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Speaker 1: Welcome to Stuff you should Know, a production of iHeartRadio.

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Speaker 2: Hey, and welcome to the podcast. I'm Josh, and there's Chuck.

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Speaker 2: And I was gonna say Jerry's here, but she totally

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Speaker 2: flaked on us.

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Speaker 3: And this is stuff you should know. Yeah, I've never

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Speaker 3: wanted to be able to make a dolphin sound.

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Speaker 1: More in my life.

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Speaker 3: I mean, that was medium better than I could do.

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Speaker 4: Well, let's hear yours. I put I've heard better out there.

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Speaker 3: No, I mean I can't. I don't even want to try. Okay,

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Speaker 3: how about this bet my nose. I'm a dolphin.

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Speaker 4: That was good.

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Speaker 2: A dolphin that speaks English is pretty impressive, Chuck.

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Speaker 4: I like that interpretation.

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Speaker 2: Yeah, Usa, so USA and Canada. So we're talking today.

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Speaker 2: The reason we're trying to speak dolphin, Chuck, is because

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Speaker 2: we're talking about anim communication. And just to clear things

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Speaker 2: up right out of the gate, we're not talking about

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Speaker 2: animal communication where we try to teach animals to speak

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Speaker 2: human languages, whether it be sign language, English, whatever. Yeah,

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Speaker 2: we already did that in our Live Coco the Gorilla episode.

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Speaker 3: Yeah.

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Speaker 2: Now we're going the other way here because there's a

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Speaker 2: whole other tranche. As the French would say, of research

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Speaker 2: that is going into listening to, decoding, understanding and potentially

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Speaker 2: speaking animal animal languages.

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Speaker 3: Yeah, and this is one. It's like one of the

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Speaker 3: rare cases whereas we'll see for a lot of year,

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Speaker 3: science was like, animals don't do this, Like they grunt

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Speaker 3: at each other, they make little instinctual noises, but they're

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Speaker 3: not really communicating with each other or us. Don't kid yourself.

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Speaker 3: And this is a rare case where I'm like, I

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Speaker 3: don't care what science says. My pets speak to me

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Speaker 3: and I understand what they're saying.

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Speaker 2: Yeah, no, this researching this, I was haunted by the

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Speaker 2: ghost of Tracy Wilson. Do you remember like how hard

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Speaker 2: core she was about not anthropomorphizing years back?

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Speaker 4: Yeah, sorry, Will, Yeah, sorry Tracy.

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Speaker 3: I do it.

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Speaker 2: And Tracy's a cat person, I know for it, she

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Speaker 2: refuses to speak to her cat. No, I'm sure that's

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Speaker 2: not true, but yeah, I mean that's a that's a

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Speaker 2: fair point, and I think we've made that point many

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Speaker 2: times whenever we've talked about pets like communicating or I

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Speaker 2: think anytime we talk about pets, we kind of both

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Speaker 2: agree that no, there's they're obviously communicating in ways we're

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Speaker 2: just not fully aware of. And it's actually pretty anthropercentric

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Speaker 2: to just assume that because we don't understand it, they're

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Speaker 2: not doing it. Luckily, Like I said, there's a whole

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Speaker 2: tranche of search that is assuming no, these these animals

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Speaker 2: have communication patterns that yeah, kind of follow the same

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Speaker 2: general idea of human language. Yeah, and as such we

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Speaker 2: have a chance of being able to understand it. But

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Speaker 2: the whole, the whole thing is based on this idea.

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Speaker 2: Like you said, science has long thought like that there

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Speaker 2: was not that any any sounds they make were instinctual,

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Speaker 2: that there wasn't any purpose behind them. It was just

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Speaker 2: that it was an involuntary response to something that evolution

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Speaker 2: had kind of bred into that animal. And that was

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Speaker 2: also predicated on this idea that Renee Descartes put out there,

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Speaker 2: which is animals have like no inner lives whatsoever, only

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Speaker 2: humans do.

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Speaker 4: Boo.

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Speaker 2: Yeah, I'm very disappointed with Descartes, and I know we've

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Speaker 2: talked about this before, probably multiple times, but it bears

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Speaker 2: repeating because he set that tone for centuries to follow.

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Speaker 3: Yeah, oh for sure. But I mean you needn't only

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Speaker 3: look at your dog and to a certain degree, your

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Speaker 3: cat but you know the first couple of animals we're

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Speaker 3: going to talk about that Olivia helped us with this

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Speaker 3: that she was keen to point out humans have been

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Speaker 3: around for a while and you know, communicate with if

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Speaker 3: you are the owner, slash, you know, mother, father, caretaker

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Speaker 3: of these animals. We're talking about dogs and horses, and

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Speaker 3: if you have ever had a dog, you know what

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Speaker 3: at least this one thing is. And that's called puppy

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Speaker 3: dog eyes. And this is a trade. It's actually a

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Speaker 3: muscle called the loam, the lam, the levator anguli oculi

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Speaker 3: media alis cristo. It's a muscle in the eyebrow basically

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Speaker 3: of a dog that evolved from a wolf. Like They've

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Speaker 3: done studies and found that wolves don't still have this

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Speaker 3: or wolves don't have this right and that the only

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Speaker 3: dog out of like the eight breeds they studied that

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Speaker 3: didn't evolve this way was the Siberian husky, which is

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Speaker 3: I guess closely related to a wolf.

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Speaker 2: It's a wolf posing as a dog.

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Speaker 3: And I don't know if you've ever known huskies. I'm

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Speaker 3: not knocking any breed. I like huskies just fine. I've

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Speaker 3: known a few, but and I've never been able to

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Speaker 3: have a good connection with them. Yeah, and that may

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Speaker 3: be part of the reason is that there haven't evolved

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Speaker 3: as far away as as you know we have with

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Speaker 3: our other domesticated friends.

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Speaker 4: That's a great point.

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Speaker 3: But they have done studies. There was one published in

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Speaker 3: the proceeding of the National Academy of Sciences who studied

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Speaker 3: dogs out for adoption at like a you know, what

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Speaker 3: do you call it? Well, I can't even think.

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Speaker 4: Of the word an adoption dry.

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Speaker 3: Sure, it wouldn't a dry, but just in the in

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Speaker 3: the pound.

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Speaker 4: Okay, what do you call those? Though? An animal shelter?

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Speaker 3: Animal shelter. You know how the easiest word won't come

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Speaker 3: to you sometimes.

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Speaker 4: Yeah, I do. I've been there, buddy, it's very frustrating.

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Speaker 3: Anyway, sheltered dogs. And they found they studied all these dogs,

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Speaker 3: like hundreds and hundreds of them over long periods of time,

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Speaker 3: and the found that the dogs to use these lone

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Speaker 3: muscles to make their eyes bigger and raise that inner

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Speaker 3: brow were adopted quicker.

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Speaker 4: Yeah.

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Speaker 2: I think it has to do with exaggerating the shape

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Speaker 2: of the eyes, which, as we've talked about the Kinder schema.

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Speaker 2: I believe in the science of cute Yeah, that would

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Speaker 2: just make them automatically cuter.

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Speaker 3: Yeah, bigger eyes. I mean, that's why those Disney characters

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Speaker 3: have big eyes.

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Speaker 2: But the exactly, but the point of this is that dogs.

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Speaker 2: We have clear evidence that dogs evolved a way to

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Speaker 2: be expressive through their facial expressions in ways that affect humans.

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Speaker 2: And that's a form of communication, exactly. That's just one

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Speaker 2: form of communication. So it isn't anthe promorphizing to say that,

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Speaker 2: you know, dogs have emotions and that they express these

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Speaker 2: emotions like things like being happy to see their their friend,

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Speaker 2: their human friend. There's a guy named ecologist Carl Sephina,

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Speaker 2: and he points out that his whole thing is like

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Speaker 2: dogs love us. Clearly, it's clear as day. Anybody who

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Speaker 2: owns a dog knows this, and anybody who says otherwise

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Speaker 2: is just being a real stick in the mud, basically.

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Speaker 2: But he pointed out that really the the whole thing

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Speaker 2: broke open. Finally, Descartes Smelly Corpse was finally cast off

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Speaker 2: of the animal behavior field in the sixties, I believe

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Speaker 2: when observational studies of animals really started in earnest thanks

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Speaker 2: to people like Jane Goodall and I can't remember who

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Speaker 2: her Lewis leaky that those people were coming back with report.

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Speaker 2: It's like, guys, these are these animals clearly interact with

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Speaker 2: one another in ways that humans would consider empathy. They're communicating,

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Speaker 2: they're doing all sorts of stuff we supposedly think they

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Speaker 2: can't and then today it's being demonstrated. All of these

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Speaker 2: these observations are being proven because we've fashioned MRI machines

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Speaker 2: that dogs can go in, and we don't like, sedate

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Speaker 2: the dog and put them in there. It's not going

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Speaker 2: to have any effect. They've made these open machines and

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Speaker 2: then they kind of introduce them to the dogs, and

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Speaker 2: the dogs free to come or go in the MRI,

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Speaker 2: and if the dog says there long enough, they can

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Speaker 2: study the dog's brain activity and what they're seeing is Nope,

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Speaker 2: these these dogs are smart. They definitely have an inner life,

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Speaker 2: and most of what we're taking as emotional communication is

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Speaker 2: probably totally correct.

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Speaker 3: Yeah, and if they sit there long enough, they're good

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Speaker 3: boys and good girls.

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Speaker 4: That's right, and they get treats, that's right.

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Speaker 3: We also have horses. If you want to look at

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Speaker 3: another animal that humans have probably had the longest relationship with,

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Speaker 3: and there's a horse trainer slash neuroscientist, which is a

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Speaker 3: very handy thing if you're going to talk about animal communication.

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Speaker 3: Her name is Janet Jones, not the former gymnasts who

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Speaker 3: was married to Wayne Gretzky. Remember, Oh, I knew.

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Speaker 2: That name sounded familiar, but I didn't know why.

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Speaker 3: I say former gymnast, former actress. She was a gymnast too, though, right.

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Speaker 2: I haven't paid that much attention to Wayne Gretzky's marital.

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Speaker 3: Life anyway, different Janet Jones, and she said that horses

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Speaker 3: and humans are are different because humans evolved into predators,

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Speaker 3: horses evolved from prey species, So like we have different

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Speaker 3: ways of looking at the world when you're riding a

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Speaker 3: horse around outside, and we communicate that to one another,

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Speaker 3: you know. Also throwing the fact that horses have a

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Speaker 3: three hundred and forty degree range of vision with those awesome,

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Speaker 3: humongous eyes on the sides of their heads.

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Speaker 2: Yeah, that last degrees really ticks horses.

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Speaker 3: It really does. But if you're like, if you're riding

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Speaker 3: along and a horse, a horse might be scared about.

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Speaker 3: And Olivia uses a great illustration of like an umbrella opening.

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Speaker 3: It might spook a horse, but the human's like, Hey,

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Speaker 3: that's just an umbrella. So they're gonna, you know, sitting

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Speaker 3: atop the horse. They're going to relax the horse by

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Speaker 3: you know, I'm not a horse rider certainly know how

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Speaker 3: you do this, but by moving your body and flexing

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Speaker 3: your muscles in a certain way with the rhythm of

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Speaker 3: the horse to let them know that it's cool.

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Speaker 4: Right.

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Speaker 2: So, what Janet Jones is basically saying is she wrote

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Speaker 2: this really cool article called Becoming a Centaur and aon

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Speaker 2: is that through this communication that horses and humans have

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Speaker 2: have co evolved together to understand with one another and

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Speaker 2: to be able to train one another with you're becoming

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Speaker 2: like kind of a super organism for that time where

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Speaker 2: you are a top of horse, yeah, and the horses

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Speaker 2: below you and you guys are working conjuncture together, sharing

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Speaker 2: sensory information.

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Speaker 3: I love that. I want to ride more horses in

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Speaker 3: my life.

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Speaker 2: There's a that's a great, great thing to try to do, Chuck, I've.

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Speaker 3: Only done it a couple of times that I loved it.

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Speaker 2: I haven't for a really long time. I used to

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Speaker 2: as a kid a lot more. I have it on

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Speaker 2: the prairie.

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Speaker 3: Yeah, when you were when you were westward bound.

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Speaker 2: Yeah, in the wagon train.

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Speaker 3: Birds, we're gonna talk a lot about birds, because obviously

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Speaker 3: bird vocalizations are you can just listen to birds and

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Speaker 3: and tell that they are specific and that that probably

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Speaker 3: means something. But humans and birds interact in different ways

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Speaker 3: around the world. Specifically, a couple of tribes, the Jahua

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Speaker 3: people in Mozambique and the Hods of Tanzania both use

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Speaker 3: what are called honeyguides, and they are birds that they

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Speaker 3: can call. They each you know, use different calls in

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Speaker 3: their inner respective places to call over these birds, and

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Speaker 3: the birds come a flying in and say, hey, follow

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Speaker 3: me and we'll show you the honey, and they go

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Speaker 3: and get the honey. And if you're asking, like, well,

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Speaker 3: why in the world would they do this, it's because

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Speaker 3: the birds get the wax after they're finished, so it's

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Speaker 3: a mutually beneficial relationship.

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Speaker 2: Yeah, And they've actually tested to make sure it's not

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Speaker 2: just the presence of humans making sounds that catch the

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Speaker 2: birds attention and then the birds associate humans with getting honey.

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Speaker 2: They've tested other like control sounds, but there are specific

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Speaker 2: calls that Jahwua people use. It's like a bird something

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Speaker 2: like that, and that is what the honey guides respond to.

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Speaker 2: They don't respond to hey, honeyguide or anything like that.

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Speaker 2: They respond to this call that the Jahua people have

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Speaker 2: been using for countless generations, and that the Jahwa have

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Speaker 2: been passing down from generation to generation. That also means

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Speaker 2: that the honey guides wild birds, not tamed in any way.

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Speaker 2: They're not coerced to do this. They're wild animals who

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Speaker 2: clearly communicate with humans. They're passing down that that burr

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Speaker 2: hum sound that a Jahua person makes in the woods

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Speaker 2: means go find that person and take them to some honey,

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Speaker 2: and they'll they'll leave. The honey comes for you. Like

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Speaker 2: people and birds passing down this common information that forms

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Speaker 2: the symbiotic relationship.

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Speaker 4: That's nuts.

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Speaker 3: Yeah, And I imagine if they said, hey, honey guides,

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Speaker 3: come over here, the birds would say, why else speak

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Speaker 3: in English?

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Speaker 1: Right?

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Speaker 3: That's really weird.

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Speaker 2: Yeah, it happened to burr hume.

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Speaker 3: They're also, of course, and we're not going to get,

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Speaker 3: you know, too much into this, but you know, for hunters,

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Speaker 3: all kinds of mating calls and and I guess you

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Speaker 3: don't have to hunt to use a maiding call if

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Speaker 3: you want to. If you want to call a mooseover

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Speaker 3: just to say hello, you could use a moose mating call,

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Speaker 3: but all kinds of and it's not just mating calls,

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Speaker 3: but usually it's some kind of mating call for any

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Speaker 3: kind of game that you're hunting, or ducks or stuff

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Speaker 3: like that.

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Speaker 4: Yeah, I crossed that part.

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Speaker 3: Out those people communicating with animals at the very least.

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Speaker 2: So you mentioned birds and how we're going to talk

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Speaker 2: about birds a lot. But birds are just an obvious

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Speaker 2: place to start, and that's where humans kind of started

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Speaker 2: in tracking animal communication, whether they realized it was animal

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Speaker 2: communication or not. Bird song has always kind of captured

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Speaker 2: the human imagination and apparently back in the day they

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Speaker 2: started to try to assign musical notation to recording bird

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Speaker 2: song by hand on paper. Good luck, right, And then

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Speaker 2: as you know, the technology progressed and we got better

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Speaker 2: at recording and reproducing sound. One of the things that

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Speaker 2: we really started compiling a lot of we're bird song.

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Speaker 2: Bird songs is bird song plural and singular?

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Speaker 3: I feel like the kind of word that would be

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Speaker 3: I think, so.

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Speaker 4: Bird's song guy.

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Speaker 2: And there's actually a really great collection at Cornell. There

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Speaker 2: are Ornithology Lab has what's called the McAuley Library. And

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Speaker 2: I've got this app called Merlin. It's a bird identification app.

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Speaker 2: It's free.

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Speaker 4: I think.

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Speaker 2: You just have to sign up with your email and

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Speaker 2: if you hear a bird call, you just open Merlin

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Speaker 2: and it's like Shazam for birds.

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Speaker 4: Amazing.

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Speaker 2: It is amazing and like it just sits there and

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Speaker 2: listens and then it goes, oh is it this burden.

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Speaker 2: It shows you a picture and tells you what that

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Speaker 2: bird is, and you say, that's exactly what bird that is.

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Speaker 2: Thanks Merlin. Merlin gives you a wink and goes back

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Speaker 2: to sleep.

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Speaker 4: In your phone.

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Speaker 3: Yeah, it's It's a very popular app and very popular

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Speaker 3: in my family. We use it all the time. A

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Speaker 3: lot of We have a lot of identification apps that

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Speaker 3: are really handy. We have like bird apps and plant

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Speaker 3: of course and flower apps, and we has those. And

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Speaker 3: then I have an art app where you can point

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Speaker 3: it at a painting or something and it'll it'll tell

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Speaker 3: you if it's in the database. Of course, it'll tell

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Speaker 3: you who it is.

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Speaker 4: Cubist.

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Speaker 3: Now it'll tell you the actual artist I got, you know,

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Speaker 3: Max Cubist.

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Speaker 2: So let me just set this up real quick, Chuck

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Speaker 2: and I say we take a break. But although people

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Speaker 2: were like, okay, animals have somewhat richer inner lives than

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Speaker 2: we had always suspected, but they're still not using anything

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Speaker 2: like what we would consider language. That carried on until

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Speaker 2: the seventies until a couple of studies came through chick

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Speaker 2: the legs out from under that what a.

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Speaker 1: Great cliffy, So I said, cliffy.

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Speaker 3: People know that means cliffhanger. I hope longtime listeners to

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Speaker 3: longtime listeners. So, yeah, you were talking about until the seventies,

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Speaker 3: and that's what I mentioned that science had always kind

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Speaker 3: of poo pooed it, and it was in the seventies

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Speaker 3: where people finally started you know, there were some sort

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Speaker 3: of rogue hippie scientists here and there that was like

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Speaker 3: nobody was listening to basically. But in the seventies is

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Speaker 3: when a couple of big studies came out that you

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Speaker 3: mentioned pre break. One was in nineteen seventy seven a

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Speaker 3: couple of primate scientists named Robert C. Farth say Farth

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Speaker 3: and Dorothy Cheney, and they were working with one of

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Speaker 3: those hippies, Peter Marler, who was an animal communication expert.

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Speaker 3: When that wasn't cool. And they were studying vervet monkeys

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Speaker 3: and Kenya, and this is a pretty big breakthrough and

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Speaker 3: pretty remarkable. They found that they they are using different

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Speaker 3: we're going to say things like words for lack of

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Speaker 3: a better term, sounds, vocalizations, but they used different words

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Speaker 3: for different threats. So like they noticed that like something

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Speaker 3: flying like an eagle versus something on the ground like

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Speaker 3: a snake like a python, they would use those to

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Speaker 3: indicate one or the other. And they learned this by

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Speaker 3: making recordings of it. And when they played it, sure enough,

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Speaker 3: the monkey would look up into the sky or search

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Speaker 3: the ground around them for the snake.

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Speaker 2: Yeah, depending on what call they played.

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Speaker 3: Back, that's right, not depending on what Brian Adams song

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Speaker 3: they played.

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Speaker 2: So okay, smarty, So you can still make a case that, okay,

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Speaker 2: so what they have these specific words for snake or

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Speaker 2: for eagle, But it doesn't mean that it's anything more

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Speaker 2: than instinct for a monkey to utter this particular cry

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Speaker 2: when it sees a snake, and other monkeys to respond

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Speaker 2: in kind, And it's all innate and none of it

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Speaker 2: means that they're using grammar or language. It's okay, fine, fine,

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Speaker 2: just keep waiting a few more years, and we're going

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Speaker 2: to go forward to Klaus Zuberbueler, great name, who is Swiss,

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Speaker 2: as people who name their family's Zuber are wont to be.

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Speaker 2: He studied the Campbell's monkeys in the Cote Duvoir, I

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Speaker 2: believe the tomato Campbell's monkeys, and he found nothing.

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Speaker 3: Not Campbell's tomato soup.

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Speaker 2: Yeah, and he found that they actually use suffixes. Right,

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Speaker 2: So if they use the alarm called crock or crack

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Speaker 2: k r a K, that's how they spelled it, not

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Speaker 2: the Campbell's monkeys, but Zuberbueler and his friends, Yeah, that

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Speaker 2: means leopard is coming, but crack ou means it's just

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Speaker 2: a general alarm like lookout or heads up or something

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Speaker 2: like this. They can also like supplement crack or crack

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Speaker 2: oo with booms that they will make. It can mean

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Speaker 2: like come this way. They can mean that there's a

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Speaker 2: falling tree branch, depending on if they amend it with

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Speaker 2: the suffix Ooh. So Zuberbueler is saying, like, guys, this

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Speaker 2: is grammar. Those are words. This cannot possibly just be instinctual.

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Speaker 2: And even if it is instinctual, then that would suggest

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Speaker 2: that animals, at least some types of monkeys have a

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Speaker 2: language instinct too. Who that's a whole other ball of wax,

359
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Speaker 2: like we talked about before. But Zuberbueler's like, dude, come on,

360
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Speaker 2: and people started to finally be like, all right, fine,

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Speaker 2: we'll kind of get on board with this idea that

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Speaker 2: the animals are using something like language possibly.

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Speaker 3: Yeah, And he even, you know, as an example of

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Speaker 3: how it's something that humans can potentially understand once they

365
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Speaker 3: learn it, he was warned off by a leopard by

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Speaker 3: hearing the leopard call apparently, and this was in a

367
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Speaker 3: radio lab at one point shout out to Radio Lab,

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Speaker 3: Yeah some of the ogs like us. But yeah, zuber

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Speaker 3: Bueler was like, hey, you know, I heard them sound

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Speaker 3: the leopard alarm, and that meant that I needed to to,

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Speaker 3: you know, be watchful.

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Speaker 2: Yeah they weren't. I didn't understand. I didn't go listen

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Speaker 2: to that episode of Radio Lab, So I'm not sure

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Speaker 2: if they were warning him or he was just paying

375
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Speaker 2: attention that they were.

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Speaker 3: I think it's that okay, So I think, but who knows,

377
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Speaker 3: Maybe they're like Zuberbueler.

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Speaker 2: He spoke, he spoke Campbell's monkey for that moment and

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Speaker 2: it helped them out. Yeah, sure so, and it wasn't

380
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Speaker 2: just a zuberbueler who did. Apparently other animals, including birds

381
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Speaker 2: and other monkeys that live around Campbell's monkeys have learned

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Speaker 2: what crock or crack means too and will respond in kind.

383
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Speaker 2: So there's evidence that there's interspecial communication, and not necessarily

384
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Speaker 2: that monkey talking to that bird, but that bird just

385
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Speaker 2: from being around these monkeys using language, picking up certain

386
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Speaker 2: words and speaking monkey ease, even though the bird actually

387
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Speaker 2: speaks parodies or something.

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Speaker 3: Yeah. I mean, it's not any different, I think than

389
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Speaker 3: you know. I have cats and dogs, and they each

390
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Speaker 3: have their own respective feeding times and programs and systems

391
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Speaker 3: and treat systems, and sometimes one will get a little

392
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Speaker 3: of the other. Like, for instance, my dog Charlie will

393
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Speaker 3: lick the wet cat food spoon after I give them

394
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Speaker 3: their wet cat food. So now Charlie knows when I say, uh,

395
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Speaker 3: do you want your good stuff, which is what I

396
00:22:17,359 --> 00:22:19,720
Speaker 3: say to the cats, and they come running in there

397
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Speaker 3: for the wet food, Charlie knows, Hey, that's when I

398
00:22:22,680 --> 00:22:26,600
Speaker 3: get to lick that spoon. So I'm speaking English and

399
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Speaker 3: each of these animals is understanding what I'm saying, even

400
00:22:30,440 --> 00:22:34,520
Speaker 3: though for Charlie I'm speaking cat, although that's really not true.

401
00:22:34,520 --> 00:22:35,119
Speaker 3: You know what I'm saying.

402
00:22:35,480 --> 00:22:37,040
Speaker 4: No, I know what you mean. You know what I mean.

403
00:22:37,240 --> 00:22:39,400
Speaker 2: It holds up and it applies. It's also you can

404
00:22:39,440 --> 00:22:42,639
Speaker 2: make the case very much like an English speaking person

405
00:22:42,720 --> 00:22:45,920
Speaker 2: in America who's got a bodega down the street. M

406
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Speaker 2: h understands what ka pasa means or aii or something

407
00:22:50,720 --> 00:22:51,040
Speaker 2: like that.

408
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Speaker 4: Right. Yeah, it's think this.

409
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Speaker 2: Living in proximity of people who speak other languages, you

410
00:22:56,080 --> 00:22:59,200
Speaker 2: pick up other languages. And that seems to very much

411
00:22:59,720 --> 00:23:02,639
Speaker 2: be analogous to what we're talking about with the birds

412
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Speaker 2: living around the Campbell's monkeys.

413
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Speaker 4: Yeah.

414
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Speaker 3: I guess the true comparison would be if my cats

415
00:23:07,359 --> 00:23:11,560
Speaker 3: made a distinctive meal when they wanted, and who knows,

416
00:23:11,600 --> 00:23:13,520
Speaker 3: they might when they want that good stuff, and that

417
00:23:13,640 --> 00:23:14,760
Speaker 3: that signal. Charlie.

418
00:23:15,040 --> 00:23:16,960
Speaker 2: Yeah, but I think your analogy still worked.

419
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Speaker 3: Okay, thanks man, No problem.

420
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Speaker 2: You want to tell them about Con, I'll.

421
00:23:22,080 --> 00:23:22,639
Speaker 4: Leave it to you.

422
00:23:23,840 --> 00:23:29,199
Speaker 3: It there's a biologist name Con slobotic cough slobota. Oh No,

423
00:23:29,280 --> 00:23:32,040
Speaker 3: that's not right at all. No, it's not slow bod Chikhov.

424
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Speaker 4: Oh, nice work. I think you totally nailed it.

425
00:23:35,320 --> 00:23:37,359
Speaker 3: Yeah, oddly enough, Con is the one that throws me.

426
00:23:37,400 --> 00:23:39,200
Speaker 3: I've never heard that as a first name.

427
00:23:40,119 --> 00:23:43,160
Speaker 2: Con like Con.

428
00:23:43,320 --> 00:23:47,119
Speaker 3: Yeah, right, this is just gone anyway. That person is

429
00:23:47,160 --> 00:23:50,360
Speaker 3: a biologist and they study prairie dogs and that they

430
00:23:50,359 --> 00:23:53,640
Speaker 3: found that they have very distinct sounds that when they're

431
00:23:53,640 --> 00:23:56,600
Speaker 3: talking about predators that basically say what kind of predator

432
00:23:56,680 --> 00:23:59,800
Speaker 3: is coming, how what color they are, how big they

433
00:23:59,840 --> 00:24:03,640
Speaker 3: are are, how fast they're coming at us, And they

434
00:24:03,680 --> 00:24:06,480
Speaker 3: can combine all those sounds in different ways. If it's

435
00:24:06,520 --> 00:24:08,439
Speaker 3: an let's say it's an animal they've never a predator

436
00:24:08,480 --> 00:24:11,159
Speaker 3: they've never seen, they can combine those other words to

437
00:24:11,240 --> 00:24:13,520
Speaker 3: kind of say this is a new thing and not

438
00:24:13,760 --> 00:24:16,440
Speaker 3: the whatever hunts a prairie dog.

439
00:24:16,680 --> 00:24:22,359
Speaker 2: Right Like if you saw a somehow a terrasaar came

440
00:24:22,400 --> 00:24:25,600
Speaker 2: through a time warp into example, modern day Atlanta, and

441
00:24:25,640 --> 00:24:28,400
Speaker 2: we were standing outside and we didn't know a TerraSAR

442
00:24:28,480 --> 00:24:31,720
Speaker 2: because we'd never seen one before. We might say something

443
00:24:31,840 --> 00:24:37,640
Speaker 2: like look a flying green dragon monster. And that gets

444
00:24:37,880 --> 00:24:44,440
Speaker 2: generally the point across what Slobodchikov found is that prairie

445
00:24:44,480 --> 00:24:47,920
Speaker 2: dogs do the same thing. Yeah, and that they also

446
00:24:47,960 --> 00:24:50,760
Speaker 2: have a tonal language very similar to Mandarin, where different

447
00:24:51,040 --> 00:24:56,200
Speaker 2: changes in intonation of the same phoneme mean totally different things,

448
00:24:56,240 --> 00:24:59,560
Speaker 2: and that they layer these different tones, that these prairie

449
00:24:59,600 --> 00:25:04,919
Speaker 2: dogs language may actually be more complex than other languages

450
00:25:05,080 --> 00:25:07,640
Speaker 2: in in that use that are used by humans.

451
00:25:07,840 --> 00:25:11,280
Speaker 3: Yeah tonally right, Oh.

452
00:25:11,160 --> 00:25:13,320
Speaker 2: I thought you were making a joke, like totally.

453
00:25:13,200 --> 00:25:16,600
Speaker 3: No, no, no, tonally yeah, tonally tonally speaking, not like

454
00:25:16,640 --> 00:25:17,240
Speaker 3: the number.

455
00:25:17,000 --> 00:25:18,879
Speaker 4: Of words or whatever, totally tubular.

456
00:25:19,520 --> 00:25:19,639
Speaker 2: Uh.

457
00:25:19,920 --> 00:25:22,840
Speaker 3: And that they he fed all this stuff through a

458
00:25:22,840 --> 00:25:26,840
Speaker 3: computer to pick out like just very I called that,

459
00:25:26,880 --> 00:25:27,280
Speaker 3: by the way.

460
00:25:27,520 --> 00:25:30,240
Speaker 2: I know, but you're giving me nothing today, are you,

461
00:25:30,320 --> 00:25:32,120
Speaker 2: mac as I keep fooling you these days?

462
00:25:32,320 --> 00:25:36,800
Speaker 3: Yeah, maybe that's it, okay to the max. Uh. So

463
00:25:36,840 --> 00:25:39,639
Speaker 3: he fed this stuff into a computer so they could analyze, like,

464
00:25:40,000 --> 00:25:41,960
Speaker 3: you know, stuff that humans can't even hear, like a

465
00:25:41,960 --> 00:25:45,760
Speaker 3: program designed to analyze little minute differences. And what they

466
00:25:45,800 --> 00:25:48,280
Speaker 3: found out was when they did experiments of like human

467
00:25:48,320 --> 00:25:51,280
Speaker 3: beings walking and approaching the prairie dogs, they would say

468
00:25:51,280 --> 00:25:54,159
Speaker 3: something different, for here comes the tall guy and the

469
00:25:54,200 --> 00:25:59,080
Speaker 3: hat rather than here comes the short woman in the

470
00:25:59,520 --> 00:26:00,160
Speaker 3: high heat.

471
00:26:01,480 --> 00:26:02,840
Speaker 2: Right walking through the prairie.

472
00:26:03,160 --> 00:26:06,480
Speaker 3: Yeah, you know, because all those short ladies and high heels.

473
00:26:06,400 --> 00:26:08,520
Speaker 2: Right, and the tall man in the yellow hat.

474
00:26:08,720 --> 00:26:10,160
Speaker 3: Yeah, I'm not sure what happened there.

475
00:26:11,520 --> 00:26:15,359
Speaker 2: So so things are starting to kind of pick up here.

476
00:26:15,680 --> 00:26:17,480
Speaker 2: There's a one more we need to throw out that

477
00:26:17,560 --> 00:26:23,320
Speaker 2: really had a big effect on demonstrating language use among

478
00:26:24,000 --> 00:26:27,960
Speaker 2: among animals, and that was among Japanese tits using grammar.

479
00:26:29,000 --> 00:26:30,280
Speaker 3: These are birds, by the way.

480
00:26:30,240 --> 00:26:33,040
Speaker 2: Cute little birds. Yes, thank you for rescuing me from

481
00:26:33,800 --> 00:26:38,560
Speaker 2: angry parents. And they have a distinct sound for snake.

482
00:26:39,040 --> 00:26:39,600
Speaker 3: Yeah.

483
00:26:39,640 --> 00:26:42,600
Speaker 2: And if you say that, if you take that sound,

484
00:26:43,480 --> 00:26:45,119
Speaker 2: I'm not quite sure what the sound is, but I

485
00:26:45,200 --> 00:26:47,720
Speaker 2: believe it's more than one part. And you play it

486
00:26:47,760 --> 00:26:51,120
Speaker 2: out of order, it means nothing to them. To the birds,

487
00:26:51,640 --> 00:26:53,360
Speaker 2: But if you play in order, they're like, oh my god,

488
00:26:53,400 --> 00:26:57,680
Speaker 2: a snake ware and they've they've That shows that there

489
00:26:57,800 --> 00:27:01,840
Speaker 2: is grammar, there's word order mountains. And if it were

490
00:27:02,000 --> 00:27:04,760
Speaker 2: just innate, if it were just an involuntary reflex, it

491
00:27:04,760 --> 00:27:07,320
Speaker 2: wouldn't matter how you said that. If they heard one

492
00:27:07,359 --> 00:27:10,159
Speaker 2: of those tones or whatever, it would it would evoke

493
00:27:10,280 --> 00:27:14,439
Speaker 2: some sort of reaction or response. So you can speak

494
00:27:15,160 --> 00:27:20,000
Speaker 2: Gibberish to Japanese tits just by switching the word order

495
00:27:20,160 --> 00:27:22,680
Speaker 2: or the order of the sounds, which is the same thing,

496
00:27:22,720 --> 00:27:24,160
Speaker 2: basically switching word order.

497
00:27:24,840 --> 00:27:27,960
Speaker 3: Yeah, pretty cool this, you know. Now we get to

498
00:27:28,040 --> 00:27:31,639
Speaker 3: the question of is this something that they've learned or

499
00:27:31,720 --> 00:27:34,919
Speaker 3: is it instinctive, Like are their older counterparts teaching in

500
00:27:34,960 --> 00:27:38,199
Speaker 3: them these languages? Are they born with it? And we

501
00:27:38,240 --> 00:27:41,000
Speaker 3: have a couple of really cool examples. One is something

502
00:27:41,040 --> 00:27:45,040
Speaker 3: we talked about at length in the b episode, which

503
00:27:45,080 --> 00:27:50,879
Speaker 3: is the waggle dance that honey bees do. Basically to

504
00:27:50,960 --> 00:27:54,239
Speaker 3: show another bee where the food is. They use their

505
00:27:54,240 --> 00:27:56,800
Speaker 3: body position in relation to the sun and do this

506
00:27:56,840 --> 00:28:00,680
Speaker 3: little vibrating waggle to indicate the distance, and that's how

507
00:28:00,720 --> 00:28:03,000
Speaker 3: they tell everyone like, hey, let's go find this honey.

508
00:28:03,760 --> 00:28:06,639
Speaker 3: In nineteen seventy three, a gentleman named Carl von Frisch

509
00:28:07,080 --> 00:28:10,760
Speaker 3: won a Nobel Prize in physiology by translating this dance

510
00:28:10,800 --> 00:28:13,119
Speaker 3: and kind of figuring it out. And then just this

511
00:28:13,240 --> 00:28:16,119
Speaker 3: year in twenty twenty three, they did a study about

512
00:28:16,119 --> 00:28:19,240
Speaker 3: whether this was learned or something they're born with, and

513
00:28:19,280 --> 00:28:21,840
Speaker 3: they found that it's super cool, but it's a little

514
00:28:21,880 --> 00:28:25,320
Speaker 3: bit of both. So they got little baby bees who

515
00:28:25,560 --> 00:28:29,520
Speaker 3: hadn't seen this waggle dance yet isolated them, and they

516
00:28:30,240 --> 00:28:33,119
Speaker 3: found that they actually did try the waggle dance, so

517
00:28:33,240 --> 00:28:35,959
Speaker 3: it is somewhat innate something they're born with, but they

518
00:28:36,000 --> 00:28:38,400
Speaker 3: weren't very good at it. And when they compared those

519
00:28:38,440 --> 00:28:42,400
Speaker 3: to other baby bees who were living with adult bees

520
00:28:42,600 --> 00:28:46,000
Speaker 3: who were ostensibly teaching them this dance, they did it

521
00:28:46,080 --> 00:28:48,920
Speaker 3: much much better, were way more accurate as far as

522
00:28:48,960 --> 00:28:51,760
Speaker 3: the distance goes. And so they found that like, yeah,

523
00:28:51,800 --> 00:28:53,920
Speaker 3: they are born with it to a certain degree, but

524
00:28:53,960 --> 00:28:56,000
Speaker 3: they get better at it by being taught.

525
00:28:56,440 --> 00:29:00,000
Speaker 2: Yes, and just a teeny teensy bit of it is mabelin.

526
00:29:00,240 --> 00:29:03,040
Speaker 3: Right, all right, there you go, you heavy?

527
00:29:03,480 --> 00:29:04,160
Speaker 4: Yes, thanks you.

528
00:29:05,840 --> 00:29:10,280
Speaker 2: So there's so bees that's a wiggle dances is almost

529
00:29:10,320 --> 00:29:14,280
Speaker 2: just entirely incomprehensible to us, except for Carl von Hirsch.

530
00:29:14,320 --> 00:29:15,280
Speaker 4: He figured it out.

531
00:29:16,920 --> 00:29:21,440
Speaker 2: A much more closely relatable to us are hand signals,

532
00:29:21,520 --> 00:29:24,040
Speaker 2: the communication that a lot of the great apes use.

533
00:29:25,480 --> 00:29:29,040
Speaker 2: And what they found is that across apes there's similar

534
00:29:29,120 --> 00:29:34,280
Speaker 2: similar gestures for similar meanings, but that groups and different

535
00:29:34,320 --> 00:29:38,000
Speaker 2: species can use slightly different gestures. Whether it's a hand gesture,

536
00:29:38,000 --> 00:29:41,280
Speaker 2: I think chewing on a leaf a certain way is flirting,

537
00:29:42,360 --> 00:29:48,640
Speaker 2: there's a lot of different communicative body language or body

538
00:29:48,680 --> 00:29:52,160
Speaker 2: gestures that apes use, but that they can be slightly

539
00:29:52,200 --> 00:29:56,000
Speaker 2: different based on groups, which is a dialect. A dialect

540
00:29:56,080 --> 00:29:59,400
Speaker 2: is the use of a similar language or the same

541
00:29:59,480 --> 00:30:04,960
Speaker 2: language in slightly different ways based on your group, your culture,

542
00:30:05,400 --> 00:30:08,560
Speaker 2: your geography, what region you live in. It's exactly the

543
00:30:08,600 --> 00:30:12,479
Speaker 2: same thing as the distinction between soda or pop or

544
00:30:12,480 --> 00:30:15,680
Speaker 2: coke depending on where you live in the United States.

545
00:30:16,040 --> 00:30:19,040
Speaker 2: Same thing, same meaning. Those are all English words, but

546
00:30:19,240 --> 00:30:21,560
Speaker 2: you would say that based on where you live or

547
00:30:21,600 --> 00:30:24,320
Speaker 2: where you were raised or the culture that you were

548
00:30:24,400 --> 00:30:24,880
Speaker 2: raised in.

549
00:30:25,360 --> 00:30:28,680
Speaker 3: Yeah, and you know, chimpanzees, of course, get a lot

550
00:30:28,720 --> 00:30:31,040
Speaker 3: of research on stuff like this, and I think people

551
00:30:31,040 --> 00:30:34,480
Speaker 3: are more apt to believe that a chimp would do

552
00:30:34,520 --> 00:30:37,640
Speaker 3: something like that because they're just more like us. But

553
00:30:37,800 --> 00:30:40,040
Speaker 3: they found the same thing in all kinds of animals,

554
00:30:40,240 --> 00:30:43,040
Speaker 3: one of which is the naked mole rat, that they

555
00:30:43,120 --> 00:30:45,600
Speaker 3: respond as far as the dialects go, they respond to

556
00:30:45,680 --> 00:30:48,720
Speaker 3: soft chirps from people in their own colonies more than

557
00:30:48,720 --> 00:30:52,440
Speaker 3: they do those a similar sounding soft chirp in a

558
00:30:52,480 --> 00:30:55,120
Speaker 3: different colony. So, in other words, it's a different dialect,

559
00:30:56,080 --> 00:30:57,840
Speaker 3: and we're going to be doing one on naked mole

560
00:30:57,880 --> 00:30:58,880
Speaker 3: rats at some point.

561
00:30:59,120 --> 00:30:59,520
Speaker 4: Easy it is.

562
00:31:00,320 --> 00:31:03,680
Speaker 3: I forgot forgot how much I love this animal from

563
00:31:04,040 --> 00:31:07,160
Speaker 3: watching the great documentary Fast, Cheap and out of Control

564
00:31:07,680 --> 00:31:12,240
Speaker 3: by master documentarian Errol Morris. And we'll talk about it

565
00:31:12,280 --> 00:31:15,160
Speaker 3: when we eventually get to that episode. Okay, so, same

566
00:31:15,600 --> 00:31:18,600
Speaker 3: great doc I have a recommend it though. You want

567
00:31:18,600 --> 00:31:21,760
Speaker 3: to hear something super amazing? Yes, do you remember?

568
00:31:21,800 --> 00:31:24,680
Speaker 2: And I think our Evolution of Human Intelligence episode we

569
00:31:24,720 --> 00:31:27,320
Speaker 2: talked about how they think the word he might actually

570
00:31:27,360 --> 00:31:31,320
Speaker 2: be so old that Neanderthals might have used it, like

571
00:31:31,360 --> 00:31:35,480
Speaker 2: it's one of the oldest sounds that humans make. There's

572
00:31:35,640 --> 00:31:38,640
Speaker 2: the hand gesture for come here that you use where

573
00:31:38,640 --> 00:31:40,280
Speaker 2: you kind of point right in front of you with

574
00:31:40,320 --> 00:31:41,320
Speaker 2: your fingers downward.

575
00:31:41,720 --> 00:31:44,040
Speaker 4: M Yeah, apes, apes use that.

576
00:31:44,600 --> 00:31:47,280
Speaker 2: Oh wow, we still use the same hand gesture that

577
00:31:47,320 --> 00:31:49,040
Speaker 2: we used back when we were.

578
00:31:49,840 --> 00:31:51,320
Speaker 4: Full on great apes.

579
00:31:51,760 --> 00:31:52,320
Speaker 3: That's awesome.

580
00:31:52,640 --> 00:31:53,440
Speaker 2: Isn't that amazing?

581
00:31:53,720 --> 00:31:53,840
Speaker 3: Ye?

582
00:31:54,440 --> 00:31:57,480
Speaker 2: It just works so well. Why why fix what ain't broke?

583
00:31:57,520 --> 00:31:59,720
Speaker 2: We decided over millions and millions of years.

584
00:32:00,240 --> 00:32:03,560
Speaker 3: That's super cool. I think we should take a break

585
00:32:05,080 --> 00:32:07,280
Speaker 3: because we have a star of the show that's about

586
00:32:07,280 --> 00:32:10,720
Speaker 3: to appear several but a big star of the show

587
00:32:10,800 --> 00:32:13,680
Speaker 3: is about to appear in Communication and that's called the Whale.

588
00:32:14,320 --> 00:32:16,240
Speaker 3: And we'll be back to talk about wales right.

589
00:32:16,120 --> 00:32:38,000
Speaker 4: After this, okay, Chuck.

590
00:32:38,040 --> 00:32:40,480
Speaker 2: So, in addition to the Naked mole Rat episode, I

591
00:32:40,520 --> 00:32:42,440
Speaker 2: want to do an episode on the Save the Whales

592
00:32:42,480 --> 00:32:43,080
Speaker 2: movement that.

593
00:32:43,080 --> 00:32:45,440
Speaker 3: Was oh yeah, yeah, yeah, Okay, we're.

594
00:32:45,280 --> 00:32:46,600
Speaker 4: Gonna do that, okay.

595
00:32:47,440 --> 00:32:49,960
Speaker 2: But central to that was a guy named Roger Payne,

596
00:32:50,000 --> 00:32:52,360
Speaker 2: who I'm sure will come quite a bit in that episode,

597
00:32:52,680 --> 00:32:55,200
Speaker 2: so we won't get into him too deeply, but he

598
00:32:55,640 --> 00:33:01,920
Speaker 2: was a I believe, a biological acoustician, some really arcane

599
00:33:02,480 --> 00:33:08,520
Speaker 2: specialty made he Basically, he, I guess, had friends in

600
00:33:08,760 --> 00:33:12,520
Speaker 2: US Navy listening stations that had been set up to

601
00:33:12,640 --> 00:33:18,280
Speaker 2: eavesdrop on ro Soviet submarines, and those navy stations were

602
00:33:18,440 --> 00:33:21,760
Speaker 2: turning up these amazing recordings of whale songs that people

603
00:33:21,840 --> 00:33:25,040
Speaker 2: just didn't realize existed up to that point, and Roger

604
00:33:25,080 --> 00:33:28,760
Speaker 2: Paine wrote a science article on it that came out

605
00:33:29,320 --> 00:33:32,680
Speaker 2: in the early seventies, and then simultaneous to that, he

606
00:33:32,720 --> 00:33:35,840
Speaker 2: introduced it to the larger public, not just the scientific community,

607
00:33:36,120 --> 00:33:39,640
Speaker 2: with an album called Songs of the Humpback Whale. It

608
00:33:39,680 --> 00:33:43,400
Speaker 2: was released in nineteen seventy. It is recordings, I think,

609
00:33:43,400 --> 00:33:46,840
Speaker 2: a thirty five minute album of recording of whale songs.

610
00:33:47,480 --> 00:33:51,880
Speaker 2: It went multi platinum because I imagine that you could take

611
00:33:52,000 --> 00:33:54,680
Speaker 2: acid or smoke pot and just sit there and zone

612
00:33:54,720 --> 00:33:58,240
Speaker 2: out to that for hours. But also it really dovetailed

613
00:33:58,240 --> 00:34:02,600
Speaker 2: with the nascent environmental movement that was coming along at

614
00:34:02,600 --> 00:34:05,880
Speaker 2: the same time too, and that actually helped contribute to

615
00:34:05,920 --> 00:34:10,120
Speaker 2: the Save the Whales campaign that was highly successful just

616
00:34:10,160 --> 00:34:11,640
Speaker 2: from releasing that album.

617
00:34:11,760 --> 00:34:15,000
Speaker 3: Yeah, I've listened to it today. You can stream it

618
00:34:15,160 --> 00:34:18,399
Speaker 3: anytime you want. It's I'm sure you listen to it, right.

619
00:34:18,840 --> 00:34:19,280
Speaker 4: I didn't.

620
00:34:19,320 --> 00:34:23,480
Speaker 2: I found it distracting, Like even though it's instrumental silent,

621
00:34:23,640 --> 00:34:25,759
Speaker 2: I was my mind kept being like, what the hell

622
00:34:25,840 --> 00:34:27,960
Speaker 2: is that? And I couldn't concentrate, so I did turn

623
00:34:28,000 --> 00:34:28,319
Speaker 2: it off.

624
00:34:28,520 --> 00:34:30,840
Speaker 3: Oh interesting, because I found it to be because I

625
00:34:30,880 --> 00:34:33,799
Speaker 3: can only listen to very specific kinds of music when

626
00:34:33,840 --> 00:34:38,719
Speaker 3: I do this study stuff, like mainly it's Brian Eno

627
00:34:38,880 --> 00:34:41,960
Speaker 3: and now songs of the humpback Whales in Brian Adams

628
00:34:42,400 --> 00:34:44,759
Speaker 3: and Brian Adams. It was good though I like it.

629
00:34:44,800 --> 00:34:48,400
Speaker 3: I mean it's not it's very much background music and

630
00:34:48,440 --> 00:34:51,800
Speaker 3: it's not even like a like I would defy anyone

631
00:34:51,800 --> 00:34:53,400
Speaker 3: to even say, like, oh, if you listen to this

632
00:34:53,520 --> 00:34:55,799
Speaker 3: like a melodic and it's like a song, it's really not.

633
00:34:55,920 --> 00:34:58,840
Speaker 3: It's whales making noises. But I just found it very relaxing.

634
00:34:59,120 --> 00:34:59,560
Speaker 4: Yeah, it is.

635
00:35:00,640 --> 00:35:03,319
Speaker 2: I can imagine like if I were, if I were

636
00:35:03,680 --> 00:35:06,120
Speaker 2: just kind of sitting around zoning out on that, it

637
00:35:06,160 --> 00:35:10,000
Speaker 2: would be extraordinarily pleasant. But my brain just wants to focus.

638
00:35:09,800 --> 00:35:13,680
Speaker 3: Yeah, on acid on PCP. There are all kinds of whales, though,

639
00:35:13,680 --> 00:35:16,799
Speaker 3: and they have all kinds of communications that we've learned

640
00:35:16,800 --> 00:35:19,640
Speaker 3: about over the years. The sperm whale they have what

641
00:35:19,680 --> 00:35:24,040
Speaker 3: they call like a coda click pattern, and it depends

642
00:35:24,120 --> 00:35:26,080
Speaker 3: It's kind of like a dialect as well, I guess,

643
00:35:26,120 --> 00:35:31,200
Speaker 3: because it depends on what clan and clans are, you know,

644
00:35:31,280 --> 00:35:34,080
Speaker 3: groups of huge, huge groups of whales. Sometimes there are

645
00:35:34,080 --> 00:35:38,120
Speaker 3: thousands of them, made up of smaller groups, usually five

646
00:35:38,160 --> 00:35:41,080
Speaker 3: to ten female adults and their kids. But they get

647
00:35:41,120 --> 00:35:44,319
Speaker 3: together in these big clans and the different clans have

648
00:35:44,520 --> 00:35:47,880
Speaker 3: you know, variations of their language, and when they have

649
00:35:47,960 --> 00:35:52,520
Speaker 3: overlapping territories, they get really really distinct because they're overlapping,

650
00:35:52,560 --> 00:35:54,040
Speaker 3: so they can tell one another apart.

651
00:35:54,520 --> 00:35:57,239
Speaker 2: Yeah, they have like a clan signifier that they use

652
00:35:57,360 --> 00:36:01,200
Speaker 2: identify themselves to others, right, because these whales don't really

653
00:36:01,280 --> 00:36:05,120
Speaker 2: navigate the world by eyesight, they mostly do it from sound,

654
00:36:05,719 --> 00:36:09,040
Speaker 2: so that's how you would do that. And these clicks

655
00:36:09,120 --> 00:36:13,680
Speaker 2: apparently can last about ten milliseconds, but they're two hundred

656
00:36:13,680 --> 00:36:18,279
Speaker 2: and thirty six decibels in volume, And to give you

657
00:36:18,400 --> 00:36:24,480
Speaker 2: a a comparison, that's the word I'm groping for. Totally

658
00:36:24,719 --> 00:36:30,200
Speaker 2: gunshot is one hundred and forty decibels. Okay, okay, your

659
00:36:30,239 --> 00:36:33,160
Speaker 2: pain threshold starts around there. So if you happen to

660
00:36:33,200 --> 00:36:38,160
Speaker 2: be sitting next to a sperm whale underwater when it

661
00:36:38,239 --> 00:36:40,239
Speaker 2: let out one of these clicks, it would blow your

662
00:36:40,280 --> 00:36:42,640
Speaker 2: ear drums right out and possibly your entire head.

663
00:36:43,040 --> 00:36:45,440
Speaker 3: I can only picture you now underwater, sitting in your

664
00:36:45,480 --> 00:36:49,920
Speaker 3: don't be Dumb chair, just floating above it or standing

665
00:36:49,960 --> 00:36:54,919
Speaker 3: awkwardly beside it, yeah, or underneath it, or who knows,

666
00:36:54,680 --> 00:36:55,760
Speaker 3: things got weird.

667
00:36:56,000 --> 00:36:57,719
Speaker 2: Yeah, it did get a little weird right out of

668
00:36:57,760 --> 00:36:58,040
Speaker 2: the gate.

669
00:36:58,120 --> 00:37:00,719
Speaker 3: Really well, that was the whole point. And I love

670
00:37:00,760 --> 00:37:04,759
Speaker 3: for your Instagram birthday posts. When I ask people to

671
00:37:04,760 --> 00:37:07,919
Speaker 3: make their favorite Josh moments, those a lot of don't

672
00:37:07,920 --> 00:37:09,240
Speaker 3: be dumb in there. I don't know if you noticed.

673
00:37:10,000 --> 00:37:12,560
Speaker 2: I did, and thank you for that. That was extraordinarily

674
00:37:12,719 --> 00:37:13,200
Speaker 2: kind of you.

675
00:37:13,480 --> 00:37:16,319
Speaker 3: Of course, man, that's all true, but people love. Don't

676
00:37:16,360 --> 00:37:16,760
Speaker 3: be dumb.

677
00:37:16,960 --> 00:37:20,719
Speaker 4: I get it, like I get it. Everybody just shut up.

678
00:37:20,880 --> 00:37:26,160
Speaker 3: No, it was great. Baby sperm whales and orcas have

679
00:37:26,400 --> 00:37:29,280
Speaker 3: like baby talk. They babble just like a little human

680
00:37:29,320 --> 00:37:33,279
Speaker 3: baby would. When they're learning. Newborn orcas make a really

681
00:37:33,360 --> 00:37:37,000
Speaker 3: high pitched call. It changes as they grow into adults

682
00:37:37,000 --> 00:37:40,800
Speaker 3: into a completely different sound, and it starts at about

683
00:37:40,840 --> 00:37:45,160
Speaker 3: two months. The adult sounding stuff they start to learn, basically,

684
00:37:45,800 --> 00:37:48,640
Speaker 3: it seems like it about two months, and then for

685
00:37:48,800 --> 00:37:50,920
Speaker 3: years and years until they hit pubert. They are learning

686
00:37:50,960 --> 00:37:54,280
Speaker 3: new vocalizations aka words.

687
00:37:54,360 --> 00:37:59,760
Speaker 2: Exactly like the development of human kids too. Yeah, orcas

688
00:37:59,800 --> 00:38:02,680
Speaker 2: all so very much like those birds that live around

689
00:38:02,680 --> 00:38:05,359
Speaker 2: the Campbell's monkeys. They can learn the calls of other

690
00:38:05,560 --> 00:38:09,719
Speaker 2: species too that they live around. Apparently, orcus can understand

691
00:38:09,800 --> 00:38:13,320
Speaker 2: what bottlenose dolphins are saying to one another. Again, bottlenose

692
00:38:13,400 --> 00:38:15,880
Speaker 2: dolphin is not trying to communicate with the orca. The

693
00:38:16,000 --> 00:38:18,680
Speaker 2: orc is just eavesdropping and if it hears like O,

694
00:38:18,719 --> 00:38:21,640
Speaker 2: there's some really great salmon over here you're clear, the

695
00:38:21,719 --> 00:38:23,520
Speaker 2: orca will be like, I'm going straight to it because

696
00:38:23,520 --> 00:38:24,720
Speaker 2: I love chinook salmon.

697
00:38:25,160 --> 00:38:28,200
Speaker 3: Yeah. The dolphin actually has my fact of the podcast

698
00:38:29,239 --> 00:38:31,920
Speaker 3: that I can't wait to tell Ruby later on what

699
00:38:33,480 --> 00:38:37,520
Speaker 3: they name themselves. Within the first few months of being born,

700
00:38:37,960 --> 00:38:41,440
Speaker 3: they create a very signature whistle to identify themselves, so

701
00:38:41,480 --> 00:38:42,799
Speaker 3: they you know, that's their name.

702
00:38:43,400 --> 00:38:46,160
Speaker 2: Yeah, And so I really want to make sure that

703
00:38:46,239 --> 00:38:51,520
Speaker 2: this lands because sperm whales have clan codas you're saying

704
00:38:51,520 --> 00:38:56,520
Speaker 2: to other sperm whales, I'm a member of the Jamboree

705
00:38:56,560 --> 00:39:00,399
Speaker 2: clan or whatever whatever they would name themselves. Sure, it's

706
00:39:00,400 --> 00:39:05,040
Speaker 2: like in click sounds, Yeah, but that's for their clan membership,

707
00:39:05,160 --> 00:39:09,120
Speaker 2: not them as individuals. Bottlenose dolphins name themselves as individuals,

708
00:39:09,200 --> 00:39:11,560
Speaker 2: like this is my name. I'm Josh the dolphin. Good

709
00:39:11,600 --> 00:39:14,120
Speaker 2: to meet you. That is what they're doing. Like that,

710
00:39:14,520 --> 00:39:18,680
Speaker 2: it's that level of identity, individual identity that they're using

711
00:39:18,719 --> 00:39:21,680
Speaker 2: to introduce themselves to other dolphins.

712
00:39:22,160 --> 00:39:23,560
Speaker 3: Amazing it is.

713
00:39:23,640 --> 00:39:25,720
Speaker 2: That really is one of the facts of the podcast.

714
00:39:25,800 --> 00:39:28,440
Speaker 2: In the podcast chock full of facts of the podcast.

715
00:39:28,760 --> 00:39:30,759
Speaker 3: So we should probably talk about the brain a little bit,

716
00:39:32,440 --> 00:39:34,799
Speaker 3: and like, you know, the question like have we ever

717
00:39:34,840 --> 00:39:36,880
Speaker 3: actually studied the brain of these animals to see if

718
00:39:36,880 --> 00:39:39,200
Speaker 3: there are anything like humans, because we know so much

719
00:39:39,200 --> 00:39:42,400
Speaker 3: about human brains and the areas of the brain that

720
00:39:42,440 --> 00:39:45,799
Speaker 3: handle communication and like emotion and stuff like that that

721
00:39:45,880 --> 00:39:50,399
Speaker 3: comes out through communication. And yes, they have. And we're

722
00:39:50,400 --> 00:39:53,720
Speaker 3: gonna talk about something called spindle cells, which were discovered

723
00:39:53,760 --> 00:39:57,560
Speaker 3: in eighteen eighty one and then basically went away and

724
00:39:57,600 --> 00:40:01,640
Speaker 3: were rediscovered in nineteen ninety five. And these are specialized

725
00:40:01,680 --> 00:40:05,960
Speaker 3: neurons that are in two very specific brain regions, the ACC,

726
00:40:06,200 --> 00:40:11,960
Speaker 3: the anterior cingulate cortex, and the frontal insula the FI.

727
00:40:12,719 --> 00:40:16,160
Speaker 3: And they've basically established that both of these regions of

728
00:40:16,200 --> 00:40:19,960
Speaker 3: the brain and humans are where we experience our emotion

729
00:40:20,480 --> 00:40:25,360
Speaker 3: and are really important for monitoring ourselves and our bodies

730
00:40:25,440 --> 00:40:28,400
Speaker 3: and how we feel, like are we in pain? Are

731
00:40:28,440 --> 00:40:32,920
Speaker 3: we hungry? Did we goof something up? Like self monitoring?

732
00:40:32,960 --> 00:40:35,719
Speaker 2: Basically yeah, and not just self monitoring like on the

733
00:40:35,760 --> 00:40:38,319
Speaker 2: individual level, but in relation to other people, like you said,

734
00:40:38,360 --> 00:40:40,440
Speaker 2: like did we goof something up? Should we feel embarrassed?

735
00:40:40,480 --> 00:40:42,879
Speaker 2: Have we made a social gaff? All of these things

736
00:40:42,960 --> 00:40:46,480
Speaker 2: are kind of controlled self monitoring, self reflection by the

737
00:40:47,120 --> 00:40:52,200
Speaker 2: anterior cingulate cortex and the frontal insula. Right, So our

738
00:40:52,640 --> 00:40:58,719
Speaker 2: ability to empathize, essentially is what we're talking about, is

739
00:40:58,719 --> 00:41:02,440
Speaker 2: from the activity of these two and they're characterized by

740
00:41:02,480 --> 00:41:06,000
Speaker 2: a large number of spindle cells, and only spindle cells

741
00:41:06,000 --> 00:41:10,040
Speaker 2: are found in these areas. Okay, so we're like, okay,

742
00:41:10,120 --> 00:41:13,600
Speaker 2: spindle cells, that's the seat of empathy, of emotion, of

743
00:41:13,719 --> 00:41:18,719
Speaker 2: understanding other people. Well, it turns out that I don't

744
00:41:18,719 --> 00:41:23,080
Speaker 2: know if it's neuron for neuron, sperm whales have more

745
00:41:23,120 --> 00:41:27,520
Speaker 2: spindle cells than human beings do. Yeah, okay, so we

746
00:41:27,640 --> 00:41:30,360
Speaker 2: have really good evidence and it's not just sperm whales.

747
00:41:30,360 --> 00:41:33,160
Speaker 2: There are other cetaceans. A lot of the great apes

748
00:41:33,200 --> 00:41:35,840
Speaker 2: have spindle cells too. Don't ask how we know this,

749
00:41:35,960 --> 00:41:38,799
Speaker 2: by the way, but where we've found that they have

750
00:41:39,040 --> 00:41:42,960
Speaker 2: the makings of what it would take to empathize with others.

751
00:41:43,120 --> 00:41:46,319
Speaker 2: And if you put that together with the assumption or

752
00:41:46,320 --> 00:41:50,640
Speaker 2: the growing understanding that they're communicating in very deep levels,

753
00:41:51,040 --> 00:41:54,279
Speaker 2: it would make sense that if we can decode what

754
00:41:54,360 --> 00:41:56,560
Speaker 2: they're saying, we'll find they have quite a bit to

755
00:41:56,640 --> 00:42:00,840
Speaker 2: say that we could conceivably, you know, understand and connect with.

756
00:42:01,520 --> 00:42:04,040
Speaker 3: Yeah, that's I mean, that's just incredible.

757
00:42:04,400 --> 00:42:06,280
Speaker 2: It really is, because what we're seeing with those spindle

758
00:42:06,320 --> 00:42:10,600
Speaker 2: cells is they're not like, you know, I'm hungry, let's

759
00:42:10,640 --> 00:42:13,319
Speaker 2: eat that snake. And that's like the extent, the most

760
00:42:13,360 --> 00:42:17,919
Speaker 2: fascinating thing a chimp says on any given day. Who

761
00:42:17,960 --> 00:42:20,080
Speaker 2: knows what they're thinking, Like, it just opens up a

762
00:42:20,120 --> 00:42:24,320
Speaker 2: whole universe of possibility about what they're thinking, what they're feeling,

763
00:42:24,520 --> 00:42:28,840
Speaker 2: because bear in mind, they're also experiencing life in the universe,

764
00:42:28,880 --> 00:42:33,319
Speaker 2: in the world and everything in a totally different way

765
00:42:33,360 --> 00:42:35,080
Speaker 2: than we are. So the idea of being able to

766
00:42:35,120 --> 00:42:37,759
Speaker 2: tap into that and then to share our experience with them,

767
00:42:38,040 --> 00:42:42,759
Speaker 2: I mean, I can't imagine what just massive impacts that

768
00:42:42,760 --> 00:42:45,440
Speaker 2: would have on humanity and hopefully on the world and

769
00:42:45,520 --> 00:42:46,799
Speaker 2: in general if we could do that.

770
00:42:47,280 --> 00:42:49,920
Speaker 3: Oh yeah, well, and to be able to figure a

771
00:42:49,960 --> 00:42:52,920
Speaker 3: lot of this stuff out, they've they've we've long realized

772
00:42:52,960 --> 00:42:56,239
Speaker 3: that some of this stuff is just beyond our abilities.

773
00:42:56,880 --> 00:43:00,440
Speaker 3: The crow is one example. They use, like have a

774
00:43:00,520 --> 00:43:04,960
Speaker 3: lot of different vocalizations of varying pitches and durations and

775
00:43:05,000 --> 00:43:10,120
Speaker 3: inflections and rhythms and cadences, and there's just no way

776
00:43:10,160 --> 00:43:14,520
Speaker 3: that humans could listen enough basically and isolate these crows

777
00:43:14,680 --> 00:43:17,920
Speaker 3: by sex and age and social status and where they

778
00:43:17,960 --> 00:43:20,600
Speaker 3: are and to be able to really learn all of

779
00:43:20,640 --> 00:43:24,040
Speaker 3: this stuff. So, you know, we talked about AI and

780
00:43:24,160 --> 00:43:27,640
Speaker 3: large language models recently and got a few emails from

781
00:43:27,680 --> 00:43:29,560
Speaker 3: people that are like, you know, there seem to be

782
00:43:29,640 --> 00:43:32,439
Speaker 3: a lot of fear based stuff in this, which is true,

783
00:43:33,440 --> 00:43:35,640
Speaker 3: and you guys didn't focus on any of the great

784
00:43:35,680 --> 00:43:39,040
Speaker 3: possibilities and maybe we didn't. So here's one cool thing

785
00:43:39,080 --> 00:43:42,640
Speaker 3: that AI is gonna potentially do and that they're already

786
00:43:42,680 --> 00:43:46,400
Speaker 3: starting to use, is helping out just sort of like

787
00:43:46,480 --> 00:43:49,520
Speaker 3: we have figured out or how AI is working with

788
00:43:49,640 --> 00:43:53,239
Speaker 3: large language models as predictors of like how a human

789
00:43:53,320 --> 00:43:56,719
Speaker 3: might type of sentence that makes sense, and doing the

790
00:43:56,719 --> 00:43:59,480
Speaker 3: same thing with animals basically and trying to figure out

791
00:43:59,480 --> 00:44:00,319
Speaker 3: their language.

792
00:44:00,680 --> 00:44:03,760
Speaker 2: Yeah, just detecting patterns, figuring out what words are important,

793
00:44:03,800 --> 00:44:06,960
Speaker 2: how they're being used, all this stuff. They've already got it.

794
00:44:07,360 --> 00:44:10,759
Speaker 2: I think Deep Squeak was the first one that analyzes

795
00:44:10,880 --> 00:44:13,920
Speaker 2: rodent sounds. And there's a couple of groups. One is

796
00:44:14,000 --> 00:44:19,960
Speaker 2: seti ceti cetacean translation initiative project that's led by a

797
00:44:19,960 --> 00:44:23,560
Speaker 2: guy named David Gruber. They have there. The way I

798
00:44:23,640 --> 00:44:26,160
Speaker 2: saw it put is that they're going all in on

799
00:44:26,160 --> 00:44:30,200
Speaker 2: one plan, the EC two clan of sperm whales off

800
00:44:30,239 --> 00:44:35,799
Speaker 2: the island of Dominica, and they are completely observing and

801
00:44:35,880 --> 00:44:39,239
Speaker 2: monitoring this clan of sperm whales twenty four hours a day,

802
00:44:39,560 --> 00:44:42,200
Speaker 2: every day of the year. They're down to using robotic

803
00:44:42,280 --> 00:44:45,360
Speaker 2: fish that are gathering video and audio and everything that

804
00:44:45,440 --> 00:44:49,680
Speaker 2: swim along with the whales. They know everything that these

805
00:44:49,680 --> 00:44:52,399
Speaker 2: whales are doing at any given moment. And so not

806
00:44:52,440 --> 00:44:55,239
Speaker 2: only are they getting these whale songs and collecting them

807
00:44:55,280 --> 00:44:59,520
Speaker 2: to feed into this the large language model to understand it,

808
00:44:59,640 --> 00:45:03,120
Speaker 2: they're all so notating this stuff so that the context

809
00:45:03,160 --> 00:45:06,239
Speaker 2: is also understood too, because what they what they think

810
00:45:06,320 --> 00:45:09,600
Speaker 2: is that a different click or coda depending on the context,

811
00:45:09,680 --> 00:45:12,560
Speaker 2: can totally change meaning. So they also need to know

812
00:45:12,600 --> 00:45:14,719
Speaker 2: this too. But it's a huge undertaking. They have like

813
00:45:14,800 --> 00:45:18,160
Speaker 2: tens of thousands of whale song right now. To probably

814
00:45:18,200 --> 00:45:22,280
Speaker 2: crack this language, they're gonna need millions. So it's they

815
00:45:22,480 --> 00:45:24,400
Speaker 2: they started on the road, but they've got a ways

816
00:45:24,440 --> 00:45:24,759
Speaker 2: to go.

817
00:45:25,520 --> 00:45:28,359
Speaker 3: Yeah, And it's like, I think what they're looking for

818
00:45:28,440 --> 00:45:32,080
Speaker 3: is that next level, which is not oh wow, the

819
00:45:32,120 --> 00:45:36,000
Speaker 3: whales told other whales where the good fish were or whatever,

820
00:45:36,560 --> 00:45:41,400
Speaker 3: is they want answers to things like do whales tell stories,

821
00:45:41,760 --> 00:45:43,960
Speaker 3: like very rudimentary stories to one another.

822
00:45:43,880 --> 00:45:45,960
Speaker 2: Or very advanced stories? Why does it have to be

823
00:45:46,040 --> 00:45:46,680
Speaker 2: rude to artry?

824
00:45:46,760 --> 00:45:46,920
Speaker 4: You know?

825
00:45:47,080 --> 00:45:51,600
Speaker 3: Yeah, exactly do they if something like something big happens

826
00:45:51,600 --> 00:45:54,040
Speaker 3: to a whale clan, do they talk about it afterward?

827
00:45:54,160 --> 00:45:56,200
Speaker 3: Like a week later? Does someone bring this up? Do

828
00:45:56,280 --> 00:45:59,040
Speaker 3: they do these maths in any way? And these are

829
00:45:59,080 --> 00:46:02,760
Speaker 3: all like questions that would just blast open the door,

830
00:46:03,840 --> 00:46:06,359
Speaker 3: like if answered, just blast open the door of our

831
00:46:06,440 --> 00:46:09,520
Speaker 3: understanding of how animals may talk to one another.

832
00:46:09,719 --> 00:46:13,000
Speaker 2: Yeah, so SETI is eavesdropping in order to understand what

833
00:46:13,040 --> 00:46:16,320
Speaker 2: whales are saying. There's another project called the Earth's Species

834
00:46:16,360 --> 00:46:19,759
Speaker 2: Project ESP that is in part looking at types of

835
00:46:19,760 --> 00:46:22,200
Speaker 2: whales not only to learn what they're saying, but to

836
00:46:22,280 --> 00:46:25,480
Speaker 2: speak to them directly. So the difference between the two

837
00:46:25,560 --> 00:46:28,480
Speaker 2: projects is almost like the difference between SETI and Mehdi

838
00:46:28,920 --> 00:46:31,759
Speaker 2: as far as searching for alien intelligence is concern. It's

839
00:46:31,880 --> 00:46:34,920
Speaker 2: very similar in nature. But one of the things that

840
00:46:35,000 --> 00:46:38,560
Speaker 2: Earth Species Project is trying to do is map all

841
00:46:38,680 --> 00:46:45,200
Speaker 2: species languages to find universal terms or universal concepts and

842
00:46:45,320 --> 00:46:48,800
Speaker 2: understand the different words, so you could translate tiger into whale,

843
00:46:48,800 --> 00:46:52,600
Speaker 2: into human into you know Japanese tit.

844
00:46:54,000 --> 00:46:57,600
Speaker 3: Right, or if they're like conceptually like you were talking about,

845
00:46:57,920 --> 00:47:01,759
Speaker 3: are there overlaps in things like grief or joy or

846
00:47:01,800 --> 00:47:06,000
Speaker 3: these other like big sort of umbrella experiences that seemingly

847
00:47:06,280 --> 00:47:07,440
Speaker 3: any living thing could.

848
00:47:07,360 --> 00:47:11,319
Speaker 2: Experience right exactly, and like you said, it would blow

849
00:47:11,320 --> 00:47:15,800
Speaker 2: things open it totally. Some people are like, this would

850
00:47:15,880 --> 00:47:19,040
Speaker 2: change humans forever, Like how could anybody eat meat after

851
00:47:19,040 --> 00:47:20,680
Speaker 2: that point? If you can understand a pig is saying

852
00:47:20,680 --> 00:47:22,680
Speaker 2: please don't kill me, Please don't kill me while you're

853
00:47:22,719 --> 00:47:26,160
Speaker 2: killing it, right, you couldn't do it, and if you did,

854
00:47:26,200 --> 00:47:29,520
Speaker 2: people would stop you kind of thing. Other people are like, well,

855
00:47:29,520 --> 00:47:32,360
Speaker 2: I'm worried that we're going to use it to manipulate animals,

856
00:47:32,760 --> 00:47:35,000
Speaker 2: and people will probably try to do that too. Up

857
00:47:35,040 --> 00:47:38,880
Speaker 2: the outcome would likely be both, like humans would be changed,

858
00:47:38,920 --> 00:47:43,480
Speaker 2: our relationship with the animal world would be forever changed

859
00:47:43,800 --> 00:47:46,439
Speaker 2: for the better and the worse. And that's just kind

860
00:47:46,440 --> 00:47:49,160
Speaker 2: of how life goes. But that kind of change, I

861
00:47:49,160 --> 00:47:52,400
Speaker 2: can't imagine how amazing it would be to witness that.

862
00:47:53,239 --> 00:47:55,360
Speaker 3: Yeah, I also thought this thing, the one thing you

863
00:47:55,360 --> 00:47:58,920
Speaker 3: said was really cool about, like because the idea of

864
00:47:58,960 --> 00:48:00,680
Speaker 3: like all right, let's say we could only get there

865
00:48:00,680 --> 00:48:05,600
Speaker 3: like or understand. You know, I think who was the

866
00:48:05,600 --> 00:48:06,640
Speaker 3: guy that was named.

867
00:48:06,480 --> 00:48:11,200
Speaker 2: Garber, he's the head of setting Ruber. Yeah, Gruber, Yeah, yeah,

868
00:48:11,360 --> 00:48:11,960
Speaker 2: Gruber was.

869
00:48:12,440 --> 00:48:14,799
Speaker 3: He was like, you know, everyone's really excited. He said

870
00:48:14,840 --> 00:48:16,440
Speaker 3: that also could be a real letdown. It could be

871
00:48:16,560 --> 00:48:20,000
Speaker 3: like the do talk is just super boring. But the

872
00:48:20,080 --> 00:48:22,759
Speaker 3: idea that you know, this thing that you said about, like, well,

873
00:48:22,800 --> 00:48:24,040
Speaker 3: what if we could communicate, what.

874
00:48:24,000 --> 00:48:26,640
Speaker 4: Would what would we have to talk about with the whale?

875
00:48:27,480 --> 00:48:29,640
Speaker 3: And that's where you start to look at like larger

876
00:48:29,640 --> 00:48:31,360
Speaker 3: commonalities of living things.

877
00:48:31,880 --> 00:48:32,040
Speaker 2: Uh.

878
00:48:32,080 --> 00:48:34,239
Speaker 3: And in the case of a whale, like you know,

879
00:48:34,480 --> 00:48:37,280
Speaker 3: I got kids, You got kids. I love to swim,

880
00:48:37,400 --> 00:48:41,200
Speaker 3: I love to sleep. We're both mammals, like we got

881
00:48:41,320 --> 00:48:44,920
Speaker 3: we have a handful of things we could actually talk about. Uh.

882
00:48:44,960 --> 00:48:48,279
Speaker 3: And then the idea of like objectivity as a scientist

883
00:48:48,400 --> 00:48:53,239
Speaker 3: comes in because it's and not objectivity in that like

884
00:48:53,440 --> 00:48:57,120
Speaker 3: I really want this to happen or not, but objectivity

885
00:48:57,160 --> 00:49:01,800
Speaker 3: of just like an experience that a whale can understand,

886
00:49:01,880 --> 00:49:05,239
Speaker 3: can't even understand, like being dry, Like we would talk

887
00:49:05,239 --> 00:49:07,560
Speaker 3: about being wet in a different context that a whale

888
00:49:07,560 --> 00:49:09,040
Speaker 3: would because a whale would just say, like, what do

889
00:49:09,040 --> 00:49:12,560
Speaker 3: you mean wet? Like all is wet exactly, and we

890
00:49:12,560 --> 00:49:14,240
Speaker 3: would say no, like wet's when you take a shower

891
00:49:14,280 --> 00:49:15,239
Speaker 3: or get in a swimming pool.

892
00:49:15,520 --> 00:49:17,720
Speaker 2: But the hope is is that if they are capable

893
00:49:17,760 --> 00:49:20,520
Speaker 2: of empathy, they're probably capable of metaphor, and that we

894
00:49:20,560 --> 00:49:24,040
Speaker 2: could explain things to one another like prairie dogs do,

895
00:49:24,360 --> 00:49:30,719
Speaker 2: like yellow tall human inhabit getting getting ideas and concepts

896
00:49:30,719 --> 00:49:32,480
Speaker 2: across just enough for the other one to kind of

897
00:49:32,560 --> 00:49:34,759
Speaker 2: understand things that are totally foreign.

898
00:49:34,440 --> 00:49:38,960
Speaker 3: To them, or whaling ship nearby swim the other way exactly.

899
00:49:40,920 --> 00:49:44,320
Speaker 2: There was a I saw somebody theorize that you could

900
00:49:44,400 --> 00:49:48,879
Speaker 2: teach like a large language model to analyze and learn

901
00:49:48,960 --> 00:49:52,279
Speaker 2: to speak teach itself to speak whale. But because we

902
00:49:52,320 --> 00:49:57,760
Speaker 2: don't understand how large language models actually work, the AI

903
00:49:58,000 --> 00:50:00,359
Speaker 2: and the whale could have a conversation and we would

904
00:50:00,360 --> 00:50:02,239
Speaker 2: have no idea what they were seeing.

905
00:50:02,520 --> 00:50:04,279
Speaker 3: It's frightening, it is.

906
00:50:04,320 --> 00:50:07,759
Speaker 2: It's frightening, but it's also like wah wah, hilarious too,

907
00:50:07,880 --> 00:50:09,839
Speaker 2: Like imagine putting all that work into it and that's

908
00:50:09,880 --> 00:50:10,440
Speaker 2: the outcome.

909
00:50:10,560 --> 00:50:11,680
Speaker 3: Yeah, you'd have to.

910
00:50:11,600 --> 00:50:14,000
Speaker 2: Build another AI to tell you what the first, AI

911
00:50:14,200 --> 00:50:18,320
Speaker 2: is talking about let's do it cool, pretty neat stuff.

912
00:50:18,880 --> 00:50:22,480
Speaker 3: I agree, And I feel like we're not done with

913
00:50:22,520 --> 00:50:23,680
Speaker 3: this topic, you know what I mean?

914
00:50:23,920 --> 00:50:25,760
Speaker 4: Oh, okay, I think.

915
00:50:25,560 --> 00:50:28,759
Speaker 3: There's more more to come, all right. Oh, by the way,

916
00:50:29,000 --> 00:50:31,080
Speaker 3: there is a listener who is Did you see that

917
00:50:31,120 --> 00:50:35,480
Speaker 3: email that has grouped our stuff into uh tranches sweets?

918
00:50:35,760 --> 00:50:36,799
Speaker 2: No, I didn't see that one.

919
00:50:37,640 --> 00:50:39,560
Speaker 3: I'll make sure I'll forward it to you. I haven't

920
00:50:39,560 --> 00:50:42,240
Speaker 3: even answered him back yet, but his name is Robert Fiddler.

921
00:50:43,200 --> 00:50:48,360
Speaker 3: Ironically enough, because he's fiddling about with with our content

922
00:50:48,800 --> 00:50:52,680
Speaker 3: and he has created sweets and subsuitees a in a

923
00:50:52,960 --> 00:50:53,879
Speaker 3: spreadsheet for us.

924
00:50:54,000 --> 00:50:55,280
Speaker 2: That's awesome, Thanks Robert.

925
00:50:55,760 --> 00:51:02,000
Speaker 3: And it's she's looking over it. Justice system, police, true crime.

926
00:51:02,040 --> 00:51:05,840
Speaker 3: Those are the big ones. Economics, finance, atmospheric science, weird,

927
00:51:06,239 --> 00:51:09,200
Speaker 3: natural disasters, natural resources. Boy, this is amazing.

928
00:51:09,360 --> 00:51:09,600
Speaker 4: Yeah.

929
00:51:09,800 --> 00:51:11,680
Speaker 3: I haven't even really looked through it yet, but anyway,

930
00:51:11,840 --> 00:51:16,319
Speaker 3: shout out to He called himself Robbie, Robbie Fiddler. Yeah,

931
00:51:16,360 --> 00:51:18,040
Speaker 3: maybe we could publish it at some point or something

932
00:51:18,040 --> 00:51:18,720
Speaker 3: on our website.

933
00:51:18,760 --> 00:51:21,200
Speaker 2: Oh I have to copyright Robbie Fiddler.

934
00:51:21,560 --> 00:51:26,040
Speaker 3: Yeah, we'll see anyway, did you say you listener mail? No, Okay,

935
00:51:26,040 --> 00:51:28,120
Speaker 3: why don't you set me up then in the traditional way.

936
00:51:28,239 --> 00:51:30,719
Speaker 2: Chuck's feeling like the chatty Kathy, so that means it's

937
00:51:30,760 --> 00:51:31,799
Speaker 2: time for a listener mail.

938
00:51:34,840 --> 00:51:37,160
Speaker 3: There you go, Hey guys, I'm an eleven year old

939
00:51:37,200 --> 00:51:39,480
Speaker 3: Canadian living in Australia and we've been listening to your

940
00:51:39,480 --> 00:51:42,160
Speaker 3: show my whole life. My dad's tell me he would

941
00:51:42,160 --> 00:51:43,719
Speaker 3: listen to stuff you should know when cuddling me in

942
00:51:43,719 --> 00:51:45,720
Speaker 3: the middle of the night when I was just a bbe.

943
00:51:46,560 --> 00:51:48,520
Speaker 3: We love your podcast and hope you make many more

944
00:51:48,560 --> 00:51:50,360
Speaker 3: for many years to come. We hope to see a

945
00:51:50,400 --> 00:51:52,480
Speaker 3: live show soon. I love it when you guys do

946
00:51:52,560 --> 00:51:54,759
Speaker 3: mysteries because they're one of my favorite things to listen

947
00:51:54,840 --> 00:51:58,040
Speaker 3: to on your channel. You've expanded my imagination and creativity

948
00:51:58,080 --> 00:52:01,759
Speaker 3: and intelligence. Me and my get into big conversations about

949
00:52:01,760 --> 00:52:04,839
Speaker 3: your episodes because you're so intriguing, and we discuss what

950
00:52:04,880 --> 00:52:07,440
Speaker 3: we've learned and what we think. I'm just emailing you

951
00:52:07,480 --> 00:52:08,719
Speaker 3: to let you know that my dad and I are

952
00:52:08,719 --> 00:52:11,799
Speaker 3: traveling across a big chunk of Australia on a road

953
00:52:11,840 --> 00:52:15,680
Speaker 3: trip in July to see the Australian Zoo. My dad

954
00:52:15,680 --> 00:52:17,160
Speaker 3: has so many stuff you should know, just to listen

955
00:52:17,200 --> 00:52:19,319
Speaker 3: to in the way, and I'm really excited to listen

956
00:52:19,360 --> 00:52:22,400
Speaker 3: to the podcast and go to the zoo. Oh and

957
00:52:22,440 --> 00:52:25,000
Speaker 3: your jokes are pretty funny, but I make them even

958
00:52:25,040 --> 00:52:26,799
Speaker 3: funnier and we all have a good laugh.

959
00:52:27,280 --> 00:52:31,040
Speaker 2: Nice, he's playing off jokes. It's collaborative.

960
00:52:31,480 --> 00:52:35,160
Speaker 3: I love it. So that says love dictated but not

961
00:52:35,280 --> 00:52:37,600
Speaker 3: read from Reese.

962
00:52:38,160 --> 00:52:40,759
Speaker 2: Reese, You're pretty cool. I just have to say.

963
00:52:41,280 --> 00:52:44,200
Speaker 3: Yeah. And a little request from Reese, I imagine the

964
00:52:44,280 --> 00:52:47,080
Speaker 3: samed at you please do one more of the voice

965
00:52:47,280 --> 00:52:49,720
Speaker 3: from the last Halloween special. It's my dad's favorite.

966
00:52:49,800 --> 00:52:50,200
Speaker 1: Yeah.

967
00:52:50,440 --> 00:52:53,680
Speaker 3: I gotta think that Reese is talking about my friend Spiegel.

968
00:52:54,160 --> 00:52:57,000
Speaker 2: Yeah, I have to go back and listen to Sniegel

969
00:52:57,040 --> 00:53:01,799
Speaker 2: again because he apparently was off last time I tried it.

970
00:53:01,840 --> 00:53:02,719
Speaker 3: So I'm not sousy.

971
00:53:03,080 --> 00:53:07,160
Speaker 2: Yeah, from what I hear, all right, some people emailed

972
00:53:07,200 --> 00:53:08,839
Speaker 2: in and were like, that was not right.

973
00:53:09,840 --> 00:53:13,120
Speaker 3: All right, I'm shooting let down an eleven year old.

974
00:53:13,480 --> 00:53:14,480
Speaker 3: I'm fine if you're fine.

975
00:53:15,440 --> 00:53:16,839
Speaker 2: Yeah, I'm prepared to do that.

976
00:53:17,400 --> 00:53:17,759
Speaker 4: All right.

977
00:53:17,800 --> 00:53:20,279
Speaker 3: Well, just listen in recent Josh. I will brush up

978
00:53:20,280 --> 00:53:21,160
Speaker 3: on his smigle.

979
00:53:20,880 --> 00:53:23,600
Speaker 2: And eventually recent when I do it next, you can

980
00:53:23,600 --> 00:53:24,520
Speaker 2: be like that was for me.

981
00:53:25,120 --> 00:53:25,560
Speaker 3: Exactly.

982
00:53:25,719 --> 00:53:28,120
Speaker 2: Okay, Well, if you want to be like recent show

983
00:53:28,440 --> 00:53:32,040
Speaker 2: how super cool you are naturally without any effort whatsoever,

984
00:53:32,400 --> 00:53:34,839
Speaker 2: we would love to hear from you. You can send

985
00:53:34,880 --> 00:53:42,200
Speaker 2: it in an email to stuff podcast at iHeartRadio dot com.

986
00:53:42,360 --> 00:53:45,239
Speaker 3: Stuff you Should Know is a production of iHeartRadio. For

987
00:53:45,320 --> 00:53:48,040
Speaker 3: more podcasts my heart Radio, visit the iHeartRadio

988
00:53:48,080 --> 00:53:51,480
Speaker 1: App, Apple Podcasts, or wherever you listen to your favorite shows.