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