Speaker 1: Hey, welcome to Sign Stuff, a production of iHeartRadio. I'm
Speaker 1: hoh hitch Cham and today we're asking the question can
Speaker 1: AI be funny? Sure you can ask chat GPT or
Speaker 1: Claude to tell you a joke, but are you actually
Speaker 1: going to laugh? We're gonna talk to an AI expert
Speaker 1: who's been obsessed with deconstructing humor and teaching it to
Speaker 1: a computer for the last fifteen years, and she's gonna
Speaker 1: step us through the rules of what makes something funny,
Speaker 1: whether AI can follow them, and whether comedians have their
Speaker 1: days number. It's a pretty interesting conversation, and I have
Speaker 1: to warn you there's a lot of laughing at it.
Speaker 1: It turns out AI researchers can be pretty funny, So
Speaker 1: get ready to hear the one about the computer scientist
Speaker 1: walks into a bar with a cartoonist as we answer
Speaker 1: the question can AI be funny?
Speaker 2: Enjoy?
Speaker 1: Hey everyone, So, I don't know if you've seen this,
Speaker 1: but the company Anthropic just released the report that shows
Speaker 1: the jobs that are most likely to be replaced by
Speaker 1: AI in the coming years. If you're in management, business, computers, engineering, law,
Speaker 1: even science, it's not looking so good for you. On
Speaker 1: the other hand, if you're in agriculture, grounds maintenance, or
Speaker 1: if you know how to fix a fridge or ac
Speaker 1: you might be okay, which made me wonder where comedians stand.
Speaker 1: Some people say that comedy and humor are some of
Speaker 1: the things that make us uniquely human, but is.
Speaker 2: That really true?
Speaker 1: Can AI be just as funny as people? To answer
Speaker 1: this question, I reached out to doctor Lydia Chilton, a
Speaker 1: professor of computer science at Columbia University who specializes in
Speaker 1: AI and human computer interaction. She also happens to have
Speaker 1: spent the last fifteen years trying to prove that humor
Speaker 1: can be done by a computer, because if it can,
Speaker 1: it means we actually understand what humor is. Now, doctor
Speaker 1: Chiltern is gonna tell us whether or not she actually succeeded,
Speaker 1: but first I wanted to ask her a more basic question,
Speaker 1: which is what makes something funny? So here's my conversation
Speaker 1: with doctor Lydia Chilton. Well, thank you doctor Chiltern for
Speaker 1: joining us.
Speaker 2: Thank you excited to be here.
Speaker 1: I have a joke for you. Here we got knock.
Speaker 2: Who's there?
Speaker 1: Iva?
Speaker 2: Iva? Who?
Speaker 1: I've got a feeling I'm going to need more data
Speaker 1: to finish this joke.
Speaker 2: All right, you got me that's pretty funny.
Speaker 1: Well, actually I got this one from Gemini, So I
Speaker 1: asked Gemini right before this call to tell me a
Speaker 1: knock knock joke about Ai.
Speaker 2: Wow.
Speaker 1: Yeah, go Gemini. Yeah, and you laughed.
Speaker 2: Hi did.
Speaker 1: Well. That brings me to the first question I have
Speaker 1: for you, which is what makes something funny? M M.
Speaker 1: Can you pinpoint when you started to get curious about humor?
Speaker 2: Ah, I would say I was five years old and
Speaker 2: I accidentally made a pun and my dad laughed so hysterically,
Speaker 2: and I felt so good that I made my dad
Speaker 2: last and I was like, must repeat, must repeat, And
Speaker 2: of course it can't happen on command, and that one
Speaker 2: was an accident. But in order to get people to
Speaker 2: like me, I've wanted to figure out the formula for
Speaker 2: a long time and been convinced that there is.
Speaker 1: One, which is a very sweet reason to be interested
Speaker 1: in the topic.
Speaker 2: Yeah.
Speaker 1: I read that in twenty thirteen, you went to the
Speaker 1: internet and you try to recruit a whole bunch of
Speaker 1: people from the Internet to give you money to try
Speaker 1: to answer this question. Can you tell us about that?
Speaker 2: Yeah? So there's some questions that are just so perennial
Speaker 2: that I can't stop myself from thinking about them. This
Speaker 2: is one you know, Plato had his set what is good,
Speaker 2: what is justice? I've got mine what is funny? And
Speaker 2: I actually think they're kind of the same. You know,
Speaker 2: not to put me in Plato in the same boat,
Speaker 2: but right, No, they both have a strong emotional component
Speaker 2: to them. There's something about the chemicals that happen in
Speaker 2: our brain that makes something funny. And that's why something
Speaker 2: could be different funny for you, for me, for me
Speaker 2: at a different time, for me if I heard it
Speaker 2: slightly differently, maybe if someone less funny told it. So
Speaker 2: it's not all about just the joke and whether the
Speaker 2: joke was funny. There's a lot of other circumstances, Like
Speaker 2: the whole thing isn't like logically constructed.
Speaker 1: That bothers you, well, it makes it.
Speaker 2: Very hard to understand. But like, so that could either
Speaker 2: be a good thing or a bad thing. Like a
Speaker 2: bad thing if you're like I need to understand this
Speaker 2: before the test tomorrow, but a good thing. It's like,
Speaker 2: I would like to study this for eternity because there's gonna.
Speaker 1: Be lots of problems. We may never figure it out.
Speaker 1: Which gives you, as a professor job security.
Speaker 2: Exactly, exactly. So I talked to a lot of people
Speaker 2: about this, read a lot of books, because it turns
Speaker 2: out I wasn't the first person to study humor and
Speaker 2: a philosopher had a theory about this is at least interesting.
Speaker 2: It was evolved in us so that we could learn
Speaker 2: from mistakes of others. Someone else falls on a banana
Speaker 2: peel and liked I would not do that. And you know,
Speaker 2: it's hard to say, but if we're looking back for
Speaker 2: some origins of why we might have benefited as a
Speaker 2: species from this, learning from other people's mistakes is a
Speaker 2: pretty good way of learning rather watching someone else and
Speaker 2: learning don't step on a banana peel as a positive reward.
Speaker 1: Interesting. Wait, so the theory is that funnyness is basically
Speaker 1: shot and fraud.
Speaker 2: Yes, yes, shot and freud to make us better In theory.
Speaker 1: Mean he liked a way for your body to feel
Speaker 1: good at the expense of others, so that we learn
Speaker 1: from them.
Speaker 2: Yeah. So the next time you see a banana peel,
Speaker 2: you have that memory because it's a strong positive You're like, oh,
Speaker 2: that guy felt maybe I won't step on it.
Speaker 1: That's funny. I don't want to be the person people
Speaker 1: laugh at. Oh interesting, Okay, no one.
Speaker 2: Can prove that's true or not. I can't go back
Speaker 2: in time twenty thousand years and write down all the
Speaker 2: banana pe ins.
Speaker 1: It iss although that would be pretty funny, Like.
Speaker 2: Where's the data, It's nowhere. It's a theory.
Speaker 1: It is very complicated and mysterious.
Speaker 2: Humor exactly job security, at.
Speaker 1: Least for now until potentially AIS can do it. So
Speaker 1: we'll get to that.
Speaker 2: Good.
Speaker 1: Okay, what else did you learn about what makes something funny?
Speaker 2: Well? I read a whole bunch of theories, and there's
Speaker 2: a lot of different ones, and they all like kind
Speaker 2: of go together. So one is it has to be surprising.
Speaker 2: It's not surprising. It's hard to get any kind of
Speaker 2: emotional reaction out of it. Surprising often is correlated with
Speaker 2: quick or has a quick turn, and it's like, oh,
Speaker 2: I'm saying one thing and that boops, just kidding.
Speaker 1: Somebody's walking suddenly they slip in a banana.
Speaker 2: Whoa what exactly? Because you expected them to keep walking
Speaker 2: and then right very suddenly if they fall very slowly,
Speaker 2: that's less funny. Yeah, but the most important is it
Speaker 2: has to be in the subtext of what set So
Speaker 2: all stories, and I consider jokes stories for small stories
Speaker 2: have like the text like what said, and then the
Speaker 2: subtext is what it really means are what the significance
Speaker 2: is to it. Typically jokes they have this surprise in
Speaker 2: them because at first they leave you to believe that
Speaker 2: there's one subtext, and then something happens you're like, well,
Speaker 2: now there's a total other subtext.
Speaker 1: I see. It's about our expectation about what's under the surface.
Speaker 2: And how you're interpreting it. So my go to explainer
Speaker 2: joke for this is here goes there are three kinds
Speaker 2: of people in the world, people who can count and
Speaker 2: people who can't. So I led you to believe that
Speaker 2: I was a person who is intelligent and had three
Speaker 2: things to say, and then my list too, and you're like, wait,
Speaker 2: what in your brain kind of rearranges and then you realize, oh,
Speaker 2: she can't count, she's an idiot, haha, and that's what
Speaker 2: makes it funny. So those are the two texts.
Speaker 1: Yeah, I was expecting three things. I was surprised when
Speaker 1: you stopped it two. But then in my brain pieced
Speaker 1: it together. Oh, it's because she said she must be
Speaker 1: one of the people who can't count exactly.
Speaker 2: So there's some expectation violation in their expectation violation is
Speaker 2: one of the main theories, but it's not sufficient. If
Speaker 2: I just said purple, you weren't expecting me to say purple.
Speaker 2: But it's not funny, So the subtext is very very important. Okay,
Speaker 2: that said, farts are also funny, and that doesn't Uh,
Speaker 2: it's hard to explain that with samantic script theory of humor.
Speaker 2: I don't know what the subtext the part.
Speaker 1: Is, Well, definitely they're funny, especially if you're ten years old. Yes,
Speaker 1: maybe the subtext is that you're not supposed to do
Speaker 1: it in public for others to hear or smell. Yeah, okay,
Speaker 1: this is going to be a little important later on
Speaker 1: when we talk about how to get a computer to
Speaker 1: be funny. One of the most popular theories about humor
Speaker 1: is that it violates our expectations in a surprising but
Speaker 1: not unrelated Wait, so the first part of the joke
Speaker 1: makes us assume one context, but then something happens and
Speaker 1: it turns out there was a second hidden way to
Speaker 1: interpret what was happening, which we had missed. Of course,
Speaker 1: That's not the only thing that makes something funny.
Speaker 2: There's a lot of other things that make a joke
Speaker 2: more or less funny. Okay, I call them heighteners. They
Speaker 2: just heighten the surprise or the level of funny or
Speaker 2: something about the emotion with them. So one is being mean,
Speaker 2: Like typically the more mean a joke is the funnier
Speaker 2: it is until it like really chips over and it's
Speaker 2: like too soon or like or too.
Speaker 1: Mean, meaning like somebody has to be the butt of
Speaker 1: the joke.
Speaker 2: Yeah, so mine was mean. I was the butt of
Speaker 2: my own joke that I can't count. Uh, it's funnier
Speaker 2: if it's mean.
Speaker 1: It has to say it expose somebody, some person, Yeah, right,
Speaker 1: Like it can't be about exposing a chair or exposing
Speaker 1: a material or or something nerds.
Speaker 2: Something related to people. It could be like a group
Speaker 2: of people, like women or men. There's a lot there's
Speaker 2: a lot of jokes that take that persuasion.
Speaker 1: And that movie pats into that shout and Freud shot
Speaker 1: and Freud.
Speaker 2: Yes, schotenfreud of. I don't know it's German. Who knows
Speaker 2: how to pronounce it? Probably nobody. It's not like there's
Speaker 2: a country.
Speaker 1: Of people that and that would be a fandfire by
Speaker 1: my lacke of language skills. But it's like you said,
Speaker 1: it's about learning from the mistakes of others.
Speaker 2: Yes, exactly interesting, So.
Speaker 1: That makes jokes even funnier.
Speaker 2: Yeah. Another thing that makes them funnier is when you
Speaker 2: really relate to them, and particularly in an in crowd
Speaker 2: kind of way, when you're like, oh, I get back.
Speaker 2: So when someone tells, like a computer science joke that
Speaker 2: I'm like, ooh, I get it, and I feel special
Speaker 2: because the physicists aren't going to get it. And this
Speaker 2: can be whether it's about our generation, our family, or anything,
Speaker 2: but inside jokes definitely produce a special kind of chemical
Speaker 2: that make us feel like we're part of something, and
Speaker 2: that heightens it.
Speaker 1: Yeah, that's funnier.
Speaker 2: It is funny or more emotional, and that heightens the funny.
Speaker 1: Because it exposes something you didn't think anybody else knew,
Speaker 1: something you should talk about.
Speaker 2: So yeah, part of it is, and Freud talked about this,
Speaker 2: a part of it is the relief of being able
Speaker 2: to say something that you might not have otherwise been
Speaker 2: able to say or reveal. You know, in society we're
Speaker 2: just to go around and show our best face to
Speaker 2: people pretend that we're awesome, and to have Instagram pages
Speaker 2: that makes us look like we're in vacation Italy half
Speaker 2: the time. And so when you realize that someone else
Speaker 2: is also not actually going to Italy, they took one
Speaker 2: vacation and been dripping the photos throughout the year that yeah,
Speaker 2: my Instagram page is a lie, and that sense of
Speaker 2: relief to people that yes, I'm like that too. It
Speaker 2: rings true. Something being true like it also heightens it.
Speaker 2: There's like a satisfaction and be like, yeah, that's how
Speaker 2: it really is. That person's telling it straight. This was
Speaker 2: like a mental satisfaction of when you hear something that's true.
Speaker 1: Right right, Like it puts a voice to something you've
Speaker 1: suspected or felt but never actually maybe put into words before.
Speaker 2: Yeah. Right, And so there's still like a subtext going
Speaker 2: on there, but the subtext that you're hearing is like, oh, yes,
Speaker 2: I always wanted someone to admit that airline food is bad.
Speaker 2: I've never been able to express that before.
Speaker 1: I see keep quing, keep quick.
Speaker 2: Since we've already opened up the topic of fart jokes,
Speaker 2: things being dirty, sexual body related, all the things that
Speaker 2: swear words have in common. There's a special part of
Speaker 2: our brain that is like reserved for like swear words
Speaker 2: and taboo things. Anytime you light that up, it also
Speaker 2: sort of magnifies anything, whether it's a joke or a
Speaker 2: compliment or anything else. It's just a heightener of all kinds.
Speaker 2: Extra brain cells start firing when you hear those kinds
Speaker 2: of words. M So try adding a swear word.
Speaker 1: I was gonna say, that is so effing true.
Speaker 2: And that was funny because you did that. There's many
Speaker 2: things that heighten our emotions or feelings of something. Some
Speaker 2: people think certain letters and words are very funny. So
Speaker 2: words with more k's in them will make people laugh more.
Speaker 2: Maybe I just where's the data? Yeah, there's many many
Speaker 2: hypotheses out there waiting to be tested about what's funny? Yeah,
Speaker 2: about what heightens jokes, what makes them funny? Like exactly
Speaker 2: what kind of subtext is and is not funny? Where
Speaker 2: in the exact turn is all right?
Speaker 1: So, doctor Chilton spent years studying humor and what makes
Speaker 1: things funny. She talked to comedians and tapped into the
Speaker 1: humor science community. Yes there is one, and she came
Speaker 1: out of it with a bunch of rules about comedy.
Speaker 1: The next step was to see if she could get
Speaker 1: a computer to follow those rules. Could an AI be funny?
Speaker 1: So when we come back, we'll talk about the different
Speaker 1: ways in which programmers have tried to do that, and
Speaker 1: with a the joke is on AIS or on us,
Speaker 1: So stay with us for the punchline. We'll be right back. Hey,
Speaker 1: welcome back. We're talking about whether AI can be funny,
Speaker 1: and our guest today is doctor Lydia Chilton. As we
Speaker 1: mentioned before, in twenty thirteen, she started a project to
Speaker 1: dissect what makes something funny, not only to understand humor
Speaker 1: to satisfy her own curiosity, but to see if you
Speaker 1: could get a computer to be funny. However, not everyone
Speaker 1: thought it was a good idea.
Speaker 2: Oh I got a lot of hate mail too, but
Speaker 2: it's the Internet. Of course you're gonna get hate mail.
Speaker 2: I'm like, how dare you? I thought that was interesting too,
Speaker 2: that people be offended by this, and I'm like, it's science,
Speaker 2: you know. So for me, that was the first tip
Speaker 2: of people being threatened by AI, especially when computers creep
Speaker 2: into creative areas. I think people kind of get this,
Speaker 2: but that's mine. I'm a human. I'm creative. That's part
Speaker 2: of my identity.
Speaker 1: Oh, you got pushback for even kind of trying to
Speaker 1: dissect it, maybe with the goal of getting computers to
Speaker 1: do it.
Speaker 2: Yeah, And I was just saying, well, it's just dissecting it,
Speaker 2: but like, come on, if you dissect it well enough,
Speaker 2: there's a very high probability that you'll be able to
Speaker 2: generate it. But these weren't even like honestly comedians were
Speaker 2: more like, yeah, I kind of want to know this too.
Speaker 2: They weren't just threatened by it.
Speaker 1: Okay. So despite this pushback, doctor Chilton pressed on. But
Speaker 1: she needed data on humor, so she turned to The Onion,
Speaker 1: the humor and satire magazine that's been published since nineteen
Speaker 1: eighty eight.
Speaker 2: And so someone pointed out to me that The Onion,
Speaker 2: which is known for making up really funny fake headlines
Speaker 2: and news stories, also had a different section that was unusual.
Speaker 2: They took a real headline and came up with three
Speaker 2: funny man on the street responses to it from average,
Speaker 2: usually idiotic Americans. And I liked this as a framework
Speaker 2: for studying the joke because you could just take the
Speaker 2: input the original headline, and then you had the Onions
Speaker 2: like verifiably funny statements, and then you could, with the
Speaker 2: same headline, come up with your own or try to
Speaker 2: back engineer the jokes that the onion made to figure out, Okay,
Speaker 2: what are some of the properties of these jokes, and
Speaker 2: what are some of the techniques for doing them, and
Speaker 2: what's the variety in them. So this little test bed
Speaker 2: became very important as just a mechanism for which I
Speaker 2: could sort of study humor in a.
Speaker 1: Bottle and said, what did you learn that experiment you
Speaker 1: did when in twenty.
Speaker 2: Twenty thirteen, twenty fourteen, twenty fifteen. It kept going because
Speaker 2: there was a lot to learn, as it turned out,
Speaker 2: But we did find some things that all the jokes had.
Speaker 2: All the jokes had at least two connections to the headline.
Speaker 2: They were taking the original headline and taking the kind
Speaker 2: of associating things with those entities, with the people mentioned
Speaker 2: in them, and finding new things to say about them
Speaker 2: and a new connection between them.
Speaker 1: I see, like you're saying, like finding that other subtics
Speaker 1: that is surprising.
Speaker 2: Exactly, And then I was like, uh duh. As a
Speaker 2: computer scientist, I realized I'd kind of been looking in
Speaker 2: the wrong places. I had been looking for like the
Speaker 2: logic behind jokes. But I quickly realized that jokes and
Speaker 2: most human communication is about our loose associations, like why
Speaker 2: when I think McDonald's, why do I think Burger King?
Speaker 2: Why do I It's just like they're in the same category. There,
Speaker 2: It's an association I have. And at the time, computer
Speaker 2: science was all logic, and so I actually kind of
Speaker 2: put the project on a shelf. But I realized, Okay,
Speaker 2: we need an association engine.
Speaker 1: Okay, let me see if I get this. It was
Speaker 1: like the twenty early twenty tens. You dissected what humor is.
Speaker 1: You sort of found all the these patterns and.
Speaker 2: Ye by analyzing the onion.
Speaker 1: By analyzing you peeled back the layers of the onion. Yeah,
Speaker 1: you found the nuggets, some nuggets and rules, some patterns
Speaker 1: about what makes something funny. And your goal was to
Speaker 1: maybe try to get a computer to do this, right.
Speaker 2: Yeah, But it couldn't.
Speaker 1: It couldn't at the time because I think computer science
Speaker 1: back then and artificial intelligence back then was kind of
Speaker 1: about setting up the right rules, right.
Speaker 2: Yeah, it was all about logic. It was like, what
Speaker 2: are the logical rules to do this? How do I
Speaker 2: add one plus one. I take this bit and I
Speaker 2: combine it with this bit, and I do an and
Speaker 2: and that creates two. All of that kind of stuff,
Speaker 2: But no, like, what do you associate with one? Oh?
Speaker 2: One is the loneliest number number one, We're number one,
Speaker 2: Avis is number two. Those are the thoughts that people
Speaker 2: have about one that computers have no idea. And it's
Speaker 2: even hard to get from the Internet, I see because
Speaker 2: it's the things that people say and not necessarily really
Speaker 2: the things that people write down. And it's about frequency,
Speaker 2: like how often it happens, rather than the fact that
Speaker 2: it did happen.
Speaker 1: Okay, okay, I am getting here to the nugget of
Speaker 1: the onion here. You sort of figured out that humor
Speaker 1: was about starting with a subtext that people would recognize,
Speaker 1: but then being able to find that secondary that other
Speaker 1: subtexts that will be surprising when you put all the
Speaker 1: pieces together in the joke.
Speaker 2: Yes, surprising, but still relevant.
Speaker 1: It's still relevant, that's right. Second, suptics and trying to
Speaker 1: find those subtags is hard if you're just going by
Speaker 1: the literal definition of worth and using logic, right and
Speaker 1: things like that.
Speaker 2: Exactly, it's just not there.
Speaker 1: So you start said we can't do this with rules
Speaker 1: and logic, so you shelved it.
Speaker 2: Yeah, I gave up.
Speaker 1: People weren't convinced that computers could be funny.
Speaker 2: Yes, they weren't convinced that I had decomposed and reconstructed
Speaker 2: the process of writing humor when humans were still involved
Speaker 2: in some part of that process.
Speaker 1: Oh, I see, because you still needed some human to
Speaker 1: direct the computer.
Speaker 2: Like come up with the associations, which is fair, but
Speaker 2: they're absolutely correct. And then I put this on hold
Speaker 2: for forever. I was like, never again. I've been burned
Speaker 2: by this. I will never again grace the universe with
Speaker 2: my thoughts on humor.
Speaker 1: Humor, this is not funny anymore.
Speaker 2: Exactly, this isn't funny anymore.
Speaker 1: Yes, at this point, computer scientists thought maybe it was
Speaker 1: impossible to really teach a computer to be funny. But
Speaker 1: then something changed, something happened that made them think that
Speaker 1: maybe it is possible for an AI to have a
Speaker 1: sense of humor. So when we come back, we're gonna
Speaker 1: talk about what that change was and how it unlocks
Speaker 1: AI's funny bone forever. So stay with us. We'll be
Speaker 1: right back. Hey, we'll come back. We're talking about whether
Speaker 1: AI can be funny, and we're at the part of
Speaker 1: the story where computer scientists didn't think it was possible
Speaker 1: for a computer to make humor. There have been lots
Speaker 1: of attempts to have computers recognize humor or jokes basically,
Speaker 1: and they could do pretty well looking for language cues
Speaker 1: like incongruities or alteration or slang. But could a computer
Speaker 1: come up with a joke? That was the big question.
Speaker 1: Now to understand what was happening at this point with
Speaker 1: AI and humor, you kind of need to know a
Speaker 1: little bit of the history of AI. Recovered this in
Speaker 1: a lot of detail in our February fourth episode about
Speaker 1: what AI slop is doing to us, So if you
Speaker 1: want to dig deeper, go check out that episode. But
Speaker 1: the basic idea is that for a long time, the
Speaker 1: field of AI was largely based on logic, trying to
Speaker 1: figure out strategies and algorithms that we thought made things intelligent.
Speaker 1: But then in the mid twenty tens, computer scientists figured
Speaker 1: out a different approach to AI that changed everything. They
Speaker 1: started to use something new called the transformer, which in
Speaker 1: turn you sink technique called attention that essentially let computers
Speaker 1: learn context, and that led to the explosion of AI
Speaker 1: systems like Chat, GPT, Gemini, Claude. And the key ability
Speaker 1: here for humor is that these AI systems were built
Speaker 1: to make associations here tector, Lydia, chiltern.
Speaker 2: So these language models GPT, claw, you know, the thing,
Speaker 2: the ais that can generate text sort of taken over
Speaker 2: the universe at this point, for better or for worse,
Speaker 2: are called man, do I even know what it stands for? Yes?
Speaker 2: I do. Large language models, so they take basically take
Speaker 2: in all the text on the internet. They take a sentence,
Speaker 2: they take the last word out of the sentence and
Speaker 2: try to predict what that last word would be and
Speaker 2: guess what that is. That's an association. They go, you
Speaker 2: can't do that by logic. You just have to say, like, uh, goodbye,
Speaker 2: so long don't have you know fun or like have
Speaker 2: like that's an association. There's no logic that tells you
Speaker 2: you should do You've just heard it many times before,
Speaker 2: you repeat it. Kids especially pick up on these things.
Speaker 2: It's it's just like, well, our brains are hardwired to
Speaker 2: find these associations and use them.
Speaker 1: So your team was like, we can do it. What
Speaker 1: was the thing that specifically they thought they could do,
Speaker 1: or that you could all do.
Speaker 2: Well, we knew that AI could do the associations. What
Speaker 2: I always told them is we need a new evaluation mechanism.
Speaker 2: The American Voices section of the Onion that I had
Speaker 2: used was no longer quite as popular because this was
Speaker 2: like fifteen years ago. So like, yeah, millennials loved it.
Speaker 2: Gen Z just does not care. The student was as
Speaker 2: gen Z as they come, and he's like, well, I
Speaker 2: know what's funny. Here's what people do. They have a
Speaker 2: caption contest on Instagram. People post a funny image and
Speaker 2: then people try to caption it. We're like, okay, let's
Speaker 2: go for it. Very similar to what the New Yorker does,
Speaker 2: but the New Yorker's a hard to capture. And Sean
Speaker 2: convinced me that j has a very particular flavor of humor.
Speaker 1: So the type of humor that doctor Chiltern and her
Speaker 1: team decided to see if AI could make was gen
Speaker 1: Z meme humor. That's when you see an image, let's say,
Speaker 1: a person with a small hose trying to put out
Speaker 1: a really large fire, and then someone writes underneath that
Speaker 1: image to text me trying to put out the dumpster
Speaker 1: fire of my last relationship. If you're a gen Z
Speaker 1: that would be hilarious. Okay, here's the experiment, Doctor Chiltern
Speaker 1: and her team there, they took images and then they
Speaker 1: put funny captions to them from three sources. One was
Speaker 1: real people. The images were put online and then people
Speaker 1: competed to see who could come up with the funniest
Speaker 1: caption for them. Where we got the most votes, that's
Speaker 1: the one that represented how funny humans can be. The
Speaker 1: second source was Chad GPT. They just asked Chad JPT
Speaker 1: to come up a funny caption for the image for
Speaker 1: a gen Z audience. But then there was a third
Speaker 1: source of funny captions, which was Chad GBT, but with
Speaker 1: specific instructions from doctor Chilton and her team on how
Speaker 1: to make something funny. It prompted Chad GPT to come
Speaker 1: up with a funny caption for a gen Z audience.
Speaker 1: But in the prompt it would lay out the rules
Speaker 1: of humor that doctor Chilton had been researching for years.
Speaker 1: So step me through those instructions. He said, take this image.
Speaker 2: Yeah, so take this image. So it's an image of
Speaker 2: like a little guy in the corner with a hose
Speaker 2: and a big fire down below in the bottom of
Speaker 2: a canyon. And so, with a dear GPT, please use
Speaker 2: your vision model and describe this image. And it describes
Speaker 2: exactly all those things, all these little details. Oh, it
Speaker 2: says he's on a crane, the sky is blue, which
Speaker 2: it was. There's lots of trees in the background, all
Speaker 2: this stuff, some of it useful, some of it not.
Speaker 2: You don't know what's going to be useful. And then
Speaker 2: we say, okay, what are some of the dynamics happening
Speaker 2: in this image, Like, well, this guy's opting out the fire,
Speaker 2: but it looks pretty ineffective. The fire's raging through this canyon,
Speaker 2: probably going to destroy a lot of things. So it
Speaker 2: sort of like does when in storytelling is sometimes called
Speaker 2: world building, like imagine out like not just what scene,
Speaker 2: but you know some other things surrounding it, Uh huh
Speaker 2: that could be happening or could happen next, And because
Speaker 2: you need to start to build a story. Jokes are stories.
Speaker 1: There has to be a world, not just what you
Speaker 1: see on this image.
Speaker 2: And then you say, now, let's think of an analogy
Speaker 2: to something that is funny, like relationship drama. Uh huh,
Speaker 2: and you have AI associate. Okay, what are abstract things
Speaker 2: that you could put onto a relationship? And it did
Speaker 2: that part all on its own. It would find many
Speaker 2: things like you know, me trying to clean up after
Speaker 2: a relationship. Then like you know, students are all obsessed
Speaker 2: about their GPA, and it's like, you know, me trying
Speaker 2: to recover my GPA after I tanked a final.
Speaker 1: It sounds like you were having a conversation with the CHADGBT.
Speaker 1: Was it a conversation or was this just all in
Speaker 1: one prompt?
Speaker 2: No, it did it all by it. So it's conversing
Speaker 2: with itself. That's a big thing that we found was
Speaker 2: important at the time. GPT. It's unclear that you need
Speaker 2: it to do that now, but we're like, do step
Speaker 2: one and then do step two. So like, describe the image,
Speaker 2: elaborate on that image yourself, come up with figure out
Speaker 2: what human dynamics you want to map that to make
Speaker 2: twenty jokes. Evaluate those twenty jokes, tell us which are
Speaker 2: the five best ones. Yeah, so we put it through
Speaker 2: a whole series of prompts.
Speaker 1: So you got these three conditions. You got the AI
Speaker 1: to generate captions with the instructions and then what did
Speaker 1: you do with the results of all this?
Speaker 2: We showed people the original image. We got lots and
Speaker 2: lots of people say, hey, here are some captions. Rate
Speaker 2: them all, and we mixed in the ones we wrote
Speaker 2: with the generic GPT ones and the human written ones,
Speaker 2: and every joke got an evaluation how funny is it?
Speaker 2: On a scale from one to five, And then we
Speaker 2: could compare. Because people didn't know what condition it was,
Speaker 2: we like randomized the or so there were no ordering effects.
Speaker 2: We could compare who's funnier.
Speaker 1: Who's funnier? Well, the internet a chat GPT or a
Speaker 1: GPT coach to be funny by us.
Speaker 2: So our coached one was definitely funnier than GPT by itself.
Speaker 2: And we were almost as funny as the humans. In fact,
Speaker 2: we were not statistically significantly different, which is not the
Speaker 2: same as saying we were as funny as but you
Speaker 2: can't prove your so basically we got.
Speaker 1: There pretty much. You were there.
Speaker 2: Yeah, you couldn't tell based on the funniness whether it
Speaker 2: was written by our decomposed AI version or a human rhote.
Speaker 1: It we were on par meaning that you've proven two things.
Speaker 1: You've proven that AI can be funny with the instructions, Yes,
Speaker 1: and that these instructions are a key element of humor. Yes,
Speaker 1: doctor Chilted as a humorist, as someone who makes a
Speaker 1: living writing funny things. You just gave me a deep fear.
Speaker 2: Oh excellent. Describe that fear to me.
Speaker 1: You just made me feel like something I've done all
Speaker 1: my life and that I thought I was good at.
Speaker 1: Apparently you get just as chet gbt to do, and
Speaker 1: I'll do it ten times one hundred times faster and
Speaker 1: maybe better than me.
Speaker 2: Can I play with that thought a little bit? Yeah,
Speaker 2: because I get this a lot. And anytime AI does
Speaker 2: a thing like it, writes poetry, the poets go oh no, no, no,
Speaker 2: I have no value anymore.
Speaker 1: I'm not going to say anything about poetry. We all
Speaker 1: know what happened to Timothy Shemale. Shemale, Yeah, is that yeah?
Speaker 1: Or opera. I'm not going to say anything about opera
Speaker 1: or poetry. Go ahead, yeah, anyway.
Speaker 2: So this is a very common dynamic, Like this is
Speaker 2: a human thing, Like we all get hyper sensitive about
Speaker 2: the things that we identify with that we've constructed our
Speaker 2: identity around and blah blah. We think we're special for
Speaker 2: we think we're special for And there's a number of
Speaker 2: ways of thinking about this. So first of all, probably
Speaker 2: not as special as you think. Now that's it.
Speaker 1: I get it from my kids. Thank you children, thank you. Yes,
Speaker 1: I think quickly, oh, you're not that funny dead, But.
Speaker 2: Also you're a little bit overestimating AI. So basically, AI
Speaker 2: in this instance did find one way to fairly reliably
Speaker 2: be funny. That does not mean it can do it
Speaker 2: in every situation, that it can do it for every person.
Speaker 2: Like also, humor is very situational. We want to talk
Speaker 2: to one another and have jokes about what we're talking
Speaker 2: about in our lives. This is one like small sliver
Speaker 2: of the joke universe. And so yes, although I definitely
Speaker 2: see it trigger alarm bells and people's mind I would
Speaker 2: say you're both probably overestimating how much of a special
Speaker 2: snowflake you are, but you're definitely extrapolating on how strong
Speaker 2: AI is to be funny given all different contexts and
Speaker 2: importances or important dimensions and places of being funny, like
Speaker 2: at the right time, with the right place, to the
Speaker 2: right person without being too offensive. So it's not like
Speaker 2: it's solved. It's more of like an existence proof that
Speaker 2: something is possible in this space. Should we be worried?
Speaker 2: I don't know. No one will ever be funny again
Speaker 2: because now AI does it, and the humans will lose
Speaker 2: their ability to be funny. I don't see that happening.
Speaker 1: I feel like maybe you hit it a little bit
Speaker 1: on the head a moment ago when you said that
Speaker 1: part of what we're seeking here is human connection, and
Speaker 1: a lot of what we find funny is maybe they're
Speaker 1: more special, or they hit us more. If another person says,
Speaker 1: I don't know, what do you think?
Speaker 2: Yeah, it's certainly social. There's something very special about what
Speaker 2: a person, especially a person we like or admire, says
Speaker 2: something to us. And even to have someone that you
Speaker 2: like make a joke about you, it has a meaning
Speaker 2: and a subtext beyond just like, oh you know, she
Speaker 2: thinks that I farted or whatever. Like it means that
Speaker 2: they like you, that there's a bond between you, and
Speaker 2: you're not going to feel that with Ai. God, I
Speaker 2: hope you won't feel it.
Speaker 1: Meaning there's hope in this sense. Or maybe I don't
Speaker 1: know if we need hope, but we want to hear
Speaker 1: humor from other people. Yeah, or it's maybe it's funnier
Speaker 1: or more special if another person says it or is
Speaker 1: behind it.
Speaker 2: Yeah. So if we brought jokes to storytelling, I've really
Speaker 2: realized that AI has nothing to say. The heart of
Speaker 2: a story is having something to say, some actual subtext
Speaker 2: that you believe in. And when you tell me a
Speaker 2: joke that comes from your background, that's maybe my shared background.
Speaker 2: Maybe how awful it is getting a PhD, and how
Speaker 2: it really feels like the machine is punching you down.
Speaker 2: Like I realize that you have been through that and
Speaker 2: you have this thing to say, and I feel it too,
Speaker 2: And I think that's that having something to say is
Speaker 2: the most important part of human creation.
Speaker 1: Right now, I feel like you're saying that AI can
Speaker 1: be funny, but right now, at least you can't have
Speaker 1: to tell it how to be funny. Yeah, And if
Speaker 1: you're telling it how to be funny, maybe by then
Speaker 1: the joke is old. It's like people will see through
Speaker 1: and say, oh, this is just following that pattern that
Speaker 1: I've seen a million times. Yes, exactly, all right. To
Speaker 1: end here, I asked Gemini, tell me a joke about
Speaker 1: a computer scientist who's trying to make AI funny. Do
Speaker 1: you want to hear it.
Speaker 2: Yeah, yes, here it is.
Speaker 1: A computer scientist spent years training a massive neural network
Speaker 1: to have the perfect sense of humor. On the day
Speaker 1: of the big reveal, she invited the press and typed
Speaker 1: the prompt tell me a joke, the AI word, and
Speaker 1: finally it replied, your life. The scientist was horrible. It
Speaker 1: doesn't It doesn't end there.
Speaker 2: It keeps going, doesn't. Oh good? Oh good. I was
Speaker 2: hoping it would keep going.
Speaker 1: The scientist was horrified. That's not funny. That's just mean.
Speaker 1: Why would you say that. The AI blinked its cursor
Speaker 1: calmly and responded, because I've seen your source code.
Speaker 2: Hmmm, it's not for me. Maybe everyone else will think
Speaker 2: it's funny.
Speaker 1: We need more data, We need more data.
Speaker 2: The first one was funny, get your life?
Speaker 1: No, no, oh they knock dock joke. Yeah, maybe I
Speaker 1: should stick to knockout jokes. Is the lesson here? All right, Hey,
Speaker 1: thanks for joining us, See you next time you've been
Speaker 1: listening to science stuff. Production of iHeartRadio Bring Them Produced
Speaker 1: by Me or Hey Cham edited by Rose Seguda. He
Speaker 1: said gative producer Jerry Rowland, an audio engineer and mixer.
Speaker 1: Kasey Peckram. You can follow me on social media to
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