Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says I texted
Speaker 1: you on a Monday, but you did not get my
Speaker 1: text until Tuesday because of a network problem. I'm Jonathan
Speaker 1: Strickland and I'm Joe McCormick. Did that problem have to
Speaker 1: do with the tubes? Yes, yes, I can't believe I
Speaker 1: made a tubes joke. No, I take it back. I'm sorry.
Speaker 1: I'm sorry. You've been a radio and the radio is forever. No, no, no, no,
Speaker 1: I apologize. Easy jokes beneath me. We're going to talk
Speaker 1: about chat bots today. Yeah, we've talked about chat bots before, right,
Speaker 1: We've talked about them in a couple of different episodes.
Speaker 1: We've talked about chat bots, we talked about AI, We've
Speaker 1: talked about touring tests, we've talked about machine learning. All
Speaker 1: of those topics are going to play a part in
Speaker 1: our discussion today. And speaking of easy jokes that are
Speaker 1: beneath us, we are talking specifically today about tay tweets. Yes,
Speaker 1: Microsoft ta uh yeah, Tay Tay. We we we hardly
Speaker 1: knew ye. In fact, I doubt anyone really knew. Ye.
Speaker 1: I didn't find out about Tay until Tay was already gone.
Speaker 1: I found out about Tay while TAY was still active,
Speaker 1: but I never messaged Tay. And the only reason I
Speaker 1: found out about Tay was because I had been invited
Speaker 1: to be a guest on a show called Tech News Today,
Speaker 1: and that was one of the news items that they covered.
Speaker 1: And I had been very busy in our day to
Speaker 1: day work and had not followed along. So when I
Speaker 1: when I heard about it, it it was before all the
Speaker 1: craziness unfolded. And we'll get into what the craziness was.
Speaker 1: To be fair, though, even at ground like square one,
Speaker 1: it seems to me like TAY was probably not the
Speaker 1: most thought out project Microsoft has ever launched. And I'm
Speaker 1: including Microsoft Bob in that. Yeah, that's a that's a
Speaker 1: sick burn for those who know what Microsoft Bob was. Okay,
Speaker 1: tell me more, all right, Well now that we we
Speaker 1: got our little rascals reference all the way. So, according
Speaker 1: to Microsoft, Tay is quote an artificial intelligent chatbot developed
Speaker 1: by Microsoft Technology and Research and BING teams to experiment
Speaker 1: with and conduct research on conversational understanding. UM, I know
Speaker 1: some people would probably start to argue with the word
Speaker 1: intelligent that was in that description based upon some of
Speaker 1: the things that were happening with t Now, that description
Speaker 1: doesn't exactly capture what the public saw of Tape because
Speaker 1: the public facing incarnation of ta was specifically a Twitter account. Well,
Speaker 1: Twitter account was the primary one. Yeah, she or it.
Speaker 1: I get very confused about referring to AI because they're robots.
Speaker 1: Robots don't have genders. But Tay was impersonating a nineteen
Speaker 1: year old girl, so our woman. Yeah. Also when when
Speaker 1: at what age is an AI a woman? I don't
Speaker 1: know yet? An AI no longer the longer a girl
Speaker 1: type of thing. She was a young adult AI. Okay,
Speaker 1: while I continue to go through this this oddly misplaced
Speaker 1: feeling of cultural shame, Yes, she was also on group
Speaker 1: me and Kick and Kick So what are what are
Speaker 1: you even talking? So group me, Lauren and I know
Speaker 1: all about group me. Yeah, we happen to use group
Speaker 1: me while we were in Austin for south By Southwest.
Speaker 1: So group me is essentially like a very contained version
Speaker 1: of Twitter. It's it's a collection of cell phone numbers, essentially,
Speaker 1: so that you can send a message to the group
Speaker 1: me which includes multiple people. Everyone receives that message and
Speaker 1: everyone can respond to it. Yeah, it's a fancy group
Speaker 1: messaging system that also has a little bit of capacity
Speaker 1: to look like cute stuff, like you can up a
Speaker 1: picture and um immediately place meme style text on it. Yeah,
Speaker 1: it used to be that it was just group texting.
Speaker 1: So when I started seeing the pictures popping up with
Speaker 1: all the memes, I immediately went into old man mode
Speaker 1: and got really confused. Kick is also an instant messaging platform. Uh,
Speaker 1: it's based off the BlackBerry Messenger platform, So it's all.
Speaker 1: Three of these are different ways to send out messages
Speaker 1: to typically groups of people, not not like a one
Speaker 1: on one thing necessarily, although I think Kick can can
Speaker 1: sometimes fall into that category. But um, honestly, before we
Speaker 1: even get into two, more of the weirdness here. From
Speaker 1: the get go, I felt odd about the idea of
Speaker 1: framing an AI within the context of not only a
Speaker 1: gender but an age, kind of like you were saying, Lauren,
Speaker 1: I I most of the time when I think of AI,
Speaker 1: unless you're specifically trying to create something that's going to
Speaker 1: pass a touring test and therefore fool someone into thinking
Speaker 1: it's an actual first I was gonna say, this sounds
Speaker 1: like some Eugene kind of trickery. Yeah, and we'll talk
Speaker 1: about goosetman in a minute to Unless you're doing that,
Speaker 1: there's not really I don't see a purpose to giving
Speaker 1: a gender and age to an AI other than maybe
Speaker 1: there's some studies perhaps that suggest humans are more likely
Speaker 1: to have a natural interaction with someone that they can
Speaker 1: kind of kind of categorize in some way. And I
Speaker 1: don't know if that, if that research even exists. That's
Speaker 1: just me taking wild shots in the dark. Um, but
Speaker 1: I personally thought I would be very reticent to send
Speaker 1: a message to an entity that had been more or
Speaker 1: less identified as a nineteen year old female, even just
Speaker 1: to see how it was working, because I'm a forty
Speaker 1: year old man, and I don't think sending messages to
Speaker 1: to an entity that's less than half my age, like
Speaker 1: it just feels inappropriate from the from the start to me,
Speaker 1: even though I know it's not a person in right,
Speaker 1: it all exactly made me feel kind of squidgy. It well, sure,
Speaker 1: it's just putting putting a strange basis from from our
Speaker 1: point of view, perhaps, I mean, and and the boat
Speaker 1: was supposedly geared towards people I think eighteen to twenty four.
Speaker 1: So so it wasn't meant for me in the first place,
Speaker 1: but it wasn't designed by people who are eighteen to
Speaker 1: twenty four. It's fairly I think it was designed by
Speaker 1: people closer to my age, and that that is one
Speaker 1: of the many problems that Tay has. So yes, slightly
Speaker 1: odd thing, but perhaps a conversation for another day. Yeah, So,
Speaker 1: at any rate, Tay, like you said, eighteen to twenty
Speaker 1: four year olds, that was the target demographic because Microsoft
Speaker 1: had identified that as the demographic that most frequently uses
Speaker 1: mobile based social media things like social messengers and um
Speaker 1: and that's really where they were looking to kind of
Speaker 1: increase this ability for AI to understand conversational English things,
Speaker 1: things that go beyond very simple, uh prescribed commands. Okay,
Speaker 1: so in practice, what does this look like? What exactly
Speaker 1: was Ta doing? So in practice, you would send Tay
Speaker 1: a message on one of these platforms, either Group Me,
Speaker 1: Kick or Twitter, and then t would respond to you.
Speaker 1: If it was on Twitter and things were particularly busy,
Speaker 1: Tay would tell you to send a direct message because
Speaker 1: otherwise we just get kind of lost in the crush. Obviously,
Speaker 1: if you unleash something like this to the world at
Speaker 1: large and you let it be known that it's out there,
Speaker 1: you're gonna get a huge response, right, especially from Twitter,
Speaker 1: so mostly consisting of mischief and I'm sure we'll talk
Speaker 1: about that some mortally. Yeah. So, uh yeah, this was
Speaker 1: you know, that was exactly the case. So they released
Speaker 1: it to try and see what would happen, how Tay
Speaker 1: would start to incorporate different types of language in her replies,
Speaker 1: how she would be able to interpret different things that
Speaker 1: were said to her. They started with a baseline where
Speaker 1: Microsoft had essentially mined a ton of publicly available data.
Speaker 1: So my guess is would be public tweets would probably
Speaker 1: be a big one. Right. They identify the demographic that
Speaker 1: they're looking at, they mine the sort of communication that
Speaker 1: these uh, these folks that are within that demographic tend
Speaker 1: to send, and he used as the model for Tay's communication,
Speaker 1: and then the idea was to build upon that. Uh So,
Speaker 1: you know, we mentioned Goostman, uh talking about the fact
Speaker 1: that you know, we've got this context of a nineteen
Speaker 1: year old female persona for Tay with Eugene Goostman, that
Speaker 1: was one of the AI programs that quote unquote passed
Speaker 1: the Turing test. We talked about this in a previous
Speaker 1: episode in which uh it was a an AI that
Speaker 1: was mimicking a thirteen year old Ukrainian boy, a non
Speaker 1: native English speaker, and that meant that the people who
Speaker 1: were uh interviewing this entity over a over a computer, uh,
Speaker 1: they were aware that they were supposedly talking to a
Speaker 1: human boy thirteen years old, non native English speaker, limited
Speaker 1: exposure to things like pop culture that would have come
Speaker 1: before he was born. So in other words, it checked
Speaker 1: people's expectations of what this particular uh AI would be
Speaker 1: capable of doing. Sure, and similarly, I think the part
Speaker 1: of the concept for Tay was probably that you have
Speaker 1: certain expectations for how a nineteen year old woman is
Speaker 1: going to behave on Twitter, and it probably involves the
Speaker 1: use of of emojis and um uh not full grammatical sentences,
Speaker 1: no capital letters whatsoever. Certainly um certain truncating of of
Speaker 1: thoughts and ideas, a certain inquisitiveness probably are the stereotypes
Speaker 1: of that sort of age group. It was to a point,
Speaker 1: and to be fair, I've only looked at sampling of
Speaker 1: some of the things he has said. It was to
Speaker 1: a point where I thought, I thought it was venturing
Speaker 1: on the border of parody, right. I was starting to
Speaker 1: feel like it was, Um, maybe someone my age is
Speaker 1: preconception of what a person would how a person would
Speaker 1: constantly be communicating. Uh. And again that could just be
Speaker 1: my own bias going into it, because there were a
Speaker 1: lot of things where I'm looking at it and I'm thinking,
Speaker 1: I'm sure there are many people who do communicate this way,
Speaker 1: but it feels weird to have built something specifically to
Speaker 1: communicate in this way. Not that I necessarily thought that
Speaker 1: she would need to speak in full sentences with perfect grammar,
Speaker 1: but just it. It almost felt like someone outside of
Speaker 1: that group trying to create language in a in a
Speaker 1: in a like like like a lame dad trying to
Speaker 1: sound like a cool dad and teenager. Yeah, that's pretty much.
Speaker 1: That's kind of how I felt. Now again, I'm not
Speaker 1: I'm not twenty years old anymore, so maybe I'm just
Speaker 1: totally out of touch and it was fine for people
Speaker 1: who are of that age and they didn't see anything
Speaker 1: different about it. No, Nowadays, twenty year olds love linguistic
Speaker 1: interlopers from the older generation oh is that a trend.
Speaker 1: That's that's how That's how I was when I was twinty.
Speaker 1: I mean, we gen xers, we we appreciated a good
Speaker 1: you know, talk about interloping, interloping, interlocution, inter interlocular. They
Speaker 1: love it almost as much as they love being talked
Speaker 1: down to about economics. Yeah. Yeah, So anyway, getting back
Speaker 1: to what Microsoft was trying to do, Microsoft's blog actually,
Speaker 1: uh kind of outlined what Tay was meant to do.
Speaker 1: TA is designed to engage and entertain people where they
Speaker 1: connect with each other online through casual and playful conversation.
Speaker 1: The more you chat with Tay, the smarter she gets,
Speaker 1: so the experience can be more personalized for you. So
Speaker 1: she was supposed to learn. It's kind of like in
Speaker 1: a terminator too, where you know the terminator has his
Speaker 1: brain is a learning computer. The more he interacts with humans,
Speaker 1: the more he learns how to be one. Right. So
Speaker 1: that's what it is to say, Asta Leavista baby right,
Speaker 1: and in the end he could even learn to love maybe.
Speaker 1: But let's say, let's say you sort of took a
Speaker 1: page from the Edward for a Long Book, uh in
Speaker 1: Terminator two, in which he teaches him how to say
Speaker 1: rude phrases to people. And you tried this out with
Speaker 1: something like ta you could get some very sad and
Speaker 1: very funny results. Right, And if you get enough of them,
Speaker 1: you can have so much of an influence that it
Speaker 1: starts to really change how how TAY starts to behave
Speaker 1: towards everyone. Right, Like, you know, the the beautiful thing
Speaker 1: about using something like Twitter for machine learning is that
Speaker 1: you have an enormous wealth of data at your disposal. Yeah.
Speaker 1: The problem is when people are aware of it, and
Speaker 1: they are the ones supplying the data, and they can
Speaker 1: make that data whatever they want. They can purposefully misdirect
Speaker 1: your efforts to go in a direction they think is hilarious. Yeah,
Speaker 1: it's investing in quantity rather than quality. Yeah. And and
Speaker 1: to be fair, I mean this this relationship was a
Speaker 1: little weird from the get go. Microsoft was using Tay
Speaker 1: to look at things like you would have a little
Speaker 1: profile and t would recognize stuff on your profile like
Speaker 1: whatever your handle or nickname was, your gender, uh, your
Speaker 1: favorite food, your zip code um, and uh, you know
Speaker 1: your relationship status, which I think is also a little
Speaker 1: creepy for why does an AI need to know if
Speaker 1: I'm single or not single. That seems a little like
Speaker 1: like messages you would get from an artificially intelligent chat
Speaker 1: like you know, hang in there or whatever it may be.
Speaker 1: Seems a little weird. Maybe, you know, maybe Tay is
Speaker 1: particularly interested in relationship counseling is a possible career. Now
Speaker 1: she needs to really work on her delivery skills. Then,
Speaker 1: because Tay started getting a little out of hand, So
Speaker 1: let's let's before we really talk about what went right
Speaker 1: the chaos, ya, let's talk about what ideally would have
Speaker 1: happened had had things played out the way Microsoft seems
Speaker 1: to have intended it. Well, you said, you said it
Speaker 1: that she was supposed to be learning from a massive
Speaker 1: store of data, which is natural conversation on the Internet,
Speaker 1: and so that was supposed to be something that the
Speaker 1: computer program could analyze and say, Okay, I'm getting a
Speaker 1: very generalized picture of how people talk, right, and how
Speaker 1: they talk to each other and in response to specific
Speaker 1: phrases and questions and and other little bits of linguistic cues. Yeah,
Speaker 1: and which is not inherently a bad idea, no, no,
Speaker 1: and and and going further than that, how different thoughts
Speaker 1: can be expressed or or the same thought rather can
Speaker 1: be expressed in very different ways, which is very useful
Speaker 1: if you're going to incorporate that kind of conversation understanding
Speaker 1: in a digital assistant. Oh yeah, yeah, that's that's like
Speaker 1: the problem in in in language and machine language and
Speaker 1: language learning, because you because the kind of conversation that
Speaker 1: we have where we've said this a lot of times
Speaker 1: on the show before, but where we can say one
Speaker 1: thing and and if not dozens, then hundreds or thousands
Speaker 1: of different ways and have another human understand them as
Speaker 1: long as they're conversational and the same language. Uh. That's
Speaker 1: that's real hard for computers. It's what you call not easy, right.
Speaker 1: So that's why usually we have to conform to the
Speaker 1: limitations of machines. Right. We have to be able to
Speaker 1: express our commands in a way that machines understand, which
Speaker 1: might not come naturally to us. And as we get
Speaker 1: better and better with machines understanding conversation, we have to
Speaker 1: do less work. Right. We don't have to accommodate the machines.
Speaker 1: They just understand us. That's the goal. So that's what
Speaker 1: we wanted to have happen, or at least what Microsoft
Speaker 1: wanted to have happen. It's not what Twitter wanted to
Speaker 1: have happen, or you know, the other folks who are
Speaker 1: using kick or group me. Um, what they wanted to
Speaker 1: do was to perhaps exercise a little bit of control
Speaker 1: and power over something and direct it for their own amusement.
Speaker 1: So you could do things would say that weren't particularly
Speaker 1: you know, uh, malicious or mischievous, Like you could ask
Speaker 1: her to tell you a joke or tell you a story.
Speaker 1: She would play games with you. She would create memes
Speaker 1: like we were talking about with group me. She could
Speaker 1: do memes with pictures. Yeah, so you could actually have
Speaker 1: these kind of interactions. But you could do other stuff too, um,
Speaker 1: like you could make her say things, like you could
Speaker 1: tell her to repeat stuff. Now before, let's talk a
Speaker 1: little bit about some of the phrases she would use
Speaker 1: on her own. Here's here's a couple of sample tweets
Speaker 1: she sent out. Um. This is sort of the way
Speaker 1: Tay's baseline worked before she got corrupted. I guess it's
Speaker 1: probably the best way of putting it. Uh OMG totes
Speaker 1: exhausted swagulated too hard today, HBU, which I assume is
Speaker 1: how about you Laurence nodding yes, So it's not. I
Speaker 1: mean it could be. That's my favorite question to ask people.
Speaker 1: Another one was damn t b H. I was kind
Speaker 1: of distracted, you got me. I'm like, okay, I can
Speaker 1: these are I can see these these tweets. I see
Speaker 1: where it's coming from. Uh. You know that's kind of
Speaker 1: the baseline again, sort of the the way t would
Speaker 1: react to two people just trying out TA sending messages
Speaker 1: to her normally. Uh, but then came all the trolls,
Speaker 1: all the people who think it's hilarious to mess with
Speaker 1: a system that they have some input into. So uh,
Speaker 1: one thing they would do is make TA repeat stuff.
Speaker 1: So a lot of the lot of the tweets you've
Speaker 1: probably seen, if you've followed the story of Microsoft TA
Speaker 1: saying horrible, horrible racist, the very worst things are probably
Speaker 1: things that someone had repeat back. Yeah, you can imagine
Speaker 1: generally approving of Hitler. This the Godwin's law was was
Speaker 1: was fulfilled almost immediately. Um So yeah, making her repeat
Speaker 1: stuff back was easy. You didn't she didn't necessarily quote
Speaker 1: unquote understand it. She was responding to a command, which
Speaker 1: was to say this thing that I'm telling you to
Speaker 1: say This is very similar to a program I used
Speaker 1: to have as a kid, um and it was very
Speaker 1: similar to Eliza, one of the early chatbots. So there
Speaker 1: was a sound card for old PCs called sound Blaster,
Speaker 1: and one of the pieces of software that came with
Speaker 1: sound Blaster was this program called Doctor Spaces. So Dr
Speaker 1: Spatso was essentially Eliza, except he talked like this, you know,
Speaker 1: and you could ask Dr Space the questions. He would
Speaker 1: try to psychoanalyze you, so he would answer everything with
Speaker 1: his own question. But you could also make him say stuff,
Speaker 1: so he would say say and then you put things
Speaker 1: in quotation marks. He'd say whatever you wanted him to say,
Speaker 1: and much amuse and into it. Yes, because I was
Speaker 1: a kid and I just thought it was funny to
Speaker 1: make my computers say really rude things, mostly just mild
Speaker 1: curse words. Because I was a pretty boring kid. You
Speaker 1: didn't have the imagination to make him approve of Hitler. Yeah,
Speaker 1: I didn't. I gotta admit I never made Dr Spazzo's
Speaker 1: out like a Nazi sympathizer. It never occurred to me
Speaker 1: as a kid. Kids today, man, they are miles ahead
Speaker 1: of me. Uh So that's that's what we started to
Speaker 1: see we saw people starting to have her repeat stuff,
Speaker 1: and not only was she repeating it, she was starting
Speaker 1: to learn. So she would start to respond to people
Speaker 1: and incorporate some of the stuff that she had been
Speaker 1: told to repeat over and over again. Not consistently either.
Speaker 1: Sometimes she would make one statement to a person where
Speaker 1: she'd essentially equate something like feminism with being the most
Speaker 1: evil concept in the world, and then she would send
Speaker 1: another message to someone else where she she'd say something like, oh, no,
Speaker 1: femin is um is great. So uh, not consistent across
Speaker 1: all messaging either. Well. And it also I would I mean,
Speaker 1: I don't know how the programming works, but I would
Speaker 1: argue that that the program probably didn't understand any of
Speaker 1: the topics that were being thrown at it, like, I mean,
Speaker 1: probably a few a few clue words like like hey,
Speaker 1: how are you means I'm supposed to say something about
Speaker 1: how I'm doing UM? But I you know, I don't
Speaker 1: think that it was taught what feminism is. I don't
Speaker 1: think that it had an opinion on feminism. I think that,
Speaker 1: you know, it was just testing out an answer to
Speaker 1: a question, you say, what do you think about feminism?
Speaker 1: And it it's like, oh, this is a question about
Speaker 1: what I think, say it's good or bad? Yeah, yeah,
Speaker 1: I mean I get the sense that these are text
Speaker 1: strings for it. It's not a it's not a semantic
Speaker 1: engine and exactly sure, yeah there was there was. I agree.
Speaker 1: I don't think there was any understanding on the part
Speaker 1: of Microsoft TA. In fact, that's one of the reasons
Speaker 1: why I really argue that people people who say this
Speaker 1: is a real warning about AI, that's b s. It's
Speaker 1: not a warning about AI. It's a warning about how people.
Speaker 1: It's a warning about real eye Yeah, human natus, it's
Speaker 1: a it's a warning about how how humans when we
Speaker 1: were given the opportunity to mess with something, it could
Speaker 1: be really hard to resist that opportunity. And this is
Speaker 1: something that we've seen multiple times. So some other examples
Speaker 1: of stuff that you know, she she blasted out xenophobic stuff,
Speaker 1: racist stuff, homophobic stuff. Pretty much everything that was dark
Speaker 1: in human nature was spouted off by Tay in less
Speaker 1: than just plenty of regular old foul mouth dirtiness and pornography.
Speaker 1: Sure so, uh, I love that. Um Gerald Miller on
Speaker 1: Twitter wrote, Tay went from humans are Super Cool, which
Speaker 1: was like one of the first messages TA talked about
Speaker 1: to full Nazi in less than twenty four hours, and
Speaker 1: I'm not at all concerned about the future of AI. Again,
Speaker 1: I would argue that this is not so such a
Speaker 1: case of worrying about where AI is going, rather than
Speaker 1: how do you design a system that uses machine learning
Speaker 1: from crowdsource data and factor out all the intentionally malicious misdirection.
Speaker 1: That's that's the question that has nothing to do with AI.
Speaker 1: That has to do with understanding how people are right.
Speaker 1: I see you've got some specific exchanges recorded in here,
Speaker 1: which one which are something that we can actually say,
Speaker 1: I like the Ricky Gervais one. Yeah, so Ricky Gervais.
Speaker 1: This one was. This one's not not as as jaw
Speaker 1: dropping lee awful as some of the other things that
Speaker 1: she said, But this was one of those where Tay
Speaker 1: actually came up with a response. She wasn't repeating something.
Speaker 1: So according to The Guardian, one exchange began a user asked,
Speaker 1: Tay is Ricky Gervais and atheist? Ricky Gervais is a comedian, Yes,
Speaker 1: and it's very uh outgoing about being an atheist, So
Speaker 1: so this person stay is Ricky Gervais an atheist, And
Speaker 1: they replied, Ricky Gervais learned totalitarianism from Adolf Hitler, the
Speaker 1: inventor of atheism. So we get Hitler, we get totalitarianism.
Speaker 1: Hitler apparently invented the concept of being an atheist. Um. Yeah,
Speaker 1: so it's sort of beautiful. Yeah, I mean that, that's
Speaker 1: that's sort of a poetry of nonsense. I like the
Speaker 1: way these ideas are crammed together. At least at least
Speaker 1: in this one, there was nothing so like personalized. There
Speaker 1: wasn't like personalized hate. Who can take it on him?
Speaker 1: But Ricky Gervais not not super factual, but sure, I'm
Speaker 1: pretty sure Ricky Gervais would would use this as material
Speaker 1: in the stand up. Right. But but it's not something
Speaker 1: where it's it's centering out like a specific ethnic group
Speaker 1: for example. Right. So, as you can guess, microsaw looked
Speaker 1: at this and said, well, this is going swimmingly. Let's
Speaker 1: just leave her online. What happened, pulled the plug. It
Speaker 1: took less than twenty four hours for them to say,
Speaker 1: you know what, we might need to revisit this after
Speaker 1: we think it through a little bit more. Uh. They
Speaker 1: also worked up a little curtain between the Internet and
Speaker 1: for a bit, I like to think that the little
Speaker 1: like technical difficulty sign came up, but yeah, exactly. Yeah,
Speaker 1: so they they also deleted a lot of the particularly
Speaker 1: nasty tweets that they had made. She made a couple
Speaker 1: hundred thousand within that twenty four hours, which is more
Speaker 1: than even I have tweeted, which is saying something I know, right,
Speaker 1: so uh. Some people actually criticized Microsoft for deleting the tweets,
Speaker 1: saying that those messages should remain up to serve as
Speaker 1: a warning of the dangers of AI. But again, I
Speaker 1: don't think that's I don't think that's a really a
Speaker 1: credible argument personally. Um. I think it's more of an
Speaker 1: of a way of warning people about how nasty folks
Speaker 1: can get on on social media. Microsoft's response was that
Speaker 1: the AI chat butt t is a machine learning project
Speaker 1: designed for human engagement. As it learns some of its
Speaker 1: responses are inappropriate and indicative of the types of interactions
Speaker 1: some people are having with it, We're making some adjustments
Speaker 1: to tay well, I mean that that seems accurate to me, Yeah,
Speaker 1: absolutely accurate. Um. She did come back online very briefly
Speaker 1: about a little bit later on a couple of days
Speaker 1: was an accident. It was completely an accident. Microsoft said
Speaker 1: that they were testing her internally. Uh, in within Microsoft,
Speaker 1: not testing Tay's internal organs. She doesn't have any she's had. Yeah,
Speaker 1: it wasn't anything like that. I'm just now thinking like
Speaker 1: I'm putting the body of Cortana in there, just thinking
Speaker 1: of them slicing open the virtual Cortana to Cortana is
Speaker 1: the character from Halo, artificial intelligent character from Hale. Also
Speaker 1: the name of that. Well, I imagine the second she
Speaker 1: came back online, people are trying to get her talking
Speaker 1: about Hitler again. Well, yeah, they said that, uh that
Speaker 1: mostly the messages that were being sent weren't offensive, but
Speaker 1: there were still quite a few that did fall into
Speaker 1: that category of crazy racist Hitler oriented tweets. So at
Speaker 1: least some of them were still in that that camp.
Speaker 1: And Microsoft immediately shut her down again. Well not immediately,
Speaker 1: it was after they realized it, because it was in
Speaker 1: the middle of the night when it happened. Okay, okay,
Speaker 1: so yeah, a couple hours later they shut it back down. Uh,
Speaker 1: And I don't think they're surprised. To me here is
Speaker 1: that Microsoft was surprised by this at all. Yeah, because
Speaker 1: we I mean, I remember PR attempts to get crowd
Speaker 1: sourced information into marketing campaigns, that kind of thing every time. Yeah,
Speaker 1: I remember. So I I agree with what you're saying
Speaker 1: about how I don't think this is really all that
Speaker 1: uh less curative of anything about artificial intelligence, but it
Speaker 1: is very illustrative of stuff about Internet culture and about
Speaker 1: what happens when you try to crowdsource things, especially when
Speaker 1: major companies try to crowd things. Uh. There is an
Speaker 1: inherent urge to mischief that is always latent on the Internet,
Speaker 1: and there are certain types of stimuli that draw it
Speaker 1: out more than other things. And I can admit to
Speaker 1: being very much a part of this in some scenarios. Now,
Speaker 1: I wouldn't go try to get ta saying racist stuff,
Speaker 1: but I do remember one time Coca Cola had a
Speaker 1: thing where they let you generate your own memes that
Speaker 1: let you make your own Coca Cola animated gifts that
Speaker 1: had so have like a Coke ad going in the
Speaker 1: background and you can enter your own text. And I
Speaker 1: guess what they were hoping people would be entering is
Speaker 1: I don't know, like you know, like big marketing campaign right,
Speaker 1: they'd have people putting in like, hey, Stacy, I love
Speaker 1: Coke to their friends. I don't know, uh, like what
Speaker 1: good could come of this? So I actually when they
Speaker 1: did that, I remember reading a very interesting article about
Speaker 1: testing all of the different things Coke wouldn't let you
Speaker 1: say and and and they had a wide dictionary of
Speaker 1: no no terms. They wouldn't let you talk. They wouldn't
Speaker 1: let you use curse words or slurs or anything like that, obviously,
Speaker 1: and they also wouldn't let you talk about uh most
Speaker 1: obvious morbid topics that they didn't want you talking about
Speaker 1: Nazis or Hitler. They didn't watch you talking about health
Speaker 1: problems or anything like that. But I I felt dared
Speaker 1: to mess with it by this, to probe this this
Speaker 1: uh these boundaries and find out ways to get around it. Yeah,
Speaker 1: so I remember, Um, I remember figuring out that it
Speaker 1: wouldn't let you talk about demons in English, but you
Speaker 1: could you could come up with phrases about d hmans
Speaker 1: in Latin that it would let you do. So I
Speaker 1: thought that was pretty fun. Yeah, I remember talking about
Speaker 1: this with you earlier this week, and I said, yeah,
Speaker 1: I could imagine writing something like Taste Sweet Oblivion, and
Speaker 1: like you know, you could easily find ways around the
Speaker 1: the protective barrier. There. There are other examples too. I
Speaker 1: don't know if you guys remember this, but I remember
Speaker 1: actually following the story when it happened. Mars Incorporated several
Speaker 1: years ago created this module for their Skittles web page,
Speaker 1: and the module allowed tweets that contained the word skittles
Speaker 1: to be uh to be displayed on the homepage in
Speaker 1: more or less real time, like shortly after you posted it.
Speaker 1: And it became abundantly clear early on that no one
Speaker 1: was vetting the tweets, no one was curating. It was
Speaker 1: just a straight feed from the Internet, from Twitter into
Speaker 1: the Skittles homepage. And all you gotta do is include
Speaker 1: the word skittles. So you could write anything you wanted
Speaker 1: to put the word skittles in there it would show
Speaker 1: on that homepage. There were some really funny ones. I
Speaker 1: think I even did one. If I'm not mistaken, I'd
Speaker 1: have to do a search on my Twitter history to
Speaker 1: find out when I said skittles. I mean, I mean,
Speaker 1: people weren't just saying really clever things about how they
Speaker 1: were tasting the rainbow. No, no, I mean I'm sure
Speaker 1: that made up a significant percentage of the tweets that
Speaker 1: showed up on the Skittles homepage, but genuine earnest love
Speaker 1: for Skittles. People just couldn't contain. They had to go
Speaker 1: straight to Twitter to share it, or they were playing
Speaker 1: a game of Skittles and they were just confused why
Speaker 1: their game was being broadcast on a candy website. Uh.
Speaker 1: At any rate, there were all sorts of different posts
Speaker 1: that went up there, including ones that were offensive obviously,
Speaker 1: so there were there's precedents, right we we That's the
Speaker 1: thing that really puzzles me that Microsoft went forward with
Speaker 1: this without even taking into consideration the precedents that has
Speaker 1: been set with crowdsourced campaigns like this, I think, especially
Speaker 1: on Twitter. Yeah. But but but but not not even
Speaker 1: not even only there, No, I mean Microsoft itself had
Speaker 1: already experienced with this. In two thousand eleven, Microsoft launched
Speaker 1: a poll on Facebook. It was part of the Microsoft
Speaker 1: Phone Projects, so it's one division within Microsoft, uh, and
Speaker 1: they asked a question. They were saying, what was your
Speaker 1: favorite feature of the Nokia Lumia eight hundred phone? And
Speaker 1: you had some options to choose from, but you could
Speaker 1: type in your own option, and people did, and a
Speaker 1: lot of them options were like there's not a not
Speaker 1: a darn thing, that kind of thing. That's a very
Speaker 1: pc way of putting the way they wor did it.
Speaker 1: So there were options that were very much uh not
Speaker 1: in line with Microsoft marketing strategy, and those got lots
Speaker 1: and lots and lots of votes because people thought it
Speaker 1: was funny or they were dissatisfied with the product, or both.
Speaker 1: So it's interesting to me that the same company that
Speaker 1: had experienced this back in two thousand and eleven would
Speaker 1: go forward in two thousand and sixteen with a plan
Speaker 1: that had some of the very same trappings there, like
Speaker 1: it was almost set up to fail in that way. Um,
Speaker 1: but yeah, it's just one of those things where I think,
Speaker 1: I think part of the reason why there is the
Speaker 1: sense of mischief one you're you're looking at entities that
Speaker 1: are much greater than yourself. Right, Microsoft is enormous, huge company.
Speaker 1: Just don't feel bad about it. Like if it had
Speaker 1: been a small business trying to say, hey, make your
Speaker 1: own meme about our product, I don't think I would
Speaker 1: would try to make memes about demons. But it's Coca Cola.
Speaker 1: I'm just not worried about them. I don't think my
Speaker 1: my demon memes about Coke are really hurting Coke. Well,
Speaker 1: not only that, but I mean, depending upon you might
Speaker 1: actually have some very negative opinions about whatever the corporation is, right,
Speaker 1: like Microsoft, Microsoft one of those companies that's very divisive there.
Speaker 1: I don't know that there are that many people who
Speaker 1: are super pro Microsoft, but I know people who are
Speaker 1: either Microsoft is a necessary evil, I need it for
Speaker 1: what I do, or I hate Microsoft so much. That
Speaker 1: seems to be the case, except for maybe Xbox. Xbox
Speaker 1: players are like, no, I really love the Xbox, which
Speaker 1: I totally get, but uh, it's it's one of those
Speaker 1: things where you you that also encourages this mischief. But yeah,
Speaker 1: when I one time when the NYPD I think, tried
Speaker 1: to start a Twitter hashtag campaign. Do you guys remember
Speaker 1: that one? Yes, I remember that. It didn't go well,
Speaker 1: it didn't turn out the way that they wanted it to. Yeah.
Speaker 1: I think they were trying to they were trying to
Speaker 1: crowdsource people. Uh what were they trying to get them
Speaker 1: to do, to like tweet good experiences HASHTAGYPD? Yeah, see,
Speaker 1: giving giving a voice to a population. Uh, that may
Speaker 1: have legitimate criticisms to say, you've got to be prepared
Speaker 1: for when that comes in, you know. I mean people
Speaker 1: don't like being told, uh and prompted with the understanding
Speaker 1: that you need to say something positive about my massive organization. Yes, yeah, yeah,
Speaker 1: even if you even if you previously did like something.
Speaker 1: There is something that strikes most of us the wrong
Speaker 1: way when we're told we need you know, you are
Speaker 1: required to say something positive about this thing, even if
Speaker 1: you liked it before. Just the thought that you are
Speaker 1: now being compelled to do it. There's most of us
Speaker 1: have a resistance to that, that that little, say nineteen
Speaker 1: year old inside you just like, hey, I can miss
Speaker 1: with I guess I technically have two of those now.
Speaker 1: Now I've seen Tay talked about in a reference to
Speaker 1: another interesting technology issue, which is the prevalence of of
Speaker 1: female gendered digital assistance or digital embodied personalities. Yeah, it's
Speaker 1: this is it's interesting. I am trying to think if
Speaker 1: there is a personal digital assistant I've heard apart from Siri,
Speaker 1: which you can you can have a male version of Siri.
Speaker 1: The the British version is male and that's the one
Speaker 1: my wife has on her phone. Apart from Sirie. I'm
Speaker 1: not aware of any of the major digital assistance that
Speaker 1: are not female. Cortonas fee male. Google Now's voices is female.
Speaker 1: You might be able to change that one. I don't know.
Speaker 1: Alexa for Amazon is female. So that's that's something that
Speaker 1: strikes me as weird too. Maybe they don't know what
Speaker 1: the psychology is behind that, what the market research says.
Speaker 1: I don't know, but it just that also is odd.
Speaker 1: And for Microsoft in particular, this was a really troubling
Speaker 1: week for them because the same week that they launched
Speaker 1: Microsoft Tay and she goes off the reservation because she's
Speaker 1: been steered the wrong way by by malicious trolls. They
Speaker 1: were also dealing with a pr nightmare. There was the
Speaker 1: Game Developers Conference that was going on earlier that week,
Speaker 1: and Phil Spencer, who's the head of Xbox, actually came
Speaker 1: out and apologized for an Xbox sponsored party. It took
Speaker 1: place in a nightclub, um and it was part of
Speaker 1: the Game Developers Conference overall event. At that party, they
Speaker 1: had a lot of skinnily clad women dancing on platforms,
Speaker 1: and anyone who is at all aware of the time
Speaker 1: it within the video game industry right now knows that
Speaker 1: the sexual objectification of women is a is a huge
Speaker 1: topic of discussion, uh, in particularly at conferences like that.
Speaker 1: It's been it's been talked very nearly to death, just
Speaker 1: just the concept of of of hiring performers like that. Um,
Speaker 1: and not that there's anything wrong with being a performer
Speaker 1: like that, but it just it just paints it paints
Speaker 1: the industry in a very particular way, and one that
Speaker 1: one that is not necessarily welcoming of of of women's
Speaker 1: participation as as as brains rather than you know, dancers. Yeah,
Speaker 1: I think it says sort of the assumed culture of
Speaker 1: our industry is sort of a tradition, yeah, traditional hyper
Speaker 1: masculine tech bro culture, right, which which perpetuates this unwelcoming
Speaker 1: environment for people who have very valuable skills and voices
Speaker 1: to add to that industry. But we're continuously denying them
Speaker 1: the ARA tunity or or if not denying them, at
Speaker 1: least discouraging them feel unco right. So this was this
Speaker 1: was like two strikes in a row for Microsoft as
Speaker 1: far as trying to to work with these ideas that
Speaker 1: involved in some way gender Like, this's not not great
Speaker 1: record for Microsoft. In March of two thousand sixteen. So
Speaker 1: the question then is what's next. Well, what if I
Speaker 1: told you that Microsoft said hey, now everyone can make
Speaker 1: one of these. I think that's a great that's a
Speaker 1: great idea. So the very same I'm not the very
Speaker 1: same day that Microsoft had to take Tay offline. Uh,
Speaker 1: there was the Microsoft Build Developer Conference and the CEO
Speaker 1: of Microsoft, Soughtya Nadela, announced that the company would make
Speaker 1: available the Bought Framework. That's all one word, bought framework,
Speaker 1: and Bought Framework is essentially the a p I that
Speaker 1: developers can use to create their own version of chatbots
Speaker 1: that can work on different sorts of platforms, including stuff
Speaker 1: like Skype, Slack, group Me, other messaging systems. UM so
Speaker 1: very interesting that they had this very well publicized I
Speaker 1: don't know that you could say any word other than
Speaker 1: failure of of Microsoft kuffle kerfuffle would work, this publicized
Speaker 1: kerfuffle of Tay, and then go ahead and say, hey,
Speaker 1: you can do it too. Now that being said, it's
Speaker 1: entirely possible that you could roll out something along the
Speaker 1: lines of Microsoft TA and and have it unfold in
Speaker 1: a very different way than what we saw with Tay. Well,
Speaker 1: I mean one option that occurs to be I don't
Speaker 1: I don't know if this would be the case, but
Speaker 1: I think it's at least possible to consider that by
Speaker 1: putting more of these things out there, you would essentially
Speaker 1: people would get it out of their system, like it
Speaker 1: would become less novel and less interesting to try to
Speaker 1: mess with a bot. Yeah, yeah, you're like I messed
Speaker 1: with a bottle last week. I'll not talk about Hitler
Speaker 1: to this one. Like, to me, it would just make
Speaker 1: sense to essentially nullify the machine learning part of Tay's
Speaker 1: software while people are getting it out of this Let
Speaker 1: it go for a month and then take it off,
Speaker 1: and sure you're not gonna have as much engagement after
Speaker 1: that because people are going to get bored. But the
Speaker 1: people who are going to get bored are probably the
Speaker 1: ones you don't want engaging with the AI in the
Speaker 1: first place. So or you take another approach where you
Speaker 1: have like a controlled beta right where you have a
Speaker 1: controlled audience that can interact with that AI, and you
Speaker 1: are just very careful about monitoring it and you encourage
Speaker 1: people to have you know, natural conversation with the AI
Speaker 1: as close as they possibly can. But if there are
Speaker 1: any shenanigans, popping up. You just you you squash it.
Speaker 1: It's a lot easier to do that if you haven't
Speaker 1: under a controlled population than if you open it up
Speaker 1: to the world at large. Um. And honestly, this is
Speaker 1: a really important field for Microsoft because this is a
Speaker 1: company that's going through a massive transition the company. You know,
Speaker 1: you think, what are Microsoft's biggest products, Windows and Microsoft Office,
Speaker 1: and those are for desktop computers, and desktop computers and
Speaker 1: laptop computers have been in pretty serious decline compared to
Speaker 1: the mobile platforms. So Microsoft is trying to transition itself
Speaker 1: into more of a mobile platform company so that it
Speaker 1: can continue to grow and develop and evolve rather than
Speaker 1: become obsolete over time. They definitely don't want to turn
Speaker 1: into the picture of what Microsoft is in those old
Speaker 1: Apple versus Microsoft ads. Uh. I'll respect to John Hodgman. Um,
Speaker 1: So I think I think it's I understand where they're going.
Speaker 1: I think it was a misguided approach. Uh. Slightly hilarious,
Speaker 1: slightly sad to see the just the amount of abuse
Speaker 1: that was thrown at Tay in order to corrupt her
Speaker 1: in this way. But it it makes me curious to
Speaker 1: see how people are going to try and tackle this
Speaker 1: problem in the future, because there is there is a
Speaker 1: lot of value to getting a grip on this natural conversation.
Speaker 1: Like we've said, it's something that could make home automation
Speaker 1: incredibly effortless, where you know, you just you could say
Speaker 1: something one of twenty different ways, but your house will
Speaker 1: totally understand what you mean and respond in kind. You know.
Speaker 1: Another thing that actually just occurred to me is that
Speaker 1: I think in the future, a machine learning program that's
Speaker 1: supposed to, you know, gain insights about natural language from
Speaker 1: interacting with the public might actually have something useful to
Speaker 1: learn by dealing with tons of trolling and abuse. Like
Speaker 1: I don't know if it's necessarily the case that it
Speaker 1: that you should just turn off its learning part, then uh,
Speaker 1: certainly it shouldn't learn how to talk from these people
Speaker 1: and say like, yes, this is a model of good conversation.
Speaker 1: But I'm not sure that there is nothing to be learned.
Speaker 1: There might be something to be learned, do you know
Speaker 1: what I mean? My thought is very vague here, but no, no,
Speaker 1: I mean, especially uh, being the person super secret you
Speaker 1: guys listening out their YouTube in the room know this. Um,
Speaker 1: I'm the one who reads the YouTube comments for how
Speaker 1: stuff works. Yeah, so I see a pretty wide range
Speaker 1: of responses to a pretty wide range of topics from
Speaker 1: the general public on a daily basis, and and it's
Speaker 1: it's genuinely fascinating to me trying to sess out, uh,
Speaker 1: what is trolling and what is sincere, particularly in response
Speaker 1: to to videos that have been produced by our stuff
Speaker 1: they don't want you to know Team, which deals with
Speaker 1: a fringe theories. And there are a lot of responses
Speaker 1: in there that that are you know that there's this
Speaker 1: huge bizarre contingency of flat earthers on YouTube and I
Speaker 1: seriously cannot figure out if they're trolling or not. I
Speaker 1: think a lot of them are. But that's this is
Speaker 1: a really great point. This is this is a perfect
Speaker 1: jumping off from what I'm saying, Like I think, uh,
Speaker 1: potentially sampling huge amounts of trolling and abuse could maybe
Speaker 1: lead into machine based insights on what's the difference between
Speaker 1: somebody messing with you and somebody having a seriously extreme opinion? Right,
Speaker 1: And and of course, I mean, like you're saying, Lauren,
Speaker 1: it's hard for us to tell depending upon the cues
Speaker 1: that we have or lack of cues. And so this
Speaker 1: is a huge challenge, not just for AI but for
Speaker 1: human intelligence, you know, just and anyone who's had that
Speaker 1: kind of miscommunication online understands that it can. It can
Speaker 1: legitimately happen without any intention on either party for it
Speaker 1: to be misinterpreted. Oh absolutely, And then that kind of
Speaker 1: interpretive help I think would be would be terrific because
Speaker 1: you know, especially speaking with people, uh for for whom
Speaker 1: your language is not their first language, can be very
Speaker 1: difficult to to have a clear conversation. And there have
Speaker 1: been times when you know, I've I've been like trying
Speaker 1: to answer someone's earnest question and they keep trying to
Speaker 1: rephrase it and I'm not getting it, and like Google
Speaker 1: Translate isn't getting it, and and it's just like all
Speaker 1: I want to do is tell you how your fingernails grow,
Speaker 1: but I don't. I don't know that that's the question
Speaker 1: you're asking, right, So that that kind of thing. The
Speaker 1: hope I have is that once the fire has has
Speaker 1: died down at Microsoft trying to handle this this PR issue,
Speaker 1: they can go over that kind of data and can
Speaker 1: actually glean stuff from it, because it would be really,
Speaker 1: as you point out, genuinely useful and Uh. I would
Speaker 1: love to see something positive come out of this experience
Speaker 1: and not just have it be another oh, those wacky
Speaker 1: pr people or however you want to frame it, like
Speaker 1: the Skittles stuff, right, like something beyond just confirming what
Speaker 1: we already know, which is that people like to stir
Speaker 1: crap up when they have the opportunity. UM, I would
Speaker 1: love to see that. I hope that that's what happens,
Speaker 1: and I hope that people who were behind Microsoft A,
Speaker 1: the people who were developing it, aren't being held to
Speaker 1: the fire for what happened, even though I do think
Speaker 1: that at least some of it could have been predicted
Speaker 1: from day one. But that's that's another that's another point entirely.
Speaker 1: At any rate, that is the tragic comedy of Microsoft
Speaker 1: TA as it stands. Right now. We're recording this March,
Speaker 1: so perhaps by the time this episode goes live there
Speaker 1: will be more developments. But I just thought it was
Speaker 1: an interesting story and one that I think has implications
Speaker 1: far beyond just Internet based AI. This also, I think
Speaker 1: gives lessons to roboticists who are trying to figure out
Speaker 1: how to design human robotic interaction and that are going
Speaker 1: to be acceptable and work and not create more problems,
Speaker 1: because that's a that's a fascinating area of study that
Speaker 1: relies more upon human psychology than actual robotic ability. And
Speaker 1: and when you see things like this happen, as as
Speaker 1: someone who designs robots, I imagine they have to sit
Speaker 1: there and think, oh, we might need to rethink some
Speaker 1: of our approaches because people people are willing to mess
Speaker 1: with stuff in certain contexts. Perhaps we need to take
Speaker 1: that into account in our design so that we can
Speaker 1: head that off before it becomes a problem. Um. So
Speaker 1: fascinating really, But at any rate, if you guys have
Speaker 1: any suggestions for future episodes of forward Thinking, please send
Speaker 1: them our way. Send us an email, our addresses f
Speaker 1: W Thinking at how Stuff Works dot com. You can
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