1: Usually it's it's related to being around thirty or third
Speaker 1: of the time, that you could argue that this machine
Speaker 1: is at least simulating the appearance of intelligence. And if
Speaker 1: it can do that reliably, why would you not just
Speaker 1: go ahead and extended the courtesy saying it is intelligent?
Speaker 1: After all, when I have a conversation with another human being,
Speaker 1: I assume that human being is intelligent because of my
Speaker 1: interactions with previous human beings and because of my own experience,
Speaker 1: why would I not extend that same courtesy to a machine.
Speaker 1: Of course, what you might be thinking is the no,
Speaker 1: I don't assume that the humans I interact with our intelligence,
Speaker 1: in which case you're mean, Well, we should observe a
Speaker 1: sort of phenomenal difference between the interactions we typically have
Speaker 1: with humans, or at least the majority of our interactions
Speaker 1: with humans, and the kinds of tests that we now
Speaker 1: would call Turing tests, because typically the Turing test as
Speaker 1: it's imagined today is a text based affair. It's talking
Speaker 1: about communication that's carried out entirely through words written text communication. Right,
Speaker 1: you're not talking about looking at a person and and
Speaker 1: seeing their face. That that's a whole different kind of
Speaker 1: We would probably still call that like life simulation or
Speaker 1: artificial intelligence, like the mimicking of say, human facial movements
Speaker 1: or something like that. And there's been tons of studies
Speaker 1: and robotics to do exactly that, to design robots that
Speaker 1: won't creep people out by having more kind of a
Speaker 1: human interaction and yet having to avoid the uncanny Valley problem,
Speaker 1: where if you get too good actually gets creepy here, right,
Speaker 1: It's it's it's too good and yet not perfect. So
Speaker 1: it's that gap between too good and perfect, are good
Speaker 1: and perfect, where if you make a robot that's good,
Speaker 1: then it doesn't seem creepy. If you make a robot
Speaker 1: that's perfect, it might not seem creepy. No one's done
Speaker 1: that yet, so it's hard to say. But if it's
Speaker 1: really close but not quite perfect, it is unnerving. And
Speaker 1: the Turing test, the various programs that have been said
Speaker 1: to pass the Touring Test in almost every single case,
Speaker 1: we're talking about lowered expectations of what the the bar
Speaker 1: would be, right, right, We're talking about text communication via
Speaker 1: computer with a bot that would purport to be say
Speaker 1: a child whose first language wasn't English, or someone else
Speaker 1: who would be uh, culturally or grammatically hampered in normal conversation, right,
Speaker 1: Or they've just got some clever tricks for changing the
Speaker 1: subject whenever they're not sure that they understand you, right, right,
Speaker 1: for example, purporting to have some kind of psychological condition
Speaker 1: that makes them act really shady. Yeah, that's a yea perfectly.
Speaker 1: All of these are real world examples. Were alluding to right.
Speaker 1: But of course when we talk about these these programs
Speaker 1: that are said to you know, there's there's an article
Speaker 1: you see where they say, oh, we passed the Turing tests.
Speaker 1: These are sort of like staged experiments, right. People set
Speaker 1: up the test on purpose and that they have somebody
Speaker 1: who's trying to determine if they're chatting on some kind
Speaker 1: of instant messenger program with a real person or with
Speaker 1: a computer program. But we actually encounter pretty much this
Speaker 1: scenario in real life in the wild all the time.
Speaker 1: No celebrity judges. Yeah, because bots, let's face it, on
Speaker 1: the web, they are everywhere, and sometimes they are again
Speaker 1: we're aware of it, and it's not a problem. Like
Speaker 1: it might be an automated response to a helpline for
Speaker 1: or some service or product that you use, So you
Speaker 1: might go to a website, and really it's just a
Speaker 1: way of filtering you down so that you either end
Speaker 1: up getting the information you want because it's somewhere buried
Speaker 1: in a help file, but it's way easier if you
Speaker 1: go through a very conversational approach to narrow down what
Speaker 1: the problem is, or it then directs you to the
Speaker 1: person who would presumably have the actual answer you need those,
Speaker 1: that's one type of butt. But we're really kind of
Speaker 1: focusing on the kind that you're not necessarily supposed to
Speaker 1: know was being run by a machine. Right, So I've
Speaker 1: got a question for you, ask away, how much web
Speaker 1: traffic do you think is a real human sitting at
Speaker 1: a browser looking for something because they want or need
Speaker 1: to know. See. If you had asked me this question
Speaker 1: like two weeks ago, I would have said, clearly, all
Speaker 1: of the traffic on the web, aside from maybe like
Speaker 1: a few researchers that like send stuff out to go
Speaker 1: look it stuff just just to see what happens, I
Speaker 1: would have said that like cent of all web traffic
Speaker 1: was actual humans, because why why Joe and Jonathan would
Speaker 1: anyone ever create a robot that looks at websites? Robots
Speaker 1: don't need websites. Pennies, nickels, dollar bills, hundred dollar bills. Yeah,
Speaker 1: we're talking money, money, money. Wow. I didn't expect we
Speaker 1: were going to get to hear that today, Prince Charles, Yeah,
Speaker 1: I busted out when I have to know you got
Speaker 1: to hear these these projected numbers. This is pretty eye
Speaker 1: opening from last year. Yeah, this, uh, there was one
Speaker 1: figure that came from the Interactive and Advertising Bureau that
Speaker 1: suggested that around thirty six of all web traffic is
Speaker 1: quote unquote fake, meaning that it was actual bots, it
Speaker 1: was it was non humans surfing to websites. And uh.
Speaker 1: The reason for this is pretty easy to under stand
Speaker 1: when you realize how a lot of funding for websites happens.
Speaker 1: You have advertising displayed on web pages, and the more
Speaker 1: often people are going to your web page, the more
Speaker 1: valuable that real estate is. It's kind of the same
Speaker 1: as if you want to buy a billboard next to
Speaker 1: the major highway running through your city versus the tiny
Speaker 1: little side street that hardly anyone goes down. The billboard
Speaker 1: next to the highway is probably going to be more
Speaker 1: expensive to use than the one on a little sites,
Speaker 1: and therefore is going to generate more revenue for whoever
Speaker 1: owns the highway or the advertising space right right right,
Speaker 1: So if you own that advertising space, what you should
Speaker 1: actually do is buy up a bunch of autonomous vehicles
Speaker 1: and spend day and night just driving them back and
Speaker 1: forth along that stretch of road. And that sounds crazy,
Speaker 1: but this is what human people are doing right just
Speaker 1: on the Internet, not on actual streets as far as
Speaker 1: you know. So, so this is this is actually a problem.
Speaker 1: This is not This is not just something where you're like, oh, well,
Speaker 1: that's weird that that more than a third of all traffic.
Speaker 1: By the way that the conservative is right, there were
Speaker 1: some experts who are saying that as much as fifty
Speaker 1: percent of traffic on the web was actually generated by
Speaker 1: non human entities. Now, it's worth saying that these probably
Speaker 1: aren't the forward the same forward facing bots you're going
Speaker 1: to interact with the ones that you see in comments
Speaker 1: sections and on social media. We'll get to those in
Speaker 1: a minute, but they're probably not Also on Twitter, bots
Speaker 1: don't usually diversify that. This is essentially algorithms are pretty
Speaker 1: simple exactly. This is. This is an algorithm that that
Speaker 1: navigates a browser to a particular web page, uh and
Speaker 1: stays there for a given length of time. That's another
Speaker 1: thing that you guys may not know is that the
Speaker 1: amount of time spent on a web page can also
Speaker 1: impact how valuable that web pages. So if it's just
Speaker 1: a hit, if that's all they're looking at, well that's
Speaker 1: that's easy. But if it's more than that, if it's
Speaker 1: how much time was spent on that web page, because
Speaker 1: if it was a fraction of a second, then clearly
Speaker 1: they didn't have enough time to view whatever advertisements might
Speaker 1: have been on that page. But as the Bureau has
Speaker 1: pointed out, it is a serious problem because it ends
Speaker 1: up devaluing digital experiences. If advertisers come to a website
Speaker 1: and say, hey, we want to advertise on your page,
Speaker 1: but we happen to know that more than a third
Speaker 1: of all the traffic going to your page is not
Speaker 1: from actual human beings. We're not gonna pay you the
Speaker 1: amount you think this page is worth because it's not
Speaker 1: really worth that. It's not actual human eyes that are
Speaker 1: seeing the stuff we want to put on your page. Right,
Speaker 1: And so I'm not a digital marketing expert, but from
Speaker 1: my understanding, the way this works is it's not typically
Speaker 1: going to be that you know, let's say you operate
Speaker 1: a web page. It's probably not that one third a
Speaker 1: half of your traffic is fraudulent. It's that there are
Speaker 1: a whole lot of sort of spammy, kind of shady
Speaker 1: sites out there that are funneling tons and tons of
Speaker 1: fake traffic to themselves. That's that's sort of changing the average.
Speaker 1: There's either I don't know if you saw this. There's
Speaker 1: even speculation that it can sometimes be a rival advertising
Speaker 1: firm sending huge amounts of traffic to bankrupt their opponent,
Speaker 1: their their competitors by saying, well, our competitor will pay
Speaker 1: out a certain amount of money every time this website
Speaker 1: is viewed. If we flood that website with tons of traffic,
Speaker 1: they're gonna have to pay out tons of money as
Speaker 1: a result because of this agreement they're in, And we'll
Speaker 1: end up hurting our competitor this way, I know. Yeah.
Speaker 1: Another thing that some advertisers do is use bots to
Speaker 1: purchase ads, like software that can identify sites that fit
Speaker 1: within the advertiser's plan and then place ads on those
Speaker 1: sites automatically at high speed. Wow. So sometimes it's bots
Speaker 1: showing ads to bots that were placed by bots. Yeah. Yeah.
Speaker 1: All I can imagine is all the Amazon robots going crazy,
Speaker 1: like we have to deliver no way that the delivery
Speaker 1: was canceled the way we got to deliver, just like
Speaker 1: an insane activity inside the Amazon warehouse. And and according
Speaker 1: to numbers that I read, it's only about four per
Speaker 1: cent of bots that I purchased that of ads that
Speaker 1: are purchased by bots as of but media marketing folks
Speaker 1: think that that could boom to in the next ten years,
Speaker 1: that it is an enormous growth. I think that might
Speaker 1: be helpful. I'm not sure. So if you are like
Speaker 1: an advertiser trying to advertise on a website and you're
Speaker 1: trying to figure out if it's traffic is real or fake. Again,
Speaker 1: from what I understand, in a lot of cases, this
Speaker 1: is pretty easy to do. Like you can look at
Speaker 1: some certain metrics and kind of say, okay, well, if
Speaker 1: you have the access to the metrics, right, yeah. See,
Speaker 1: That's the thing is that actually it behooves the the
Speaker 1: people who are running the website to keep an eye
Speaker 1: on those metrics to make sure that they are in
Speaker 1: fact legitimate, because if it were ever discovered that they aren't,
Speaker 1: whether whether ultimately the owner of that website was at fault,
Speaker 1: it's going to negatively impact that owner. Right, So, if
Speaker 1: the owner is employing bots essentially to visit the website
Speaker 1: to drive up numbers, if that ever gets public, that's
Speaker 1: a huge black mark against that that website and that owner.
Speaker 1: If in fact someone else is doing it and the
Speaker 1: web owner is completely innocent of it, it can still
Speaker 1: be a black mark. So it's one of those things
Speaker 1: that now, honestly, if you really look at the long term,
Speaker 1: you don't want this happening because it ends up hurting
Speaker 1: everybody in the long run. It's only the short term
Speaker 1: where it seems to have some gains. Right. But let's
Speaker 1: if you are one of these people who's trying to
Speaker 1: use one of these sneaky tricks to like generate a
Speaker 1: bunch of fake traffic. Obviously, the artificial intelligence capabilities are
Speaker 1: getting better, and this is an interesting variant on the
Speaker 1: type of artificial intelligence we usually talk about. Usually we
Speaker 1: talk about a kind of um single agent oriented artificial
Speaker 1: intelligence simulation, Like when you're doing our official intelligence, you're
Speaker 1: trying to create a program that can talk like a
Speaker 1: single person or behave like a single person. This would
Speaker 1: be artificial swarm intelligence, like it's trying to behave the
Speaker 1: way massive groups of people actually look in statistics. Yeah,
Speaker 1: and you can imagine, like, if you want to get
Speaker 1: super clever with this one, you could try and design
Speaker 1: an algorithm that would surf to a page as if
Speaker 1: it had been human, in other words, to follow a
Speaker 1: trail as opposed to just directly navigate to a site
Speaker 1: so that if someone were looking into it, it would
Speaker 1: look as if it was an actual person. And if
Speaker 1: you really wanted to be tricky, you could look for
Speaker 1: times when it's the most advantageous to initiate a swarm
Speaker 1: activity at a website. For example, let's say that there
Speaker 1: was a big press release. Then that ends up naturally
Speaker 1: saying well, curiosity would be raised because we've they've got
Speaker 1: some thing new to announce. Therefore you would expect an
Speaker 1: increase in traffic. We're just artificially boosting that increase. So
Speaker 1: it gets super like super secret spy type stuff going on.
Speaker 1: And this is all on the corporate end, right. But
Speaker 1: let's say you're not involved in any kind of web
Speaker 1: advertising or anything like that. You're just a normal Internet user.
Speaker 1: You use some social media occasionally, you look at the comments.
Speaker 1: You know you shouldn't, but you do. Yeah. I'm pretty
Speaker 1: much with you, except for the normal parts. So go ahead.
Speaker 1: Yeah I haven't figured it out. Well, it turns out
Speaker 1: normal is encountering tons of bots all the time. Yeah,
Speaker 1: we are all going to be encountering lots of bots
Speaker 1: all the time. So you're gonna find bots on Twitter
Speaker 1: if you use online dating, you're going to find bots
Speaker 1: on their trying to scam you body accounts. Uh. There
Speaker 1: is their bots on Facebook. Their bots in the comments
Speaker 1: section of almost any article or anything you look at. Yeah,
Speaker 1: they're everywhere. Whenever you see a like you go to
Speaker 1: an article or a video, anything really that allow has
Speaker 1: a comment section. And whenever you see someone who is
Speaker 1: posting a link to something that is truly unrelated to
Speaker 1: whatever the content is, that's more likely than not a
Speaker 1: bot account. Sure, And even if it's posting a link
Speaker 1: to something that is related, that can still be about account.
Speaker 1: And even if it's just seeming to participate in the
Speaker 1: conversation like a human would, it can be about account. Yeah, yeah,
Speaker 1: it can be. Yeah, we're going from the the easy
Speaker 1: to detect to the more difficult to detect. Sometimes, depending
Speaker 1: upon the interaction, it may be really hard to detect.
Speaker 1: And we'll get into that. Okay, So just for the record,
Speaker 1: what do we mean when we say butt, Like, what
Speaker 1: what distinguishes a bot from you know, a full fledged
Speaker 1: artificial intelligence program or something. Complexity is a big part
Speaker 1: of it, right for for a truly like depending upon
Speaker 1: why you're talking about, when you say AI, because, as
Speaker 1: we've discussed before, AI covers a wide variety of of
Speaker 1: disciplined behaviors, that sort of thing like you could argue
Speaker 1: that having a sensor that sends a message to a
Speaker 1: program that then has a reaction based upon the information
Speaker 1: the sensor is gathered, that's the type of AI. But
Speaker 1: it probably it might be a reach. You could even
Speaker 1: argue that, like a robot that successfully squirts ketch up
Speaker 1: on your plate or on your hot dog has some
Speaker 1: kind of well yeah, because it has to detect where
Speaker 1: the hot dog is and then detect you know, how
Speaker 1: much pressure it needs to use. There is some there's
Speaker 1: intelligence involved in catch up. Yeah. No, if you ever
Speaker 1: see the way that I end up trying to put
Speaker 1: ketch up on something, you realize I lack that intelligence.
Speaker 1: It does we I thought we agreed we weren't going
Speaker 1: to talk about the lunch meeting at the podcast, but yeah, so.
Speaker 1: But at any rate, if we're talking about the type
Speaker 1: of artificial intelligence that is meant to maintain a prolonged
Speaker 1: conversation or the type that is supposed to at least
Speaker 1: simulate UH intelligence on a more general level, what we
Speaker 1: would start to look at as potentially being strong AI,
Speaker 1: or at least simulating the appearance of what we would
Speaker 1: consider strong AI. Strong AI being a machine that can
Speaker 1: truly think, right, a machine that is capable of thinking
Speaker 1: and making decisions, perhaps even having consciousness, Although that there's
Speaker 1: an argument over whether or not that would be necessary
Speaker 1: to truly be strong AI. We're still talking about weak AI.
Speaker 1: In that sense. Bots can be even simpler than that.
Speaker 1: Bots can be so simple as to really be uh,
Speaker 1: you know, closer to that that model of a sensor
Speaker 1: that sends information and then the computer reacts in a
Speaker 1: specific way. It may be as simple as that. Yeah.
Speaker 1: Typically it's thought of as something that's very simple. It
Speaker 1: automates a task on the internet. So you can have
Speaker 1: like hacker bots a that's another kind of thing that
Speaker 1: we're probably not really going to talk about today. If
Speaker 1: somebody might create a bot army of a bunch of
Speaker 1: computers with with programs secretly installed the hijack those computers
Speaker 1: to do what the hacker wants, and some of the
Speaker 1: butts we're talking about here may in fact be the
Speaker 1: result of that, because it means that the person who
Speaker 1: is ultimately responsible for creating the butts has a level
Speaker 1: of protection because if it gets traced back, it's traced
Speaker 1: back to the victims computer, not to the hackers computer. Right.
Speaker 1: But then also a lot of bots on the web
Speaker 1: that we interact with our they're so simple that they're
Speaker 1: not even really like artificial intelligence. I mean, they're they're
Speaker 1: a collection of tricks. They might have like three tricks,
Speaker 1: and they just do them over and over and most
Speaker 1: of the time they're not going to fool anybody, but
Speaker 1: every now and then they do, and they're so cheap
Speaker 1: you might as well use them. It's a shotgun approach
Speaker 1: of just like, well, these aren't going to fool everyone
Speaker 1: all time, obviously, but like if that one or two
Speaker 1: get through, then same same same philosophy strategy as as
Speaker 1: people who send out or create networks to send out
Speaker 1: spam emails, right, the idea being that, sure, the people
Speaker 1: who get this are never going to respond to it. Yeah, yeah,
Speaker 1: they're not going to say, I believe you Nigerian Prince,
Speaker 1: thank you, thank you for this information exactly. That one
Speaker 1: percent that does respond, however, if you're sending it to
Speaker 1: a large enough group of people is still a significant number.
Speaker 1: So it's the same kind of strategy there. In fact,
Speaker 1: if we want to give an example to explain this
Speaker 1: simple behavior, you could use Twitter. It's a very easy
Speaker 1: platform to describe. Let's say that you set up an
Speaker 1: algorithm that does a search for certain keywords. Whenever those
Speaker 1: keywords pop up, it identifies the person that sent the
Speaker 1: tweet that contained those keywords and sends an apt reply
Speaker 1: to that person with a link attached. Uh, the goal
Speaker 1: being for that person to end up clicking through that link. Hey,
Speaker 1: you mentioned lunch, are you interested in weight loss? Right
Speaker 1: through to the spam page? Right? And it may it
Speaker 1: may even be more more um uh, you know, subtle
Speaker 1: than that. It maybe like I think you meant and
Speaker 1: then the link or that's cool, have you seen this
Speaker 1: and then the link? You know, something so that it's
Speaker 1: harder for you to initially identify that as a sales pitch. Yeah,
Speaker 1: a lot of them work by i would say, limiting
Speaker 1: interaction or information. Yeah, it's just it's a pretty simple trick,
Speaker 1: but sometimes it works. Another thing is I think a
Speaker 1: lot of times Twitter bots overcome their simplicity by hiding
Speaker 1: in plain sight, like they don't seem to care all
Speaker 1: that much if they look like a real human they're
Speaker 1: just enough of them working in tandem that they can
Speaker 1: blend in with a mixed crowd of real humans and
Speaker 1: other bots, especially for certain kinds of uses. And one
Speaker 1: of those that I would identify is swaying public opinion. Sure,
Speaker 1: so have you all heard about propaga into bots? Yeah,
Speaker 1: I've heard about these, and uh it's incredibly upsetting. So
Speaker 1: there are political bots out there, It's a phenomenon on
Speaker 1: comments sections on social media. Uh. And they typically work
Speaker 1: by like polluting political conversations with garbage or propaganda or
Speaker 1: even sometimes perhaps attempts it straight up intimidation. Just one
Speaker 1: example is that, according to some sources, it looks like
Speaker 1: the Kremlin has created a Russian Twitter bot army to
Speaker 1: sort of like shout down criticism on the web with
Speaker 1: pro government messages. Again, this is this allegations. But and
Speaker 1: and we have seen in the past it doesn't even
Speaker 1: have to be a an officially state sponsored project, right
Speaker 1: because there can there can at times be people who
Speaker 1: have the capability of pulling something like this off who
Speaker 1: sympathize with the state and i'd eological faction. Yeah, it
Speaker 1: could be like and and the challenges finding out whether
Speaker 1: or not in fact finding out whether or not a
Speaker 1: state sponsor tends to be incredibly difficult unless someone has
Speaker 1: made a poor life decision, right someone has, or someone right,
Speaker 1: or someone is a whistleblower, which can also be a
Speaker 1: poor life decision depending upon the regime you're talking about. Um,
Speaker 1: it could be a valuable service, but it can, depending
Speaker 1: upon the state, be really dangerous as well. Yeah, one
Speaker 1: incident I was reading about earlier was that people have
Speaker 1: been referring to this thing that I was talking about
Speaker 1: earlier that the Kremlins supposed Russian troll army, so they
Speaker 1: show up, which just on the phrasing of that, sounds
Speaker 1: like Tolkien. Yeah, I like think of like all these
Speaker 1: giant Tolken esque trolls wearing fur hats marching through Siberia,
Speaker 1: which is kind of awesome. But that's not what we're
Speaker 1: talking about. Know. What these do is that. So let's
Speaker 1: let's let's say that your comments section in or your
Speaker 1: Facebook feed is Helm's deep, Okay, So you're speaking. They
Speaker 1: show up in droves to leave comments and post on
Speaker 1: social media, trying to rally public opinion to a pro
Speaker 1: government position on issues like Russia's alleged involvement in the
Speaker 1: Ukraine UH in the February murder of the Russian dissident
Speaker 1: Boris Nimistov h so I found an interesting post on
Speaker 1: a blog network called Global Voices Online by a guy
Speaker 1: named Lawrence Alexander and if he's correct, he claims that
Speaker 1: he used some open source tools to analyze the Twitter
Speaker 1: activity of these accounts that were tweeting the same pro
Speaker 1: government messages after the murder of Nemstov, and he he
Speaker 1: came to the conclusion that by analyzing their activity, this
Speaker 1: was a bot army. These were bought accounts that were
Speaker 1: created to sort of like shout uh, you know, political
Speaker 1: pro government messages. And I don't know, I think that's
Speaker 1: a really interesting and kind of worrying development, and like,
Speaker 1: what what does that mean? Yeah, it's it's tricky too,
Speaker 1: because when we're talking about Twitter, when you know how
Speaker 1: Twitter works, you realize that this is something that only
Speaker 1: only works in specific ways. So for example, if I
Speaker 1: tweet out something that would trigger this sort of bought attack, uh,
Speaker 1: when I tweet that out, the people who see that
Speaker 1: tweet are going to be the folks who follow me, right,
Speaker 1: the people who the the bots that respond to me.
Speaker 1: The only people who would see the responses would have
Speaker 1: to be people that are already following those bots, because
Speaker 1: otherwise they and they'd have to be following me if
Speaker 1: it's a direct at reply, because that's the way Twitter works,
Speaker 1: right If if people want to see what I tweet,
Speaker 1: they have to follow me um or they end up
Speaker 1: having to search a term that I have used, and
Speaker 1: then my tweet, assuming that I'm tweeting publicly, will pop up.
Speaker 1: So uh, using Twitter as this platform is difficult in
Speaker 1: that if you're trying to sway public opinion, you have
Speaker 1: to hope that it's because people are searching certain terms
Speaker 1: and because you're using a massive bot army, most of
Speaker 1: the conversation ends up being from your perspective. Then that works.
Speaker 1: But if if you're talking about just the public discourse
Speaker 1: in Twitter, you know you have to first get people
Speaker 1: to follow your bots if they're going to even see
Speaker 1: what you have to say. Well, I would say that
Speaker 1: in order if you're just looking to to sway opinion,
Speaker 1: to cause some kind of doubt in someone's mind about
Speaker 1: the the opinion that they currently hold, having what appears
Speaker 1: to be another human person, send you what appears to
Speaker 1: be an earnest tweet response to something that you've said,
Speaker 1: going like, hey, have you thought about this other thing?
Speaker 1: Like should like have you considered this? Have you read
Speaker 1: this article? Something like that? That could be enough to
Speaker 1: start sewing those seeds and different ideas. So in that is,
Speaker 1: you're talking about on a case by case basis on
Speaker 1: the actual people if you're trying to change their minds,
Speaker 1: as opposed to changing everyone's mind at once. Your your
Speaker 1: pinpointing the the quote unquote trouble spots and addressing those sure,
Speaker 1: and furthermore, the the way that the general public uses
Speaker 1: the metadata, the the big data of Twitter is that
Speaker 1: a lot of researchers will go in and and search
Speaker 1: these kinds of terms and kind of get a reading
Speaker 1: on overall public opinion based on the number of people
Speaker 1: that are tweeting and right a thing, and and so
Speaker 1: that could be stilting journalism or or research point to
Speaker 1: political parties. Yeah, that's that's a very good point. Also,
Speaker 1: I mean I thought about how even if it's not
Speaker 1: necessarily convincing you of the correctness of the pro government position,
Speaker 1: I mean, what is the like effect on your emotions
Speaker 1: of if you say something critical of a government and
Speaker 1: suddenly like five Twitter accounts starts shouting at you, and
Speaker 1: I mean you could have an intimidation effect exactly. Of course,
Speaker 1: of course, if you realize that it's a bout and
Speaker 1: you suspect that your government has sent this. I mean,
Speaker 1: even if it's just one or two people or accounts
Speaker 1: that are tweeting this kind of thing at you, like,
Speaker 1: there's definitely an intimidation fact, right if you if you
Speaker 1: don't know it's about, then it's intimidation simply because you're
Speaker 1: getting people who are disagreeing with your perspective, and that
Speaker 1: can if there are enough of them, you can start
Speaker 1: to question whether or not your point is. It would
Speaker 1: depend on the tone of the tweets and stuff like, right,
Speaker 1: if if it was you, like, if you do realize
Speaker 1: they are boughts, then you're thinking, oh, now I've got
Speaker 1: the attention of a state that's not good. Uh necessarily,
Speaker 1: so yeah, it's you could even Joe and I were
Speaker 1: talking about this, you could even make the argument that
Speaker 1: this is an indirect form of censorship because you start
Speaker 1: to intimidate people into being silent. Sure, if used in
Speaker 1: the right way, I think it could be something like that.
Speaker 1: Another thing is that this obviously isn't limited just to Twitter.
Speaker 1: Twitter is sort of what we're focusing on because it
Speaker 1: is one of the most open kind of ways to
Speaker 1: look at this, because you know, Twitter is generally it's
Speaker 1: easy to retrieve a lot of data, and it's specifically
Speaker 1: what Alexander was looking at when when he was analyzing that.
Speaker 1: It's also the easiest to automate um of any of
Speaker 1: the social media platforms because it's it's very simple too
Speaker 1: that there are very few checks and balances in creating
Speaker 1: accounts and sending out tweets, right, But you could also
Speaker 1: look at the activity of bots on Facebook and stuff
Speaker 1: like that. But sticking with Twitter for a minute, I
Speaker 1: think we should drill down and look at what it
Speaker 1: looks like to be a Twitter bot. What what is
Speaker 1: that existence like and how do you know him when
Speaker 1: you see Well, I mean, this one is really super
Speaker 1: tricky for Twitter in particular because like we were talking
Speaker 1: about with the Turing test, where you had that lowered
Speaker 1: set of expectations because of whatever, like like the the
Speaker 1: supposed entity you are speaking to is a thirteen year
Speaker 1: old boy who does not speak English as his first language,
Speaker 1: then you have a difference of expectations. You know, you
Speaker 1: realize that that entity is not going to have the
Speaker 1: world experience of an adult. They are not going to
Speaker 1: have the linguistic sophistication in English of someone who is
Speaker 1: a native English speaker, presumably anyway, depending upon your your
Speaker 1: past experiences. But the same sort of thing applies with
Speaker 1: Twitter because you have the hard limit of one forty
Speaker 1: characters to get your point across, which means a lot
Speaker 1: of us, even as being like, even even the people
Speaker 1: who are masters of the English language, people other than myself,
Speaker 1: for example, uh, they would find it challenging sometimes to
Speaker 1: express a particular thought within those hundred forty characters, and
Speaker 1: often we end up having to take various shortcuts or
Speaker 1: we have to, uh, you know, take we have to
Speaker 1: make compromises in the way we're trying to communicate our
Speaker 1: our message, particularly really if we want to share a
Speaker 1: link within that as well. For example, I would want
Speaker 1: to share a link of a forward thinking video episode
Speaker 1: where I have the idea of what I want to communicate,
Speaker 1: but I realize I can't say it and share the
Speaker 1: link within forty characters, so I have to start making cuts,
Speaker 1: which means sometimes what I communicate ends up not sounding
Speaker 1: entirely human. You say, watch or suffer like something like
Speaker 1: something along those lines, uh, or you will be assimilated
Speaker 1: or whatever. Um So. In other words, because Twitter places
Speaker 1: this limitation, because we have all encountered it, and because
Speaker 1: we have all made those compromises, it lowers our bar
Speaker 1: for what we expect when we encountered that. And it's
Speaker 1: also a pretty casual medium, and and people are not
Speaker 1: always using it in order to compose poetry dissertation level tweets.
Speaker 1: You know, people will just be like, like you obey,
Speaker 1: watch this, Like I will not click. I realized that
Speaker 1: I have just invited a ton of those messages to
Speaker 1: come at me. But I will not click on those links.
Speaker 1: I don't care what the link goes to. If you write, yo, bai,
Speaker 1: watch this, it is not happening. I don't get it.
Speaker 1: You don't get it. Well, when it starts, when they
Speaker 1: start getting the flood, Yeah, then you'll get it. Yeah. Well,
Speaker 1: you know, sometimes Twitter bots can be harmless or even delightful.
Speaker 1: I'd say, I'm sure. I have to admit I get
Speaker 1: intense pleasure from the old favorite horse e books. Sometimes
Speaker 1: I just revisit that when I need a laugh. Uh
Speaker 1: the if you're not familiar there, it was an automated
Speaker 1: Twitter account, or at least at some point I think
Speaker 1: it was automated. At another point I think it was
Speaker 1: actually taken over by a human user maybe, but it
Speaker 1: was a Twitter account that was famous for tweeting out
Speaker 1: these lines of garbled you know, garbage text that from
Speaker 1: somewhere yeah, beauty full things right. It was sold in
Speaker 1: two thousand eleven to an alternate reality game A K
Speaker 1: A and a r G. These are those. Have you
Speaker 1: ever played an Energy? Yeah? I participated a little bit
Speaker 1: in a I Love Bees Halo two campaign from Circo.
Speaker 1: Oh what was that play? As many years ago? Something
Speaker 1: like that. It was a game where you use your
Speaker 1: smartphone camera to shoot swarms of bees at your coworkers
Speaker 1: did not have smartphone cameras. I didn't right now though.
Speaker 1: An ERG is one of those games that has a
Speaker 1: real world component to it, where you have to do
Speaker 1: certain actions within the real world or certain things happened
Speaker 1: to you within the real world that also carry over
Speaker 1: into a virtual world, and you progress through the game
Speaker 1: that way. They usually they almost always have a very
Speaker 1: defined beginning, middle, and end because they employ lots of
Speaker 1: people to run the back end, and you can't perpetually
Speaker 1: do that. So anyway, in this case, they thought, here's
Speaker 1: a Twitter account that already has a dedicated number of followers,
Speaker 1: let's go ahead and end up using this for a
Speaker 1: viral marketing approach. Idea. Yeah, it's a clever idea. It
Speaker 1: can sometimes work. Um, it doesn't always work. And there
Speaker 1: were other ones too, Like I remember there there were
Speaker 1: certain chat uh like like Twitter butts that would respond
Speaker 1: if you use certain phrases to grammatically correct you, well,
Speaker 1: actually you probably meant this rather than what you actually wrote. Um.
Speaker 1: How often were they wrong? It depends, It depends. I can't.
Speaker 1: I want to say. There's one that um that Bernie
Speaker 1: Burns of of Rooster Teeth has talked about before because
Speaker 1: it was a particular uh phrase or word that he
Speaker 1: hated seeing because he knew that that people either were
Speaker 1: using autocomplete or they were mistakenly using this one word
Speaker 1: when they in fact meant a totally different word. And
Speaker 1: I wish I could tell you what it was, but
Speaker 1: I can't remember what it is off the top of
Speaker 1: my head. If I do remember, I'll make sure to
Speaker 1: tweet it out. With this episode goes live. But but yeah,
Speaker 1: so there's some bots that are out there for for
Speaker 1: fun entertainment reasons like those, or or to just tweet
Speaker 1: like big Lebowski quotes at you. If you quote big
Speaker 1: Lebowski on Twitter. I've had that happen. There are a
Speaker 1: lot of others that are maybe a little less helpful,
Speaker 1: a little less delightful. There there for lots of purposes,
Speaker 1: not just trying to sway public opinion in terms of propaganda,
Speaker 1: but also maybe for marketing and advertising, or we might
Speaker 1: say spamming, for trying to trick you into downloading malware.
Speaker 1: Those are the worst, trying to funnel you into some
Speaker 1: place where you can be taken advantage of on the web.
Speaker 1: So there's a lot of research that's going into how
Speaker 1: to identify these spots. And I mean Twitter has a
Speaker 1: has a policy that if it finds a bot that
Speaker 1: you're using that is malicious, it will shut it down.
Speaker 1: So let's go way back to the early Twitter times,
Speaker 1: not that early, but well when did atter start? About
Speaker 1: two thousand seven? So yeah, like so around two it
Speaker 1: was that some researchers from Texas A and M came
Speaker 1: up with a scheme for catching bots on Twitter. So
Speaker 1: it was a phenomenon back then too, and they took
Speaker 1: an advantage of some observable facts about Twitter bot behavior.
Speaker 1: For example, twitter bots like to follow and they like
Speaker 1: to retweet, and this is sort of part of the
Speaker 1: Twitter bots strategy, right, you need people to follow you,
Speaker 1: but normally people don't want to follow a bot because
Speaker 1: you're not friends with them and whatever. So they'll take
Speaker 1: advantage of the fact that a lot of people just
Speaker 1: automatically follow back. Right, they'll follow you hoping you don't
Speaker 1: really investigate and you just follow them back and now
Speaker 1: you're on their span. Well, especially if they if a
Speaker 1: bot retweets you, then it's also you know, trying to flattering, Yeah, exactly,
Speaker 1: it's trying to to to stroke your ego and hope
Speaker 1: that you will also either follow or you'll click on
Speaker 1: something that the that's in the message. Right, So that's
Speaker 1: a sort of observable fact about the Twitter bots. They
Speaker 1: like to follow, they like to retweet, but they don't
Speaker 1: have very good taste in friends, if any at all.
Speaker 1: So the researchers set up these boring, garbage filled bait
Speaker 1: accounts that no real human should be interested in as
Speaker 1: far as they were concerned, only hipsters and bots follow them. Well,
Speaker 1: apparently then they waited for the non discriminating bought love
Speaker 1: to pour in, and using about sixty bot bait accounts,
Speaker 1: they identified thirty six thousand candidate bought accounts. Back then,
Speaker 1: that seemed like a lot how young we were. Yeah,
Speaker 1: I I you know there are tools that you can
Speaker 1: use that uh, that will supposedly go through your your
Speaker 1: Twitter followers and alert you to the accounts that appear
Speaker 1: to be butts. I'm going to talk about one in
Speaker 1: a second. Yeah, So I don't want to use one
Speaker 1: of those simply because I'm afraid of seeing that I have.
Speaker 1: You have three h human followers and six thousand non
Speaker 1: human followers. Well as as of twelve, you could buy
Speaker 1: the follows of twenty five thousand bots for just two
Speaker 1: hundred and forty seven dollars. Yeah, this was from a
Speaker 1: company that was called by Real Marketing Buy. It does
Speaker 1: not appear to be operational anymore. Um, but but big
Speaker 1: companies and celebrities are purported to have purchased follows like
Speaker 1: this is a kind of cheap way to puff up
Speaker 1: their online image. Right then, the idea of being able
Speaker 1: to to say, look how important I am because this
Speaker 1: many people are interested in what I have to say.
Speaker 1: I had a conversation with somebody I won't name the
Speaker 1: person I talked to, but another online personality who at
Speaker 1: one point had said, Hey, I'm not saying that this
Speaker 1: is what you should do, but this is something you
Speaker 1: could do where if you want, you know, if if
Speaker 1: your value as an employee is somewhat based upon the
Speaker 1: number of people who follow your account, you should just
Speaker 1: on the down load, go and buy these because if
Speaker 1: that's going to be the metric, then here's how you
Speaker 1: can gain the system. So that way you can, you know,
Speaker 1: have that metric hit the goal. And I was thinking
Speaker 1: the whole time, I was like, I could never do
Speaker 1: that personally because I couldn't live with myself, but I
Speaker 1: could totally see how if that's if that's how you're
Speaker 1: being judged, well, I mean it's There were news stories
Speaker 1: at back when cloud was was more popular, when it
Speaker 1: first came out of marketing people and and and public
Speaker 1: persona type people being hired based on partially their cloud score, right,
Speaker 1: and that might end up getting a little bit of
Speaker 1: a boost if you suddenly have you know, twenty people
Speaker 1: following you. Yeah, but then that also ends up encouraging
Speaker 1: services like clout to build in elements of their algorithm
Speaker 1: to look for red flags like that. So one of
Speaker 1: the interesting byproducts that is happening with this is that
Speaker 1: we're starting to see improvements in both the detection systems
Speaker 1: to try and find bots and the systems used to
Speaker 1: create bots so that they are harder to detect, which
Speaker 1: is very similar to what we see with security systems,
Speaker 1: and it's it ultimately means improvements and artificial intelligence, which
Speaker 1: is which generally a good thing, but the actual particulars
Speaker 1: might be really irritating. Yeah. And in one advantage of
Speaker 1: having this large of a sample size of this kind
Speaker 1: of data is that people can study it pretty well.
Speaker 1: Right Well, you can, for one thing, compare them to
Speaker 1: the accounts of suspected real humans, suspected real humans, right,
Speaker 1: suspected bots and suspected real humans there, and what's what's
Speaker 1: different between them? That that would be an interesting thing
Speaker 1: to know. Well, people have studied that. Just one was
Speaker 1: a group from Indiana University in Bloomington's they did the
Speaker 1: analysis on I think it was based on the sample
Speaker 1: set create aided by the Texas A and M researchers,
Speaker 1: and they did the analysis on that, and they came
Speaker 1: up with this little web app you can check called
Speaker 1: Bot or Not, which will evaluate a Twitter account and
Speaker 1: let you know if it's a bout And in fact,
Speaker 1: it gives some kind of interesting feedback on bought probability,
Speaker 1: Like it's not just like probably a bot. It gives
Speaker 1: you these different like metrics of of human nous. Have
Speaker 1: you have you tried this? Did? It said? I was
Speaker 1: probably not a bot? But it wasn't real Sure? Now
Speaker 1: I gotta try it on me. Yeah, you gotta try
Speaker 1: it on yourself. It's sort of like a void comp
Speaker 1: test for Twitter, like I imagined it. Tweeting that leon
Speaker 1: is like the tortoises on its back hashtag you're not helping?
Speaker 1: Why is that? Oh? Man? Uh? This is this is both.
Speaker 1: I'll tell you about my mother. Sorry anyway, So we
Speaker 1: discovered some things about body account. It here a few
Speaker 1: They retweet others more often than humans. Makes sense. Um,
Speaker 1: they've typically been Twitter users for shorter periods of time.
Speaker 1: They typically have longer user names. Uh. There was another
Speaker 1: study that I found it. It was published in plus
Speaker 1: one back in that looked solely at tweet times and patterns,
Speaker 1: and according to it, u bots tweet randomly at all
Speaker 1: hours of the day and night, whereas humans are more
Speaker 1: likely to tweet from like like a in bursts and
Speaker 1: be from the hours of like seven am to midnight. Obviously,
Speaker 1: depending on the human at hand, it could also things
Speaker 1: like that. I mean, obviously, if you're using something like
Speaker 1: tweet deck where you can schedule out tweets, then that
Speaker 1: might end up making you look more botish than than
Speaker 1: other human beings. But let's face it, if you're using
Speaker 1: tweet deck, you really are sort of a robot tweet
Speaker 1: tech all the time. I have it up right now.
Speaker 1: In fact, I'm sorry, I'm just kidding, but no, it's um,
Speaker 1: you know, it's it's fair beautiful. But another thing is that, like,
Speaker 1: and again this isn't a dead giveaway, but it is
Speaker 1: one of those flags is how many people does the
Speaker 1: bot follow? Right, Because if you are a human being
Speaker 1: and you really do wish to use Twitter in some
Speaker 1: sort of useful way, you're probably limiting how many accounts
Speaker 1: you're following, because otherwise you can't keep up with what's happening.
Speaker 1: It's just a fire hose of content and it's constantly updating,
Speaker 1: and you will never be able to see anything from
Speaker 1: anyone you're actually interested in following. So if you're following
Speaker 1: like I follow, I think it's between two hundred and
Speaker 1: three hundred people, and you know, occasionally I'll add folks
Speaker 1: to it, or I might drop people off if I
Speaker 1: realize that I haven't seen a tweet from them in
Speaker 1: a really long time. Um, but that tends to be
Speaker 1: my comfort zone because that's what I'm interested in seeing.
Speaker 1: But some of these bought accounts, you look in, like
Speaker 1: a hundred and twenty six thousand people follow, and you
Speaker 1: think there's no way anyone could ever follow a hundred
Speaker 1: twenty six thousand people and actually have an idea of
Speaker 1: what is going on. So well, you can use you
Speaker 1: can use filtering options to only follow, to only immediately
Speaker 1: follow certain lists. But even so, I mean, it's it's
Speaker 1: one of some. In some cases, if it's a celebrity
Speaker 1: who wants to end up encouraging as much interaction with
Speaker 1: fans as possible, it may very well be completely legitimate.
Speaker 1: But if it's some, if it's a name, like just
Speaker 1: a long string of of of numbers after a noun
Speaker 1: of some sort or a name of some sort, and
Speaker 1: otherwise you don't recognize that person at all. It's an
Speaker 1: indication that it could be not necessarily is, but could
Speaker 1: be a butt. Yeah, there's another sort of statistically informed
Speaker 1: fact that you still shouldn't use too much, because this
Speaker 1: fact is that bots more often create a sort of
Speaker 1: persona for themselves that is female gendered. Right, And that's because,
Speaker 1: according to research, female gendered social bots get more attention. Uh.
Speaker 1: This is basically because gender bias exists in our society.
Speaker 1: But this this was a study that was out of Brazil.
Speaker 1: It released a hundred and twenty boats on Twitter for
Speaker 1: thirty days. These boats could post tweets generated by algorithm
Speaker 1: um and also retweet things, and Twitter caught and deactivated
Speaker 1: less than a third of them. Uh. Twitter users themselves
Speaker 1: didn't always catch on or maybe care that these were
Speaker 1: bought accounts. The body accounts received a total of just
Speaker 1: one shy of five thousand followers during that month. Five
Speaker 1: thousand followers. Uh, some some boughts. Some of some of
Speaker 1: the accounts had like repeat followers from from the other
Speaker 1: body accounts that the follows came from a total of
Speaker 1: one thifty two users. I wonder how many of those
Speaker 1: were bots? Good question. I'm not sure. Yeah, it could
Speaker 1: be that's it's a bot followed by bots, but we
Speaker 1: don't know sure. But but over twenty of these bot
Speaker 1: accounts received over a hundred followers, which puts them in
Speaker 1: like the forty six percentile of the most popular Twitter users.
Speaker 1: That's more followers than I got, right, so I'm kind
Speaker 1: of I'm kind of a low engagement Twitter user. You are, yeah,
Speaker 1: you you're under the radar for a lot of people.
Speaker 1: Then we can get you more than a hundred just
Speaker 1: the show alone, if we were sharing your your Twitter handle.
Speaker 1: The researchers also checked these bots cloud scores and and Joe,
Speaker 1: I think you you were actually the one who originally
Speaker 1: quoted this at me, So if you would like to
Speaker 1: say it out loud because you were so tickled by it, No, well,
Speaker 1: I wrote it down, but now I can't remember where
Speaker 1: the quote came from. Well, the quote came from the researchers. Okay, okay,
Speaker 1: so their cloud scores were the same or higher than
Speaker 1: quote several well known academicsions and social network researchers. Uh, well,
Speaker 1: and you know from my horse then again, who wants
Speaker 1: to follow an active mission? From my own experience, like,
Speaker 1: more often than not, I don't get I don't notice
Speaker 1: follows from Twitter accounts usually, I mean, I don't notice
Speaker 1: like bots following me. By definitely notice bots tweeting at me,
Speaker 1: so they're not always following users. Sometimes it's just an
Speaker 1: apt reply uh, and I can I can usually detect
Speaker 1: it pretty early on if I don't notice it directly
Speaker 1: from that tweet. What I tend to do if I
Speaker 1: see a weird tweet from somebody, I will click through
Speaker 1: their name to look at their most recent tweets, but
Speaker 1: don't click through to their website. I don't do that.
Speaker 1: I just look to see their Twitter feed to see
Speaker 1: what kind of tweets they've been sending out. And usually
Speaker 1: it's just a line of apt replies to various people,
Speaker 1: often with the identical phrasing. Sometimes it ends up being
Speaker 1: um uh, peppered in between nonsensical tweets that clearly have
Speaker 1: been scraped from some other Twitter account or have been
Speaker 1: formed seemingly at random. Because you'll read it and you
Speaker 1: think all the words in that sentence makes sense, but
Speaker 1: collectively it doesn't mean anything. You know, the like made
Speaker 1: up tweets that these bots tweet. I feel like very
Speaker 1: often a characteristic of them is you can't tell what
Speaker 1: they're talking about, but they sound like they really mean it.
Speaker 1: You could you could probably end up making a very
Speaker 1: successful book of nonsensical butt tweets where it's one per page.
Speaker 1: You could totally yeah, but at any rate that you know,
Speaker 1: the thing I always do is I always flag those
Speaker 1: accounts of spam accounts every single time I encounter one.
Speaker 1: For two reasons. Well, no, I don't not for not
Speaker 1: for spam. I I don't mind marketing if it's done properly,
Speaker 1: but I don't want spam. Uh. And two, it means
Speaker 1: it automatically blocks that from you know, I won't see
Speaker 1: tweets from that that entity anymore. Says the two reasons
Speaker 1: why I do it. And it's not like Twitter is
Speaker 1: going to pounce on an account if it gets one
Speaker 1: flag for being a butt. So it may be that,
Speaker 1: you know, it'll take a certain threshold of of reports.
Speaker 1: But I always encourage people who use Twitter. If you
Speaker 1: encounter this, go ahead and flag that account for for
Speaker 1: one thing, you won't see anything else from that account again.
Speaker 1: But if you're curious, you can always go and use
Speaker 1: bot or not. That's true, you could get although I
Speaker 1: find that just looking at the again the past few
Speaker 1: tweets usually gives you an indication. You know, there's one
Speaker 1: other place other than than social media and comments sections
Speaker 1: that you very often might encounter bots on the internet,
Speaker 1: and that would be in online gaming. Yeah, and in
Speaker 1: this case, it may not be the purpose of the
Speaker 1: bot to fool other players into thinking it's a human.
Speaker 1: It's more to fool the game into thinking it's a human.
Speaker 1: Because generally speaking, most games have policies against automated UH
Speaker 1: player behavior. They don't want players to use bots to
Speaker 1: play the game. I mean really, depending on the game
Speaker 1: they the owners might not care because if it means
Speaker 1: that someone is paying the account for that bot to
Speaker 1: do the thing it's doing, yeah, they're like, hey, you know,
Speaker 1: you're paying the same amount whether you're human or a robot.
Speaker 1: I don't care, sure. But but in a more responsible
Speaker 1: concept of game ownership, they might care about the user
Speaker 1: experience of their real players and not want people to
Speaker 1: be automating processes that either make the game unfair. Exactly,
Speaker 1: if they are being responsible, they don't want an unbalanced
Speaker 1: experience from their players, and they don't want people to uh,
Speaker 1: to end up having an unfair advantage because they purchased
Speaker 1: a an account from someone who had just set this
Speaker 1: bot this automated behavior, so that this this automated player
Speaker 1: account ends up doing a specific task so many times
Speaker 1: that it builds up the character in some way or form,
Speaker 1: usually through experience. So it's a bot that's seeking out
Speaker 1: very low level mobs or monsters. Uh. And you know,
Speaker 1: if you were to grind that way to build up
Speaker 1: your character as a human, it would get very boring
Speaker 1: very quickly. But bots don't get bored. They just keep
Speaker 1: doing what they were programmed to do. Well, we don't
Speaker 1: know that they don't get bored, Well, they don't complain.
Speaker 1: So or that you might have it where it's mining
Speaker 1: gold or the equivalent within whatever game so that you
Speaker 1: get a massive amount of gold that you know it
Speaker 1: may take. Let's say that it takes four hours to
Speaker 1: get a significant amount of golds. So therefore the the
Speaker 1: most players aren't going to bother doing that because it's
Speaker 1: such a huge time commitment. But again, a bot's going
Speaker 1: to keep doing it until it's told not to do it. Anymore. UM.
Speaker 1: These are the sort of things that that game systems
Speaker 1: have to look for, and it's not usually up to players,
Speaker 1: although if a player sees it and reports it, then
Speaker 1: an administrator might take a look at it. It's usually
Speaker 1: something that requires UM software on the game side, the
Speaker 1: back end, maybe on the server side, to look at
Speaker 1: this behavior. Uh. But uh, you know they're and they're
Speaker 1: even UM competitions to build bots that are really really
Speaker 1: good at doing what they do without being detected, so
Speaker 1: they're not always massively multiplayer online role playing games. That's
Speaker 1: the one that I'm most familiar with as far as
Speaker 1: bots go. But these can even be in first person shooters. Uh.
Speaker 1: And there are competitions to build convincing bots in first
Speaker 1: person shooters. So you could, in theory, build a bot
Speaker 1: that navigates through a first person shooter map and targets
Speaker 1: people in the head and shoots them with unerring accuracy.
Speaker 1: Because it's a boat, not a human, this particular approach
Speaker 1: would end up giving it away that's not in fact
Speaker 1: a robot. It is in fact or it is not
Speaker 1: in fact a human. Rather, it is in fact a
Speaker 1: robot because it's so accurate that no human being could
Speaker 1: do that. It's able to react in uh split second
Speaker 1: of a time of time as opposed to a humans
Speaker 1: reaction time. So you're saying that every time that I
Speaker 1: suspected that's one was it go Ram Cheater and Halo
Speaker 1: it was probably right. Um, I'm going to go ahead
Speaker 1: and say yes, because frankly, I'm scared of you and
Speaker 1: I don't want to suggest that you are not a
Speaker 1: good Halo player. I did just clenchman. I mean I
Speaker 1: honestly like, there have been times where I have been
Speaker 1: in online games where I thought, Wow, that person, that
Speaker 1: person is so good I can't even imagine what they're
Speaker 1: playing like. And to be fair, people who play on
Speaker 1: a professional level play there there they seem inhuman, but
Speaker 1: sometimes there really are in human entities in these games.
Speaker 1: And there was a competition in two thousand twelve called
Speaker 1: the Bot Prize that was the challenge was for teams
Speaker 1: to try and build a convincing bot that would play
Speaker 1: Unreal Tournament at a level that was similar to how
Speaker 1: a human would play. So so it's the Turing Test
Speaker 1: for violent video games exactly. Yeah. The idea being that, Okay,
Speaker 1: here's a robot that is essentially a robot. This control rolling, this,
Speaker 1: this game character, but you don't want it to be
Speaker 1: a dead giveaway that's a robot. So it has to
Speaker 1: play more like a human would play. It has to
Speaker 1: be able to make mistakes. It has to have a
Speaker 1: delay in the time when it detects an enemy and
Speaker 1: when it can aim at that enemy and shoot at
Speaker 1: that enemy. It has to have all this built into it,
Speaker 1: so it seems fallible like a human is, rather than
Speaker 1: having machine precision like a robot does. And uh, there
Speaker 1: was a bot called ut to the Power of two
Speaker 1: that convinced enough judges that was actually a human player
Speaker 1: in Unreal tournament that it won the prize. Uh, there
Speaker 1: was supposed to be one in two thousand fourteen, although
Speaker 1: I did not see anything about that, so maybe the
Speaker 1: it might have been that the competition fell apart after
Speaker 1: the two thousand twelve one. But uh, it's it's pretty fascinating,
Speaker 1: which means that ultimately, as a player, you might not
Speaker 1: be able to tell if another entity within that game
Speaker 1: is in fact controlled by human or controlled by a butt.
Speaker 1: But ultimately, if it's controlled by a butt, it needs
Speaker 1: to be a butt that's bad enough that you could
Speaker 1: beat it. Anyway, if it's always beating you, then you
Speaker 1: might at least suspect that something hinky is going on. Sure, well,
Speaker 1: that's that's kind of fascinating though. It's it's another area
Speaker 1: in which your your interaction with a human person would
Speaker 1: be limited enough. Uh, and you might make assumptions about
Speaker 1: that human person that they're distracted or that I mean,
Speaker 1: you know, you're you're probably not talking to them of
Speaker 1: a headset because that would be a really advanced bot, right,
Speaker 1: but they probably all use that irritating robo voice that
Speaker 1: was an optional Xbox Live that I hated so much.
Speaker 1: But but yeah, it's another situation in which you're making
Speaker 1: assumptions about the people that you're playing with, and so
Speaker 1: you could perhaps be more easily fooled by basically your
Speaker 1: expectation for human is lowering, rather than your expectation for
Speaker 1: robots being improved. And of course this isn't always used
Speaker 1: to give someone an unfair advantage or to frustrate other players.
Speaker 1: Sometimes times we are using bots and video games for
Speaker 1: entertainment purposes. You guys have heard about the bought tournament
Speaker 1: that's going on in Civilization. Okay, it is a guy
Speaker 1: who has installed all these different mods and civilization and
Speaker 1: is having a game of forty two computer controlled players
Speaker 1: set on Earth. All of the starting positions are a
Speaker 1: correlative to where the starting positions should be for all
Speaker 1: the civilizations, all of them set to deity level ability,
Speaker 1: and the only way to win is to destroy everybody
Speaker 1: else on the plant. That's the parameters they've set just
Speaker 1: to see who would win at the end with those, uh,
Speaker 1: that criteria and uh, that's still going on right now.
Speaker 1: Can you all right? Knowing that we're talking about both
Speaker 1: historical civilizations and current ones. The two uh civilizations that
Speaker 1: were eliminated at approximately the same time shocked me. It
Speaker 1: was Germany under the control of Hitler that was the
Speaker 1: That was the first one, and North Korea was the
Speaker 1: second one. And it turns out pressing people doesn't really
Speaker 1: work out. So the question right now is will Gandhi
Speaker 1: last long enough to go totally nuclear on everybody? Because
Speaker 1: when civilization was developed, Gandhi was set so that he
Speaker 1: would be the most peaceful. And the way they had
Speaker 1: said that the piece meter was that it was a
Speaker 1: a a number that went from like zero to two
Speaker 1: fifty six. But Gandhi, if enough conditions happened to make
Speaker 1: him more peaceful, like like you had built the United
Speaker 1: Nations and it would lower everyone's k And to the
Speaker 1: other end, yes, he would go from instead of going
Speaker 1: from one to zero and staying there, he go from
Speaker 1: zero to two fifty six and go as as aggressive
Speaker 1: as you possibly could. It was an error that was made,
Speaker 1: and in everyone identified Gandhi as being the most insanely
Speaker 1: aggressive player and they kept it on purpose from that
Speaker 1: point forward. They discovered the bug and decided it was funny. Yeah,
Speaker 1: but anyway, that's that's example. I can only hope that
Speaker 1: that Gandhi will eventually actually we'll have to see if
Speaker 1: he lasts long enough to develop what nuclear weapons. But
Speaker 1: at any rate, that's just an example of of body
Speaker 1: behavior being used for entertainment purposes as oppose to these
Speaker 1: other irritating means. Yeah. Yeah, And we we need to remember,
Speaker 1: of course, that creating simulated behavior, simulated and automated behaviors
Speaker 1: for these environments is not always malicious or inconvenient or
Speaker 1: sometimes it's sometimes it's for fun, sometimes it's benign or
Speaker 1: for greater research, which is yeah, yeah, that's cool too.
Speaker 1: I'm all for creating bots to research bots. Yeah, But
Speaker 1: so I'm wondering what do you all think as just
Speaker 1: citizens of the internet. You know, as a web user,
Speaker 1: you're on social media sometimes you look at the comments.
Speaker 1: What do you see as the future of our relationship
Speaker 1: with bots? I mean, because there's sort of an arms race, right,
Speaker 1: like the spam filters and the bot detectors that are
Speaker 1: automated or getting better. We're we're sort of evolving too,
Speaker 1: like we're all learning just personally how better to spot
Speaker 1: bots as web users, but also the bots are getting better.
Speaker 1: So what do you think is gonna happen? Well, I
Speaker 1: think I mean, if I had to guess, I would
Speaker 1: say that we would see the bots get sophisticated enough
Speaker 1: where upon, at least casual observation, you would not be
Speaker 1: able to tell the difference easily between a bot and
Speaker 1: a real person, and that it will reach a saturation
Speaker 1: point until there is some form of collapse, not necessarily
Speaker 1: a collapse of the platform, but maybe collapse of confidence
Speaker 1: in that platform, and that as a result, the bots
Speaker 1: will lose their value because no one will be no
Speaker 1: one will care or about it anymore. And then we'll
Speaker 1: see kind of a cycle where the bots will get
Speaker 1: kind of not used so much, that's my guess. Or
Speaker 1: what if, and this might be kind of outlandish to suggest,
Speaker 1: but what if bots, by becoming so sophisticated that it's
Speaker 1: hard to tell them apart from real people, become worthy
Speaker 1: of our attention. I mean, what, you know, a very
Speaker 1: very sophisticated bot that can easily trick a human observer
Speaker 1: into thinking it's a human operating a Twitter account? Do
Speaker 1: you mind interacting with them on Twitter? Yeah, that's what
Speaker 1: I was kind of just thinking about. And I mean,
Speaker 1: if I were to find out that someone that I've
Speaker 1: had conversations with on Twitter is actually a bot, I
Speaker 1: I wouldn't be mad at it. You know, like like
Speaker 1: at that point, I would kind of want to talk
Speaker 1: to the person who had written the program and and
Speaker 1: you know, I'd like like come to interview them on
Speaker 1: a show and be like, hey, you did this really
Speaker 1: cool thing. You totally fooled me at any rate, So
Speaker 1: that's pretty rad. But yeah, like I would be impressed
Speaker 1: rather than upset, as long as it wasn't threatening to
Speaker 1: turn me into Mother Russia. Yeah, and then again, I
Speaker 1: think if it weren't malicious, I would definitely be impressed
Speaker 1: of course, we would be impressed. I think to counter this, perhaps,
Speaker 1: I think you would be impressed by the novelty of it.
Speaker 1: So what if we live in an age where bots
Speaker 1: like this you can, you know, buy a million of
Speaker 1: them for a penny, and they're just they're everywhere. Well,
Speaker 1: I mean they're already everywhere, but like really really good
Speaker 1: bots are everywhere. You're you're talking at that point about
Speaker 1: the potential collapse of entire industries because they're collapse of
Speaker 1: Internet society basically, which would be incredibly destructive. So here's
Speaker 1: hoping that we never reached. I mean, I assume we'll
Speaker 1: probably reach some semblance of that and then get through it.
Speaker 1: It'll just be the point where that happens will be
Speaker 1: really ugly and it'll take some time. It'll it'll I
Speaker 1: would assume it'd be similar to something like a Dot
Speaker 1: Calm crash, where you know, the consequences would be pretty
Speaker 1: grim at least for a while, and we would eventually
Speaker 1: get through that. Um So I think that would happen
Speaker 1: to I mean, I hate to be kind of doom
Speaker 1: and gloom about it, but ultimately, bought behavior on a
Speaker 1: wide scale basis can be pretty dangerous. You know, I
Speaker 1: wonder if and this might not be doable just because
Speaker 1: of the way, um, the way bots are incorporated into
Speaker 1: the sources through which we access them, like social media
Speaker 1: platforms and stuff. But if we could end up we're
Speaker 1: with browsers that have bought blockers in the same way
Speaker 1: we now have ad blockers, maybe I mean that would
Speaker 1: probably be more like a virus blocker in this kind
Speaker 1: of like an ad blocker, because we probably have a
Speaker 1: database that would have to depend upon and it wouldn't
Speaker 1: necessarily automatically detect whether something was a bot, but if
Speaker 1: it fit in a database that would say, all right,
Speaker 1: this is we're just blocking this interaction. And I suspect
Speaker 1: that kind of countermeasure is going to I mean, it's
Speaker 1: still going to be an arm sprace, but I but
Speaker 1: I picture it more or less evening itself out at
Speaker 1: a certain way. I don't I don't foresee a collapse
Speaker 1: of Twitter as we know. I would hope not. And
Speaker 1: I also think that, you know, bots can potentially be
Speaker 1: very helpful. What if I go on Twitter and I
Speaker 1: expressed that I need a certain thing, like there's something
Speaker 1: that I'm looking for that I haven't been able to find.
Speaker 1: But then there's a Twitter bot that totally can point
Speaker 1: me in the right direction at John Strickland. Your keys
Speaker 1: are in your front left pocket. Thanks so much. Now now,
Speaker 1: now my Twitter handle is out there for everybody. No,
Speaker 1: I have self promote all the time. I'm not gad
Speaker 1: about that. I'm just glad you said and I didn't
Speaker 1: have to, uh but no, no, I mean that it
Speaker 1: could really be very useful depending upon what it is
Speaker 1: you're looking for. Like, there are times where people complain
Speaker 1: about their experiences with a brand, and then the brand
Speaker 1: will get in touch with them, especially if they have
Speaker 1: a lot of followers, uh, to to try and resolve that. Well.
Speaker 1: Bots could make that happen much more easily for a
Speaker 1: lot more people. So it's not just the folks who
Speaker 1: have fifty thousand followers who get, you know, sponse from
Speaker 1: a brand that they've had some issue with. It's everybody.
Speaker 1: And it may be that we see more problems resolved
Speaker 1: because of that. So I see a lot of potentially
Speaker 1: good things coming out of bought interactions. But again, those
Speaker 1: kind of interactions tend to be more on the Hey,
Speaker 1: I'm I'm aware that this is a bot that's that
Speaker 1: I've encountered. But that's okay because it's getting me to
Speaker 1: what I need stuff because I'm upset or or at
Speaker 1: least addressing whatever the problem is. Yeah. Um, as opposed
Speaker 1: to a bot that's designed specifically to fool you into
Speaker 1: thinking it's a person, I mean, that's always gonna be
Speaker 1: a little more because you've been tricked, right, right. Nobody
Speaker 1: likes to be right unless you're unless you're specifically going
Speaker 1: to a stage magic show where you're like I want
Speaker 1: to see amazing allusions where I don't know how he
Speaker 1: did it or she did it. This is not something
Speaker 1: that you generally seek out. I think the takeaway of
Speaker 1: this episode is that the future of social media is
Speaker 1: a stage magic show. I can only hope. Yeah, I
Speaker 1: can make a coin disappear into a vending machine, which
Speaker 1: we did that episode about those previously. I know you
Speaker 1: can't jump. Thanks, well, uh, you know, this has been
Speaker 1: a really fun conversation. We had talked about how this
Speaker 1: was gonna be a super short episode, and of course
Speaker 1: we found so much to talk about. It was a
Speaker 1: really long one, but it was so much fun to
Speaker 1: talk about. So we want to encourage our listeners if
Speaker 1: you guys have ideas for for topics we can tackle
Speaker 1: in future episodes, or you have your own thoughts about
Speaker 1: what we have said in this episode. We want to
Speaker 1: hear from you, so send us an email. The address
Speaker 1: is FW Thinking at how Stuff Works dot com. Or
Speaker 1: get in touch with us on social platforms and let
Speaker 1: us know you're not a bot or that you are
Speaker 1: a bot. We don't really discriminate. You can get in
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Speaker 1: Twitter and Google Plus, we are f w thinking. Just
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Speaker 1: leave us a message, and we'll talk to you again
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