Flatus Ex Machina, Part 1

Like the machines, we can't stop. So I want to start with a particular example, probably my favorite funny thing on the Internet these days, the hilarious almost successes of artificial intelligence trying to generate examples of human language almost but not quite human. Yes, I don't know why this does it for me, but aside from Highlander to The Quickening, pretty much nothing makes me laugh harder than language generated

by artificial neural networks and machine learning. And we'll explain a little bit more about exactly how this works in a minute, but first I just thought we should look at a few examples of what this is like. If you're on the Internet, you've probably encountered this at some point, because it's become popular in the past few years. Especially for its comedic value. If you're not on the internet, boy,

are you in for a treat um by far? I would say the best of this stuff I've come across is traceable back to the blog of a person named Janelle Shane, who I think her day job as she works in industrial researching optics, but as a hobby, she trains neural networks with text based data uh, text based data sets to spit out these amazing simulations of types of human language, and so they'll they'll be in categories

like it. She gets an AI program to write recipes for foods or to come up with the names of paint colors or something. And the way, of course you would do this is, and we'll explain more of the details in a bit, but you'd train it on existing names of paint colors, or you'd train it on existing recipes of food. Right. So the end result here is you essentially have a machine trying to human but not quite pulling it off, but but but doing so in

such hilarious fashion. Yeah, so you should absolutely look up Janelle Shane's blog. It's called AI Weirdness dot com. It's reliably such a source of joy. But I want to start. In fact, I will say that if you were if you're scratching your head and you're thinking, oh, didn't I see something hilarious in this vein recently, there's there's a there's a high probability that it originated from AI weirdness dot com. Yes, that that blog is just awesome, but I wanted to look at a few examples of what

this is like. So one thing is consider the work, uh that that Jennell Shane did training in neural network to come up with names for D and D spells. So you take the Dungeons and Dragons manual and you'd feed in all the actual names of spells to the neural network, so it gets a sense of what these things are like, and then it tries to come up with similar types of names on its own. Now, to give everybody just a quick idea first of what actual

Dungeons and Dragon spells are named. You have everything from Magic Missile or Crown of Madness to Evard's Black Tentacles or anytime there's a wizard name in there, you know you're in for some good stuff. Um, well, there's you know, stuff like Glyph of Warding or what's the what's the one I'm trying to think of? Oh, stuff like Leo Moon's Secret Chest or Leoman's Tiny Hut. That's another great one. Well, so here are the ones that it came up with.

How about selections like Mister of Light, Confusing, Storm of the Giffling, Song of Goom, song of the Darn, Ward of Snay to the pooda Beast, primal rear. You've got to watch out for primal rear. Someone's storm Bear. Now that one sounds legitimate, because that's one of the beauties of these exercises is is when there's one that either

almost works or actually does work. Because I think some in storm Bear, I can I can easily imagine I can describe the storm Bear blasting out of the portal and rushing into combat on behalf of your you know, your your storm mage. Yeah, it's almost kind of effortlessly evocative. A divine boom. How about that? That one sounds pretty good too. Soul of the Bill. Now now we're flying back down the hill. Farc mate about Charm of the CODs, Death of the Sun. Okay, that that's that would have

to be a high level spell. But okay, okay, three more greater flick. Okay, that's that's just a can trip there, that's just a flick, a magical flick of the ear

curse clam. I like it. Daving Fire's confusing. Now. While these phrases I think are mostly funny on their own, I think they're probably even funnier if you're an actual D and D player, because you not only get the pleasure of the nonsense words and the you know, the syllables that seem out of place in their context, like Dave does not really seem to go very well with some kind of magical fire spell, But if you actually play D and D you probably also get some humor

from just like seeing the little resonances that these spells have with actual spells that you would recognize. I now want to run a gaming session where there's some sort of warp effect in place where suddenly all the magic users are forced to use of spells from this list, and they don't know what they're going to exactly do until they cast them. Even better, what if they had to What if your character suddenly had amnesia and then had to act as if they had bios that were

also generated by an artificial neural network. That's right, because AI weirdness dot Com also has a wonderful piece titled D and D Character bios now making slightly more sense. In fact, I would say this post from I think this was last week or something we were looking at. I think this inspired me to want to do this episode. We should read a couple of these. I'll read the

first one here. Quote. Frick found his old family's fortune and his curiosity, and he went to a small city to see if he could find a work in the goldfish. He heard stories of a goldfish, a goldfish, a sea monster, and a silver fish, a sea monster, and a ship that was a ship of exploration. The ship was full of fish and evil some treasure, but it was not to be When Frick found the ship. He rushed back and found the ship full of treasure and full of fish.

He wanted to be a pirate and fight it. Now. I like that because there's a there's a lot of silliness in there, but it does have the basic shape of a bio and and and and and it's weirdness, and it's the stuff that is more nonsensical actually feels suitably magical and fantastic. You know that there's this this fish that's not a fish, that's also a ship that's full of treasure and fish, and he's going to become a pirate and fighted, and so I think you could

run with that. This is one of the fascinating things about neural net generated text is that it often has the format of what you're going for correct, It just doesn't have the sense of it correct like it will esthetically and in shape be very much like what you're looking for, but keywords do not make any sense at all.

Then again, another one that I thought was kind of interesting in what it showed about common themes in in D and D character bios, or I guess maybe fantasy more generally, was one bio that included the lines um the orc Warlock was captured and killed by a group of Orcs. He was imprisoned and forced to work for a giant tome. He was imprisoned and imprisoned for a while until he was rescued by a group of adventurers.

He was imprisoned and imprisoned for a while until he was rescued by a group of adventurers who were looking for a group of adventurers to help eradicate the orchest ferocity. See, now that's wonderful. I actually really like that when it's a little you know, nonsensical. But uh, first of all, you know, being an orc is hard. It's it's it's a warlike world for the orc. But but then the idea that he's working for a giant tone, the idea that there's like a magical book employing him, and the

repetitive imprisonment. Yeah, it's a hard knock life. You're always getting imprisoned and then escaping and encountering a band of adventurers. Yeah, and working for weird magic books. So that one, that one works, I think. Of course. Another great one on this page is an injury that just seems to devolve into endless like dozens of repetitions of variations of the

phrase big cat. Yes, little did he know. During the monastery's course of time, when the monastery's training and growth was complete, his mother told big cat, big cat and big cat to drive their own path and test big cat with big cats, Army of big cats, Army of big cats and big cats and big cats. Big get is big cat, big cat and goes on, yes something for for many many lines. I'm trying to picture big cat. Uh, though I can't. I don't actually go to like tiger

or lie in there. I think more of a kind of mental power great basilisk type of fat house cat. I think that would work. I also can't help but think of cheshire cats, and of course cat Bus from Totoro is being sort of in the vein of big Cat, Big Cat, big Cat. Um. How about a neural networks trained on a corpus of more than forty three thousand questions and answer style jokes. I'll just read one of them. Why do you call a pastor a cross the road?

He take the chicken? Yeah. Another great one from AI Weirdness dot Com was a neural network designs Halloween costumes. Okay, so you just feeded a bunch of Halloween costume names. Yeah, and this one, this one was tremendous fun. I remember when it first came. I just made me laugh so hard. Highlight It's include uh, Saxy Pumpkins, Um, Disco Monster, Spartan Gandalf. I really like that one. Starfleet Shark. Yeah, that's good. Um,

let's see Martian Devil. That was too believable. Panda clam though, that's that's pretty good shark cow. That's very interesting given some of our past discussions on was it a shark cow that we were discussing. I believe it was. Oh, that was That's Daniel Dennett's thought experiment exposing what he believes to be flaws in Donald David's Donald Davidson's Swampman thought experiment. It was the cow shark where he says, is it actually meaningful if you posit a cow shark

to ask whether it's a cow or a shark? A little accidental thought experiment here from this list. Uh, you'll find less thought experimentation in such entries though, as Snape scarce snape scarecrow, or or how about Lee garbage Lady garbage. Lady garbage is good. So yeah, there's there are a lot of great, great entries on that list. Okay, one last thing from General Shane's work, I just must mention some some titles for recipes that she wrote a script

to come up with. Um. These include chocolate pickle sauce, whole chicken cookies, salmon beef style chicken bottom, artichoke, gelatin dogs, and crock pot cold water. Where any of these attempted these recipes well, actually at one point she so those

were just names at some point. I don't have these selected, but she did have it based on a corpus of recipes generate new recipes which are just nightmares, you know, like huge lists of ingredients that would be like, you know, a third cup s'more goals, you know, a cup of Horseradishuh. Then there was one website I don't remember who actually did it, might have been Super Deluxe. Somebody made a video where they took one of these AI generated recipes

and just literally made it. Now, they had some trouble because some of the ingredients were not real words. They're just you know, like so they had to I guess substitute something or they put in instead of smart goals it was coco powder or something. But they ended up with these like, uh, pasta shells. I think that had like chocolate and stuff all over them. But anyway, so that I think that's currently one of the best websites on the internet. Go to AI weirdness dot com if

you want more joy and humor. But um, but I was wondering, why is this the funniest thing out there for me right now? What? What is so inherently funny about the ways machines create things that are sort of like real language, just close enough to be in the zone where they are funny, but at the same time far enough off that they're they're totally hilarious. And I think that they're at least two parts it's uh that

make this this stuff so golden. One is that there's something inherently funny about machines trying to behave like humans and failing. Specifically, it's the ways that they fail, and we'll we'll definitely explore this more throughout the episode. But the way that they failed demonstrates a kind of pristine, oblivious quality of stupidity. It's like a kind of platonic stupidity,

isolated from the ability to appreciate itself. It's like the funniness of, you know, watching automatic doors repeatedly trying to close on something that's blocking them like that that's not itself so funny, but you see an inkling of the same thing there that comes through in these AI generated texts.

But then we sort of mentioned this earlier. It's also funny because it tends to reveal something interesting about the human culture game that it's trying to play like it brings this sort of cold objectivity to phenomenon that we don't necessarily always bring, and it can identify a quardly replicate trends and behaviors that we might fail to notice in the same way that like you might notice funny things that are actually present in the names of D

and D spells by watching a computer try and replicate them. Yeah, yeah, I think these are these are these are two strong reasons. Okay,

we're going to take a quick break. We'll be right back with more than thank Alright, we're back now with all things humor, And of course we'll get into this in this episode as we continue to discuss what humor is and then why machines in some cases achieve it, Like the absurd is is funny, Like the absurdity is hilarious, And it seems it seems like we're in a situation where a lot of times, some of these, especially these

neural net situations, were we were accidentally creating absurdity engines. We're creating machines that that produce absurdity, and uh, you know, you know, what can you say, like why is I is something that is absurd funny? You know? Because we'll get into all that in a bit. But but sometimes I feel like the answer might be it's funny because it's funny. Well, yeah, I mean, there's definitely an ineffable quality of of of humor. That is one of the

reasons there's so many different theories of humor. Then, as I said, we'll explore them more as the episode goes on. But um, yeah, it's obviously something that's really hard to to narrow down and put your finger on. It's that there seemed to be all these strange, conflicting, overlapping reasons we find things funny. But I do feel like this strain of machine humor machine failure humor being one of the funniest types of humor UH is bigger than just

the AI text. Because it got me I started thinking about what are some of the funniest scenes in movies I can think of? And when I tried to think about that, I can't help but think of the dark humor of the hyper violent board room scene in Robo Cop. With ed to own mind, I would argue one of the I mean, it's not for children, this is like a hyper violent, horrible UH scene, but it's also in in a in a morbid way. One of the funniest scenes I think in film history, Like and so, why

is it so funny? I think it hinges on parallels between humans and machines and the scene and the similarities in the ways they fail. So brief recap of the scene is, uh, you know, Ronnie Cox plays this you know self, serious bloviating businessman who's proudly proclaiming, you know, I've got the technology that's the future of policing. And he brings out this robot called ED two oh nine that's got these big guns on it, and they're saying that it's going to take over the police force and

it's this great new technology. And then they demonstrate it on a guy and it malfunctions, and uh, it tells him to drop his weapon in the demonstration, and he does, and it doesn't seem to notice he has and then it shoots him like a hundred times, just a ridiculous amount of times. And but it's something about the way that the the people in the room fail in the

same way the machine does. Like he just plows through this this horrible, violent encounter, and then afterwards somebody in the background is like, can we get a paramedic after this guy has been shot like a hundred times, as if, like the machine, they're just sort of like carrying on their like route behaviors without understanding what they're doing or thinking about them. Yeah. The edd to oh nine is such a great design in the original RoboCop because it

it resembles it has animal qualities to it. It looks kind of like a bipedal dinosaur. Uh, and yet it's also smooth and abstract in so many ways that it looks like either a highly designed piece of technology, which of course it is. It has you know, it looks like it's a nice piece of stereo equipment, but but then it also lacks any additional details, like it's almost a silhouette of a lie of an animal. Yes, um,

and the growls and it growls as well. But then also later a really funny scene in the movie is the discovery that this horrible violent killer robot is can be defeated by stairs. Like it can't use stairs. It has these wonderful like all terrain looking um legs that it walks on, and that it can't manipulate stairs. It falls down them, and then it's it's like a you know,

a ridiculous upside down duckling. I mean it's so hilarious too, because I mean it dad to a nine is highly effective in other situations if it's just filling a guy with bullets, highly effective. Um, just battling RoboCop also highly effective for the most part um. And this falls in line I think pretty well with our experiences of machines and of AI, we can create highly effective specialist in

many areas of AI and robotics. So you create a machine that just puts bullets into this guy, uh, you know, it does a great job. But when it comes to creating a general AI or a machine that can navigate the complex natural or even or the human created world such as the stairs, Uh, there's continual challenge there. Like that's kind of that's that's what a lot of of what's going on in robotics and AI is about. Yeah, or the just recognizing that it's not actually supposed to

shoot the board executive during the demonstration exactly. Yeah. But then also just the idea that it's it's wonderful, it's something and terrible at another thing. That imbalance is often where we find a lot a lot of hilarity in other um, you know, another comedic stories and bits of fiction or just situations like one of my favorite cut scenes of all times from Conan the Barbarian. So there's a scene in it where this is the Arnold Schwarzenegger original.

There's it's like a blooper out take. That's a blooper Outare you find it? I don't think most of the special DVDs. It's available on YouTube as well, but basically Conan has just escaped or been released from servitude and he's running across the you know, the waste land essentially, while dogs are chasing him in order to to to eat his flesh. He manages in the finished film to scramble up some rocks and that begins this new story

arc him discovering this great old sword. But this is like young corporal beef body giant Arnold Schwartz, and this is the this is the Arnold Schwarzenegger that was so ripped he had to like lose muscle so that he could actually hold the sword correctly. Uh So there's this out take though, where he's running in chains, the dogs are chasing him in the movie Dogs, and then he's scrambling up the stones and the dogs catch him and drag him back down, and he's just screamed a and

and cursing the whole time. Yeah, it's I watched it after you linked it very very funny, and you see the p as come in to get the dogs off the dog, and he's like, yeah, it's it's funny because the idea of Conan the barbarian, or even just Prime Arnold failing like this, it's just start contrast to actual

or perceived strength of a character and or individual. And it's also funny because he wasn't actually mauled to death by movie dogs, right, of course, he wasn't actually seriously harmed in this incident, but he was apparently inconvenienced and had his pride wounded. Right, And it will come back to that idea to the degree to which you know, the severity the outcome um comes into play and determining

if something is funny or not. Yeah, I think I can definitely see what you're talking about that the failures of technology are especially funny when there are other ways that the technology is highly advanced or presented as highly advanced, and as long as like nobody of course dies, you know, um, but but I do wonder too if there's a darker streak and all of this too, you know, something that ties into a deeply rooted human disdain for the other,

especially for others of species. There is a quote from C. S. Lewis's The Lion, The Which, the Wardrobe and the Four Children. That's my son suggested alternate title for it. He's okay, like, why don't they call it the Land the Witch Wardrobe the Four Children? Like they're in it too, and they're not in the title. Seems like a missed opportunity. But anyway, there's this wonderful quote from it that I find I

found kind of creepy on a recent reread of the book. Quote, but in general, take my advice when you meet anything that is going to be human and isn't yet or used to be human once and isn't now or ought to be human, and isn't you keep your eyes on it and feel for your hatchet. Well, that is very creepy. Now, I think in the context of the book, this is going to be referring to, like, you know, possessed objects and stuff like creepy and magical. Basically, yeah, basically, the

message here was talking. Animals are cool, you know, you can hang out with them in Narnia, But there are other things in Narnia that are dangerous, and you can tell if they're dangerous based on how human they seem, they're they're trying to be or used to be. Well, that's one thing in a in a fantasy context, in a in a science fiction or even just a real technology context, that's that's a different thing entirely, and starts making you think about uh, well, you know, fear of

advancing technology mimicking human behavior, fears of AI. Yeah, yeah, yeah, To what extent do we delight in the falls and errors of inhuman entities because we don't wish to see them succeed? You know, we we celebrate that the telltale signs of their otherness because we kind of dread the day when there will be no way to tell. And I think there's a lot there's a strong argument to be made that that day will be here sooner than

we think, and in many respects it already is. We've talked about, for instance, robocalls on the show before um and and just how and also chat bots and to it becomes frightening when you look at where we are with the technology. Now, UM, certainly you get a robocall, it's not going hopefully not going to um deceive you long term. But I think a lot of us are having that experience where you pick one of these up and at first you think it is a human you

are talking to, and then you realize that it is not. Well. I think one of the funny things about stuff like chatbots, which also deal in language but can be much much more convincing than these, uh, these neural networks that generate you know, lists of uh, lists of character bios or something, is that the things that are generally more convincing these

days are programmed. I think with more explicit rules, they tend to have more kind of human meddling and exactly what they're going to be doing, and by having less freedom to be creative and all that, and ultimately having less potential, they can actually be more kind of narrowly convincing the I think one of the things that's interesting about the the neural network generated text is that it's not anywhere close yet. You know, you can't really use it yet to make things where you go like, yeah,

that's definitely real human. I mean, maybe you can in some again narrow scenarios, but we like, for instance, if you have something that will tell you what your Wu Tang clan name will be. You know, they sort of random generation and uh systems which are totally different, different thing. Uh. And we'll get into the distinction here. But but you know, systems like that, just through sheer random um matching of words,

those can be effective. Yeah, but it doesn't mean that it's you know, it's an entirely different kettle of fish in terms of of what's going on with neural networks and where they seem to be going. Well, maybe we should take a quick break and then we come back. We can explain just the basics of how this these kind of things actually work. Alright, we're back. So I'm gonna try to do the simple version of how a neural network works, because if you get in the weeds,

obviously neural networks become extremely complicated. I know, I I spent a lot of time deep in a bunch of articles trying to understand technical details that I'm not actually gonna end up using here. Um. So the simple version is think of a neural network as a machine that transforms values. That's it. You know, it has values that come in, like number variables, and then it puts out values at the end. It's like it's kind of like the toaster conveyor belt at quiz Nos or one of

those sandwich shops. You know, you're untoasted. Sandwich comes in, toasted sandwich comes out. If everything goes according to plan. Now, if it doesn't go according to plan, maybe it splits out something that's on fire or something that you know, who knows what goes on in now, or nothing comes out because the bagels are building up inside of it, right exactly. But a neural networks core job is to just accept inputs and produce outputs. Okay. An example would

be image recognition. Their neural networks designed to look at an image, a digital image, and come up with a text string that says this is what that is. So look at a picture of a dog and say that's a dog. And you've probably seen examples of this on

the web. So you'd have numerical values going in. It might be things like, you know, be a field of pixels with numerical values for their color and placement, and then it would have an output that's, uh, say a string of text, which would actually be like a ranking, like the top ranked string of text that matches with those pixels in that configuration. But so the question is what's going on inside the machine? How does it turn that input into the correct matching output, and then and

then of course, how does it fail? Because I've also found this tremendously amusing. Tumbler Um recently changed their guidelines about what's acceptable content and what's not and stuff to blow your mind has has Slash had a tumbler account recently just rebranded it as the Transgenesis tumbler account, but it had a lot of old stuff to put your mind content on there. And suddenly I got a page of all the things that have been flagged for for

potentially violating the new terms. And it was amusing because some of it was like, okay, well that has a classical painting on it. It's got like a you know, it's something there's something that might look like nudity, and so it got flagged, makes sense, or the topic is something that is a little too sketchy for them, and they're like, okay, that's been flagged by the machine. But the most hilarious one was a picture of a baby bat on on somebody's pall them and that was flagged

as as as likely inappropriate. And so I was just trying to figure that one out, Like what is it about a baby bat that it? I mean, did it think this was genitalia or like, like what because because I know it's somehow clicked off a number of boxes and when and then the automated result was no baby bats on tumbler. Well, I guess I don't know if you know, but I don't know what the mechanism for identifying that is. It might be something like this, but

yeah that I love seeing that kind of failure. And notice that we are laughing now like it or we were laughing. It is funny that it looks at that and says, I don't know about this bad I think this might be porno. Yeah, I mean then the stakes are pretty low. I ultimately, one picture of a baby bat no longer on a tumbler page that we don't really use. It doesn't really affect me personally, but you could see where this could lead to, uh too far

worse problems if given the right scenario. Okay, so so back to the neural network. So you've got this machine. Inside the machine, values are being transformed. You have inputs and output and so inside on the inside, and neural network consists of layers of sort of stations of value

transformation that are called referred to as neurons. And each neuron essentially accepts a series of numerical values as input and then it just performs some kind of mathematical transformation of those values based on what's known as the weights of its connections with the sources that received the inputs from. So you've got these interlinking sources of information, inputs and outputs throughout, and each neuron takes inputs, sums them, does

some transformation, and produces an output. So for each neuron, you've got a bunch of numbers coming from different sources. Each one gets treated with a certain bias based on where it came from, and then the neuron spits out a new value. And these neurons exist in layers, so there are these waves of inputs, say the pixels and image getting passed and transformed through one layer after another of these neurons, until finally the network produces numbers that

constitute its final outputs. In this case, this would be something like it's top guesses at the word string that describes the image you put in at the beginning. Now you'll see from this that the value of neural network depends completely on how well those connections between neurons are weighted to produce the correct results. If they just have random weights, then the network will just produce random output. It won't be any better than making up numbers at random.

So the network has to be calibrated or trained somehow to produce outputs that are correct, and there are multiple ways to do this. Uh. It of course could be programmed to some degree by hand, right, you could have a program or explicitly uh going in and tinkering with waiting rules to try to get the outcomes to be better.

But can it can also be trained through machine learning, which is a process where inputs are already associated with correct outputs, Like you've already got a text string associated with the image that you put in and you say this is what you should say when it comes out, And each time it runs the process, it checks to see how far off from the correct known output that it was, and then tries to change the internal waiting

to get closer to the correct answer. And of course, with automatic machine learning, you can do this at scale. You can do it thousands of times. You could potentially do it millions of times just training over and over, and you might be able to see a parallel here with one of the ways that we actually learn. Uh,

you know, we we learned in multiple ways. Sometimes we learned by being taught explicit rules to follow, like if we're learning in school what an insect is, we might learn that an insect is a small animal with an exoskeleton that has six legs. Or sometimes, on the other hand,

we learned to generalize from particulars. We might see lots of animals pictures of lots of animals and notice that the ones that are called insects all happen to have six legs and exo skeletons, and therefore we derive this category called insect from that survey. And in logic, of course, this this process where we come up with general rules

from lots of individual examples, is known as induction. So machine learning to train your oal networks is kind of like allowing computers to learn categories by induction, kind of like we do when we just go out and look at the world and see what we find. But one of the things that really sets humans apart from computers is that humans seem to have this amazing, remarkable ability to generalize from particulars. We can often get the gist of a category from just a tiny handful of examples.

You know, when you're giving somebody examples of something to like, give them the gist of what you're talking about. You don't usually need to list a million examples. You can list two or three maybe, or sometimes even just one. Yeah. This gets into the idea of judging a book by its cover, right, not supposed to, but we often do, and sometimes you you can if you pick up on particular things on you know, specifically to use the book, example,

specific things about the design or the era of the cover. Yeah. Yeah, And in fact, sometimes you can you can judge things about the contents of a book just by knowing certain things about how certain types of books end up with certain types of covers. Like you might think I tend to like books that have hand drawn illustrations on the cover more than I like books that have sort of like c g I stock image cover photos, which means you probably like books from like, you know, at least

the nineteen eighties and before. Yeah, because it seems like we have far fewer hand drawn, uh you know, covers on books these days. Yeah, why are people putting stock photos on the covers of books? I do not get it. But you you didn't need to read a million books to come to that conclusion. You could probably come to that conclusion after reading I don't know three books like you, really we get the gist of things really fast. And that's in contrast to computers, which really really don't at all.

This is one of those strange and amazing things about neuroscience, about the human brain. How do we solve such a difficult problem as generalizing from particulars with so few examples

to draw from? And of course, another example of the generalizing power of the human brain is in language, Like we've been talking about it, like, how is it that most of the time kids learn how to speak a language without being taught explicit rules of syntax and grammar and the definitions and usages of all the common words, and without hearing billions and billions of examples of sentences. They just pick it up. Well, there's there there are

some specific answers to that. If we've talked about that on the show before. Yeah, well, I mean I think that there there is a good case to be made that the human brain is specially geared towards language acquisition in childhood. That's sort of like one of our species superpowers. And then those windows close, or they don't completely close, but they the windows become much smaller later on in life.

You know. Speaking of children, they are you know, they are also frequently a font of weirdness and beauty as they two are learning to function in the human world, in the in the adult human world, and then they say and do things that sometimes hit a weird zone that is either hilarious or sometimes a little frightening, or or even a little bit elegant. Yes, I know exactly what you mean. Like, kids often do the same funny outputs based on induction that these machine learning algorithms do.

Like you can see it's funny in the same way that they might say something that's a little bit off and kind of absurd, but you can sort of understand the rules that got them there, right. Like one example I always refer to is from years and years ago, I went to a children's puppet shows before I had a kid, my my wife and I went to check this out because it was actors from a local improv group, Dad's Garage, Atlanta. They put on the show Uncle Grandpa's

Who Deal His story Time? I think it was, and so you had these sort of seasoned, uh you know, improv vets, and they were doing a puppet show for kids, and they're taking ideas from the audience and they said, like, who should our main character be? And so they hand the mic to some little little girl in the front row and she says Batman the Girl, which which is so hilarious and I don't think an adult would be able to come up with that, but you can sort of tease it apart and figure out how she got

there and you know, with it. But uh, but it's it's just one of you know, many examples I'm sure that that anyone with children in their life can can turn to where they come with something that is just so goofy or weird or or sometimes terrifying. Well, I think the real funniness and pleasure in that is that it's Batman the Girl and not bat Girl. Right, Yeah, Like it's almost there, and but by not being there, it's it's also like it's it's even better like it's

not it's not Backgirl, it's Batman the girl. It's just so nonsensical, uh and beautiful at the same time. You know. Another one of the great AI text generation experiments that in El Shane did on her blog was was generating that you know those Valentine's Day candy hearts. Yes, yes, She had a programmed sample those and then try to come up with examples and ended up saying things I think like like sweat, pooh and hole and time hug.

Time hug sounds good time hug time like time cop. Yeah, but it also sounds like something that like it might be a term that aliens come up with for human love. It's like they engage not in a hug, but in a time hug. It is as if they are hugging for the rest of their lives, you know, or something

like that. Another one all hover and then finally bog love. UM. My son, who as of this recording is is six almost seven, he drew a picture for my wife and I for for Valentine's Valentine's Day CARDI did his school and it depicted dinosaurs as these want to do um and they're there are some herbivores walking about, there are some carnivores eating the flash of fallen dinosaurs, and then, as is typical in paleo art, there may be a

volcano in the background. But then there is a meteor coming in hard and fastid Yeah, and he writes, uh, I love you on the on the meteor, which which is absolutely wonderful because it's like at once it is like like this is beautiful, Like he totally means all of this, and it's like the most beautiful Valentine I've ever received and yet to put it on the the instrument of of the of the extinction events. It's just

so weird and like it it's accidentally brilliant, you know. Yeah, I saw that when you put it on the internet. It was the sweetest thing I've ever seen. It was so good, and it's like, yeah, this is that. This is how love works. Love is a is a destroyer as well as the creator. Now, we do want to stress it with the especially with these text based situations.

We're not talking about merror random mashups of text, uh, such as like the Wu Tang clan name generator or um or more literary example would be the cut up technique popularized by author William S. Burrows, where you just have like a random um mechanism and play to sort of splice words or or sentences together to get something that may have sort of accidental meaning to it. No, the neural net programs are They are algorithms attempting as best they can to approximate the quality of the the

input texts, the text they're trained on. So they're doing their best to make something like this. Uh. And then, of course, I mean one of the things is you might say, well, why don't they just perfectly spit back out the text you've trained them on. In fact, if you don't tell them not to, they'll do that. You know, they'll just spit back in exactly what you fed in. You have to sort of like change some values and tinker with it to prevent what's known as overfitting in

this world. Uh, to sort of force the algorithms to be more creative and try to come up with new versions of the kind of thing they've seen instead of just completely copying what you fed them. Yeah, because we want these machines to rule the world, not just Hollywood, right, Um, But you know we also have to to distinguish it. We're also not talking about fake i AI generated text,

which can certainly be tremendously entertaining as well. Yes, that's such a great genre, people pretending to be neural networks creating machine learning generated text, which it's such an amazing reversal of principles that humans have intuitively detected what's funny about machine learning generated text and then made fake human designed versions of it to exploit that inherent humor. Right, I don't I don't know how you get deeper on

the irony pit than that. Yeah, there's a wonderful two thousand eighteen tweet by comedian Keaton Patty and this was the tweet quote, I worked a boat to watch over a thousand hours of Alive Garden commercials and then asked it to write an alive garden commercial of its own. Here is the first page, uh and uh. And then he proceeded to include the images of this text that

script rather for and alive garden commercial. And and it's filled with hilarious nonsense like I shall eat Italian citizens and unlimited stick and playing seeming you know, to play upon the whole catchphrase of what when you're here? Your home. I think when you're here your family and you hear your family. There's there's one from this UH, this fake script that says leave without me, I'm home, which I just I love. I remember laughing so hard at this when it came out as well. I love that the

waitress says lasagna wings with extra italy. Yeah. There's also like really funny stage directions in it. Yeah, well, oh yeah, you mentioned the infinite uh where it says like we the gluten classic O. We believe the wage risk that it is from the kitchen. We have no reason not to believe. Now, of course, this is just a comedian

doing this thing, trying to pretend to be an AI. UM. But I was reading an article where they quoted Janelle Shane, the author of the AI Weirdness blog, who trains all these neural networks to come up with all this funny stuff we were talking about at the beginning of the episode, And you know, she talks about that there are ways to notice UH when something was probably written by a human instead of by an actual AI. Both can be

absurd in similar funny ways. But one of the problems with this script UH in passing as a real AI generated text is that it's actually too coherent, like it's memory. Its memory is too long. It remembers characters from many lines earlier and still has them appearing and saying things. Actual AI generated texts of a much shorter memory. They they're not consistent in that way. They don't make Actually, they make even less sense than the fake all of

Garden commercial. So she's saying, don't reach for your match it on this one because a real neural net generated text only mimics forms, it doesn't mimic meaning. And this thing it's it means too much. It's too clever. Okay, So we're gonna go ahead and close out this episode now, but again there's going to be a part two where we continue this discussion and really get more into the meat of the topic. In the meantime, check out Stuff to Blow your Mind dot com. That's the mothership. That's

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