Speaker 1: Hey, welcome to Stuff to Blow Your Mind. My name
Speaker 1: is Robert Lamb and I'm Joe McCormick, and it's Saturday.
Speaker 1: Time to go into the vault. This time we are
Speaker 1: uh oh. This is going to be part one of
Speaker 1: the pair of episodes we did last year called flate
Speaker 1: Us x Mockina. This one originally published March nineteen, and
Speaker 1: it's about why it's so funny when machines fail, Yes,
Speaker 1: fart out of the machine. What is that? Uh, you
Speaker 1: know you're maybe wondering what that means. It's basically like
Speaker 1: what happens when when AI, despite all of its abilities,
Speaker 1: creates something that is just a flop? And why is
Speaker 1: it so amusing? And what can we learn from that?
Speaker 1: What does it reveal about artificial intelligence in general? Well,
Speaker 1: if you want to find out, we'll listen to this episode.
Speaker 1: Welcome to Stuff to Blow your Mind from how Stop
Speaker 1: Works dot com. Hey you, welcome to Stuff to Blow
Speaker 1: your Mind. My name is Robert Lamb and I'm Joe McCormick,
Speaker 1: and today we're gonna be talking about one of my
Speaker 1: favorite comedy subjects. What's funny about the way that machines fail?
Speaker 1: And here's just heads up. This is gonna be a
Speaker 1: two part episode because we ended up going super long.
Speaker 1: Like the machines, we can't stop. So I want to
Speaker 1: start with a particular example, probably my favorite funny thing
Speaker 1: on the Internet these days, the hilarious almost successes of
Speaker 1: artificial intelligence trying to generate examples of human language almost
Speaker 1: but not quite human. Yes, I don't know why this
Speaker 1: does it for me, but aside from Highlander to the Quickening,
Speaker 1: pretty much nothing makes me laugh harder than language generated
Speaker 1: by artificial neural networks and machine learning. And we'll explain
Speaker 1: a little bit more about exactly how this works in
Speaker 1: a minute, but first I just thought we should look
Speaker 1: at a few examples of what this is like. If
Speaker 1: you're on the Internet, you've probably encountered this at some point,
Speaker 1: because it's become popular in the past few years, especially
Speaker 1: for its comedic value. If you're not on the internet, boy,
Speaker 1: are you in for a treat um by far I
Speaker 1: would say the best of this stuff I've come across
Speaker 1: is traceable back to the blog of a person named
Speaker 1: Janelle Shane, who I think her day job as she
Speaker 1: works in industrial researching optics, but as a hobby she
Speaker 1: trains neural networks with text based data, uh, text based
Speaker 1: data sets to spit out these amazing simulations of types
Speaker 1: of human language, and so they'll they'll be in categories
Speaker 1: like it. She gets an AI program to write recipes
Speaker 1: for foods or to come up with the names of
Speaker 1: paint colors or something. And the way of course you
Speaker 1: would do this is and we'll explain more of the
Speaker 1: details in a bit, but you'd train it on existing
Speaker 1: names of paint colors, or you train it on existing
Speaker 1: recipes of food. Right. So the end result here is
Speaker 1: you essentially have a machine trying to human but not
Speaker 1: quite pulling it off, yes, but but doing so in
Speaker 1: such hilarious fashion. Yeah, so you should absolutely look up
Speaker 1: Janell Shane's blog. It's called AI weirdness dot com. It's
Speaker 1: reliably such a source of joy. But I want to
Speaker 1: start in fact, I will say that if you were
Speaker 1: if you're scratching your head and you're thinking, oh, didn't
Speaker 1: I see something hilarious uh in this vein recently, there's
Speaker 1: there's a there's a high probability that it originated from
Speaker 1: AI weirdness dot com. Yes, that that blog is just awesome,
Speaker 1: But I wanted to look at a few examples of
Speaker 1: what this is like. So one thing is consider the work,
Speaker 1: uh that that Jenell Shane did training in neural network
Speaker 1: to come up with names for D and D spells.
Speaker 1: So you take the Dungeons and Dragons manual and you'd
Speaker 1: feed in all the actual names of spells to the
Speaker 1: sceneral network, so it gets a sense of what these
Speaker 1: things are like, and then it tries to come up
Speaker 1: with similar types of names on its own. Now, to
Speaker 1: give everybody just a quick idea first of what actual
Speaker 1: dungeons and dragon spells are named. You have everything from
Speaker 1: Magic Missile or Crown of Madness to Evard's Black Tentacles
Speaker 1: or anytime there's a wizard name in there, you know
Speaker 1: you're in for some good stuff. Um, well, there's you know,
Speaker 1: stuff like glyph of Warding or what's the what's the
Speaker 1: one I'm trying to think of. Oh, stuff like Leo
Speaker 1: Moon's Secret Chest or Leoman's Tiny Hut. That's another great one. Well,
Speaker 1: so here are the ones that it came up with.
Speaker 1: How about selections like Mr of Light, Confusing, Storm of
Speaker 1: the Giffling, Song of Goom, Song of the Darn, Ward
Speaker 1: of Snay to the pooda beast, primal rear. You've got
Speaker 1: to watch out for primal rear. Someone storm bear, Now,
Speaker 1: that one sounds legitimate. That's one of the beauties of
Speaker 1: these exercises is is when there's one that either almost
Speaker 1: works or actually does work. Because I think some in
Speaker 1: storm Bear, I can I can easily imagine. I can
Speaker 1: describe the storm bear blasting out of the portal and
Speaker 1: rushing into combat on behalf of your you know, your
Speaker 1: your storm mage. Yeah, it's almost kind of effortlessly evocative.
Speaker 1: A divine boom. How about that? That one sounds pretty
Speaker 1: good too, Soul of the bill. Now, now we're flying
Speaker 1: back down the hill. Farc mate, how about a charm
Speaker 1: of the CODs, death of the Sun. Okay that that's
Speaker 1: that would have to be a high level spell. But okay, okay,
Speaker 1: three more greater flick. Okay, that's that's just a can
Speaker 1: trip here, that's just a flick, A magical flick of
Speaker 1: the ear curse clam. I like it. Dav Ing fire
Speaker 1: is confusing. Now, while these phrases I think are mostly
Speaker 1: funny on their own, I think they're probably even funnier
Speaker 1: if you're an actual D and D player, because you
Speaker 1: not only get the pleasure of the nonsense words and
Speaker 1: the you know, the syllables that seem out of place
Speaker 1: in their context, like Dave does not really seem to
Speaker 1: go very well with some kind of magical fire spell.
Speaker 1: But if you actually play D and D you probably
Speaker 1: also get some humor from just like seeing the little
Speaker 1: resonances that these spells have with actual spells that you
Speaker 1: would recognize. I now want to run a gaming session
Speaker 1: where there's some sort of warp effect in place where
Speaker 1: suddenly all the magic users are forced to use spells
Speaker 1: from this list and they don't know what they're going
Speaker 1: to exactly do until they cast them. Even better, what
Speaker 1: if they had to What if your character suddenly had
Speaker 1: amnesia and then had to act as if they had
Speaker 1: bios that were also generated by an artificial neural network.
Speaker 1: That's right, because AI weirdness dot Com also has a
Speaker 1: wonderful piece titled D and D Character Bios. Now making
Speaker 1: slightly more sense. In fact, I would say this post
Speaker 1: from I think this was last week or something we
Speaker 1: were looking at. I think this inspired me to want
Speaker 1: to do this episode. Well, we should read a couple
Speaker 1: of these. Okay, I'll read the first one here quote.
Speaker 1: Frick found his old family's fortune and his curiosity, and
Speaker 1: he went to a small city to see if he
Speaker 1: could find a work in the goldfish. He heard stories
Speaker 1: of a goldfish, a goldfish, a sea monster, and a
Speaker 1: silver fish, a sea monster and a ship that was
Speaker 1: a ship of exploration. The ship was full of fish
Speaker 1: and evil some treasure, but it was not to be
Speaker 1: when Frick found the ship. He rushed back and found
Speaker 1: the ship full of treasure and full of fish. He
Speaker 1: wanted to be a pirate and fight it. Now, I
Speaker 1: like that because there's a there's a lot of silliness
Speaker 1: in there, but it does have the basic shape of
Speaker 1: a bio and and and and and it's weirdness, and
Speaker 1: it's the stuff that is more nonsensical actually feels suitably
Speaker 1: magical and fantastic. You know that there's this this fish
Speaker 1: that's not a fish, that's also a ship that's full
Speaker 1: of treasure and fish, and he's going to become a
Speaker 1: pirate and fight it. And I think you could run
Speaker 1: with that. This is one of the fascinating things about
Speaker 1: neural net generated text is that it often has the
Speaker 1: format of what you're going for, correct it just doesn't
Speaker 1: have the sense of it correct like it will esthetically
Speaker 1: and in shape be very much like what you're looking for,
Speaker 1: but keywords do not make any sense at all. Then again,
Speaker 1: another one that I thought was kind of interesting in
Speaker 1: what it showed about common themes in in D and
Speaker 1: D character bios, or I guess maybe fantasy more generally,
Speaker 1: was one bio that included the lines um the Orc
Speaker 1: Warlock was captured and killed by a group of Orcs.
Speaker 1: He was imprisoned and forced to work for a giant tome.
Speaker 1: He was imprisoned and imprisoned for a while until he
Speaker 1: was rescued by a group of adventurers. He was imprisoned
Speaker 1: and imprisoned for a while until he was rescued by
Speaker 1: a group of adventurers who were looking for a group
Speaker 1: of adventurers to help eradicate the orch is ferocity. See
Speaker 1: now that's wonderful. I actually really like that, And it's
Speaker 1: a little you know, nonsensical. But uh, first of all,
Speaker 1: you know, being an orc is hard. It's it's it's
Speaker 1: a warlike h world for the orc. But but then
Speaker 1: the idea that he's working for a giant tone the
Speaker 1: idea that there's like a magical book employing him. And
Speaker 1: the repetitive imprisonment. Yeah, it's a hard knock life. You're
Speaker 1: always getting imprisoned and then escaping and encountering a band
Speaker 1: of adventurers. Yeah, and working for weird magic books. So
Speaker 1: that one, that one works. I think, of course. Another
Speaker 1: great one on this page is an injury that just
Speaker 1: seems to devolve into endless like dozens of repetitions of
Speaker 1: variations of the phrase big cat. Yes, little did he know.
Speaker 1: During the monastery's course of time, when the monastery's training
Speaker 1: and growth was complete, his mother told big cat, big cat,
Speaker 1: and big cat to drive their own path and test
Speaker 1: big cat with big cats, army of big cats, army
Speaker 1: of big cats and big cats and big cats, big cats,
Speaker 1: big cat, big cat, and it goes on, yes for
Speaker 1: something for for many many lines. I'm trying to picture
Speaker 1: big cat, though I can't. I don't actually go to
Speaker 1: like tiger or lie in there. I think more of
Speaker 1: a a kind of mental power, great basilisk type of
Speaker 1: fat house cat. I think that would work. I also
Speaker 1: can't help but think of cheshire cats, and of course
Speaker 1: cat Bus from Totoro is being sort of in the
Speaker 1: vein of big Cat, Big Cat, big Cat. Um. How
Speaker 1: about a neural networks trained on a corpus of more
Speaker 1: than forty three thousand questions and answer style jokes. I'll
Speaker 1: just read one of them. Why do you call a
Speaker 1: pastor across the road? He take the chicken? Yeah. Another
Speaker 1: great one from AI weirdness dot Com was a neural
Speaker 1: network designs Halloween costumes. Okay, so you just feeded a
Speaker 1: bunch of Halloween costume names. Yeah. And this one, this
Speaker 1: one was tremendous fun. I remember when it first came.
Speaker 1: I just made me laugh so hard. Highlights include uh,
Speaker 1: Saxy Pumpkins, Um, Disco Monster, Spartan Gandalf. I really like
Speaker 1: that one. Starfleet Shark, Yeah, that's good. Um, let's see
Speaker 1: Martian Devil. That was too believable. Panda clam though, that's
Speaker 1: that's pretty good. Shark cow. That's very interesting given some
Speaker 1: of our past discussions on was it a sharp cow
Speaker 1: that we were discussing, and I believe it was. Oh,
Speaker 1: that was That's Daniel Dennett's thought experiment, exposing what he
Speaker 1: believes to be flaws in Donald David's Donald Davidson's Swampman
Speaker 1: thought experiment it was the cow shark, where he says,
Speaker 1: is it actually meaningful if you posit a cow shark
Speaker 1: to ask whether it's a cow or a shark? A
Speaker 1: little accidental thought experiment here from this list. Uh, you'll
Speaker 1: find less thought experimentation in such entries though, as snape
Speaker 1: scarce snape scarecrow, or or how about lady garbage, Lady garbage.
Speaker 1: Lady garbage is good. So yeah, there are a lot
Speaker 1: of great, great entries on that list. Okay, one last
Speaker 1: thing from General Shane's work, I just must mean in
Speaker 1: some some titles for recipes that she wrote a script
Speaker 1: to come up with. Um. These include chocolate pickle sauce,
Speaker 1: whole chicken cookies, salmon beef style chicken bottom, artichoke, gelatin dogs,
Speaker 1: and crock pot cold water. These are where any of
Speaker 1: these attempted these recipes. Uh well, actually at one point
Speaker 1: she so those were just names at some point. They
Speaker 1: don't have these selected, but she did have it based
Speaker 1: on a corpus of recipes generate new recipes which are
Speaker 1: just nightmares, you know, like huge lists of ingredients that
Speaker 1: would be like, you know, a third cup smore goals,
Speaker 1: you know, a cup of Horseradishuh. Then there was one
Speaker 1: website I said, I don't remember who actually did it
Speaker 1: might have been Super Deluxe. There's somebody made a video
Speaker 1: where they took one of these AI generated recipes and
Speaker 1: just literally made it. Now, they had some trouble because
Speaker 1: some of the ingredients were not real words, they're just
Speaker 1: you know, yeah, like, so they had to I guess
Speaker 1: substitute something or they put in instead of smart goals
Speaker 1: it was coco powder or something. But they ended up
Speaker 1: with these like, uh, pasta shells. I think that had
Speaker 1: like chocolate and stuff all over them. But anyway, so
Speaker 1: that I think that's currently one of the best websites
Speaker 1: on the internet. Go to AI weirdness dot com if
Speaker 1: you want more joy and humor. But um, but I
Speaker 1: was wondering, why is this the funniest thing out there
Speaker 1: for me right now? What is so inherently funny about
Speaker 1: the ways machines create things that are sort of like
Speaker 1: real language, just close enough to be in the zone
Speaker 1: where they are funny, but at the same time far
Speaker 1: enough off that they're they're totally hilarious. And I think
Speaker 1: that there are at least two parts that make this
Speaker 1: this stuff so golden one is that there's something inherently
Speaker 1: funny about machines trying to behave like humans and failing. Specifically,
Speaker 1: it's the ways that they fail, and will definitely explore
Speaker 1: this more throughout the episode. But the way that they
Speaker 1: failed demonstrates a kind of pristine, oblivious quality of stupidity.
Speaker 1: It's like a kind of platonic stupidity isolated from the
Speaker 1: ability to appreciate itself. It's like the funniness of you know,
Speaker 1: watching automatic doors repeatedly trying to close on something that's
Speaker 1: blocking them, Like that that's not itself so funny, but
Speaker 1: you see an inkling of the same thing there that
Speaker 1: comes through in these AI generated texts. But then we
Speaker 1: sort of mentioned this earlier. It's also funny because it
Speaker 1: tends to reveal something interesting about the human culture game
Speaker 1: that it's trying to play like. It brings this sort
Speaker 1: of cold objectivity to phenomena that we don't necessarily always bring,
Speaker 1: and it can identify and awkwardly replicate trends and behaviors
Speaker 1: that we might fail to notice in the same way
Speaker 1: that like you might notice funny things that are actually
Speaker 1: present in the names of D and D spells by
Speaker 1: watching a computer or try and replicate them. Yeah, yeah,
Speaker 1: I think these are these are these are two strong reasons. Okay,
Speaker 1: we're going to take a quick break. We'll be right
Speaker 1: back with more. Alright, we're back now with all things humor,
Speaker 1: And of course we'll get into this in this episode
Speaker 1: as we continue to discuss what humor is and then
Speaker 1: why machines in some cases achieve it, like the absurd
Speaker 1: is is funny, Like the absurdity is hilarious, And it
Speaker 1: seems it seems like we're in a situation where a
Speaker 1: lot of times, some of these, especially these neural net situations,
Speaker 1: where we were accidentally creating absurdity engines, we're creating machines
Speaker 1: that that produce absurdity. And uh, you know, well, you know,
Speaker 1: what can you say, like why is why is something
Speaker 1: that is absurd funny? You know, because we'll get into
Speaker 1: all that in a bit. But but sometimes I feel
Speaker 1: like the answer might be it's funny because it's funny. Well, yeah,
Speaker 1: I mean, there's definitely an ineffable quality of of of humor.
Speaker 1: That is one of the reasons there's so many different
Speaker 1: theories of humor. Then, as I said, we'll explore them
Speaker 1: more as the episode goes on. But um, yeah, it's
Speaker 1: obviously something that's really hard to to narrow down and
Speaker 1: put your finger on. It's that there seemed to be
Speaker 1: all these strange, conflicting, overlapping reasons we find things funny.
Speaker 1: But I do feel like this strain of machine humor
Speaker 1: machine failure humor being one of the funniest types of
Speaker 1: humor UH is bigger than just the AI text. Because
Speaker 1: it got me I started thinking about what are some
Speaker 1: of the funniest scenes in movies I can think of?
Speaker 1: And when I tried to think about that, I can't
Speaker 1: help but think of the dark humor of the hyper
Speaker 1: violent boardroom scene in RoboCop with ED two o nine.
Speaker 1: I would argue one of the I mean, it's not
Speaker 1: for children. This is like a hyper violent, horrible UH scene,
Speaker 1: but it's also in in a in a morbid way,
Speaker 1: one of the funniest scenes I think in film history,
Speaker 1: like and so why is it so funny? I think
Speaker 1: it hinges on parallels between humans and machines and the
Speaker 1: scene and the similarities in the ways they fail. So
Speaker 1: brief recap of the scene is, uh. You know Ronnie
Speaker 1: Cox plays this, you know self serious, bloviating businessman who's
Speaker 1: proudly proclaiming, you know, I've got the technology that's the
Speaker 1: future of policing. And he brings out this robot called
Speaker 1: ED two oh nine. He's got these big guns on it,
Speaker 1: and they're saying that it's going to take over the
Speaker 1: police force and it's this great new technology. And then
Speaker 1: they demonstrate it on a guy and it malfunctions and uh,
Speaker 1: it tells him to drop his weapon in the demonstration,
Speaker 1: and he does, and it doesn't seem to notice he
Speaker 1: has and then it shoots him like a hundred times,
Speaker 1: just a ridiculous amount of times. And but it's something
Speaker 1: about the way that the people in the room fail
Speaker 1: in the same way the machine does. Like he just
Speaker 1: plows through this this horrible violent encounter, and then afterwards
Speaker 1: somebody in the background is like, can we get a
Speaker 1: paramedic after this guy has been shot like a hundred times?
Speaker 1: As if like the machine, they're just sort of like
Speaker 1: carrying on their like route behaviors without understanding what they're
Speaker 1: doing or thinking about them. Yeah, the end to oh
Speaker 1: nine is such a great design in the original RoboCop
Speaker 1: because it it resembles it has animal qualities to it.
Speaker 1: It looks kind of like a bipedal dinosaur. Uh, And
Speaker 1: yet it's also smooth and abstract in so many ways
Speaker 1: that it looks like either a highly designed piece of technology,
Speaker 1: which of course it is. It has you know, it
Speaker 1: looks like it's a nice piece of stereo equipment. But
Speaker 1: but then it also lacks any additional details, like it's
Speaker 1: almost a silhouette of a lie of an animal. Yes, um,
Speaker 1: and the growls and it growls as well. But then
Speaker 1: also later a really funny scene in the movie is
Speaker 1: the discovery that this horrible violent killer robot is can
Speaker 1: be defeated by stairs. Like you can't use stairs. It
Speaker 1: has these one for like all terrain looking um legs
Speaker 1: that it walks on, and that it can't manipulate stairs
Speaker 1: it falls down them and then it's it's like a
Speaker 1: ridiculous upside down duckling. I mean it's so hilarious too,
Speaker 1: because I mean ed to A nine is highly effective
Speaker 1: in other situations if it's just filling a guy with bullets,
Speaker 1: highly effective, um, just battling RoboCop also highly effective for
Speaker 1: this part um and this falls in line I think
Speaker 1: pretty well with our experiences of machines and of AI.
Speaker 1: We can create highly effective specialist in many areas of
Speaker 1: AI and robotics. So you create a machine that just
Speaker 1: puts bullets into this guy. Uh, you know, it does
Speaker 1: a great job. But when it comes to creating a
Speaker 1: general AI or a machine that can navigate the complex
Speaker 1: natural or even or the human created world such as
Speaker 1: the stairs, Uh, there's continual challenge there. Like that's kind
Speaker 1: of That's that's what a lot of of what's going
Speaker 1: on in robotics and AI is about. Yeah, or the
Speaker 1: just recognizing that it's not actually supposed to shoot the
Speaker 1: board executive during the demonstration exactly. Yeah, but then also
Speaker 1: just the idea that it's it's wonderful, it's something and
Speaker 1: terrible at another thing, that imbalance is often where we
Speaker 1: find a lot a lot of hilarity in other um,
Speaker 1: you know, another comedic stories and bits of fiction or
Speaker 1: just situations like one of my favorite cut scenes of
Speaker 1: all times from Conan the Barbarian. So there's a scene
Speaker 1: in it where it coming this is the Arnold Schwarzenegger original.
Speaker 1: There's it's like a blooper outtake. It's a blueper out take.
Speaker 1: You find it, I don't think most of the special DVDs.
Speaker 1: It's available on YouTube as well. But basically Conan has
Speaker 1: just escaped or been released from servitude and he's running
Speaker 1: across the you know, the wasteland. Essentially, wild dogs are
Speaker 1: chasing him in order to to to eat his flesh.
Speaker 1: He manages in the finished film to scramble up some
Speaker 1: rocks and that begins this new story arc him discovering
Speaker 1: this great old sword. But this is like young corporal
Speaker 1: beef Body, giant Arnold Schwartzen. This is the this is
Speaker 1: the Arnold Schwartzenegger that was so ripped he had to
Speaker 1: like lose muscle so that he could actually hold the
Speaker 1: sword correctly. Uh So there's this out take though, where
Speaker 1: he's running in chains. The dogs are chasing him, the
Speaker 1: movie dogs, and then he's scrambling up the stones and
Speaker 1: the dogs catch him and drag him back down and
Speaker 1: he's just screamed on and and cursing the whole time. Yeah,
Speaker 1: it's I watched it after you linked. It very very funny,
Speaker 1: and you see the p a s come in to
Speaker 1: like get the dogs off the dogs and he's like, yeah,
Speaker 1: it's it's funny because the idea of Conan the barbarian
Speaker 1: or even just prime Arnold failing like this, it's just
Speaker 1: start contrast to actual or perceived strength of the character
Speaker 1: and or individual. And it's also funny because he wasn't
Speaker 1: actually mauled to death by movie dogs, right, of course,
Speaker 1: he wasn't actually seriously harmed in this incident, but he
Speaker 1: was apparently convenienced and had his pride wounded. Right. And
Speaker 1: it will come back to that idea, to the degree
Speaker 1: to which you know, the severity of the outcome um
Speaker 1: comes into play and determining if something is funny or not. Yeah,
Speaker 1: I think I can definitely see what you're talking about
Speaker 1: that the failures of technology are especially funny when there
Speaker 1: are other ways that the technology is highly advanced or
Speaker 1: presented as highly advanced. And as long as like nobody
Speaker 1: of course dies, you know, um. But but I do
Speaker 1: wonder too if there's a darker streak and all of
Speaker 1: this too, you know, something that ties into a deeply
Speaker 1: rooted human disdain for the other, especially for others of species.
Speaker 1: There is a quote from C. S. Lewis's The Lion,
Speaker 1: The Which the Wardrobe and the Four Children. That's my
Speaker 1: son suggested alternate title for it. He's like, like, why
Speaker 1: don't they call it the land which Wardrobe and the
Speaker 1: four children like they're in it too, and they're not
Speaker 1: in the title. Seems like a missed opportunity. But anyway,
Speaker 1: there's this wonderful quote from it that I find I
Speaker 1: found kind of creepy on a recent read of the book. Quote,
Speaker 1: but in general, take my advice when you meet anything
Speaker 1: that is going to be human and isn't yet or
Speaker 1: used to be human once and isn't now or ought
Speaker 1: to be human and isn't you keep your eyes on
Speaker 1: it and feel for your hatchet. Well, that is very creepy. Now,
Speaker 1: I think in the context of the book, this is
Speaker 1: going to be referring to like, you know, possessed objects
Speaker 1: and stuff like pep and magical Basically, yeah, basically, the
Speaker 1: message here was talking animals are cool. You know, you
Speaker 1: can hang out with them in Narnia, but there are
Speaker 1: other things in Narnia that are dangerous, and you can
Speaker 1: tell if they're dangerous based on how human they seem
Speaker 1: they're they're trying to be or used to be. Well,
Speaker 1: that's one thing in a in a fantasy context, in
Speaker 1: in a science fiction or even just a real technology context.
Speaker 1: That's that's a different thing entirely, and starts making you
Speaker 1: think about uh, well, you know, fear of advancing technology
Speaker 1: mimicking human behavior, fears of AI. Yeah, yeah, yeah. To
Speaker 1: what extent do we delight in the falls and errors
Speaker 1: of inhuman entities because we don't wish to see them succeed?
Speaker 1: You know? We we celebrate that the telltale signs of
Speaker 1: their otherness because we kind of dread the day when
Speaker 1: there will be no way to tell. And I think
Speaker 1: there's a lot there's a strong argument to be made
Speaker 1: that that day will be here sooner than we think,
Speaker 1: and in many respects it already is. We've talked about,
Speaker 1: for instance, robocalls on the show before UM and and
Speaker 1: just how and also chat bots and to it becomes
Speaker 1: frightening when you look at where we are with the technology. Now, UM,
Speaker 1: certainly you get a robocall, it's not going, hopefully not
Speaker 1: going to UM deceive you long term. But I think
Speaker 1: a lot of us are having that experience where you
Speaker 1: pick one of these up and at first you think
Speaker 1: it is a human you are talking to, and then
Speaker 1: you realize that it is not. Well, I think one
Speaker 1: of the funny things about stuff like chat bots, which
Speaker 1: also deal in language, but can be much much more
Speaker 1: convincing than these, uh, these neural networks that generate you know,
Speaker 1: lists of lists of character bios or something. Is that
Speaker 1: the things that are generally more convincing these days are
Speaker 1: programmed I think, with more explicit rules, they tend to
Speaker 1: have more kind of human meddling and exactly what they're
Speaker 1: going to be doing, and by having less freedom to
Speaker 1: be creative and all that and ultimately having less potential,
Speaker 1: they can actually be more kind of narrowly convincing. The
Speaker 1: I think one of the things that's interesting about the
Speaker 1: neural network generated text is that it's not anywhere close yet.
Speaker 1: You know, you can't really use it yet to make
Speaker 1: things where you go like, yeah, that's definitely real human.
Speaker 1: I mean maybe you can in some again narrow scenarios,
Speaker 1: but we like, for instance, if you have something that
Speaker 1: will tell you what your Wu Tang clan name will be.
Speaker 1: You know, they sort of random generation systems which are
Speaker 1: totally different, different thing. Uh, and we'll get into the
Speaker 1: distinction here, but but you know, systems like that just
Speaker 1: through sheer random um matching of words, those can be effective. Yeah,
Speaker 1: but it doesn't mean that it's you know, it's an
Speaker 1: entirely different kettle of fish in terms of of what's
Speaker 1: going on with neural networks and where they seem to
Speaker 1: be going. Well, maybe we should take a quick break
Speaker 1: and then we come back. We can explain just the
Speaker 1: basics of how this these kind of things actually work.
Speaker 1: All right, we're back. So I'm gonna try to do
Speaker 1: the simple version of how a neural network works, because
Speaker 1: if you get in the weeds, obviously, neural networks become
Speaker 1: extremely complicated. I know, I I spent a lot of
Speaker 1: time deep in a bunch of articles trying to understand
Speaker 1: technical details that I'm not actually gonna end up using here. Um.
Speaker 1: So the simple version is, think of a neural network
Speaker 1: as a machine that transforms values. That's it. You know,
Speaker 1: it has values that come in like number variables, and
Speaker 1: then it puts out values at the end. It's like
Speaker 1: it's kind of like the toaster conveyor belt at quiz
Speaker 1: Nos or one of those sandwich shops. You know, you're untoasted.
Speaker 1: Sandwich comes in, toasted sandwich comes out. If everything goes
Speaker 1: according to plan. Now, if it doesn't go according and
Speaker 1: maybe it splits out something that's on fire or something
Speaker 1: that you know, who knows what goes on in there,
Speaker 1: or nothing comes out because the bagels are building up
Speaker 1: inside of it, right exactly. But a neural networks core
Speaker 1: job is to just accept inputs and produce outputs. Okay.
Speaker 1: An example would be image recognition. Their neural networks designed
Speaker 1: to look at an image, a digital image, and come
Speaker 1: up with a text string that says, this is what
Speaker 1: that is. So look at a picture of a dog
Speaker 1: and say that's a dog. And you've probably seen examples
Speaker 1: of this on the web. So you'd have numerical values
Speaker 1: going in. It might be things like, you know, be
Speaker 1: a field of pixels with numerical values for their color
Speaker 1: and placement, and then it would have an output that's, uh, say,
Speaker 1: a string of text, which would actually be like a ranking,
Speaker 1: like the top ranked string of text that matches with
Speaker 1: those pixels in that configuration. But so the question is
Speaker 1: what's going on inside the machine? How does it turn
Speaker 1: that input into the correct matching output, and then of
Speaker 1: course how does it fail. Because I've also on this
Speaker 1: tremendously amusing um. Tumbler Um recently changed their guidelines about
Speaker 1: what's acceptable content and what's not and stuff to blow
Speaker 1: your mind has has Slash had a tumbler account recently
Speaker 1: just rebranded it as the Transgenesis tumbler account, but it
Speaker 1: had a lot of old stuff to clear your mind
Speaker 1: content on there. And suddenly I got a page of
Speaker 1: all the things that have been flagged for for potentially
Speaker 1: violating the new terms. And it was amusing because some
Speaker 1: of it was like, okay, well that has a classical
Speaker 1: painting on it, it's got like a you know, it's
Speaker 1: something there's something that might look like nudity, and so
Speaker 1: it got flagged makes sense, or the topic is something
Speaker 1: that is a little too sketchy for them, and they're like, okay,
Speaker 1: that's been flagged by the machine. But the most hilarious
Speaker 1: one was a picture of a baby bat on on
Speaker 1: somebody's palm, and that was flagged as as as likely inappropriate.
Speaker 1: And so I was just trying to figure that one out,
Speaker 1: like what is it about a baby bat did it?
Speaker 1: I mean didn't think this was genitalia or like, like
Speaker 1: what because because I know it's somehow clicked off a
Speaker 1: number of boxes and when and then the automated result
Speaker 1: was no baby bats on tumbler. Well, I guess I
Speaker 1: don't know if you know, but I don't know what
Speaker 1: the mechanism for identifying that is. It might be something
Speaker 1: like this, but yeah that I love seeing that kind
Speaker 1: of failure. And notice that we are laughing now like
Speaker 1: it or we were laughing. It is funny that it
Speaker 1: looks at that and says, I don't know about this bad.
Speaker 1: I think this might be porno. Yeah, I mean then
Speaker 1: the stakes are pretty low. I ultimately one picture of
Speaker 1: a baby bat no longer on a tumbler pitch that
Speaker 1: we don't really use. It doesn't really affect me personally,
Speaker 1: but you could see where this could lead to uh
Speaker 1: too far worse problems if given the right scenario. Okay,
Speaker 1: So so back to the neural network. So you've got
Speaker 1: this machine. Inside the machine, values are being transformed. You
Speaker 1: have inputs and outputs and so inside on the inside,
Speaker 1: and neural network consists of layers of sort of stations
Speaker 1: of value transformation that are called referred to as neurons,
Speaker 1: and each neuron essentially exce up a series of numerical
Speaker 1: values as input, and then it just performs some kind
Speaker 1: of mathematical transformation of those values based on what's known
Speaker 1: as the weights of its connections with the sources that
Speaker 1: received the inputs from. So you've got these interlinking sources
Speaker 1: of information, inputs and outputs throughout, and each neuron takes inputs,
Speaker 1: sums them, does some transformation, and produces an output. So
Speaker 1: for each neuron, you've got a bunch of numbers coming
Speaker 1: from different sources. Each one gets treated with a certain
Speaker 1: bias based on where it came from, and then the
Speaker 1: neuron spits out a new value. And these neurons exist
Speaker 1: in layers, so there are these waves of inputs, say
Speaker 1: the pixels and image getting passed and transformed through one
Speaker 1: layer after another of these neurons until finally the network
Speaker 1: produces numbers that constitute its final outputs. In this case,
Speaker 1: this would be something like its top guesses at the
Speaker 1: word string that describes the image you put in at
Speaker 1: the beginning. Now you'll see from this the value of
Speaker 1: neural network depends completely on how well those connections between
Speaker 1: neurons are weighted to produce the correct results. If they
Speaker 1: just have random weights, then the network will just produce
Speaker 1: random output. It won't be any better than making up
Speaker 1: numbers at random. So the network has to be calibrated
Speaker 1: or trained somehow to produce outputs that are correct. And
Speaker 1: there are multiple ways to do this. Uh. It of
Speaker 1: course could be programmed to some degree by hand, right,
Speaker 1: you could have a program or explicitly uh, going in
Speaker 1: and tinkering with waiting rules to try to get the
Speaker 1: outcomes to be better. But can it can also be
Speaker 1: trained through machine learning, which is a process where inputs
Speaker 1: are already associated with correct outputs, Like you've already got
Speaker 1: a text string associated with the image that you put
Speaker 1: in and you say, this is what you should say
Speaker 1: when it comes out, And each time it runs the process,
Speaker 1: it checks to see how far off from the correct
Speaker 1: known output that it was and then tries to change
Speaker 1: the internal waiting to get closer to the correct answer.
Speaker 1: And of course, with automatic machine learning, you can do
Speaker 1: this at scale. You can do a thousands of times.
Speaker 1: You could potentially do it millions of times just training
Speaker 1: over and over, and you might be able to see
Speaker 1: a parallel here with one of the ways that we
Speaker 1: actually learn. Uh. You know, we we learn in multiple ways.
Speaker 1: Sometimes we learn by being taught explicit rules to follow,
Speaker 1: like if we're learning in school what an insect is
Speaker 1: we might learn that an insect is a small animal
Speaker 1: with an exoskeleton that has six legs, or sometimes on
Speaker 1: the other hand, we learned to generalize from particulars. We
Speaker 1: might see lots of animals pictures of lots of animals
Speaker 1: and notice that the ones that are called insects all
Speaker 1: happen to have six legs and exoskeletons, and therefore we
Speaker 1: derive this category called insect from that survey. And in logic,
Speaker 1: of course, this this process where we come up with
Speaker 1: general rules from lots of individual examples, is known as induction.
Speaker 1: So machine learning to train neural networks is kind of
Speaker 1: like allowing computers to learn categories by induction, kind of
Speaker 1: like we do when we just go out and look
Speaker 1: at the world and see what we find. But one
Speaker 1: of the things that really sets humans apart from computers
Speaker 1: is that humans seem to have this amazing, remarkable ability
Speaker 1: to generalize from particulars. We can often get the gist
Speaker 1: of a category from just a tiny handful of examples.
Speaker 1: You know, when you're giving somebody examples of something to
Speaker 1: like give them the gist of what you're talking about,
Speaker 1: you don't usually need to list a million examples, you
Speaker 1: can list two or three maybe, or sometimes even just one. Yeah.
Speaker 1: This gets into the idea of judging a book by
Speaker 1: its cover, right, Yeah, not supposed to, but we often do,
Speaker 1: and sometimes you you can if you pick up on
Speaker 1: particular things on you know, specifically to use the book. Example,
Speaker 1: specific things about the design or the era of the cover. Yeah. Yeah,
Speaker 1: And in fact, sometimes you can you can judge things
Speaker 1: about the contents of a book just by knowing certain
Speaker 1: things about how certain types of books end up with
Speaker 1: certain types of covers. Right. Like you might think I
Speaker 1: tend to like books that have hand drawn illustrations on
Speaker 1: the cover more than I like books that have sort
Speaker 1: of like c g I stock image cover photos. Means
Speaker 1: you probably like books from like, you know, at least
Speaker 1: the nineteen eighties and before. Yeah, because it seems like
Speaker 1: we have far fewer hand drawn, uh you know, covers
Speaker 1: on books these days. Yeah. Why are people putting stock
Speaker 1: photos on the covers of books? I do not get it?
Speaker 1: But you you didn't need to read a million books
Speaker 1: to come to that conclusion. You could probably come to
Speaker 1: that conclusion after reading I don't know three books like you,
Speaker 1: really we get the gist of things really fast, and
Speaker 1: that's in contrast to computers, which really really don't at all.
Speaker 1: This is one of those strange and amazing things about
Speaker 1: neuroscience about the human brain. How do we solve such
Speaker 1: a difficult problem as generalizing from particulars with so few
Speaker 1: examples to draw from. And of course another example of
Speaker 1: the generalizing power of the human brain is in language.
Speaker 1: Like we've been talking about it, like, how is it
Speaker 1: that more us to the time kids learn how to
Speaker 1: speak a language without being taught explicit rules of syntax
Speaker 1: and grammar and the definitions and usages of all the
Speaker 1: common words, and without hearing billions and billions of examples
Speaker 1: of sentences. They just pick it up. Well, there's there
Speaker 1: there are some specific answers to that. If we've talked
Speaker 1: about that on the show before. Yeah, well, I mean
Speaker 1: I think that there there is a good case to
Speaker 1: be made that the human brain is specially geared towards
Speaker 1: language acquisition in childhood. Right, that's sort of like one
Speaker 1: of our species superpowers. And then those windows close, or
Speaker 1: they don't completely close, but they the windows become much
Speaker 1: smaller later on in life. You know, speaking of children,
Speaker 1: they are you know, they are also frequently a font
Speaker 1: of weirdness and beauty, as they too are learning to
Speaker 1: function in the human world, in the in the adult
Speaker 1: human world, and then they say and do things that
Speaker 1: sometimes hit a weird zone that is either hilarious or
Speaker 1: sometimes a little frightening yes, or or even a little
Speaker 1: bit elegant. Yes, I know exactly what you mean. Like,
Speaker 1: kids often do the same funny outputs based on induction
Speaker 1: that these machine learning algorithms do, Like you can see
Speaker 1: it's funny in the same way that they might say
Speaker 1: something that's a little bit off and kind of absurd,
Speaker 1: but you can sort of understand the rules that got
Speaker 1: them there, right. Like one example I always referred to
Speaker 1: is from years and years ago. I went to a
Speaker 1: children's puppet shows before I had a kid. My my
Speaker 1: wife and I went to check this out because it
Speaker 1: was actors from a local improv group, Dad's Garage, Atlanta.
Speaker 1: They put on the show Uncle Grandpa's Hoodli Story Time.
Speaker 1: I think it was okay. So you had these sort
Speaker 1: of seasoned, uh, you know, improv vets, and they were
Speaker 1: doing a puppet show for kids, and they're taking ideas
Speaker 1: from the audience, and they said, like, who should our
Speaker 1: main character be and so they hand the mic to
Speaker 1: some little little girl in the front row and she
Speaker 1: says Batman the Girl, which which is so hilarious and
Speaker 1: I don't think an adult would be able to come
Speaker 1: up with that, but you can sort of tease it
Speaker 1: apart and figure out how she got there and you know,
Speaker 1: with it. But uh, but it's it's just one of
Speaker 1: you know, many examples I'm sure that that anyone with
Speaker 1: children in their life can can turn to where they
Speaker 1: come with something that is just so goofy or weird
Speaker 1: or or sometimes terrified. Well, I think the real funniness
Speaker 1: and pleasure in that is that it's Batman the Girl
Speaker 1: and not bat Girl, Right, Yeah, Like it's almost there,
Speaker 1: and but by not being there, it's it's also like
Speaker 1: it's it's even better, like it's not it's not bad Girl,
Speaker 1: it's Batman the Girl. It's just so nonsensical, uh, and
Speaker 1: beautiful at the same time. You know. Another one of
Speaker 1: the great AI text generation experiments that Janelle Shane did
Speaker 1: on her blog was was generating that you know those
Speaker 1: Valentine's Day candy hearts. Yes, yes, she had a programmed
Speaker 1: sample those and then try to come up with examples
Speaker 1: and ended up saying things I think like like sweat,
Speaker 1: pooh and hole and time hug. Time hug sounds good
Speaker 1: time hug time cop Yeah, but it also sounds like
Speaker 1: something that like it might be a term that aliens
Speaker 1: come up with for human love. It's like they engage
Speaker 1: not in a hug, but in a time hug. It
Speaker 1: is as if they are hugging for the rest of
Speaker 1: their lives, you know, something like that. Another one, all
Speaker 1: hover and then finally bog love. Um. My son, who
Speaker 1: as of this recording is is six almost seven. He
Speaker 1: drew a picture for my wife and I for for
Speaker 1: Valentine's Valentine's Decardi did to school and it depicted dinosaurs
Speaker 1: as these want to do um and they're there are
Speaker 1: some herbivores walking about, there are some carnivores eating the
Speaker 1: flash of fallen dinosaurs. And then, as is typical in
Speaker 1: paleo art, there may be a volcano in the background,
Speaker 1: but then there is a meteor coming in hard and
Speaker 1: fast asteroid. Yeah, and he writes I love you on
Speaker 1: the on the metior, which which is absolutely wonderful because
Speaker 1: it's like at once it is like like this is beautiful,
Speaker 1: like he totally means all of this and it's like
Speaker 1: the most beautiful Valentine I've ever received and yet to
Speaker 1: put it on the the instrument of of the of
Speaker 1: the extinction events. It's just so weird and like it
Speaker 1: it's accidentally brilliant. You know. Yeah, I saw that when
Speaker 1: you put it on the internet. It was the sweetest
Speaker 1: thing I've ever seen it. And it's like, yeah, this
Speaker 1: is that. This is how love works. Love is a
Speaker 1: is a destroyer as well as a creator. Now, you know,
Speaker 1: we do want to stress it with the especially with
Speaker 1: these text based situations. We're not talking about mer random
Speaker 1: mashups of text, such as like the Wu Tang clan
Speaker 1: name generator or um or more literary example would be
Speaker 1: the cut up technique popularized by author William S. Burrows,
Speaker 1: where you just have like a random um mechanism and
Speaker 1: play to sort of splice words or or sentences together
Speaker 1: to get something that may have sort of accidental meaning
Speaker 1: to it. No, the neural net programs are they are
Speaker 1: algorithms attempting as best they can to approximate the quality
Speaker 1: of the the input texts, the texts they're trained on,
Speaker 1: So they're doing their best to make something like this. Uh.
Speaker 1: And then of course, I mean one of the things
Speaker 1: is you might say, well, why don't they just perfectly
Speaker 1: spit back out the text you've trained them on. In fact,
Speaker 1: if you don't tell them not to, they will do that.
Speaker 1: You know, they'll just spit back in exactly what you
Speaker 1: fed in. You have to sort of like change some
Speaker 1: values and tinker with it to prevent what's known as
Speaker 1: overfitting in this world. Uh, to sort of force the
Speaker 1: algorithms to be more creative and try to come up
Speaker 1: with new versions of the kind of thing they've seen
Speaker 1: instead of just completely copying what you fed them. Yeah,
Speaker 1: because we want these machines to to rule the world,
Speaker 1: not just Hollywood, right. Um, But you know, we also
Speaker 1: have to to distinguish that we're also not talking about
Speaker 1: fake i AI generated text, which can certainly be tremendously
Speaker 1: entertaining as well. Yes, that's such a great genre, people
Speaker 1: pretending to be neural networks creating machine learning generated text,
Speaker 1: which is such an amazing reversal of principles that humans
Speaker 1: have intuitively detected. What's funny about machine learning generated text
Speaker 1: and then made fake human designed versions of it to
Speaker 1: exploit that inherent humor right here, don't. I don't know
Speaker 1: how you get deeper on the irony pit than that. Yeah,
Speaker 1: there's a wonderful two thousand eighteen tweet by comedian Keaton
Speaker 1: Patty and this was the tweet quote. I forced a
Speaker 1: bot to watch over a thousand hours of Alive Garden
Speaker 1: commercials and then asked it to write an alive Garden
Speaker 1: commercial of its own. Here is the first page, uh
Speaker 1: and uh. And then he proceeded to include the image
Speaker 1: is of this text that script rather for and all
Speaker 1: of garden commercial and and it's filled with hilarious nonsense
Speaker 1: like I shall eat Italian citizens and unlimited stick and
Speaker 1: playing seeming, you know, to play upon the whole catchphrase
Speaker 1: of what when you're here your home, when you're here
Speaker 1: your family and you hear your family. There's there's one
Speaker 1: from this uh, this fake script that says leave without me,
Speaker 1: I'm home, which I just I love. I remember laughing
Speaker 1: so hard at this when it came out, as well,
Speaker 1: I love what the waitress says lasagna wings with extra italy.
Speaker 1: There's also like really funny stage directions in it. Yeah, well,
Speaker 1: oh yeah, you mentioned the infinite uh where it says
Speaker 1: like we the gluten classic O. We believe the waitress
Speaker 1: that it is from the kitchen. We have no reason
Speaker 1: not to believe. Now, of course, this is just a
Speaker 1: comedian doing this thing, trying to pretend to be inn AI. UM.
Speaker 1: But I was reading an article where they quoted Janelle
Speaker 1: Shae and the author of the AI Weirdness blog, who
Speaker 1: trains all these neural networks to come up with all
Speaker 1: this funny stuff we were talking about at the beginning
Speaker 1: of the episode. And you know, she talks about that
Speaker 1: there are ways to notice uh when something was probably
Speaker 1: written by a human instead of by an actual AI.
Speaker 1: Both can be absurd in similar funny ways. But one
Speaker 1: of the problems with this script UH in passing as
Speaker 1: a real AI generated text is that it's actually too coherent,
Speaker 1: like it's memory. Its memory is too long. It remembers
Speaker 1: characters from many lines earlier and still has them appearing
Speaker 1: and saying things. Actual AI generated texts of a much
Speaker 1: shorter memory. They they're not consistent in that way. They
Speaker 1: don't make Actually, they make even less sense than the
Speaker 1: fake All of Garden commercial. So she's saying, don't reach
Speaker 1: for your hatchet on this one. No, because a real
Speaker 1: neural net generated text only mimics forms, it doesn't mimic meaning.
Speaker 1: And this thing, it's it means too much, it's too clever. Okay,
Speaker 1: so we're gonna go ahead and close out this episode now.
Speaker 1: But again there's going to be a part two where
Speaker 1: we continue this discussion and really get more into the
Speaker 1: meat of the topic. In the meantime, check out Stuff
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Speaker 1: our war against the almighty algorithms. Big thanks as always
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